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]]>“AI can accelerate misalignment just as easily as it can accelerate execution.” – Yvette Cronje
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Yvette Cronje, Head of UX Design for HP’s unified consumer app experience, about how design leadership is shifting as AI compresses production time. Cronje argues that AI hasn’t removed the hard part of design so much as relocated it: craft is shifting from executing pixels to judgment, taste, and critique, and teams need shared principles and decision frameworks in place before they diverge into AI-generated options, or misalignment simply accelerates alongside execution. She and Ferguson trace how this plays out in practice, from workshops that align teams on principles before production, to using AI as a critique partner and coaching tool rather than a production shortcut, to the delegation and communication skills leaders increasingly need as ICs manage AI collaborators. They also discuss trust, both customers’ trust in AI-driven product features and organizational trust in people through constant change, as a central design and leadership problem. The conversation closes on reframing AI anxiety as an opportunity for curiosity and growth rather than a threat to craft or job security.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:02:14] Designers Fearing Job Loss To AI
[00:05:10] Learning While Doing, Not Separately
[00:08:40] Craft As Judgment, Not Pixel Pushing
[00:12:05] Aligning On Principles Before Production
[00:18:30] AI Can Make Weak Thinking Look Polished
[00:24:50] Friction And Conflict As The Pearl
[00:31:15] Customer Trust In Automated AI Features
[00:35:40] Delegation Skills For Former Individual Contributors
[00:42:20] Building A Personal AI Coach
Yvette Cronje is a product design leader who currently leads UX Design for HP’s unified consumer app experience, bringing previously separate products and services together into an ecosystem used by more than 23 million people each month. She describes herself as a designer at heart and a leader by practice, someone who is happiest moving between big-picture product strategy and the smallest details of craft. On the show she speaks about cultivating team autonomy, running workshops that align teams around shared principles and decision frameworks before diving into AI-assisted ideation, and building psychological safety so designers can grow through difficult, conflict-filled moments rather than avoid them. She is especially focused on the intersection of design, technology, AI, and human behavior, including how design teams stay relevant as AI changes the nature of craft.
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So, why are organizations still stuck? Because the friction didn’t disappear. It moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. Today, I’m joined by Yvette Cronje, head of UX design for HP’s unified customer app experience, where she leads the work of bringing a complex ecosystem of products and services together into one experience used by millions of people. Yvette is a designer at heart and a leader by practice, someone who loves the messy space between product, technology, and human behavior, and believes the best design work happens when you’re willing to get close to both the problem and the people experiencing it. Welcome to the show, Yvette.
Yvette Cronje: Hi. Thank you, Douglas. So, good to be here.
Douglas Ferguson: Yeah, I’m looking forward to chatting. It’s such a fun moment to be in right now with all this change. And especially as a designer, I’m sure if you’re looking at that intersection between the problem and the people, it’s such a great moment to be thinking and designing.
Yvette Cronje: Yeah, absolutely. There’s always problems and there’s always people. We’re never going to be about a job here.
Douglas Ferguson: Right. What have you been noticing lately? I mean, there’s so much change in there. I’m curious, what are some of the things that have been popping up for you lately?
Yvette Cronje: Yeah, there’s a couple of things actually, but I would say the main ones is, I mean, I think this is a very natural response, is designers specifically thinking my job is going to be taken over by AI and the fear that is coming with that. And I think as a leader, what I’m trying to facilitate is rather than fear, how can you get excited about it? What does this change about how you do your job and the value that you specifically bring as a designer? So, that the only thing that I always say is the only constant in life is change. And the people who are able to work through that change and live with that change are the people who thrive. You want your people to thrive and design to thrive. And so, with that comes the ability to adapt with change and how I always think about be like water, go around the rocks, don’t be a rock. You’re not the rock, you’re the water that flows and brings all of it together. And that’s the real power of design and the value that they bring. So, I think to sum that up, just managing a lot of the emotional and mental thought around how AI is changing their world. And then once I get over that, it is, “Well, okay, well, how do we use this? The tool keeps changing under our feet every, I don’t know, couple of days.” It’s this constant change now. It’s not just about, okay, let me learn it and I’m once and done. It’s constant learning and how do you keep that going on your team while you’re delivering product? So, you want to be doing learning and curiosity and teaching, but also, and we have deadlines and deliveries, let’s keep going on that.
Douglas Ferguson: Yeah. How have you struck that balance between skating in front of the puck, but also keeping the trains running and the deliverables flowing?
Yvette Cronje: That’s hard. It’s not easy. Many years ago, I did Seth Godin’s altMBA workshop course, and the one thing that I found really valuable in that course was the work is in the doing. You don’t go and do a course and come back and you spend 12 hours on doing the course and then you spend another 12 hours on doing your actual work. “Hey, let’s compress that and let’s figure out how we can learn while we are doing.” And sometimes you have a bit of ebb and flow. Sometimes you need to just get in and get something done, and sometimes you have a bit more time. And I think it’s helping the team notice when you can explore a bit more, use the tools to help you get to your end deliverable. But basically it’s like learn while you’re doing, and maybe you’re taking a bit of extra time for a little bit, but that’s okay because you’ve learned something that’s going to help you accelerate in your next delivery. And making space for that I think is important.
Douglas Ferguson: I think also too, sometimes it’s not about efficiency, but finding a better approach or finding a novel new solution. It could be a whole new revenue stream that we unlock because we took the time to explore a different path.
Yvette Cronje: Yeah, that’s actually a really good point. Yeah, because I mean, in many ways we are reshaping how we do design and within that space comes, like you said, discovering new ways and new things. And if you think about generative AI, that actually made me think of something now is the paths and the use cases and the outcomes could be very different from what we traditionally would’ve thought because AI makes us diverge a lot quicker. And in that divergence, you’re like, “Oh, well, I never thought of that. Thank you so much.” But also what are our constraints that we’re working in and how do we keep the goal that we’re trying to keep? But I think it’s awesome. It’s great.
Douglas Ferguson: Coming back to the emotional piece, I feel that so many folks are in this moment of they’re feeling a sense of loss, like the craft is slipping away from them. But I would argue, and I see this in design and product and engineering, I think maybe engineering and design more so than product just because I feel like engineers and designers have a deeper sense of love of craft, right? And not that it’s not there on product, but it’s just super, super deep and almost like sometimes it’s like they’re zealots, right?
Yvette Cronje: Yeah.
Douglas Ferguson: And I think the issue is when the practices start to take a hole more deeply than the principles. That’s something I’ve found as helpful is helping root people back in the principles, because if we apply the principles in this new era and how these new tools are influencing how the principles show up, then we can throw away the practices and say goodbye to them and be okay with that.
Yvette Cronje: That’s actually, yeah, really good point. It’s interesting because actually we just had a really fun workshop on a new feature that we want to launch. And one of the big things in the workshop was discussing our principles, was creating that initial alignment because I mean, I’ll get to your point about craft in a minute, but it just made me think of how important that beginning part of the alignment is and experience principles very much falls in that initial bucket of, “Hey, do we all agree on these things?” And then it makes the craft and the production that much easier. But I’ve also thought that, I don’t know, the output of AI, I’m a bit of a zealot. The output of AI for me is not always super amazing. It’s not always around what I would call beautiful, innovative craft. And that’s where I think I want to help my designers and the team to not have that fear that their job’s going to be taken away because your craft is super important still. You have the eye, you have the taste, you’ve honed your judgment and your taste over years of being in challenging situations and living next to the customer and hearing from the customer and understanding businesses. And sure, AI can arguably understand all of that as well, but it’s only going to be as good as the input that you give it. And that’s where our design systems come in and the attention to detail and AI can definitely make our production faster. I mean I haven’t personally seen how it necessarily up levels craft as much as humans still can in many ways.
Douglas Ferguson: Yeah, I agree. And I think of it less as upleveling craft and more that it’s transitioning or evolving craft. Because to your point, judgment, critique, attention to detail, these are what I would classify in the principles bucket. We have to hold onto those because you’re not doing design if you’re not doing those things. Whereas moving the pixels around inside of Figma, maybe that’s not really the craft after all. What are the principles that we need to hold dear to and what do we need to let go of, I think is an interesting conversation. And it’s awesome that y’all are having the conversation around principles in your workshop because rerouting, grounding in them is really helpful in this moment because to your point, if everything’s changing around us, what do we need to hold tightly and what is that footing that we can stand on?
Yvette Cronje: Yeah, because ultimately in those moments, everyone’s a designer, a product manager’s a designer, an engineer is a designer. We’re all going to be making little prototypes. I mean, our product managers, which has been pretty awesome actually, make little prototypes early on to illustrate their requirements. It’s not the final product by any means, but they’re using that. And engineers make proof of concepts. And so, the whole process is fraught with a lot of visual stimulation and input coming from AI. But if we align on our principles early on, when that engineer is sitting doing his prototype and the product manager is doing their version, whatever, it can cause problems if we don’t have those principles early on. But if you do have them, you’re like, okay, well, this is the approach that I’m going to take. I’m asking myself those five questions while I’m doing it, and therefore it creates more alignment actually earlier on, which is a really fun way of working because you’re making those thoughts tangible quicker into something that I can see and use quicker. And I think that’s really fun.
Douglas Ferguson: I love that. And it kind of gets a little bit into how there’s layers of principles, right? We can be principled about how we do our engineering. We can be principled about how we do our design. We can be principled about how we approach this project, how the organization operates, right? And in the examples you were just giving, it’s kind of in that category of being principled about how we work together as a team and how we make decisions, what happens when there’s conflict. And I think it’s really powerful for groups to have those conversations up front when you’re kicking off a new project or even just there’s a moment of reflection or review on a long-running project because so often, it’s easier for us to get in our little silos, whether that’s in our functional silo or our individual personal silo. And the more we can discuss these things and get clear about them, the more it’s easier for us to collaborate.
Yvette Cronje: Yeah. And it’s also easier to not over-index on our personal taste or our personal opinions. It up levels the whole team and we say, “Well, we agreed on, what can we come back that we agreed on? It doesn’t mean we can’t change those things, but let’s have a discussion around it.” And so, it becomes this constant bubble of a team, this organism that’s moving together through the process to get the work done. And one of the other really helpful things that we did in that workshop after the principles was thinking through our decision frameworks and decision matrices. And every org has its bias because it has to, it’s what it specializes in. But that’s another thing that helps us when we think about, “Okay, well now we’re all going to dive into AI and go create multiple hundreds of ideas and things.” But if we have that decision matrix, not only for our own brains, but also for AI to input that into the AI, AI becomes your teammate. It becomes another aligned teammate that can just superpower, make a hundred options very easy.
Douglas Ferguson: Yeah, that reminds me, we were just talking yesterday in the mastermind about how often the AI isn’t aware of the decisions we decided not to make because the context we give it is really kind of, I would say, idealistic or it’s clean. We filtered it down to here’s where we decided to go, here’s what we want to do, and here are the requirements, but we rarely come back to these things that we decided not to do and the rationale for why not to do them, right? And so, that’s a fascinating thing that the humans often have context on, but our AI companions don’t necessarily see that stuff.
Yvette Cronje: Yeah, exactly. The things that you didn’t think of or the things that you’re purposely leaving out, giving it that context. Yeah, really important point. And that’s one of the things that I’m also seeing is the ability to make sure that the AI is also asking you good questions so that it’s not just a workhorse, but you’re saying, “Well, go ahead and ask me these questions. Go ahead and tell me what gaps are you seeing, especially if you weren’t detailed enough upfront that it becomes a critical conversation before it becomes a solution.”
Douglas Ferguson: Yeah. I think often because AI is so powerful at generating pretty convincing stuff, I mean, you criticized it earlier saying that you still feel like there’s some gaps there and whatnot, but at the end of the day, it is pretty convincing and pretty finished, finite looking. And I think that’s another skill that, to your point earlier, this important skill of being comfortable with change and curious and constantly learning. But also I think we need to build the skill of planning and interrogating with the AI before just having it build stuff.
Yvette Cronje: Yeah. And then also to go hand in hand with that is the ability to critique it is like you said, AI can make, I don’t want to call it weak thinking, but for the lack of better words, let me just say that. It can make weak thinking look beautiful and look polished if you don’t have those clear boundaries and the principles and everything we spoke about earlier to be able to make good judgment of it and to say, “Well, this is not going to have the last say. How do we as a team come together and decide how did it think about it?” And that’s our value now is how are we thinking about the problem? Is it solving the right problem? Is it solving problems I didn’t even ask it to solve in the first place? Because it looks pretty potentially and it’s, “Oh, the pixels are aligned, therefore it’s great. No, no, what is the problem it’s actually solving?”
Douglas Ferguson: I think oftentimes that’s a real risk. I mean, we saw that before the arrival of AI where folks were starting with the solution and they hadn’t really articulated the problem yet. And I think the fact that you can build so many solutions so quickly now, people are at risk of continuing to skip that step. And it’s almost, I would say, amplifying that dysfunction around if we don’t have that alignment, we don’t have that visibility into the why. It’s funny, I’ve seen that show up a ton too around folks like, “How are we going to put AI into our product?” And I always say, “I can critique this architectural approach to using bedrock or whatever other tools that you’re considering, but we haven’t even begun to talk about the why yet. What’s the business need for this, right? And to your point, where’s the intersection of this problem and the people experiencing it, right? If we don’t understand that, then we probably need to stop and go.” And we might use AI to figure some of that out or help aid us in our exploration. We certainly don’t want to just spin up a bunch of AI agents to build that solution when we don’t understand it deep enough.
Yvette Cronje: Yeah, that’s a really good point. And it’s funny that you say that. Well, there’s two things that really stood out of what you said. The first one was how you jump to the solution too quickly. And that’s an interesting space for design because I feel like in many organizations for a long time, that’s kind of how organizations operated. They would come to design and say, “Hey, I need a screen with a blue button and a heading,” and the designer would do it or whatever. And I think the industry has worked so hard to get us to, well, are we solving the right problem? And the value, that upstream value of doing that, AI is just, like you said, amplifying the problem in a different way now because that designer that had to do that took a couple of days and now it’s instant. But it’s sort of the same problem that was before. And I think therein lies the opportunity for teams to be like, “Hey, no, no, this looks like a familiar pattern. Let’s go back to what we know we need to be doing, which is solving the right problem.”
Douglas Ferguson: So, let me bring us back to something you were saying earlier around how you were starting with the principles and purpose. And it got me to thinking about this idea of creating a symbiotic organization where currently a lot of what we see is engineers are experimenting with creating prototypes and they’re maybe moving a little bit more into the design space. You’ve got folks that are, to your point, designers can even create functional prototypes and whatnot. And if everyone’s just figuring that stuff out on their own, then they’re not necessarily operating in ways that are going to be most helpful to how the organization evolves together. And so, having these workshops that either start with principals or check-ins where we’re talking about this stuff means that we can influence each other’s thinking. So, then when we are experimenting outside of our classical wheelhouse, we’re doing it in a way that’s informed by what the others need and how the others are thinking. And then if we’re sharing our experiments and there’s touch points and moments where that stuff can get bubbled up, it allows us to be a little bit more symbiotic, even though we might be doing our own experiments. If we think of our own function as being an organ in the body, it’s like, yes, we’re optimizing for what the stomach needs to do, but we’re not doing it at the detriment of the blood system or whatever.
Yvette Cronje: Yeah, that’s probably the best analogy. It’s such a human analogy to this technical problem. I actually love it. I feel like you can write a book on it, Douglas. But no, it’s a really good point because it gets to that moment of look, AI can accelerate misalignment just as easily as it can accelerate execution, right? And so, for us to be able to get to this aligned space means getting those principles upfront, getting the decision matrices upfront, because everyone’s going to use a different tool. Engineering, product, design, everyone’s using different. It’s not about, “Hey, everyone needs to be using the same tool and in the same space.” And it’s not about the tool. It’s about how do we create shared context? How do we create shared evidence? How do we make sure that we are all sharing the same problem? What is the problem that we’re trying to solve? Shared criteria, shared outcome. It’s this shared, the body system has a shared holder, it has a shared constraint, your organs have constraints, and it’s the same for us. There’s a book called, it’s actually called A Beautiful Constraint, and it’s a beautiful book because it describes how it is the constraint that helps you make something beautiful and useful. And I think having those upfront before you unleash and go and diverge helps create a shared understanding of teams.
Douglas Ferguson: Yeah, it’s interesting that you’re talking about the constraint. It made me think about the pearl, right? It’s like the pearl doesn’t form under ideal circumstances. It’s like this impurity or this thing got in there that made it start to form. And it’s similar to this concept we talk about, which is conflict or friction can often point to the fact that the good stuff is happening. Or if we really attend to it, that might point us toward where we need to go. And quite often, people ignore it or they try to optimize around it, right? And if instead, if we open that up as a conversation and it’s an opportunity for the team to heal or to explore some new territory that was unclear to them, but that opacity was what was causing that friction. And until we confront it, we can’t really expose it, understand it, and move through it.
Yvette Cronje: Yeah. And if we want to call it conflict or misunderstanding or challenges or difficulty, I think it’s in line with what you’re saying, it’s reminding me of this notion of it almost can feel like because AI is so easy, I’m wondering, does it feel to people like, “Oh, it’s removing the conflict or it’s making my life so easy. I don’t need to think about conflict or difficult moments.” And especially if I think about junior designers and people, new people coming into the team and how we grow people, it’s like the fundamental things that you need to be good at needs to be learned in those difficult moments. We need to understand the conflict and how to navigate that and, yeah, how to stay curious. And what’s one of my biggest principles I think is, and it’s maybe a bit cliche, but it’s true, right? If you stay curious around the problem, it can lead to really, really amazing outcomes at the end of the day. Embrace your challenges, all those. And those are the skills that we’re having to learn up front, because if you think about it, when you’re in these workshops, and that’s where a lot of that stuff bubbles up, and then it makes production just fun and exploratory and getting to the deadline, and you can have those friction moments, which are growth moments upfront. But that’s the magic really, really. It’s the pearl.
Douglas Ferguson: Yeah. Yeah. We always talk about creating almost these temporary moments that feel very special or different. We’re asking people to step into behaviors that they don’t embody every day. And then I feel that gives them the freedom to maybe be more curious than they normally would be or to let go of some of the guards a bit. And so, I do think these imaginary worlds that we ask people to step into can be quite powerful. I think teams ought to do it more.
Yvette Cronje: Yeah. It makes me think of when we are using AI in our production and our ideation, all of that, you can almost think of it’s not going to be perfect out the gate. And how do we become comfortable with that? Because if you can become comfortable with “failure” or you tried something and it didn’t work, but what did you learn? And AI does a bit of that because it’s going to spit out, it’s going to create a hundred options for you. Not all of them are going to be great and that’s okay. So, then, okay, let’s work through this. Let’s decide, let’s hone our judgment, let’s work on our taste. Let’s be comfortable with that discomfort moment.
Douglas Ferguson: Yeah, I’ve been hearing a lot of folks shifting from, or maybe not shifting from, but adding to their typical metrics more of almost innovation accounting type of techniques where they’re measuring lessons learned. So, they’re using this moment to not only measure business outcomes and value direct bottom line revenue type goals, but also what are we learning? Especially in this moment right now, it’s like, are we leveling up in a way, not only as far as what the tools can do and how we might function in this new AI world, but also what are we learning about the customer? What are we learning about the market? What are we learning about where the business could go? Those are all very valuable things because those are things we can build on top of.
Yvette Cronje: Yeah. And I think arguably because AI is making some of our production easier and taking away some of the slog work, it allows us to focus on those aspects a lot more potentially and to highlight them to the organization and once again, share it with each other. It’s so easy to sit in a silo and have this understanding, but you’re having to come together more with people and share that understanding. And if I think about research and getting to know the customer, this is that moment that that can be highlighted way upfront in the process strategically and we can stop changing even how we make strategic decisions. What is this product that we’re creating? Why are we doing this? Are we solving the right problem for the customer? Who is our customer? Our customers are changing as well. As much as what we are changing, our customers are also changing. And that’s kind of a full circle moment for us to realize.
Douglas Ferguson: And I think we’re only in the beginning there. You think about AI LLM adoption, it’s somewhat gated by… Well, I’ll just say this. Once it’s pervasive across mobile devices and embedded in really significant ways, I think we’re going to start to see expectations from consumers in ways that are unprecedented, right? So, that to your point, the customer changing, we’re just in the beginning there.
Yvette Cronje: Yeah. That workshop that I was referring to earlier, some of the initial concepts, there was a lot of AI baked into our concepts of, let’s say, automated things that it’s doing for you upfront because it knows who you are. “Oh, Douglas, I know you’re on Zoom between 9:00 and 5:00. I’m just going to make your lighting and audio and everything perfect for you and pull that all together with your devices.” And it was very interesting, the mixed feedback that we got from customers, because I would say the majority of them had massive trust issues before they were excited about what it could do for them. And now we are having to learn, okay, it’s not just about, oh, this is a really cool feature. How do we get people to use it? Or getting excited about that. It’s also about how do we build trust and allow people to keep the control, help them feel like, okay, I’m still in control of this. This thing isn’t taking over my life. It’s going to become an interesting problem, I think, with the pervasiveness of AI and products.
Douglas Ferguson: Trust is such a fascinating one because there’s so many layers and so many attributes to think about, because you talked about folks having a sense of loss of control, so that’s an element. There’s also people under trusting it, and so then they don’t want to necessarily. In some cases, it’s not lacking control. It’s a fear of it’s going to blow up on them and cause them huge headaches. So, they’d be happy to let go of the control if they actually trusted it. And then the other side is over trust where people think it can solve all these problems and it can’t. So, it’s a fascinating one. And then also I’ve seen trust come up in these conversations too around trust in the organization. Is the org going to support you? Are they going to continue to invest in finding ways to keep the humans around? Trust is a huge complex issue as it relates to all this work.
Yvette Cronje: It is. It really is. Yeah, the org is a difficult one as well, I think, because there’s a lot of that that you’re not in control of as a team. You live at the edge of after the decision has been made, and then having to work within that constraint and figure out, “Well, okay, how are we going to work with that?” It’s often not as ideal as you would wish. I think employees are always like, “Well, I wish this if I had my ideal world.” But that’s rarely the case. And so, yeah, once again, being able to work and produce and be really proud of your work in environments that are constantly changing because the orgs are now changing about as quick as the technology in many spaces. It’s people coming and going and there’s a lot of change. So, a lot of our role I think as leaders is change management, not only of how the work’s being done, but what’s happening in the company and our organizations. But the other thing I was thinking about was also the value of designers. And if we think about these experiences in our customers and how they think about trust in AI, I wonder if it’s also just shifting a lot towards, “Okay, now we as UX people need to help the customer understand what is the AI doing? Why is it doing it? How do you correct it if it’s doing the wrong thing? How do you control it?” But it needs to not be a laborious extra thing that you now need to manage in your life. It needs to feel easy and light and delightful, and this is helpful in my life. It’s not another chore. Great, thank you company X, you’ve given me another chore with your product that I didn’t want.
Douglas Ferguson: It makes me think about how, especially the builders, so the engineers, designers, if you’ve gone down this route of being the IC, the principled engineer, the distinguished designer, you maybe made that choice intentionally down that path because you didn’t want to manage people. And so, you’re never trained on delegation skills. And now you have to delegate pretty effectively to get the best out of these tools. And so, I think as leaders, the more we can help people get comfortable with delegation, I’ve seen that be a big point of friction with some of the folks that are actively resisting. Once I peel back the layers, the delegation is a big piece of it. It’s like, I didn’t sign up to be a manager. And it’s like, okay, well the future of creating this stuff is going to demand some of these skills.
Yvette Cronje: Yeah. You hit the nail on the head because one thing that I’ve always tried to cultivate on my teams is the ability for team members to give input into each other’s work. I never want the leader to be the be and end all of decision making. And if it hasn’t passed through me, we’re not doing anything. The team needs to become autonomous enough. There’s this thing that I keep saying is I want a team that is so strong that no one knows who the leader is. And so, I try to hone that. I’ve always tried to hone that ability for people to, okay, well tell me what you guys think of it. Give each other critique. And now to your point, it’s becoming give the AI critique. It’s making you almost be a bit of a creative director and a manager to your point. And don’t just accept what it gives you because it looks good. It is such a good point actually. Yeah, you’re right. The strength of the craft is shifting.
Douglas Ferguson: Yeah. Absolutely. And I think it’s only going to grow over time as people get more comfortable using agents and sub-agents and there’s just a lot more things will happen in parallel. And so, how do we describe the work? How do we evaluate it? All these things that are going to be critical to managing a fleet of these coworkers, if you will.
Yvette Cronje: Yeah. But I also think it’s helping us be better communicators when we are in those initial alignment workshops, and especially in a world where many of us are still working virtually. Many of us, I mean, most of the people I work with are not in my location and communicating and creating connection over a virtual space is really hard. We all know when you meet a human in person, it becomes different, but you need to learn to communicate well so that one meeting doesn’t turn into 10 meetings. And arguably the skillset with AI is going to help us also. I think it could help us be better communicators.
Douglas Ferguson: Absolutely. Never have we been in a better position to tell good stories. I argue that a good story really hinges on our ability to understand the audience and to meet the audience with adapt the narrative to the audience, right? And so, gosh, if you built the prototype and you’re going to share it with engineering, heck, even three different engineering teams might care about different things, right? The security team might care about hearing about different stuff about that prototype. They might have different questions than the front end team or even leadership or the customer. I think these tools can help us adapt the narrative, the pitch, et cetera, to whoever we’re speaking to. And that’s something I really encourage folks to lean into is it doesn’t have to be one size fits all now because we can generate so many different versions.
Yvette Cronje: So true. And it also elevates us being able to explain our thinking because how you explain your thinking to an engineer is different to how you explain your thinking to a leader who’s trying to sign off on the project. It might be similar thinking, it might have a similar outcome, but that thought process and what matters to that person shapes very much how you describe your thought process about the solution. And I think that’s just awesome. That’s fun. It’s like iron sharpening iron is what it’s doing to us.
Douglas Ferguson: I love that. You’re making me think about metacognition, thinking about thinking and not a lot of people are comfortable with that. Some folks just, when it gets too introspective, maybe there’s a quarter turn of the crank and then they just jam up and stop. Whereas some people just take to it very naturally and they can describe how they do things and how they came to conclusions. But others might be a little more inherent or they can’t necessarily put words to it, but they can arrive at the answer, at the conclusion. They can maybe back it up with some evidence. But I’ve found AI, if you interrogate it well, can help you with some of that reflection, especially for folks that get a little stuck. It can be that thing, that coach that keeps nudging you and asking questions to get you to find those deeper answers. And I just encourage folks to find those novel ways to use it versus just say, “Build a thing for me,” because there’s so much more you can get out of it if you keep nudging at it and finding novel ways to explore maybe almost even personal weaknesses.
Yvette Cronje: Yeah, that’s a good one. That’s a really good one. You’re making me think of something that I now want to go do immediately after this.
Douglas Ferguson: Love it.
Yvette Cronje: Is what if you had every person on your team create a personal coach for themselves, like a business coach or a, “Hey, I’m a junior designer. I want to become a senior designer. I want you to be a coach to me.” And have that constantly running for themselves. Because one of the things that I want to start doing as my team, and we’ve been working on a structure for this, is this sort of couch to 5K program is looking at each person on the team and thinking, “Okay, what does this team need?” Every designer is different and you have a visual designer and a UX designer and a creative technologist and a researcher, and they all have different needs and purposes within the process. How do we say we’re going to use AI to our advantage as a team? And a lot of that came down to the craft was really the very last step. All of the beginning stuff was, how do you think? How do you get it to be a partner for you while you’re thinking? How do you get it to ask critical questions to you while you’re going through your thought process? The base of that took a lot more of that sort of program than the actual production of it. And it made the production a lot more fun and less frustrating because also you’re getting to this place were you’re like, “Oh no, that’s not right, make this green. Oh no, that’s not right.” And you’re getting super frustrated versus all of the fun of the thinking process. And it also makes me think of psychological safety, how you mentioned some people might not be comfortable with thinking and talking through that thought process. It’s like, okay, so how do we as leaders really hone in on creating safety within our teams to be able to just say what you want to say? That’s how you learn. Let’s not shame it. Let’s not critique it. Let’s correct and ask good questions to the person to help them really grow.
Douglas Ferguson: Yeah. And to that point, you were making me think how valuable it is as a one-on-one tool. A lot of people record meetings and then use the meeting summaries, but I highly recommend leaders before, don’t just send out the summary, have a conversation with the agent about the transcript and shape it as a leader. Include things you know about that individual and push back on things that they’ve determined. Don’t let it just pick the next steps. Work with it as a companion, as almost like your coach to then give better coaching to the individual. And then if they also have their coach set up, they could take the transcript from the one-on-one plus your additional thoughts and bring that in. It’s like your one-on-one turns into a much deeper interaction because it’s not just the 15 or 30 minutes that you can spend with each employee. It’s like that plus the three to five minutes you spent with the agent refining it and then plus the time they spend with their agent refining it. And I don’t know, it can go much deeper and folks can maybe find better footing.
Yvette Cronje: That’s so true. That is such a good point. I did something, I myself also record meetings and have AI tools summarize them for me. And the other day, I was reading through one of them and I was like, “I feel like the summary is missing some things here.” So, I fed it into another AI that knows a bunch of stuff about the project and the people and whatever, and it was like, “Yeah, it’s missing a couple of things.” So, that summary came back quite different. And then I was like, “Ah, yeah.” I think there’s a couple of other things also. There’s nuance that it doesn’t pick tone of voice and skill sets of who the people are. And that’s where you as a people manager, you know these things, use them to grow your people and to move the project forward. And that’s your superpower now.
Douglas Ferguson: Yep. It’s funny because the same person that says, “This thing’s coming from my job,” is the same person I hear saying, “It’s not getting everything right.” And it’s like, do you realize how profoundly disconnected that is? You should realize that, “Oh, it’s not coming from my job because I’m noticing it’s leaving things out, but yet I can use this as a way for me to refine my thinking.” It might’ve pointed out something I had forgot because unless you have a photographic memory, you’re probably going to miss some things. And honestly, it’s a great stimulus for being reminded of the things that it left out because it mentioned X, Y, and Z. You’re like, “Oh, but we talked about A between B and C,” or whatever. And so, I feel like rather than being over judgmental on it or overly worried about it, why not lean into it and think about, “Well, what can this do for me?”
Yvette Cronje: Yeah. And this reminds me of that principle, since we were talking about principles of, maybe this is because one of my core skills is being a learning, I enjoy learning. I could have been a student the rest of my life and been happy if I could get paid for it, but it’s that notion of nothing is ever wasted. If you have the perspective of always, literally always trying to take something out of something, there’s nothing that you can say, “Nothing gets wasted in my life.” Sure, I might not have used the 10 rules and or the 10 steps exactly like that, but what did I use and how did this shape me as a person and how did this impact or influence another person or a project or whatever? And sometimes, it’s things that you’re not going to see right now. You’re going to see them come to fruition months or years from now in a person or probably whatever that might be. And it’s like that sort of skillset to be able to say, “Well, I believe that I’ve learned something anyway.”
Douglas Ferguson: I love that. It comes back to that growth mindset and curiosity, and it also reminds me of the adage, “All models are wrong, but some are useful.” And if you can be the kind of person that finds as many models as possible useful, how can I apply this versus the kind of person that’s like, “Well, this is garbage, it doesn’t work,” right? It’s like being overly critical and dismissive of too many things doesn’t really leave us much ability to wonder and repurpose and reapply.
Yvette Cronje: Wonder.
Douglas Ferguson: And that’s where most innovation comes from, is when we’re able to take something and reuse it for a new purpose. The microwave oven is one of the prime examples of that, right?
Yvette Cronje: Yeah. Yeah. Yeah, notability, I love what you said about wonder. One of our leaders constantly says that, they say, “Always keep your sense of wonder.” And even was it Leonardo da Vinci that said, “I’m not really a good artist at all. I’m just extremely curious.” And it made him do amazing things, and that’s a pretty amazing space to be in, I think.
Douglas Ferguson: Absolutely. Just hang around with kids more if you want to tap into your wonder. They have endless amounts of it. Somehow we lose it over time or it gets beaten out of us or something. I’m not sure. Well, we’re kind of coming up on our end here, so I want to give you an opportunity to leave our listeners with a final thought.
Yvette Cronje: Yeah. I hope that designers and folks in our industry can get really excited about this versus fearful that they can move into a space where let the AI do all of the production and the slog work for me, but really get excited about how this changes the value that you bring towards the world, not just your product or whatever, really the world, because we’re applying all of these things in the rest of our lives as well. And it’s a very exciting moment. I think, like you said, if we can lean into the excitement and the opportunity of it, I think it’s going to open up a lot more doorways and innovation, thinking about new products or new ways of doing things. I just think there’s a lot of space for that and I’m hoping people can get excited.
Douglas Ferguson: Totally agree. Well, it’s been a pleasure chatting with you and hopefully, we’ll chat more again sometime soon.
Yvette Cronje: Yeah, absolutely. It’s always good to chat with you, Douglas. Thanks for the lively conversation. I really enjoyed it.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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]]>“AI will not take your job, an engineer who’s better at using it will.” – Alyssa Coughlin
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Alyssa Coughlin, Director and Chief of Staff for Autodesk’s Data, AI, and ML Platform organization, about what it actually takes to move a large engineering company from AI-enabled to AI-native. Coughlin describes how bottlenecks have shifted away from writing code toward code review, deployment permissions, and design decisions now that AI has made execution fast and cheap. She walks through concrete changes her org has made, including abandoning two-week Scrum sprints for Kanban flow, moving from PRDs to spec-driven development consumable by both humans and AI, and building shared “knowledge graph” brains to catch duplicated work before it ships. Throughout, she frames change management as a balance of carrot and stick, arguing that engineers aren’t losing their jobs so much as shifting from author to orchestrator, and that managers themselves must stay hands-on with the tools to coach effectively. She closes by describing AI as an amplifier that exposes organizational seams rather than a fix, so the real work is continuously finding and addressing the friction it reveals.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:00:00] Framing The New Friction Series
[00:02:15] Shifting From AI Enabled To AI Native
[00:04:00] Bottlenecks Move Upstream To Reviews And Design
[00:06:45] Balancing Change Management With Carrot And Stick
[00:09:15] Mourning The Coder Identity As Orchestrator
[00:11:30] Ditching Scrum Sprints For Kanban Flow
[00:16:00] Building An Autodesk Brain To Avoid Duplication
[00:19:45] Spec Driven Development As A Living Document
[00:22:15] Roles Blurring Across PM, Engineering, And Design
[00:25:30] Closing Thought: AI As An Amplifier
Alyssa Coughlin on LinkedIn
Voltage Control
Alyssa Coughlin is Director, Chief of Staff for the Data, AI, and ML Platform organization at Autodesk, a role she describes as spanning operating model design, change management, and helping the organization transition from using AI as a tool to treating it as a genuine working partner. Her background is in project management, holding a PMP credential and years of experience across Scrum, Agile, and Kanban practices before landing in this role. She speaks about leading her organization through practical shifts like moving from Scrum to Kanban, adopting spec-driven development, and building shared knowledge graphs to reduce duplicated work, all while managing the human side of a fast-moving AI transition. She frames her core philosophy as treating AI as an amplifier of both strengths and weaknesses in an organization, and as a partner rather than a replacement for human judgment.
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved and it multiplied. It’s no longer in building, it’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore, the conversation before the work is. That’s the work this show is about. Today, I’m with Alyssa Coughlin at Autodesk, where she is the chief of staff for the organization that builds the data and ML platforms and agentic AI infrastructure. Welcome to the show, Alyssa.
Alyssa Coughlin: Thank you so much for having me. I’m excited to be here.
Douglas Ferguson: Yeah, it’s great to have you. It’s been a minute since we spoke. In fact, I think you embarked on a cross country trek, so lots of change, not only the AI, but you’re having change of environment as well.
Alyssa Coughlin: Yeah. Life wasn’t hectic enough, so why not throw in a cross country move?
Douglas Ferguson: There you go. Amazing. So yeah, I’d be curious just for starters, what are some of the things you’ve been noticing as AI has been more and more prevalent in the workforce?
Alyssa Coughlin: Yeah, the shift has been really interesting. What my teams have been primarily focused on recently is transitioning from AI enabled to AI native. And so what I mean from that is AI adoption is really just do people use the tools? That’s where you start looking at token maxing and are they vibe coding? Are they connecting MCPs? Are they doing all the things, versus AI native is making AI part of the process in the workforce holistically. So from beginning to end and kind of redesigning how we work to incorporate AI as a partner and not just as a tool. So that shift has been really interesting because there’s obviously a really big people and process component to that. And so trying to navigate what that looks like in all of its forms end to end is where we’re really focused right now. And it’s interesting. I mean, we call this podcast the New Friction and the friction’s definitely part of that process.
Douglas Ferguson: What friction have you noticed most prevalent or the most maybe difficult to get your hands around?
Alyssa Coughlin: I think an interesting shift we’ve seen twofold when it comes to friction. One is embracing friction. In an AI native environment, friction becomes another metric. It shows you where there might be seams in your processes or your workflows that weren’t really surfaced before. So looking at that friction and figuring out why it exists, how does being AI native play into that and what does this new way of working that we’re learning based on this friction become? And then related to the friction is, as we shift to AI native, one of the really big things we’re seeing is the bottlenecks shift. So not all that long ago manually coding was fairly time intensive. And so now with AI assisted coding, that’s not the bottleneck anymore. It’s shifted further upstream. So we’re seeing it in reviews, we are seeing it in deployments, things as mundane as permissions. All of a sudden, there’s this backlog of PRs that need to be reviewed before they can be pushed to prod. We’re also seeing it on the other end of the spectrum from an XD perspective. So it used to be that XD and coding were moving at similar speeds, but while XD is leveraging an AI native mentality, there’s still a lot of human intervention in that practice right now. And so that’s another new bottleneck that we’re seeing, is that determining what that true user experience should look like takes longer than actually coding that experience. So we’re seeing the bottlenecks and we’re seeing the friction move to different parts of the workflow than where they’ve historically existed.
Douglas Ferguson: I like to say that these frictions were already there, now they’re becoming the breaking point. Or because we’re sending more stuff through a process that was maybe broken to begin with or something we hadn’t spent a lot of time rehearsing or getting good at.
Alyssa Coughlin: Yeah. And I think a really interesting way to look at it is, AI is an amplifier in all aspects. It amplifies the positive, but it also really amplifies the things that maybe aren’t working as well in your workflows. And so it’s really hard to hide anything in the era of AI. You’re moving faster, you’re doing more. And so these seams and these bottlenecks and these frictions just become more apparent.
Douglas Ferguson: And the role as chief of staff, how is it impacting you personally? Are you being asked to help identify some of these problems, address them or facilitate the conversations where it’s getting addressed? How are you showing up to meet this moment?
Alyssa Coughlin: Yeah, it’s all of the above. Part of becoming AI native is helping to identify what processes need to shift and where. But it’s also really rethinking the operating model and our strategies. We need an operating model that is based on spec driven development and working with AI, not as a tool, but as a partner. We affectionately refer to AI as the intern because it’s quite capable of doing many things, but it doesn’t quite have the expertise to make the hard decisions. And so figuring out what does that look like? How do we shift our ways of working? One of the things we’re doing right now is we are transitioning away from Scrum to Kanban because that’s more conducive to an AI native working speed, especially when you can have multiple agents working on multiple repos simultaneously. So a lot of my job has been trying to figure out what this new way of working looks like in an AI native environment. And obviously there’s a huge people component there. So figuring out how to balance the change management, because obviously everything in this AI revolution, evolution, whatever you want to call it, is moving at a breakneck speed. But if you aren’t thoughtful with how you’re pairing that with change management, the human cost of that can be massive. So in my job, I’m trying to balance how we work from a day-to-day basis, what needs to change, what can we keep, and what do we honestly just need to reinvent?
Douglas Ferguson: So you talk about balance and change management, and it sounded like a big piece of that was just trying to prevent overwhelm or maybe even revolt from folks. And so I’m kind of curious, what are some of the principles or tactics that you’re thinking about when you’re looking at balancing that change management?
Alyssa Coughlin: Yeah, I think the core principle of successful change management is buy-in. It’s getting people to move along with you. And in order to do that, it’s been a little bit of a balance between a carrot and a stick. And so by carrot, I mean making people really excited to meet this moment. I mean, this is one of our biggest technological shifts since the invention of the internet. This is going to completely change the entire world, hopefully for the better, but we’re still sorting that part out too. It makes so much more room for innovation and experimentation and creativity. So really trying to get people to hone in on that excitement and that opportunity, but balancing that with the tough love. AI’s here to stay. We work in tech. It’s not going anywhere. If you don’t like it, I’m going to be honest, this might not be the job for you anymore. So trying to make sure people have an honest understanding of the reality of the situation. But that does not necessarily translate to doom and gloom, it translates to opportunity.
Douglas Ferguson: Yeah. And when you think about the cross section of the org, the team, what would you say… Is this resistance prominent or is it a small subset? And follow-up question would be, I’ve seen a wide array through these conversations and dinners and things I’ve been having, a wide array of causes for the resistance. So I’m kind of curious what you’ve been noticing? How prevalent is the resistance and then what are some of the causes that you’ve been seeing?
Alyssa Coughlin: Yeah, I think the prevalence continues to decline. I think at first, I mean, you had tech CEOs who were like, “Oh no, this is going to be apocalyptic. This is going to take everybody’s jobs.” Even this week, Bill Gates is like, “Ah, I don’t know about this.” But I think the more we have understood what working with AI truly means, the more we’re able to realize that it’s not getting rid of jobs, it’s not getting rid of work, it’s changing what that work looks like. And so helping people understand that shift, I think has led to a continual decline of people who aren’t really bought in on the whole AI native transition. So I think that’s one side of it, the people who are like, “AI is going to take my job.” And one of my favorite sayings is, “AI will not take your job, an engineer who’s better at using it will.” So it’s really a matter of do you change? Do you transition? Do you meet this moment? And so that’s the second category of resistance that we do sometimes encounter. And I would say it’s not very frequent, but you do have some people who are just like, “This isn’t what I signed up for. I am an engineer. I’m a coder. I want to put my headphones on and be heads down all day just cranking out lines of code.” And that’s not the job anymore. And so you do have some people who I think are still mourning what being an engineer used to mean, and they’re struggling a little bit to adapt that you’re not a coder anymore, you’re a systems thinker. You’ve moved from the author to the orchestrator. Not everyone wants to be the orchestrator.
Douglas Ferguson: Yep. And a lot of that has to do with the skills they developed over time because to be an orchestrator is a lot more akin to a manager. You got to delegate, you got to evaluate, you got to organize, and you’re not necessarily the one doing the direct work, you’re reviewing the work. And it’s just different skill sets. And engineers have the transferable skills to be able to evaluate good engineering work. In fact, they have to do pull requests quite often, and that’s a key part of the job. That’s just becoming more and more a part of the job. And so I do think there is something to this idea of having this identity loss and even acknowledging the fact that people need to go through that mourning process.
Alyssa Coughlin: They do. And you see that with any change. I’ve heard a lot of people compare this AI revolution to the industrial revolution, and honestly, that scared a lot of people and it made a lot of people think that they were no longer going to be relevant, their jobs weren’t going to matter. We still need just as many, if not more people. It’s what they’re doing that is different. So we have replaced some of that more trivial work with machinery, but that just elevated what the human needs to do. And so this is really similar and it’s not a direct correlation of the progression isn’t human to AI, it’s human with AI, human doing, human directing, human orchestrating, human judging. So the human’s more important than ever because until we have AGI, there’s no reasoning coming from AI. It’s an order taker. And so you still need that human judgment in the
Douglas Ferguson: Loop. Yeah, it’s so true. One example would be your shift to Kanban. So talk about that for a moment. Was Kanban something that you were familiar with prior or is it something new that you had to learn as the team were starting to adopt it?
Alyssa Coughlin: Yeah, my background is in project management and I’m a PMP, so Waterfall, Agile, Kanban, they were all kind of already in my repertoire, but working in tech for the last decade, everything’s obviously been very Scrum heavy. And even though a sprint is traditionally two weeks, in the era of AI native, that’s a long time and that sort of time boxing doesn’t work. And even when you think about the Scrum ceremonies and how you do sprint planning and you have two weeks of work that you’re committing to, and that does not leave a ton of time for experimentation. So it’s not that when we switch to Kanban, Agile’s dead, it’s more the actual practice is shifting. So we’re moving away from a more regimented Scrum ceremony, Scrum timeline, Scrum metrics into we have a prioritized backlog and you pull things out, you work on them. If you get stuck, you put it into a blocked row and it gives us a chance to honestly be more agile and more nimble than we could with Scrum. And with Scrum, you’re committing to story points and these blocks of work where honestly, it might make more sense to just prototype a really small piece of that, learn from it, either keep building or try something new. And so I just don’t feel like Scrum leaves enough room for experimentation. And that’s one of the really big unlocks with being AI native, is you’re able to transition more so away from a very regimented way of working to a way of working that’s a little bit more flexible. So I think in the spirit of Agile, like hypothesize, build, test, learn, adjust, constantly inspect and adapt, this is actually more conducive to that than traditional Scrum.
Douglas Ferguson: I’ve used Kanban for years as a CTO. I was a big fan of it just because it’s nimble. And one of the things I’ve noticed when organizations move from a rigid adoption of Scrum, because some folks have a loose adoption, but especially the ones that have a rigid adoption when they transfer to Kanban, they tend to step more intentionally or more purposely into the Agile principles. Because it’s funny that often the Scrum rituals are preventing people from truly adopting the principles.
Alyssa Coughlin: Right. It’s meant to be a framework, but it really in practice becomes more of a gospel, and people get so stuck in, “This is my job today, these are my story points.” And honestly, it worked in the old way of coding, but in the new way, it’s just better to be nimble. And the more you experiment, the more you learn, the more you correct, the more you’re gaining context. And if you’re doing it correctly, if you are learning with your AI tools, then they’re learning context as well. So it’s really a way to expand the knowledge graphs of both the humans and the AI. It’s a better way of working in this environment.
Douglas Ferguson: Yeah, that’s really interesting. And I’m curious how meta that gets for you. Do you have the AI observing your Kanban board and giving you feedback on how you’re thinking about the roadmap and sequencing of tickets? Is it involved in those steps currently?
Alyssa Coughlin: A little bit. We’re just starting to build some of that out. Because that’s been honestly the most challenging part of shifting to Kanban is losing the traditional Scrum metrics. So how are we measuring the productivity and the efficiency of our organizations without those? Because the thing is, not every experiment’s going to be a winner. And so we’re not necessarily looking for how many things got pushed to prod, token maximizing, it’s so easy to fudge. It doesn’t necessarily translate to business outcomes. So we have been struggling to try to figure out how do we measure success in this new way of working? And one of the things we are doing is, we have created a formula within Jira to help us gauge how well our individual teams are transitioning over to Kanban. And it’s been really interesting because we’ve got the whole gamut. We’ve got some that still really have one foot in the Scrum lane and they’re struggling with this change. They like the predictability, they like the standardization. And then on the other end of the spectrum, we have teams that are fully embracing Kanban and they’re enjoying the kind of Wild West of just pick a story out of the backlog, grab your agent and have at it. And then of course, everything in between. So I think it really comes back to the change management. It’s everywhere, because we are completely changing operating models, we’re completely changing operating rhythms. Every person who would wake up and be like, “I know exactly what my day’s going to look like,” now has to grapple with, “Well, let’s see where it takes me.” Which as a chief of staff, it’s every single day for me. When somebody asked me to describe my job, I’m like, “I don’t know. What day is it?”
Douglas Ferguson: “What day of the week is it?” So one of the things that I find liberating with Kanban was the fact that the measurement was about the cumulative flow diagram. And so we were basically estimating our average lead time. So it’s not necessarily what are we going to get done or planning this two-week chunk, but just on average, it’s taking X days to get something to production right now, and we can easily see that because the system is designed just for the flow of work. The release train is the work, right? And so we can just say, “Hey, it looks like these things are running at about this speed.” And so if we look at the backlog, we would guesstimate this is how long it would take to get through. And even though it feels less predictable because it’s less like we’re putting this chunk of thing on this thing and planning it in a super structured way, it’s almost more predictable in the sense that we have a sense of what the system is doing. Even though it’s a bit more emergent, we have a handle on its throughput. And so even if we rearrange the deck chairs a bit, we know it’s going to flow through.
Alyssa Coughlin: Yeah. And I think in addition to that, the value proposition changes. With Scrum, you don’t really review whether or not something worked until your full two-week sprint is up and you’re in your sprint review. And then that’s where you review it, you get customer feedback, you take a look at whether or not something worked. And so the emphasis within Scrum is really on completion of work versus I think a huge opportunity with Kanban is the emphasis switches to learning. It’s not, “Did I complete X amount of story points in this sprint?” It’s, “Did I learn? Did my agent learn? Was I able to share that learning?” That’s a really big part of it. And that’s another organizational challenge I’m struggling with a little bit, is when you start moving at such a fast speed, identifying and catching silos and duplications becomes a lot harder. And so that’s kind of coming back to where an AI native environment really does expose the seams in your organization. Where are things connected versus where might there be a little bit of a gap? And so that is another consideration with Kanban, is people are just kind of grabbing things and running with them and I love it. Let’s move fast, let’s learn, let’s break things. But at the same time, let’s not build the same thing in three different places.
Douglas Ferguson: Yeah, absolutely. Yeah. I wonder how much of that are you anticipating that agents might help with? For instance, scanning for duplicate work, or de-duplicate even some of the things in the backlog to even get ahead of it or even just finding issues with stuff that, because we talked about the review cycle getting so heavy and that creating such a burden on the team. So I’m just wondering about some of these kind of adjacencies even though it’s not necessarily a step in the product development lifecycle, but it’s the tooling that helps make things keeps things from falling apart, if you will.
Alyssa Coughlin: Yeah. And that’s really where context and knowledge graphs are so important. And it’s not just the technical documentation, it’s also the organizational knowledge, so much of which lives in either disparate disconnected locations or it just lives in people’s heads. And so the more we can pour that knowledge into context and knowledge graphs that AI can read, AI can turn into semantic search, the more we can help mitigate some of these circumstances. So one of the things we are doing in my organization is each group is creating their own brain as we call it. And so every organization is expected to develop some form of a knowledge graph, which can then be connected to all of the other organizations’ knowledge graphs and really create an Autodesk brain for us. And so obviously it’s down the line, we’re still working on just even gathering that knowledge and connecting the right MCPs and figuring out what that looks like. But eventually, ideally the AI can help us catch some of these things before we go too far down the road. And then we realize in some sort of review or QBR that we built the same thing twice.
Douglas Ferguson: Yeah, I’m super curious about these kind of. It’s almost like AI DevOps or developer experience kind of stuff. How are we investing in the infrastructure surrounding the experience of building the software? And because it’s not just about site reliability and how we deploy and get stuff live, but it’s also to your point, how are we identifying that there’s not duplicate tickets in the backlog or that one developer built a feature, but the AI decided to be super thorough, so it superseded three other things in the backlog because hey, it was there, it did it, it figured it out, and how do we discover that stuff and patch it? And it’s like I’m even experiencing that to some degree on my own. I built a harness and I have some agentic employees that are helping augment the team and support them on a lot of laborious, tedious kind of things or even stitch together stuff that was just really difficult to do because there was multiple systems involved. So one of the things it does is, as we’re building stuff, the agents will identify things that maybe we decided to set aside for later or that an idea that we came up with that we said, “Let’s just not focus on that now.” And so my backlog just grows and grows. And so every now and then I’m looking at, I’m chatting with the agents about what we might pull from the backlog. And so much of it’s been superseded because we just fixed it along the way while addressing some other thing. And sometimes the agent didn’t even call it out. I’m noticing in a pull request or we’re even noticing it when I go into planning cycle. So I think there’s a million ways to solve it, whether you have some sort of Damon that’s scanning the system constantly for these things or even a rule set in your planning phase that’s saying, “Hey, let’s double check that nothing’s been released to production or to the dev environment that actually addresses this issue.”
Alyssa Coughlin: Totally. And I think the most important thing about that entire point is the significance of the human in the loop. AI is just a worker bee, it’s going to keep going forward. It’s not necessarily stopping to think about is this the right thing? And so that’s really where the human expertise shines. And when you’re working with AI, telling it what to do is just as important as telling it what not to do. These are your processes, these are your checks, these are your guardrails, here’s your harness. All of that requires a human making the call. There’s human judgment and that is really what powers all AI development. Another thing that we’ve been doing is we’ve been requiring everyone to move towards spec driven development. So instead of just writing a PRD for a human, we’ve transitioned to writing specifications so that it is consumable by both humans and AI. And it’s more about bringing AI into the loop, treating AI as a partner and a member of the team and learning how to work with it versus just treating it as a tool. But yeah, completely to your point, the AI is just going to run a muck if there’s not a person in there to give it some coaching and some guidelines.
Douglas Ferguson: Absolutely. And not necessarily in a bad way, you just might not get efficient business outcomes. Because you hear all these horror stories around, “Oh, AI broke into hugging phase.” Or AI went and did this thing that was unasked for. And even in the most innocuous ways, it might be, to your point, spinning up duplicate work or doing things that are redundant. So I think it behooves us. And this is where people mourn the loss of the craft. I think the craft is just shifting. How are we being thoughtful and intentional about how we deploy these tools in ways that don’t consume tokens egregiously and our efficient use of them? And it’s not super simple. It’s not always like sometimes reaching for the cheaper model is a more expensive route to go.
Alyssa Coughlin: Yeah. And I think that’s been a really interesting industry trend, is everybody is really moving towards those open weight models because faster’s not always better right now. Just because you’re working with AI and it can build five things in the amount of time it used to take you to build one, well, if all five of them are wrong, have you still accomplished any business outcomes?
Douglas Ferguson: Right.
Alyssa Coughlin: Faster isn’t always better. And so again, that’s where that human expertise comes from. And so I think when people have this existential mindset, I think they’re really missing that component. And I think they’re missing a really exciting opportunity in that the scope boundaries of positions are really shifting and melding during this era of becoming AI native. For example, I’ve got product managers who are coding prototypes now as requirements. So they’re kind of teetering into that engineering section a little bit, and engineering is teetering a little bit more into kind of a people and operations perspective because they are that orchestrator now. And then you have XD is now kind of a little bit of everybody’s job because everybody needs to be thinking about that end user experience and that customer value. And so what it means to be an engineer, what it means to be a product manager, what it means to be a designer, I think all of that’s going to shift. And I think having a mindset of this is opportunistic versus existential, it’s going to be really critical in people succeeding in this environment.
Douglas Ferguson: Have you gotten to the point where role definitions have started to officially change or it’s still in this kind of liminal space where behaviors are shifting, but we haven’t necessarily documented it in a role or title shift or a responsibility, maybe not documented yet? I’m kind of curious where things are on the journey so far.
Alyssa Coughlin: Yeah. Well, we’re a large enterprise, so actually changing people’s job descriptions in the formal HR way has absolutely not taken place. However, the ways of working, everybody uses AI, everybody is a creator. That is very much in place. As far as what is the scope boundary for product management versus engineering, we haven’t necessarily formally defined that. Because I think we’re still figuring it out. Right now we’re still trying to learn what does our business model look like with AI? And then I think from there we can back into, okay, what roles are needed to support this model? So there’s definitely a spirit of experimentation. There’s definitely product management getting in there and kind of vibe coding something versus trying to explain it in a PRD. But we haven’t gotten to the point yet where we are formally shifting those roles. We joke that these big companies are big ships and they don’t turn quickly.
Douglas Ferguson: Well, also it kind of gets back to this idea of experimentation versus exploitation. And if you want to be innovative, you got to stay in this experimental mindset. And titles and definition and specificity is more about optimization. That’s when you’re in the exploitation phase. We’re starting to exploit the knowledge and the innovation that we learned and landed on. And so I think prematurely defining those things would be a hindrance, because it’s harder to adapt once we’ve locked in new titles or new definitions.
Alyssa Coughlin: Yeah. I think the biggest shift we’re seeing right now is actually with managers. Our expectations for them are shifting quite a bit in that all managers, nobody is just a people manager anymore. Our managers are expected to also be part of the team, to be technical experts as well, and to be able to lead the team, but to also be able to contribute. Because down the line when we eventually have these teams of people and agents, a manager’s going to have to be able to manage both. And so having them jump into the work and jump into the technology is really important. It’s also requiring a shift in their mindset and how they manage and that it’s not just about outputs anymore, it’s about outcomes. So again, coming back to token maxing is a really easy way to fake productivity. It’s not just how many things did you put out there? You’re really coaching your team and rating your team based on what business outcomes did they unlock? And to make that a reality, you do have to leave room for experimentation and you have to leave room for failure. I think freedom to fail, honestly, reducing the fear of failure. It’s something that really thrives in the startup world and it’s not as readily embraced in large companies. But I think that’s a really big transition we’re seeing as well, is it’s okay to try something, that’s kind of the whole point of this rapid fire Kanban way of working, is it’s not a failure because you still learned something even if it didn’t work. And learning is how we really want to measure success going forward.
Douglas Ferguson: Yeah, I hear that and I think of two things that I’ve seen across clients and just listening and paying attention to where folks are at these days. And one is, this managers have to get comfortable with these tools if they’re going to coach through the use of them, because if you think back to the days of coding with punch cards, if that’s all you know, then using modern abstracted languages through a keyboard, it’s going to be very difficult to manage a team doing that work if all you know is punch cards. And so I think managers need to upskill and be comfortable. And it’s not just upskilling, it’s really a behavioral paradigm shift. So learning the new ways of leaning into these tools and working in the ways that these tools can open up for you is really important for folks to experience firsthand. And then second, I think that it’s really critical that managers understand the new competencies that are critical for people to survive in this era, in this moment. For instance, we already talked about you’re shifting to the orchestrator. And so if engineers need to learn how to delegate because now they’re orchestrating, or if they need to get better at eval, the manager cluing in on those things and knowing where the got yous are and knowing what’s uncomfortable and use the word friction again, understand and diagnose the friction, they’re going to be a lot better at coaching their team through those moments too.
Alyssa Coughlin: Yeah, absolutely. I mean, I know a lot of companies are starting to creep up on Q4. And so when we think about giving feedback to your teams, how can you accurately do that if you no longer understand their work? And that aspect of being able to be an efficient manager and mentor, being hands-on is really important. And the other benefit is not only are they learning the tools, but they’re working with their team’s technology firsthand, which again allows everyone to think through that end user perspective. Call it eat your own dog food, drink your own champagne, whichever trope you prefer. But it gives them a chance to really experience what it’s like to be a member of their team and what it’s like to consume the technology they’re creating.
Douglas Ferguson: Absolutely. A few things I wanted to come back to, you mentioned the shift towards spec driven development, and you also mentioned that product managers are creating basically vibe coded prototypes. Are you seeing that the prototypes become part of the spec that is given to the LLM or what’s that kind of ritual or the shape that these specs are taking?
Alyssa Coughlin: Sometimes yes. We’re still very early in the journey and there’s definitely a change management aspect to it. I had one PM make a really great prototype that did end up going into production, but engineering originally kind of ruffled their feathers at how dare you. So coming into that, everybody has an opportunity to be more AI is an amplifier mentality. We’re still learning the best way to incorporate spec driven development into our existing development workflows. We don’t want to throw the baby out with the bath water. There’s a lot of good processes and tools already in place that we want to learn how to weave AI into. And I think what’s important as well when you’re going through these processes is slapping AI on everything is not being AI native. That doesn’t fix your problems. It’s really inspecting what does your workflow look like? What does your operating model look like? What are tasks or areas that you could leverage AI in and then free up your human bandwidth and capital to focus on more challenging topics that require that human judgment? So we’re still navigating best practices and we’re still learning, but it seems to be, it’s becoming pretty well embraced, and it’s becoming the operating norm across our organization. So yes and no. Some of it’s gone into prod, but we’re still learning and that’s kind of part of the fun of this adventure.
Douglas Ferguson: Yeah. I ask a question out of curiosity because I found that even if the prototype is throwaway, it tends to be really valuable during the spec process. I’ve always been a big fan of visual specifications. If we can show what we imagine the product looking like before we build it it’s a lot easier to build it because then we can start poking holes on edge cases or where are we going to need to put an error handling or what if someone’s name is really long? It might push out of the space. You can start asking a lot of these questions that are a lot harder to ask when you just read about something. And so I’ve found taking the visual spec or prototype or different mock-ups or even customer research plus some technical maybe architectural things and requirements and constraints, putting it all together and letting the AI have access to that, super powerful when we start putting together the plan for how we’re going to build.
Alyssa Coughlin: Yeah, definitely. And I come back to if you learned something, it wasn’t a failure. And that’s such an important mindset shift when it comes to AI and when it comes to experimentation and innovation, you don’t have to get it right for it to be valuable. You could have learned exactly what not to do or you could have learned where you had a gap that you want to fix in the next iteration. And that’s the really cool thing about specs too versus requirements, is they’re kind of living documents. They’re always evolving. You’re always adding to them as you learn more. And so they’re valuable not only because they are consumable by both humans and agents, but they learn.
Douglas Ferguson: Yeah, absolutely. I love this evolving living document concepts. It also comes back to the knowledge graph you were talking about where if teams have knowledge graphs, departments have them, orgs have them, it gives us a lot more power to use these tools in a more deeper fashion. I think a lot of organizations that are kind of handcuffed are ones where the agents don’t have access to the knowledge they need, to the tacit information that are in the heads of the humans. And if we don’t unlock that stuff, if people are hoarding that stuff because they’re afraid, it’s really going to be more limiting than anything. And it doesn’t bode well for the individuals that are doing that, because they’re only delaying the inevitable because the more that we can share and safely share and understand what’s safe to share, the more that we can understand how these things work, the better guardrails we have in place, all that stuff. So yeah, I totally agree. As we start to head toward wrapping up, you mentioned quite a few changes to the nature of the work, this adopting spec driven development, shifting to Kanban. Anything else shifting or changing as it relates to the product development life cycle and how folks are putting the software together?
Alyssa Coughlin: I think my biggest takeaway is lean into learning. So instead of going into a project or approaching a team with what’s the plan, rephrase that as what are we trying to learn or what did we deliver versus what did we unlock for our end users? It’s transitioning away from this very black and white, I deployed, I executed, it’s done, to how can I completely evolve and grow and transition as I learn? So it’s much less of a regimented stagnant process. It’s ever evolving. I mean, consider yourself to be one of the specs. You’re learning and you’re growing and you’re connecting new data and making new insights. And my favorite way of thinking of AI is, we have transitioned from a bicycle to a car. So a car can take us the same place as a bicycle can take us and it can get us there much faster. But what’s really fun about the car is it can go so much further to places that we’ve never been able to go before. And I think going in with that learning and that experimentation mindset is more important than any of the tools themselves.
Douglas Ferguson: Yeah. Maybe another way to think about that too is, once you get there, you’ll have a lot more energy to enjoy the place that you got to.
Alyssa Coughlin: Way less sweaty.
Douglas Ferguson: Yes, that’s right. Amazing. Well, as we come to a close here, I’d love to leave you with an opportunity to offer up a final thought to our audience.
Alyssa Coughlin: Yeah. Well, I think my biggest recommendation to everyone is, AI is an amplifier in every capacity and lean into that. Don’t be afraid of it. Don’t be afraid that it’s going to highlight the bad right along with the good. See all of it as an opportunity to figure out where that real friction is, where do you need to focus your energy, and then move on. Once you fix that one place, AI is going to amplify something else that needs to be fixed. And so it’s a really great partner for evolving your people, your technology, your processes. It’s going to show you everything and all you have to do is just respond.
Douglas Ferguson: Amazing. Well, it was a pleasure chatting with you. Thank you for spending some time with us and we’ll chat with you again sometime soon.
Alyssa Coughlin: Always a pleasure. Thanks for having me.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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]]>“AI is not displacing the team. It’s just giving you a stronger, sharper mission and a sharper why on what you’re doing and clarity of purpose.” – Jeff Chow
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Jeff Chow, Chief Product and Technology Officer at Miro, about where friction is showing up inside product teams as AI reshapes how work gets done. Jeff describes how PMs prototyping in high fidelity, designers coding, and engineers orchestrating agents are collapsing old role boundaries, and argues the underlying tensions — who owns a decision, who gets paged at 2am, how a designer receives feedback — aren’t new, just amplified. Much of the conversation centers on decision-making as the real bottleneck: Jeff makes the case that 10x individual output doesn’t translate into 10x business results unless organizations can also accelerate cross-functional alignment and cascade decisions with their underlying context, not just their conclusions. He and Douglas dig into Miro’s bet that a shared visual canvas — carrying both artifacts and a decision log of why choices were made — is the connective layer that keeps AI-assisted work and human teams moving together instead of fragmenting into isolated single-player sessions. The two also trade observations on viral AI adoption patterns inside organizations, the psychology of design crits applied to AI-generated work, and the early signs of “ephemeral UI” and custom, vibe-coded widgets reshaping what software teams expect to build for themselves.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:00:00] Introducing New Friction With Jeff Chow
[00:02:15] Where Friction Is Showing Up At Miro
[00:05:45] Guarding Against Dehumanizing AI Mandates
[00:10:30] Lo-Fi Versus Hi-Fi Prototyping Debate
[00:15:20] Visual Specs Replacing Product Requirement Docs
[00:18:40] Chasing Magic Moments Of Invention
[00:22:10] Decision Bottlenecks And Cascading Alignment
[00:26:30] The Canvas As A Shared Context Layer
[00:31:00] Multiplayer AI Adoption Across Departments
[00:36:15] Ephemeral UI And Custom Widgets
Jeff Chow on LinkedIn
Voltage Control
Jeff Chow is the Chief Product and Technology Officer at Miro, where he oversees a visual collaboration platform used by tens of millions of people to align on strategy, product development, and decision-making. He describes himself as a “recovering founder” and a self-professed “ways of working nerd,” with a career spent building customer-centric digital products and leading teams through periods of disruption. Before Miro, he held senior product and executive leadership roles, including CEO and CPO at InVision, and product leadership positions at companies focused on travel and consumer technology. Jeff is especially focused on how organizations make and cascade decisions at scale, and on using a shared visual canvas to keep human teams and AI working from the same context rather than fragmenting into isolated, single-player workflows.
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved, and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Jeff Chow, chief product and technology officer at Miro. Welcome to the show, Jeff.
Jeff Chow: Hey, Douglas. Great to be here.
Douglas Ferguson: Yeah, thanks for coming and really looking forward to the conversation. And this is the New Friction series, and we’re talking about how AI is reshaping roles and the ways that organizations work and how the friction is shifting and appearing in different places. So I’d love to just start there. What are you noticing at Miro as you’re building the products of the future, as we’re thinking about how AI impacts work and as you’re building these tools, what are you noticing friction-wise? Where are folks struggling or what’s showing up as new problems to solve as we think about the product development life cycle?
Jeff Chow: Yeah, I mean, first and foremost, probably my favorite topic. I’m a little bit of a ways of working nerd, and anytime there’s disruption, I’m always like, “Ooh, what is this going to do?” And not being dramatic, I think we all would all agree this is likely the mother of all disruptions in terms of challenging ways of working. So always great for that mindset. Yeah, I mean, I think it’s a very interesting thing for Miro because we both have a product that is driving some transformation of how teams work, and we ourselves are going through that transformation. So it gets very meta within Miro. I think the standard ones that you’re seeing around friction is starting to present itself. And it’s like product managers are now prototyping, designers are now coding, developers have product mindsets, they have their orchestrating agents as opposed to coding themselves. And so I think there’s that just fundamental skills shift is driving a rethinking of the PDLC. And then maybe I think from a pure friction perspective, I see that that’s actually introducing very interesting tensions maybe we’ve always seen. It’s like how does a designer and a product manager align when the product manager creates a high fidelity workable prototype to begin with? There’s a lot of psychology that gets into that around how do you actually align? If a designer can check in front-end code, what does that mean around a lot of the sensitivities around code management, SLAs, who actually gets paged in the middle of the night? And so there’s always a lot of those things. But again, I think there’s also the glass half full, which is like, it’s worth the friction because it’s a really interesting time to drive impact for an organization.
Douglas Ferguson: Yeah. I mean, folks that enjoy organizational design, it’s a perfect moment to really reflect and think about, well, what are the impacts here and how do we really put folks in positions and give them context so that they can be successful?
Jeff Chow: Yeah, 100%. And I would actually just maybe one other thing to say is this is kind of what we’ve always wanted. And I would say the opportunity, and maybe that’s sometimes when we get into the minutia of the pain points, we can kind of lose the plot and we have to stick to that, which is the, isn’t it great that teams are able to co-create even more together? Isn’t it great that we can actually reduce the doing of the work so we can think more about the highest impact work? And how do we harvest that more? And my job as a leader in org is to try to keep the change to a glass half full take as opposed to a glass half empty take.
Douglas Ferguson: Yeah, and I agree. And the thing I’ve noticed that sure you have folks that are, let’s say, resistant that that goes with any kind of change, and it’s just attending to that and understanding the values that are driving people’s needs. The thing that I think that’s unique in this moment that we had to attend to as leaders is if people are feeling dehumanized by this stuff. A great example is I ran into my neighbor not long ago and she actually works in public sector and they were starting to adopt some AI stuff and her work. And it was a project that the team had been advocating for a while and the leaders finally said, “Oh yeah, we should do this because the AI told us to do it.” And so that’s very dehumanizing because they’ve been advocating, let’s do this, let’s do this. And then now the AI is saying doing it so the leaders are on board. And so we have to watch out for those things. We really want people to feel empowered and-
Jeff Chow: Yeah. I will say though, it’s really interesting because of, I actually don’t think anything is new around the friction. It’s just amplified. It’s like as a leader, if a team gets a mandate, say we wanted to change a strategic direction or we wanted to shift our product priorities, if the team goes to their team and says, “Hey, we’re redoing this because Jeff said so,” they absolutely would be demoralized and whatever. And it’s no different from AI saying that. And I just think that that’s what we’re feeling. There’s nothing new about product managers coming with a high-fidelity wireframe or clickable prototype and a designer rolling their eyes.
Douglas Ferguson: Yeah, just more of them can do it.
Jeff Chow: More of them can do it. I used to be that person. I would use keynote because I’m old and back in my day I would use magic move and keynote and I’d have these amazing transitions and I’d have a mobile skin and I’d be like, “Hey guys, I have an idea.” And they’re like, “Oh, here we go.” And so it’s not new, it’s just scaled. And I think so that means things that we should have probably addressed in the past. It’s like, well, how do you get a designer to yes and that process? And how do you get Jeff with the really beautiful keynote to be willing to kind of boogie and expand their thinking as a way to communicate, not just like, can you please build this? That’s literally human nature since the dawn of time.
Douglas Ferguson: Yeah, that’s fascinating. I was also thinking about when you have PMs checking in code, I love your analogy or your point around who’s going to get paged in the middle of night, but it ultimately comes down to the rigor of the process and are we following the standards? And I think that gets a lot more difficult when you have folks that aren’t completely trained in those things or aren’t thinking about those concerns because they’re dispositionally oriented somewhere else. And so I’m just kind of curious, what are y’all noticing there as far as how to open it up so that you can support those kinds of things? Because it is valuable if a PM can just change a typo, why pull in a ticket for a developer to have context switching just to do that work? But at the same time, how do we have the guardrails to keep things smooth?
Jeff Chow: Yeah. We’re going through that journey right now, and I would say we by no means have nailed it, but ultimately I think it’s about getting people to be willing to say, “I can see how this could be really great on the other side.” And then it is a deep, deep, deep amount of empathy. You might not have had to actually have the discussion around who holds the bag, but guess what? Engineers talk about that all the time. Another team contributing to your code base because we should have platform level thinking. Again, not new. Again, I’m a PM by trade, so I can make jokes about PMs. But having a PM with that attitude of how hard could it be to. And then all of a sudden that permeates. Of course, engineers are going to be like this, and then they’re going to be like, “Let me tell you all the bad things that could happen,” even if it’s just a text change. “Have you thought about localization? Ah.” Stuff like that. So I think there’s a little bit of, there’s a really dynamic change if people feel like they’re in it together. If they’re being told, “Not going to work,” and things like that. So we’re going through that. So one is getting everyone to accept that, is this a problem we want to solve together? Yes. Intuitively, designers from the dawn of time have created these amazing experiences that have been air quotes approved. And then by the time it hits production, it looks nothing like it. Now you actually can have a designer iterate 25 times until they feel like they’re really happy with what they can do. And that’s an amazing opportunity. And that frees up the other front end engineer to do some amazing things as well. So I think the opportunity is huge, and I think it’s really plays to the strengths of each individual. So the change management is worth the journey.
Douglas Ferguson: I think about prototyping culture, and the reason it’s so powerful is that you can share your ideas quickly and get feedback and correct them before you’ve really fallen in love with them and they’re too resistant to change. And so I wonder if there’s a flip side here with things being so high fidelity and people being able to iterate so much by themselves, is there a risk of getting into this false sense of confidence before you even share it with anyone that you’ve fallen in love with it and so you don’t want to change it?
Jeff Chow: We talk about this all the time. We have our own prototyping product. We integrate with all the prototyping products. And a lot of our users at Miro are of the kind of UX experience design, concepting and validation. So it’s really a big topic. And I’ll tell you where we’ve gone, one, there’s no hard and fast rule. And so I think there’s two paths. One is tried and true practice, start lo-fi. It keeps the barrier there and even create design systems where it’s intentionally lo-fi so that when you’re sharing it, you’re visually communicating, it is at the right fidelity. That’s a really powerful one. There’s also one which is what we’ve seen a lot is the visual communication of the art of the possible, telegraphing what is possible. Actually, high fidelity experiences really help, especially for what we would call brownfield development, existing work, you’re trying to try something out. And it’s actually inspired higher levels of ambition from designers too, by the way. It’s like designers, product managers, even engineers, they have this idea in their head that will solve a strategic problem that as a business we’re trying to solve. I’ve seen more invention coming out of this, and that’s amazing. And so on that track, what we’ve decided is, okay, what are we trying to avoid? Well, that first high fidelity prototype is more a visual storytelling mechanism than it is like, I want to check this. It’s like if we accept that, then what’s the next step that would be bad? Probably treating it like a design crit. All of a sudden people are just throwing rocks and you’re like, “That’s horrible.” So we’re trying to create these kind of collaborative techniques that invites ideation because that’s the step. The step is like, okay, maybe you need to diverge and converge, but you started a great conversation. And so if you treat it like that, then we have these capabilities like variations. It’s like, okay. And our ways of working is like, okay, you share a prototype, you tell your story of what it’s trying to solve. But when people give feedback, especially around the design, we first start with variations to get truly divergent ideas of solving the same problem. And then people are now reacting to what they like and don’t like against a sample size of three or four. And it becomes less personal. It’s not a crit. It’s just actually more of a open kind of ideation session. And I think that’s one way of maybe not reverting to just the lo-fi to high-fi. It’s more of like, okay, what’s the new way of what I call boogying together and creating that experience? And to us, it’s all psychology. It’s like if a product manager comes with a prototype and you’re like, “I want to build this,” then the designer’s like, “Okay, I’m going to critique. Why is there a sixth button? That’s crazy.” And you’ve lost the plot. You’re not actually talking about the value you’re trying to communicate. You’re just nitpicking the pixels.
Douglas Ferguson: Yeah. And also, I think it’s fascinating if folks sit with the opportunity or the challenge or the problem that it’s solving, and they critique that. It’s like, “What is this prototype telling us about the questions we’re trying to ask? And how satisfactory is it at answering those questions?” And to your point, are there variants, other ways to get at that problem? Because the prototype in a way can help us understand a challenge or problem or opportunity better. That might be the value it brings to us. It’s this genius spark of innovation that we’re getting from this developer that now the whole team can start, to your point, boogieing on it and coming up with other variations and things. And the thing that I’ve found about the high fidelity, there’s two things I really love. It’s the folks that really have trouble thinking or communicating in visuals. Then they can write their specifications. They can sit there and describe it all day long or even just speak into the AI and prompt it and then generate these great visuals and it democratizes that ability to communicate in visuals. And the more things are common and similar and familiar, then we’re not critiquing the differences in style or presentation. It’s the merits of the idea.
Jeff Chow: Yeah. I’ll say 100% agreed. I think it’s truly democratizing work in a way that I find amazing. The other way we think about it, and I think this is along your lines, is there’s a little bit of work theater to writing a product requirements doc. You have a kickoff and you’re like. Because most of the time people have an idea in their head and then they try to reverse engineer the how to the why and the what. And then they’re being very academic about it. And so we’re sort of like, “That’s cool.” And you have the classic founder, and I’m a recovering founder myself, you’re always painting in the air being like, “What if we did this?” And then you rely on the product team to abstract the requirements from that and all this other stuff. I think there’s something really relieving about just saying, “You know what? Share the how.” And it’s actually a visual spec. It’s like, great. And as a team, we’ll abstract the requirements from that. We’ll talk about this. Because it’s a real human, a natural human behavior of when you have an idea, you kind of know exactly a little bit of a thing. And so you might as well just share it. We don’t have to go through the work theater of it. And then we can align on what are the moving parts, what are the principles, what are the et ceteras? And even when you say that, you have a visual example of what the principles might be. So it’s just way easier for people to align. Whereas the other approach, which is the classic PDLC, is you start with the product requirements doc, you then wait a couple of weeks to get your first design. Engineers don’t actually have a visual proxy. They’re architecting in the void. It just feels like looking back, it just feels very antiquated at this point.
Douglas Ferguson: Yeah. So it’s definitely more fun, more explorative. And it reminds me of this awesome innovation concept of, and it goes like this, the main impediment to innovation is your first good idea. You get to something good that works and it’s hard to get that out of your head. But the system you’re describing where it’s like you just, okay, there’s the how, let’s just use AI to just manifest that quickly, and then let’s decompose it together from the materialized endpoint.
Jeff Chow: Yeah, that’s right. That’s right. And I think that’s for us, I think there’s this kind of north star, the how becomes immediately launched and we’re all fine. And that might be the case for certain JIRA ticket level improvements, if you will. But I do think keeping the actual prototype as not the design handoff, but as the visual communication vehicle, it’s a pretty good mental model to get teams to understand, okay, I get it. It used to be pros. Now it’s like, okay, it’s a way to symbolically have that. And then I think the change management, the friction, if you will, is we just have to be honest with ourselves. Probably that product manager does want to ship it. And probably a designer’s having trouble with that. They want the ones who created the first version. And how do you handle that level and create a great collaborative environment despite that? And that’s where I go back to, that has always been the friction. So we have to solve it anyways.
Douglas Ferguson: Yeah, I mean it comes down to identities and how folks think of themselves and what their responsibilities are. And if we can break those things down and reimagine them, then I think it creates a lot more possibility.
Jeff Chow: Yeah. I’m sure you’ve seen this in the past, but there’s nothing more magical than when somebody creates an idea that is so cool, that is so amazing that the front end engineer can’t shake it out of their mind and they would just want to build it. And the team just kind of rallies. And because there’s oftentimes not that, those magic moments, that’s when magic happens. Somebody does something that unlocks everything and then the teams happen. I’ve for my entire career have been chasing that endorphin hit. It’s always fun. When that magic happens, business trajectory changes, all this. It’s true invention. And the reason I get so excited about this stuff is this has actually now democratized that. It’s like, okay, anyone can do that. And we at Miro have seen these kind of magic moments where somebody’s just like, it’s usually a very introverted junior or somebody. And they’re like, “Hey guys, I had an idea.” And they share it, they record a talk track on it. And then you can just see people like, “Oh my God, yes, let’s do that.” And all of a sudden the energy shifts. And I think that’s, especially if I look at the companies we talk to, they’ve built for decades an optimization culture. And now they’re sitting in the face of like, “Oh wait, it’s pretty competitive right now, pretty existential. The only way out of this is innovating your way out of it.” And I do think that’s the starting place here. It’s like, okay, there’s some really great things. But I do also think it’s not about displacing. AI is not displacing the team. It’s just giving you a stronger, sharper mission and a sharper why on what you’re doing and clarity of purpose. It’s like, okay, I could run 25 experiments on sizes of this button, or I can actually do something that actually changes the trajectory of this organization. And I think that’s really exciting.
Douglas Ferguson: Yeah, it’s definitely shaky ground for folks because it’s just kind of uncertain how roles are going to shift. But I’m also of the camp that I don’t think we’re going to see massive job loss, but we’re definitely going to see massive job redefinition.
Jeff Chow: Yeah. Yeah, for sure. I think about all the roles blurring 100% is changing. I think a lot about product operations and every product ops leader I talk to both has a little bit of an existential feel to it, but also an excitement because it’s like, okay, I didn’t necessarily like pushing teams to follow processes versus making sure that we are actually creating something that can scale an organization and deliver impact. And so I think these kind of shifts are pretty deep and exciting if you make it that way.
Douglas Ferguson: One piece of friction that we see a lot, and I’m curious what you’ve noticed, middle managers especially, or just anyone that’s in a position to receive output from others are getting overwhelmed because it’s so easy to generate output. So the reviewers, the people that have to evaluate and review things are just getting hammered with so much content and stuff to review. I mean, luckily the tools allow us to review things faster, but ultimately if we want human eyes on things, that is a bottleneck right now.
Jeff Chow: For sure. I mean, I think you can’t get 10X business outcome with just 10X individual velocity. And a lot of that is around accelerated decision-making. And it could be reviews, it could be decisions, it could be alignment. A lot of the times it’s where we see the bottleneck is the cross-functional alignment, like strategic decisions, et cetera. And so we see that as a huge opportunity. And when we think about when you’re talking about the middle layer, et cetera, I would say the honest truth is a lot of the times the middle layer is more people and process managers. And now’s the time that. And frankly, most of them don’t want to just be that. It’s just like one day we woke up and our ways of working were treated that way. And now it’s like, no, actually we need you to make 10 strategic decisions a week. And how do you do that? And I think that’s fantastic. That is true, the ball moves forward on every layer of the organization. Fantastic. Now, how does that happen? That’s going to be a tough one. And that’s where collaborative decisioning is back to the old bottlenecks are still there, which is if you have a lot of reviews, then you have to do it asynchronously. Very difficult to have asynchronous feedback loops happen at pace to make a decision. If you want to do it synchronously and it’s a really tough problem to solve, how do you empower our team to share it? Or how do you create the right scaffolding of decision options, pros and cons? Because there’s no perfect solution. And oftentimes organizations tend to iterate 93 times to get to the, there must be a silver bullet solution. And guess what? We make money because we have to make decisions that aren’t easy to solve. And so I think that’s just what we have always had to do, and it’s an important opportunity. And we obsess over this at Miro because ultimately we view the canvas as that alignment and decisioning layer and accelerating it. So we’re trying to figure out ways to make it easier to get people to do that.
Douglas Ferguson: Yeah. I’ve been super excited about where Miro’s headed AI-wise and how the Canvas can provide such an interesting alternative to this kind of linear text chat history context. Because I saw a good example of that earlier today in a mastermind I was running where one of the members was sharing some of their clawed workflows. And I was just kind of looking at how they. I was noticing a lot of it was baked into one project and they were shifting between these different sessions and stuff and was just reminded how tough it is for folks to just sit back and think and design how they’re going to manage their context. Where Miro is much more intuitive when you’re looking at the canvas and things are sitting out there and you can just marquee, select whatever you need to go into the context. Or you have to set up flows so it’s visual what’s going where. I think it’s going to be a game changer for folks. The more and more folks that start to work in that way and start to realize how much I’m shaping the context visually and intentionally I would say. Because when you’re in a chat, you have no control. You can’t say, ignore this piece of the context, right? It’s like it’s there.
Jeff Chow: Yeah, for sure. At the end of the day, what’s the gap towards delivering company level impact on the AI transformation is making decisions and cascading those decisions. It’s always been the case, you just have to make so many more faster decisions and you have to pivot more often around just given how fast the world is transforming. And so our point of view is a single shared space with all the context visually where teams can align, they can get the context, AI can help produce that. Even if you started single player, even if you started with say Claude Cowork and you developed a point of view, there still is a gap around how do you socialize, align on that? And then as a team that’s delivering impact that can cascade, how do you make that decision, get really crisp on it? And how do you cascade that throughout the organization? And I think that’s for large enterprise organizations, that’s one of the hardest things. Because you imagine when you make a strategic shift, you then have to socialize it for the next level leaders. Maybe you have to do it all hands, then you have to cascade it, you have to talk about the implications. A month later, you’re still seeing in product review the OG product strategy. And that’s just not fast enough. So we really don’t talk enough around alignment decisioning and operationalizing and cascading those decisions so that everyone understands that without feeling like they were told what to do. I think. And so how do you cascade a strategic why shift? How do organizations at scale be nimble? These have always been a hard problem. And now it’s just the magnifying glass on those friction points are just going to just get amplified.
Douglas Ferguson: Yeah, I think the cascading is an interesting one to think about, especially when you design scaffolding for that to happen. If you’re intentional about how you’re meeting and your rituals, the canvas can provide an interesting place for that to happen almost instantaneously as you think about things flowing. You have to be careful about where you put things and what goes where. And that comes into that intentional scaffolding. The thing that dawned on me not that long ago, because I was doing a workshop with some folks around flows and prototyping and whatnot, and they were kind of mesmerized by it all, but they came back to this concern or this friction around, well, I’ve got a bunch of memory in our corporate ChatGPT setup, or I’ve been using Claude and the memory’s there. And then my thought was like, man, that’s just one node in the entire organization. And the canvas becomes this connective layer and through MCP. And if things are cascading and flowing that way, it’s really powerful. And it’s funny because at the time when they mentioned it, I was like, “I’m going to think about that a little bit more.” But the more I reflected on it’s like, oh, I already have a proof point inside my own company because we meet and do everything in the canvas. So the group decisions are happening there. I’m doing solo work in Claude code. It has access to the team Miro board where the executive meeting is happening. And so it’s constantly peering into that and giving me context on the decisions we made or it’s aware of that stuff. And so it’s not like that you lose it becomes unified.
Jeff Chow: Yeah. And it is not just Miro, I think this is just a big gap around organizational context, includes the process of making a decision. Sometimes I call it the work exhaust. And all of that decision, you can imagine, let’s just take our favorite topic of prototyping. It’s easy to call a process. You have V1 prototype shared by a product manager, you guys ideate, and then you have the aligned prototype at the end. And that is the context that’s shared back to the agents or AI tools. The missing component in the context that you’re talking about is what were those decisions? What were those decisions? Is there a decision log? What’s the true context? The why of those decisions is more rich and important to feed the agents so that when they make the next iteration, you don’t burn tokens by just revisiting the same decisions over and over again. So it’s not actually about the final prototyping output. It’s about the prototyping output plus a decision log that’s clear on why some decisions were made. And so I think those are really clear. Now, beneficial for teams too. It’s like you expect X, you end up with Y, same thing. You need to cascade the why to humans, but you also have to cascade the Y to agents so that they can do their job better as well.
Douglas Ferguson: Yeah, it’s like the context behind the context.
Jeff Chow: That’s right.
Douglas Ferguson: Sure, we made this decision, but why did we make it? And that can help with avoiding the revisionist history or whatever. It’s reminded me of, did you ever see that CIA field guide that they basically distributed and to folks that were embedded in access countries?
Jeff Chow: No.
Douglas Ferguson: Oh, it’s really fascinating. You should check it out. It’s public domain now, and so you can buy it off Amazon. They have printed copies that are like three bucks or whatever. It’s basically corporate sabotage, but when you read it feels like all the dumb stuff that people do in organizations that just break down and create poor work environments. But we were intentionally telling folks to go do that in the companies and enemies. And the funny part is you find it rampant just in companies anyway, but one of the things is revisit every decision.
Jeff Chow: Yeah, exactly. Yeah, let’s really double click into every single one.
Douglas Ferguson: Yeah. Well, maybe we should talk. I know we decided that last week, but what if?
Jeff Chow: Yeah, exactly.
Douglas Ferguson: And so if the AI’s doing that, then we got problems. And so to your point, if they don’t have the context on why we made decisions, they might be pushing back in ways that aren’t helpful.
Jeff Chow: Yeah, for sure.
Douglas Ferguson: So I know y’all are going through a big clawed code, well, maybe more co-work across the org. So I’m curious, as someone who has been overseeing a group that probably uses AI way more than the rest of the org, what’s it been like watching marketing and HR and legal and all the other departments start to lean into co-work? Any epiphanies or observations there?
Jeff Chow: Yeah, I mean first the adoption in the engineering product and design org was very viral. And so mostly cloud code, but also co-work as well. And I think that’s because there were just the themes of, wait, I used to have to negotiate this, now I can just do it myself, and was pretty clear and amazingly sophisticated. And we started building tools on top of tool. We started testing the factory approach for agentic coding and all these other things. So I think right now 95% of the EPD org is already on Claude or Cursor or others, and that’s amazing. The rest of the org has also seen the same kind of virality in, I would say, different ways. I think we’ve seen more, you have to show the specific use case and solution more specifically to then get that light bulb moment versus here’s a sandbox. God speed. But I would say once that’s happened, it’s been just as viral. People have been kind of. And again, it all starts with maybe the low-hanging fruit rote work that just takes up your day and like, oh wait, I can write my weekly update really quickly. I can have these experiences pushing forward. So I think those are the start. Clearly there’s a real value in analytics, and so a lot of value of like, “Hey, I have this question. Traditionally, I’d have to file a ticket to get some dashboard or do something else, and now I could just ask.” And we’re keyed directly into our snowflake, and we have a really good graphs and structures to really give people the ability to ask the nuanced questions to help them self-serve. So these are really powerful things that we’re seeing. And again, it’s just getting more and more and more. And I would say maybe not as much as EPD, but every function has what I would call the AI maker. It’s the kind of viral person that hacked the system to do something that wasn’t planned. And they just can’t help themselves but to share it with everyone, which then gets somebody else to use it and then others. And I love that stuff. That’s probably the most fun when you see an organization get galvanized on something that would save you time, and it organically creates new processes.
Douglas Ferguson: Yeah, I love that. And I think that’s a big selling point for multiplayer AI as well, because that infectiousness, that virality is increased dramatically when folks are together watching other folks use it in novel ways. Because to your point, not everyone can be thrown in the sandbox and just thrive and figure stuff out. Some folks need to be oriented a little more. They need to be sparked a bit more. And in fact, people talk about AI fluency or literacy and training. I think that’s all a waste of time. In fact, there’s some Gartner data that proves it. But if you can spark people, if you can give them that, I don’t know, that X factor of like, “Oh wow, this is amazing.” Help them see where there’s some potential, watch someone else use it, and then boom, they’re along for the ride. And so it reduces that gap between the folks that are really far out and doing amazing stuff and the laggards.
Jeff Chow: Yeah. I would even take that one step further and say when we look at it, we want everyone to be AI fluent, of course, but the multiplayer collaborative impact is maybe that second touchpoint. So say you’re planning a QBR deck for a customer and you’re on the go-to-market team. The one person who could have created that QBR deck maybe with Claude or our sidekicks quickly means that the five other people on the account team who are there can start and obsess less on the doing of the work, aggregating all the data, and do more of the thinking of the work where they’re now in it being like, okay, how can we strategically help our customer? How can we plot a path for them moving forward? And so even the benefits, even if you weren’t the one to click the button or write the prompt, there’s a value in the force multiplier of anybody who got that first move, which is producing say the board in our case of that experience. And so we really obsess internally and externally to look at that. The value is not just, it’s the multiplayer impact of AI. And what’s really interesting now that we’re seeing, and maybe probably every org, is every org starts with everyone just plays a sandbox, just figure it out until you realize, oh my God, the tokens. So what we’re realizing now is the fact that multiplayer collaborative AI where maybe one person does the task, a team converges, and then ultimately a decision or something’s settled and the agents then learned around that context has greatly reduced our token costs, right?
Douglas Ferguson: Yeah, absolutely.
Jeff Chow: Because you’re not doing repetitive work.
Douglas Ferguson: Yeah. The other thing is, it’s making me think about there’s just a lot of power in the design crit process. And essentially we’re bringing that process to every part of the business. Because oftentimes someone will be responsible for doing their thing and it took so long to do it. Maybe there would be some rehearsal, some feedback cycles, whatever. But the fact that we can spend more time, more extended time in that moment together deciding if it’s quality work, tearing it apart, is it good? Is it what we want? There’s so much value in that time spent. And I love that thinking that other parts of the business will start to engage in those behaviors.
Jeff Chow: That is such a great way of thinking about it. And it tracks really well because just like a crit or human nature, if you know somebody took two weeks to build this thing, psychologically, you’re a little bit softer with them. If you know that they stubbed it out in an hour with the help of say Claude or Sidekicks, and it looks great, but you’re actually more willing to. And they’re more willing to receive the feedback. You’re more willing to give the candor to get it to a better outcome. And so yeah, I think actually you’re right. The mindset of a crit, and I love crits. They’re so painful, but I love them and everyone comes out way better for it. But I think that you’re right, that’s permeating through the organization. There’s a more welcoming force for that.
Douglas Ferguson: Always invoke Cunningham’s law. In fact, I’m sure I’ve already mentioned it on the podcast many times, but are you familiar with it?
Jeff Chow: No.
Douglas Ferguson: If you want to know the answer to anything, to post a wrong answer on the internet.
Jeff Chow: Yes.
Douglas Ferguson: And so I think of that a lot in the multiplayer AI. It’s one of my favorite ways to use AI in Miro. And it’s that same point you just mentioned is, and it’s not that it took me no time to build it because I used AI. It’s like we just hit a button in Miro. You know what I mean? It just came out of the tool. And so there’s no sense of ownership. And so let’s just beat this. We have a common enemy. Let’s beat this thing up and destroy it until we find the essence of what matters and what’s right.
Jeff Chow: That’s right. That’s right. Yeah, that rapid ideation makes a ton of sense. And you’re right. I think it really lowers the barrier of you can be a little bit more raw, you can be a little bit more honest, and that leads to better iteration cycles.
Douglas Ferguson: There’s two things that you mentioned earlier that I want to double stitch on. One, you mentioned some folks using Cursor, some folks using Claude. And I hadn’t coded in years and a few years back started using Cursor. And then I switched over to Claude code pretty much exclusively. And I noticed that those two surfaces had a bias to me in how I use the AI. And then I would argue Miro even biases me different because it’s like one, you’re in a text space, one you’re in a visual space. I’m curious, given that you have developers using both, do you have a sense of how that impacts development craft or whether you’re in an IDE or on the command line, how that’s impacting how people think about code and how they approach it?
Jeff Chow: Yeah, I think it’s still early days, and I would say it’s probably not necessarily the tool itself, but it’s the mentality that we’re still mining right now. It’s the artisanally crafted engineer that just wants to code versus the ones that are like, “Okay, I need to create my agentic surface where I’m more orchestrating a team of agents to do the work for me and iterating with them.” Both do it just fine. So I think we’re still at that, how do we get people to at least try? And even if you’re skeptical, see what happens. Nothing’s a silver bullet. But if you can get into that mentality, I think there’s a pretty high ceiling to get it to work, and that’s where our investments are. And I think we try to be tool agnostic just because the competitive landscape is, you might think now just because Claude’s the breakout winner and they have amazing traction is just going to be the winner. But I just think the market, if I look at, and I squint and look at the disruptions of the past, cloud, mobile, others, I think that it’s so early days, who knows? So we want to invite our organization to try things, to get that aha moment so that we can learn. There’s no top-down mandate so much as bottoms up discovery.
Douglas Ferguson: Yeah, exactly. That makes a ton of sense. I guess the thing I was kind of hinting at was my experience was being in the ID, I was still putting my stamp on it. I was still reviewing it as like, “Is this Douglas code?” Whereas when I switched to purely agentic, Claude code aside, there’s many tools you can do agentic development with, but it almost felt like I was just reviewing other people’s code. I had a group of interns that I was like, “Is this acceptable?” Which is different than me thinking, “Is this Douglas code?” So I feel like my hypothesis is that that’s going to be the future is less us putting our stamp on it and that’s more reviewing it. Is it acceptable? Can we allow this to be in production?
Jeff Chow: Totally. And by the way, your agents just heard you calling them interns and you’re going to have to watch your back.
Douglas Ferguson: It’s definitely heard that analogy before. We all have room to grow, so they’re happy that I acknowledged that they’re still learning every day.
Jeff Chow: Yeah, a strong performance review with your team.
Douglas Ferguson: Yes. The other thing I wanted to come back to is you mentioned the power of dashboards and you didn’t have to put in a PR and wait for a business analyst to come in and make a thing for you. And one of the phenomenons I’ve been noticing is just the richness of some of these little HTML tools that’ll just build instantaneously for just some random question I asked, which made me start thinking about this concept of ephemeral UI. No one designed or thought about or premeditated the need for this UI to exist. And so it just made me wonder, are we going to start building products that intentionally anticipate ephemeral UI? We just set up the conditions where every user gets the thing they need. I even think your custom widgets is almost an example of this.
Jeff Chow: Yeah, for sure. I think the rise of personalized software, which is just another fun name for customization, et cetera, is a real key. Early days for that. And so I think there’s a really interesting aspect. You think about vibe coding apps. Not everything has to be an app, yet all of a sudden it’s an app. You think about maybe organizational ways of working where people have to retrain themselves every time they get some artifact that’s a little bit different. And so I think there’s back to friction. I think the friction here is just because you can do these types of things and share them doesn’t necessarily mean you should, just given the cognitive change that if you have to collaborate with someone, what does it take? So I think the value is clear. What’s going to happen in the industry is that over time there’s going to be shared permissioning structures. Where do you put it? What’s actually the organizational template so that if you open a product review, there’s not one that sounds like Claude, that sounds like a dramatic reading. Here’s the hook, and stuff like that. And you’re like, oh my God, that’s not your voice, stuff like that. But yeah, I think our approach to the custom solutions is what we call it, is there should be some consistency so teams don’t have to relearn some stuff. In our case, I know how to drop a sticky, I know how to mark things up, I know how to drop a comment, and I know how to share and do permissions. But then the workflows within that to drive some alignment has the potential to be very specific, very tailor-fit, very unique. And so that starts with all of our personal artifacts and how do we get agents to lay them out in an intelligent way that helps people align. But that also means what we have are custom widgets, which is basically a vibe coding platform that adds real-time multiplayer widgets on the canvas. And that’s connected to your data. And I think that means together with existing patterns, with some maybe one new one together, that creates very specific bespoke workflows that organizations could scale, and it truly meets their need. And I think that used to be a really far path away. You’d have to get a developer to create something, you’d have to do something else. It would cost a lot of money, so it’d be prohibitive. And I think that’s another path around speed to decisioning and alignment for us. And then you think about scaling that. Scaling organizations have used our workflows and maybe a department within a large enterprise organization does it. But how do you scale that to 500 teams, a thousand teams, 5,000 teams? These are the sizes of organizations. That’s where these kind of connective solutions really becomes. I’ll tell you, and you know this, there’s not a single PDLC process that’s the same. And everyone says they subscribe to this pattern or not, and then you go into their organization and they’re like, “Oh, sort of.” But we don’t really do it that way. And so I think that’s where we’re seeing real opportunity and honestly great traction with our customers is the, great. Well, we didn’t want to vibe code an end-to-end workflow. We want to use most of what you have, but take us the last mile. Connect the dots for us so we can use it across as our single operating system for product. Great, we got you.
Douglas Ferguson: Yeah, and I’m excited about where some of these customer solutions can go. And I’ve even been chatting with some of my manufacturing pals. We’ve been doing some work there with some lean guys because their rituals and ceremonies are very similar. I’ve studied them. I’m very fascinated. So got some colleagues there. We’ve been collaborating recently, and some of these guys are old school, retired, but still tinkering and speaking and whatnot. And then that reaction’s typically like, “Oh, you got to be in person for an obey, and I don’t see how AI could play a role,” and whatnot. But after a few sessions, the gears start turning and they’re like, “You know, this is the piece that was always problematic. Every group that comes has their way of wanting the world to be seen or they have the format they want to present things in, and AI could translate all that.” I’m like, “Now you’re getting it. Now you’re getting the power of this stuff.” And so I think it’s only going to be a matter of time before we just see more and more impacts across how people are coming together in ways that were just impossible before. It’s just too much contention or it’s just too fraught to really work together in a cohesive way.
Jeff Chow: Yeah. At the end of the day, manufacturing is one of great interest for myself as well as the organization. And a lot of it is because the same patterns that we see everywhere is the truth, which is manufacturing has hundreds of disparate data sources to maintain resources, supply, et cetera. Real visual ways of working like Lean or Baya, Kaizen. But that connected workflow of supply chain management and Lean is always disconnected because it’s so complex. And so there’s this opportunity to smooth even some of the hardest experiences out in a way that makes it more dynamic, faster decisions against some things that are just incredibly difficult. And so a lot of our manufacturing partners, we have mad respect. You jump in there and you talk to them about what they do. And you’re like, oh, that’s not just a Salesforce database in a Jira database. Then it’s some real, real stuff there.
Douglas Ferguson: And even each department has their own level of complexity. So then when you’re integrating across those lanes through the whole value stream, it’s tricky.
Jeff Chow: Yeah, absolutely.
Douglas Ferguson: It looks like we’re running out of time here, so going to have to bring things to a close. But before we do, I want to ask you to leave our listeners with a final thought.
Jeff Chow: No, I think when we started this around both friction and opportunity, I would just say it is such early days. It is very easy. I have existential dread all the time, but I choose to think about where there are opportunities that have always been friction points in the culture of work that has been happening. Where is this an opportunity for us to finally debunk those, those points of friction, those cultural cross-functional inertia moments? And the minute you shift gears to thinking about that, I think the sky parts and the world opens up and you’re like, “Okay, let’s actually solve the things that kind of pissed me off since the dawn of time.” And then it’s like AI becomes this amazing opportunity. And I think that’s probably the best way to ride this wave, because otherwise it’s pretty paralyzing.
Douglas Ferguson: Great words to live by. Amazing. It’s been a great chat, Jeff. We appreciate you coming on, and we’ll talk more soon.
Jeff Chow: All right, thanks for having me, Douglas. Great time.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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]]>“Where it’s wrong or where people disagree is where the conversation needs to happen.” – Kristi James
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Kristi James, a Lead Change Manager at the World Health Organization’s Health Emergencies Programme in Berlin. Kristi traces her facilitation instincts back to childhood event planning and an early corporate career at DHL, where she discovered that sketching out processes and user journeys helped teams see where they belonged, resolve role confusion, and surface disagreement productively. She describes how a coach, Mary Beth Maines, helped her recognize creativity she had dismissed in herself, and how that shift now shapes the way she draws out scientists and doctors at WHO who are more comfortable debating than co-creating. Kristi walks through concrete practices, from visual cues and whiteboards that anchor group memory to 1-2-4-All exercises that convert passive listeners into active participants, and a recent example of using a user-journey diagram to unstick a stalled budget negotiation. The conversation closes on her core belief that visualizing a process doesn’t just clarify tasks, it gives groups a safer, more concrete way to name resistance and work through conflict together.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:01:40] Drawing Out Prom As A Kid
[00:05:30] Team Baffled By Her Event Mapping
[00:09:00] Realizing Leadership In Her 30s
[00:13:15] Best Presenter Praise From Karen Jones
[00:17:40] Grounding Presentations In Real Stories
[00:21:20] Learning Event Craft With IMG
[00:24:50] Coach Helps Her See Her Creativity
[00:28:30] Diagramming A Stalled Budget Proposal
[00:32:10] Moving Groups From Listening To Doing
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Voltage Control
Kristi James is a Lead Change Manager at the World Health Organization’s Health Emergencies Programme, based in Berlin, where she helps teams navigate complex global health challenges and strengthens collaboration across diverse stakeholder networks. Her background spans public health, technology and intellectual property, global logistics, and community events, with prior roles in change management, fundraising, communications, and event and sponsorship marketing at companies including DHL. She now brings that range of experience into designing interactive workshops that turn ideas into action for scientists, doctors, and program teams. Outside of her WHO role, she is a mixed media artist and creativity coach known for bringing empathy, curiosity, and creativity into her facilitation work.
Douglas Ferguson: Hi, I’m Douglas Ferguson. Welcome to the Facilitation Lab Podcast, where I speak with Voltage Control Certification alumni and other facilitation experts about the remarkable impact they’re making. We embrace a method agnostic approach so you can enjoy a wide range of topics and perspectives as we examine all the nuances enabling meaningful group experiences. This series is dedicated to helping you navigate the realities of facilitating collaboration, ensuring every session you lead becomes truly transformative. Thanks so much for listening. If you’d like to join us for a live session sometime, you can join our Facilitation Lab community. It’s an ideal space to apply what you learn in the podcast in real time with peers. Sign up today at voltagecontrol.com/facilitation-lab. And if you’d like to learn more about our 12-week facilitation certification program, you can read about it at voltagecontrol.com. Today, I’m with Kristi James at the World Health Organization where she works as a lead change manager for the emergencies program. She’s also a creativity coach and mixed media artist. Welcome to the show, Kristi.
Kristi James: Thank you.
Douglas Ferguson: So good to have you and looking forward to chatting. I guess to start off, we can go back. In your alumni story, you mentioned that even as a kid, you are always the person bringing people together. Looking back, what do you think you understood about energy and connection before you ever knew what facilitation was?
Kristi James: I mean, as a kid, I liked creating experiences and not the decor. I would try to imagine what’s the experience of receiving the invitation? When do you receive the… What happens next? What happens next? What happens next? I like process. I love a process, but I have to draw out my processes. So even as a kid, I would draw out the steps of what I wanted to be doing, the events that I wanted to do. I was in charge of the prom committee one year and I had to draw it all out. First start talking about it here, tickets go on sale here, how do you buy the tickets? Da, da, da, da, da. So I don’t know, that’s just how I’ve always viewed the world. And I was surprised when people don’t view the world that way.
Douglas Ferguson: Yeah. What was one of the first events you remember doing this for? Was it the prom or were there things that came before that?
Kristi James: I’m sure it was things before that, but prom is probably the first thing that jumps out in my mind of being an event for other people. I mean, it’s one thing to have your own birthday party or do the smaller things, but prom is for everybody and it’s a big group of people, so that’s probably the first big event that was put in my hands.
Douglas Ferguson: Did that feel different, that it was for everybody? And how did that shape how you approach things?
Kristi James: So long ago, and I have to think, so much of my life happened I think by accident, but we know when you look back on it, you’re like, “Oh, that makes sense.” These things fell into place. So I don’t think in the moment I really thought too much about it. It was just somebody needs to do prom and I’m happy to do it. So I did it, but of course it is a teamwork. I was one person on that, but I was the person drawing out the process and saying, “Okay, so now we’re going to need this and we’re going to need that and we’re going to need this because these are the steps that we’re going to take.” And my more creative friends were the ones who were doing the decor. I was there making the paper flowers with them, but they were the ones like, “We need paper flowers.” So it was definitely a team effort, but for me to be able to visualize what we wanted to do from your very first experience, all the way through to the actual delivery and people going home at the end of it, that is what I brought to the group. And then everyone else had their other expertise. They can make the posters.
Douglas Ferguson: Yeah. What was your approach to visualizing that process?
Kristi James: I sketched things out. I still sketch things out. It’s trying to understand when I’m… So even today, I’m working on a project and people were trying to explain the problem that we’re solving and what we’re going to do about it. And so as I’m getting bits and pieces of information, I started drawing it out. What is that process that we’re trying to do? And every once in a while, I would hold up my paper going, “Do you mean it looks like this? Would it look like this?” And then they’d be like, “No, no, no, no, no, it’s… Changed that. That wouldn’t happen before…” I find it easier to draw it out because oftentimes words are just words and they get jumbled and you can write it all out, but then you forget what you read three pages ago and whatever. So I don’t know what started that in me. It’s just how I’ve always operated. I need to draw it.
Douglas Ferguson: Yeah. And you said it surprised you that others didn’t operate that way, so it came naturally. What have you noticed through the years around that surprise or that not everyone operates that way?
Kristi James: I think probably the first time I really realized it is when I was a manager and I had a team. And so we were planning a corporate event and I had been planning sales meetings and stuff for years and then switched jobs and moved around and we were doing an all-hands meeting and I had people helping me with that. And it kept saying, “Okay, so if I’m a participant, when am I first going to hear about it?” “Well, there’ll be an email.” Okay. And so then if I’m a participant and I know that I’m going to this location, how do I know where to park? How do I know how to get there? How do I know where to park? How do I know where to go from the parking lot to this? What happens? Do they walk in? My team was just looking at me like, “Why are you such a maniac with all these details?” And I’m like, I’m trying to walk through what my experience is going to be so that we can plan for, we need signage here, we need signage there. We need to have these things ready to go on the tables. And they just tolerated me. They dealt with me, but it was surprising. I was like, “Wait, you guys don’t do this too. You don’t visualize… You put all these things in place, but you don’t actually visualize what it means. Have we missed a step? Have we gone back? Have we missed something that would be important that makes this experience better for other people?” So that was surprising to me.
Douglas Ferguson: Yeah. It sounds like, intuitively, the mapping the journey was something really critical for you to identify the points of friction or the areas that we might want to lean in and make special.
Kristi James: Yeah.
Douglas Ferguson: Have you found that your process for visualizing those types of things has adapted over time or are there new tools or ways that you’re approaching that now?
Kristi James: Yeah, for sure. Well, more experience helps you understand different business processes more. So when you’re going in and you’re working with a team and they’re talking about a strategy, there’s generally a process that they’re following for that and we can make sure that they’re checking all the boxes of things that they need to do, which is different than planning an event. So you learn different processes and everyone’s just slightly different or writing guidance and how we’re going to get guidance through all the reviews and things. So there’s a process flow for how things work. But I have found that in meetings and in workshops, it’s also helpful to show the process to people so they know where we’re at so that they can provide the input that we need at that point in the process and they know that we haven’t forgotten that these other steps are coming up.
Douglas Ferguson: Yeah. And to your point earlier, if they see what we are visualizing and how we’re mapping things out, it’s much easier to interject corrections or tweaks versus if we’re just using language, it’s much harder to identify those things and interject.
Kristi James: Yeah.
Douglas Ferguson: In your alumni story, you also talked about being the student body president, which may have been related to the problem, I’m not sure. But the point is you kind of pointed back to that being a moment of leadership, but that wasn’t necessarily obvious to you at the time. And then looking back, you realized that some of your instincts, some of the ways that you showed up were actually strengths in the professional setting and pointing toward leadership abilities. And so I’m curious, at what point did you start to connect the dots and realize that just some of the ways you were intuitively showing up and that you were learning through doing were actually setting you up for a career that you maybe hadn’t even planned?
Kristi James: Well, first of all, nothing about my career is planned. Everything’s a happy accident. I think it was probably somewhere in my 30s. I think of my 20s as just being that negativity, that blind just like, “You want me to go up in front of a stage and deliver a speech? Sure, why not? Let me just do it,” and not even stopping to think that I should be nervous about these things. It’s just those things. But it was in my 30s when I was working at DHL and I was in a role that I actually had to lead another team, a consultancy team. And I was doing things and the feedback that I’d gotten on my review from that team was how much they appreciated my leadership. And I was like, “What leadership?” Because, again, I was just that blind nativity of youth. And so then I started thinking about, okay, what are the things that I’m doing well and where does that come from? I really do attribute it back to growing up in a small town. I graduated with 70 students. It’s small. One, you have to get along with everybody, and two, somebody needs to do it. So you just do it. And that’s been how I’ve gone about things. Somebody needed to lead the strategy for this, so okay, I’ll just do it. No one else is raising their hands, so I’ll just do it. But I recognize, going back to the prompt thing, I recognized I’m not great with the core. I’m not great at making posters. There are things that I’m not great at, but I can design the process and I know what I need, and I can get the other people involved who can bring their expertise and the things that they like to do into that process. And they know where they fit into the process and they know the value that it brings. So I think that helped me with leadership, being able to understand what the process is, what skill sets we need, how to plug people in, how to make them feel like they understand the bigger picture because they can see where it’s leading to.
Douglas Ferguson: Yeah. It makes me think that it’s a great lesson or observation for folks that are wanting to do more leadership. And I’m curious how to step into those moments. There’s a few patterns that I might call out. One is just taking initiative, showing up and doing stuff. Also, being able to articulate a vision. You’ve talked several times now about the ability to see the patterns, map out the overall journey or experience. And I would say the final thing that jumped out to me was this notion of understanding your own limitations. And if you can map out a vision and you know which parts that you’re not good at, it’s going to be a lot easier to step out of the way and let folks step up that can do those things. And it’s more clear of where they’re needed because, A, you can point out that there’s a gap for you there. Plus, if the vision and journey and experience is well mapped out and visualized, people see all the places where they can step up. So that’s interesting. I think that for listeners, that’s a really important pattern to maybe tap into as they’re wanting to step into more leadership or more influence because you don’t always have to have the title, that positional authority. Sometimes it’s just bringing the right perspective, the right attitude to the table.
Kristi James: Yeah, I agree. And again, I work in a multicultural, multinational organization, so I find drawing things out is a way to bridge language gaps. And it’s a way where people can see, “Oh, I belong there. I belong at this part in the process. I belong at this part in the process.” Or they can understand why they’re not in that part because they’ve already done their part or their part’s coming after, because sometimes we think we need to be in every meeting because we need to know what’s happening, da, da, da, da, but if you understand where we’re at in the process, it can help you say, “You know what? Actually, you guys need to do this, and then I’ll be looped back in at this point.” So in my drawings I find helpful just in conversations with other team members and other stakeholders when they’re like, “Why am I not there?” “Okay, this is what we’re trying to accomplish here. We’re going to bring you back in at this point.”
Douglas Ferguson: Yeah, there’s a difference between being left out and being looped in later.
Kristi James: Yeah.
Douglas Ferguson: I love that. And also helping people understand the mechanisms that are in place, just sets expectations, give people more awareness, because at the end of the day, people just want to be informed and understand so that they can play their role and do their part.
Kristi James: Yeah, if they have their role clarity… When you get into a lot of facilitation, you’re facilitating some team conflict. And over and over again, it’s we need role clarity. We need role clarity. That comes up all the time. And we just go back to where are we at in the process? What do we need right now in this process? We need this. Who’s doing these things? And giving the clarity for this moment in time. Because you can do all the RACIs in the world, but it’s either so detailed that everyone goes, “No, my name’s not next to that. We’re not going to do it.” And then there’s a critical piece that doesn’t get done or it is you looked at it once and then you never look at it again. I mean, I’m not the huge fan of RACI, but I do like to have that process because your process evolves too. You think you have it, but as you’re learning more, you have to make adjustments to it. And then you have that visual that we can have that discussion and go, okay, well, we’re evolving this. We’re adding in another feedback loop. We’re adding in another approval layer because of X, Y, and Z. Here’s who’s doing the approval. But I’ve also found it really helpful to remind reviewers that they’re essentially approvers because they’re reviewing and saying, “This is okay to send to the vice president.”
Douglas Ferguson: Yeah. And especially in this day of AI-generated content and human in the loop or human on the loop, this idea of reviewers and approvers is becoming more and more prevalent.
Kristi James: And everyone wants to be the approver. So again, it’s where are we at in the process? Are you approving your piece of the puzzle so that all the collective stuff goes to the director or the executive director or whoever, right?
Douglas Ferguson: Yeah.
Kristi James: So boosting or elevating their role for the importance that it has.
Douglas Ferguson: Yeah, and then how are we compartmentalizing so that everything doesn’t grind to a halt. Can the approvals happen in parallel and making sure that people are approving their piece and not gumming up the work somewhere else?
Kristi James: Yeah.
Douglas Ferguson: You were just talking about DHL, and I recall that there was a VP, I think, there that had labeled you the best presenter in the organization, which took you off guard.
Kristi James: Karen Jones.
Douglas Ferguson: There you go, Karen. What felt natural to you or stood out to everyone when you were presenting that maybe you hadn’t really had realized at the time?
Kristi James: Well, first, I’ll just clarify. She probably doesn’t even remember saying that, but you hear those things and it’s the thing that sticks in my head, right?
Douglas Ferguson: Oh yeah. Yeah, yeah.
Kristi James: But I go back to that blind youth. I was new in the role. I just didn’t know to be nervous. But from that time, she had put me up in front of the entire department to present my strategy, and I did. You give me a microphone and I have to tell a joke or I have to do something. So I was talking about different people who had applied. I wasn’t just reading slides. I was telling a story as I was going through my slides. And I think that’s why she… People clapped at the end of my thing. Well, who does that in a business setting? But people laughed and they clapped and I was done and I was like, “Okay, well that’s done.” And I just thought that’s how you do it. I also attribute that blindness for growing up in a small town, I didn’t know anyone in a corporate job. I didn’t know anyone whose parents were in corporate jobs. I didn’t know what it meant to have a corporate job. So I really had no role models for… My parents weren’t coming home and preparing for presentations. My dad was drawing pictures. Well, maybe that’s where the process comes from. He’s an electrician and he would draw out the grids.
Douglas Ferguson: Oh, yeah.
Kristi James: Yeah.
Douglas Ferguson: So you saw schematics from a young age.
Kristi James: Yes. This is how this works.
Douglas Ferguson: There it is. Yes, that procedural thinking is a way of infecting our brains, doesn’t it?
Kristi James: Yeah.
Douglas Ferguson: Oh, man, that’s cool. And also, I mean, one thing I picked up on was this lack of nervousness. So yeah, you didn’t see these models of parents stressing out in the evenings about a big presentation the next day or it didn’t seep in that’s, oh, we have to get worried about these things. But also, I think just some people are just naturally less nervous in those kinds of situations. It’s pretty amazing how much a little bit of anxiety can send us just out of whack. If we’re not in the moment, we’re not in the flow. If that’s what’s occupying our brain, it’s going to be less energy, less cognition devoted toward doing a great job.
Kristi James: Yeah. Again, it’s a small school. I did a little bit of everything. I was a cheerleader. So there is part of me that’s like, “I’m on stage. All right, let’s go. Let’s have fun. Everyone’s smiling. Let’s do this.”
Douglas Ferguson: I heard some great advice once around speaking. I was talking about this being around the nervousness stuff. I think when folks get nervous, they get in their head that folks have poor thoughts about them or like, “Oh, Douglas is bombing this presentation,” or whatever. And then if you put yourself in the shoes, in the audience, and you think about when you saw someone really struggling on stage, whenever I’ve been in that situation, I don’t know about you, but whenever I’ve been in that situation, I’ve always felt really bad and hope that they just get back on track. It’s like they’ll right the ship any second now because it’s like, I’m not hoping they bomb. I don’t want to keep seeing them tripping up. And so I think that’s a really great thing to remember. So if folks do struggle with anxiety and don’t have this natural lack of nervousness like you stepped into, I think that’s a great thing for folks to think about is like, “Hey, the audience is really rooting for you. They came. They’re there. They cared enough to show up. So just know that they want you to do a good job. And so don’t get hung up on thinking they want you to fail or they’re judging you.”
Kristi James: Well, exactly. I mean, you do. As an audience member, you’re like, “Is somebody going to help this person?” You want to help the person. “Okay, you collect your thoughts. I’ll distract people. Whatever you need.” You want to help them. And it’s not that I’ve never been nervous on stage. Of course I have. But I think early in my career, I just didn’t know enough to be nervous. I just went out and did things. I’m told you’re going to present. I’m like, “All right, I’ll present.” I have terrible slides because, again, I don’t do posters and I don’t do decor, so my slides are terrible. This is fine because I’m not reading from them. I’m telling you a story.
Douglas Ferguson: Yeah, I think that’s the other thing I picked up on is not only the confidence and lack of nervousness, but the storytelling and ensuring that there’s a great narrative for folks to hang onto. And so I’d be curious there, any advice you have for folks on… I mean, you mentioned jokes. What are some other elements or criteria for a good story to make sure it really captivates the audience and pulls them in and gets that applause at the end?
Kristi James: Yeah. Well, first of all, humor can be hard, so you really have to know your audience. So if it is your peers, then fine, go ahead and make jokes, but otherwise, you want to proceed with caution. If it feels comfortable, go ahead and make a joke, but if not, then don’t. Don’t force yourself. We do a series here that’s part of our culture work called Behind the Build. And every Friday, we have a presenter, somebody who’s presenting on the work that they’re doing for the Health Emergencies Program. It’s a way for us, because we’re a global organization, it’s a way for us to have an understanding of all the different work that’s happening because oftentimes our work can be interrelated and maybe there’s an opportunity to collaborate. It’s also a way to get to know our colleagues. We have a prep call and I always remind people, it’s what is the problem you’re trying to solve? What is the problem? Ground me in something real, intangible. Tell me the story of somebody with cholera, and then tell me what your project can do to help that person. So what is the problem we’re trying to solve? Why do we need to do it now? And then, what it is that your project is doing to address that issue? Maybe it isn’t going to solve the issue, but it’s going to address a piece that makes it better for somebody. And then you start going into some of the details, but you need to ground people in that story. What is that vision? What is the purpose of the work that you’re doing?
Douglas Ferguson: Yeah, I love that. And as you’re sharing that, I was thinking about how your natural intuition and instincts around mapping stuff out and visualizing. I’m curious if you visualize the narrative at all. Beyond the slides, is that something that you map out and visualize before you start building the story up?
Kristi James: Yes. I’m big on outlining. I like to outline. Even when I was doing all-hands meetings and the order of presenters, I would draw it out. This person’s doing the introductions and they’re going to present on the state of the organization and then the supporting pieces. And drawing it out helps me sometimes reorder the speeches.
Douglas Ferguson: That conceptual arc starts to shift as you start to map it out.
Kristi James: Because sometimes you try to do it by hierarchy or by whatever, and that’s not the audience experience. That’s not always interesting for the audience, and it can feel like you’re jumping all over the place. So if you draw it out… And again, I go in with my picture and be like, “Well, this is why I’m recommending this order. So we’re not going to follow hierarchy.” Usually, leadership is fine with that. When I can draw it out and show them why, why we’re presenting the information, this follows on this and follows on this, and then we’re going to segue into that, then they’re like, “Oh, well, okay. Yeah, sure.”
Douglas Ferguson: Yeah, that’s a good point to think about because often I see folks struggling to convince leaders to make a change away from something they’d envisioned. And so that’s where these visuals, these maps can help articulate our thinking in a way that can get others on board. You spent some years creating immersive brand experiences and marketing campaigns. And I’m curious, did that impact your storytelling and experience design or even your facilitation work today?
Kristi James: Yeah, totally. At DHL, I oversaw the sports and entertainment marketing for the US. That’s all of our sponsorships with Major League Baseball and Miami Dolphins and US Olympic team, that kind of thing. And I had the privilege to work with a team at IMG for our agency, and they know the space forwards and backwards and how to create experiences. So working with them was great because they also got the process. So us together, we’d be like, we’re in a boardroom and we’re mapping it out and we’re identifying what are the different pieces. And then they would go back and identify even more pieces that just make it even better. And then they would handle the majority of the execution and we would get the people there. So I learned a lot from them on that process because before that, I had been doing sales events and stuff, and I was just going on my intuition. And then I’m working with other people who do think similarly to me, but have very specific areas of expertise. And so that also helped me understand those areas of expertise better. I knew I didn’t have that expertise, but really, what makes somebody who is excellent at registration, what is it about that that makes it really great? I still can’t do it, but it’s having a deeper appreciation for all those different skills.
Douglas Ferguson: Yeah. That’s another component, I would imagine, that you can put into the maps that you build. If you have an appreciation for those things, you can at least make an affordance for it, even if you’re not going to actually do the actual implementation pieces.
Kristi James: Yeah. Or knowing that I know that Lori always worried about these things, so let’s make sure that those are in there. And I know Justin always worried about these things and Rachel always worried about these things and all that’s accounted for in my process.
Douglas Ferguson: Yeah, I love that. I think those experiences as well, once you start to get a handle on what Lori’s concerned about, what Justin’s concerned about, even in future projects, you start to build an intuition around some of those things. Even if the finer details might be lost, at least we know, oh, we might want to check on this. There might be a category of things that we need to account for in this plan or in this workshop or whatever.
Kristi James: Exactly. Yeah.
Douglas Ferguson: This is interesting, a nice segue. I was just mentioning our prior experiences. We learned new things that we can bring into our toolkit, new awarenesses, new things we can account for as we visualize and we bring people along. But there’s also gaps and blind spots that we have or things about ourselves that we might overlook. You were talking about your coach and mentor, Mary Beth Maines, helping you recognize some of these talents that you had overlooked. So I’m curious what your thoughts are around why is it so hard to see our own strengths sometimes?
Kristi James: It’s like a psychotherapy session at this point. I think it’s hard because we’re not really a culture that encourages us to, I’m the best at this. And are you the best? Am I the best at that? I’m not. Maybe I’m not the best at that, but I need to be able to appreciate the things that I like doing and the things that I do well. I also think that in your 20s and 30s, you’re doing all the things that everyone said you should do. You should do, you should climb the ladder, you should be getting promoted. Why aren’t you being promoted? You should have a bigger team. Y should, you should, you should. And you’re not really spending a lot of time to think about what I want to be doing and what are the areas that I’m good at? Do I really need to climb the ladder or do I really like doing this thing? I mean, the more I climb the ladder, the more I was like, well, I don’t like it here. It’s so much administrative work. I want to do creative stuff.
Douglas Ferguson: Yeah, I want to build and make things, right?
Kristi James: Yeah. I want to draw my pictures of a process. So I think part of it is we’re so focused on the shoulds that we’re not really focused on what I’m bringing. I always feel like I should be doing more and I’m not enough because I should. I should, I should, I should. And Mary Beth was always like, “What do you mean?” Because I actually had convinced myself that I wasn’t creative at all. Even though I draw out everything. In my head, I had convinced myself that I wasn’t creative. I’m just doing my job. And Mary Beth was like, “You’re one of the most creative people I’ve ever worked with.” And it took me a couple of years to actually recognize that because I was like, “That’s not true. No.”
Douglas Ferguson: The fact when organizations, we especially see this in agencies, there’s the creative team. And so when you have these labels and titles, it’s easy to say, “Well, I’m not in that group,” or, “I’m not doing that kind of work,” but the fact of the matter is there’s creativity in quite a lot of roles.
Kristi James: Exactly. And it took me a while to see that because I’m not a graphic designer and I can’t do decor. If you come to my house, you’ll see that. It’s eclectic. It’s just not going to be in decor. Yeah, I am a mixed media artist in other ways, but it even took me… I only started painting about 10 years ago because I had told myself that I wasn’t creative, but the creativity comes out in other ways.
Douglas Ferguson: Yeah, absolutely. And I’m curious if that realization about yourself, and that’s to be something that’s now evident to you that it impacts everyone, has that influenced how you facilitate and how you lead and bring people together? Is that something that you try to draw from people?
Kristi James: Yes. I work with a lot of brilliant scientists and doctors, and it’s amazing when you can see them start using their hands to do things, because so often, we have very dignified discussions and they’re good. Those are great discussions. But then, when they start co-creating things together and are making something tangible, whether they’re drawing it out or using the Post-its or whatever method we’re using, how much they open up, how much they can push each other to do something. So I do lean on my creativity a lot, trying to come up with unique ways to get them doing something. It’s not always easy because they come in sometimes and they’re like, “No, I have three PhDs and I want to talk about things.” And so also trying to find that balance. It can’t be all fun and games. So discussion, game, discussion, game. In the end, hopefully there’s a little bit of fun, but getting them to make things. Also with my energy, sometimes that also helps them loosen up and be like, “All right, she’s going to sing and dance if we don’t just start doing things, so let’s make that stop.”
Douglas Ferguson: Yeah, I love that. Especially folks that have gone deep, deep into specialization, it’s sometimes hard for them to break out of that myopic view they have of the world because they’ve specialized so much, they’ve actually made a dent in the knowledge that exists in the world. Their contribution has expanded the knowledge. It’s not like they’re regurgitating or they’ve learned some things. They’ve actually added and contribute to what’s the knowable, learnable stuff in the world. And when you get to that level of depth and specialization, it’s really hard to see anything, notice other things because you’re so lasered in on it. And so I love that you’re able to use games and play and just get people to maybe bust out of that, the little repetition cycle they’re maybe in.
Kristi James: Yeah. The one thing that I always insist on is there needs to be a visual cue in the room. So if we’re going to have a discussion, that’s fine, but there needs to be something visually… If we’re in the room or online, we’re using a whiteboard, there needs to be some reminders of what was said previously. If there was something that came up, because if we’re just talking, then somebody needs to be making notes somewhere that we can go back to and say, “Oh, wait, we did actually cover that before, so let’s move on,” or whatever. Because oftentimes, they just want to talk.
Douglas Ferguson: Yeah, I found that too so powerful to simply write down some words or phrases that seem like key milestones or key demarcations of the conversation so that as they’re having the conversation, they can tie back to those prior moments. It’s really nice grounding.
Kristi James: And part of my job as a change manager is to break things down into simple steps. So if I can take this discussion and summarize it in just a few short, simple words, and I’ll try that, and then they’re like, “No, it’s not like this,” and whatever. But then we have something that we can take back to the masses. We have something that we can take outside of that room of non-experts so they can understand, okay, what do I need to do when there’s a cholera outbreak? Or what do I need to do for meningitis? Because when you’re an expert in that, we need that. We need the people who have made that dent, as you said, but then we need to be able to relate it to somebody who doesn’t think this way, doesn’t think about these things to be able to explain the importance of it. So that’s what I try to do, and it doesn’t always win me friends when I’m trying to condense it down into something really short and simple, but I insist on having the visual cues, otherwise, it’s just things get lost.
Douglas Ferguson: Yeah, absolutely. And that’s been a through line on the entire conversation here, the visualization piece. I am curious, I think this idea of we’ve had five meetings and nothing’s moving as a thing we’ve all encountered, and I think you mentioned it in your alumni story. When you encounter that situation today, what’s the first thing you look for?
Kristi James: When we’re having multiple meetings and nothing’s really moving?
Douglas Ferguson: Mm-hmm.
Kristi James: Well, I mean, today, we were working on a proposal, and you know how when you have a bunch of people making comments into a document, and then after a while you don’t really know what you’re making comments on anymore because there’s track changes and then there’s comments, you’re not sure if the track changes address the comments and whatever? I drew it out because everyone was getting bogged down in the budget because they had words and then numbers, but then everyone had different definitions of things. And so they were plugging in more words with numbers. And then they’re saying, “Well, no, this one shouldn’t cost this much as this.” But the other person’s saying, “No, no, no, this is what it should be.” I mean, it’s those kinds of things. So I went to the user journey and I’m like, “All right, this is a training and development thing. So step one, what is change management? Step two, how do I apply it? Step three, in-depth, master level kind of thing. And then we broke down through the user’s lens. Okay, I’m going to come here. This is online learning. It’s going to be this. What do we need to get it? If that’s going to cost this much, who owns it? And then the next step, okay, we’re going to do these courses. It’s instructor-led, blah, blah, blah, blah, blah. What do we need? We need to update the frameworks. We need to do these things. So I started drawing it. I can’t even show you. I started drawing it. So I had it all drawn out.
Douglas Ferguson: Love it.
Kristi James: And then I plugged it into Excel and I put it in my colors. And so I showed up at the meeting and I was like, “I’m trying to make heads or tails of this. This is what I have. What do you guys think?” And they were like, “Oh, yeah, no. Yeah, that doesn’t make sense. No, no, no. Change that number to this. Yeah, no, that’s everything that we need for that piece of the user journey, and that’s everything that we need for that piece, and so that’s going to cost this.” But we had rounds of reviewing. Anyway, I try to come with solutions. If we’re getting stuck on something and nothing’s moving, I try to figure out where is it that we’re getting stuck? And again, I draw. I try to visualize what a solution could look like. And then I propose this. Could this be a solution?
Douglas Ferguson: Yep. Even if it’s the wrong one, oftentimes that clarity can help folks get back on track.
Kristi James: Yeah.
Douglas Ferguson: Yeah. I think it also sometimes comes back to purpose. If folks are getting hung up on a pricing conversation, but there’s a more critical purpose at hand, sometimes coming in with like, “Okay, I tried to distill down everything everyone was saying. Here’s my stab at what I think that comes to.” Can be a moment to reground in purpose too, bring it back to what are we really trying to accomplish here?
Kristi James: Yeah, exactly. Which is what I was trying to do with the user journey because everyone’s like, “Oh, this costs this and da, da, da, da.” And I’m like, “Who’s it for? Here’s the different steps. The different steps in the journey, how much are we spending on each of those steps? You have different people, da, da, da.” And they’re like, “Yeah, that works. That actually, yes, it’s a now an addendum in our proposal.”
Douglas Ferguson: Yeah. As we’re coming up on the end here, there are two other things I wanted to come back to. One was you had mentioned this idea of moving people from passive listening to active participation. I’m curious if you could share any examples, even small design changes that transform the outcome of moving people from that passive listening to more active participation.
Kristi James: So before I did my facilitation training, I was kind of just drawing on the same tools, running workshops just on tuition and drawing on the same processes. So when I took the training course and we were actively doing 1-2-4-All, I read 1-2-4-All. I’m like, oh, okay, sure, fine, whatever. But actually participating and experiencing it, that has been my go-to. When you have people who are just sitting back, you can’t really sit back when you’re in pairs. You have to be there. So that is often my default thing. If I find that people really aren’t participating, which the next exercise is going to be a 1-2-4-All. You’re going to write something down and you’re going to sit and talk to somebody about it.
Douglas Ferguson: Yeah. Yeah, yeah. Any small group breakouts are great at that. It’s hard to social loaf if it’s just two or three of you.
Kristi James: Exactly.
Douglas Ferguson: You described your journey as starting from this, I just like to bring people together, and that instinct or passion led you to facilitating global collaboration at the World Health Organization. I’m curious what the student body president version of Kristi would have to say about that journey and where it led to.
Kristi James: I think that the student body president of me would be probably flabbergasted. I’m like, “How on earth did you get there?” That was nowhere on my radar when I was in high school. The only thing that was on my radar was I knew that I wanted to have an international experience. I knew that I wanted to work outside of the US at some point in my career, and I had no idea how to make that happen. And even trying to do a study abroad thing, my parents didn’t have the funding, I didn’t have the money, but I went every year to listen to the speeches, and then I would get the price tag and be like, “Well, I can’t go.” And then I would go again the next year and I’m like, “Still don’t have enough money.” So that was something that I wanted that experience. We had a lot of exchange students come to our school, and I always found it really interesting to get different perspectives on how they see the world and how their interpretation of my culture is and things like that. So I knew that I wanted to have that experience at some point, but I never would have guessed that I would be living in Berlin, ever would have guessed that. I studied Spanish. And I never would have guessed that I’d be at the World Health Organization. That’s super cool to be here and super lucky to be here.
Douglas Ferguson: Yeah. The thing that I’m hearing that could be helpful to point out to listeners is this idea of don’t let the logistics or the circumstances discourage you from showing up, because I assume going to those presentations kept the dream alive. It also probably gave you context on when the opportunities did present themselves. Now you’ve got this rich tapestry of knowledge that you picked up from attending these presentations and hearing the stories. Whereas if you had not have gone because you couldn’t afford it and just wrote it off anyway, you would’ve maybe missed some of the opportunities that presented themselves later.
Kristi James: Yeah, I think so. I mean, and just understanding the opportunities that were out there. There’s all these opportunities to study and what you could do and being a little bummed that I couldn’t do it again. But hello, hope, one day I’m going to do this. Somehow I’m going to figure it out. Applying, again, that blindness, sending out my resume, all kinds of companies all over the place. I have no visa to work in your country, whatever, but I just sent them. I don’t know. You throw it out in the universe and somehow, some way, it sorts itself out and maybe it takes 20 years. I don’t know.
Douglas Ferguson: Well, as we wrap today, I want to leave you with an opportunity to share a final thought with our listeners.
Kristi James: I mean, given that we talked a lot about drawing today, I would think that probably if you are stuck or if you’re with a group of people who aren’t moving, visualizations can help a lot. So draw out the process as best you can because that is where it’s wrong or where people disagree is where the conversation needs to happen.
Douglas Ferguson: I love that. Where it’s wrong or where people disagree is where the conversation needs to happen. And we often, well, I say we, lots of folks tend to shy away from conflict. That’s something we didn’t talk about much is this idea that the visuals can allow us to step into the conflict or the places of friction with maybe a little bit more ease and a little bit more care and grace because if we don’t have the words to approach it, at least we can say, “Hey, look, there’s some tension here. Let’s talk about it abstractly because we’re looking at the tension on the diagram.”
Kristi James: Exactly. You think about root causes of resistance. Oftentimes, it’s people are resisting and it’s natural. We all resist and we’re all guilty of it. We all resist in different ways. But for status, I give the example of hierarchy for an agenda. And so it could be a perceived status thing, but when you can draw it out and explain why you’re doing it this way, then it removes that threat for them. Or if you’re autonomy, like I just want to sit over here and do the thing, but if you can show them where their work fits in and where it is in the process, then they can see where they have their autonomy, but where they also need to be collaborating and doing other things. So being able to show that, it’s not like we want to look over your shoulder on everything you’re doing. We just need this check-in point. So you go do your thing, but then bring it back here because everyone’s bringing their stuff back here. We’re doing it. So you can remove some of those friction points or that resistance that you’re getting from people.
Douglas Ferguson: Love that. The other thing that’s jumping up to me is this idea of if people are feeling a lack of autonomy, but then they see the overall picture, they might better understand where their autonomy resides and they can embrace the autonomy that exists versus pushing back on where it doesn’t exist.
Kristi James: Exactly. Exactly. And again, if they disagree, that’s where you have the conversation, because sometimes people are resisting, but they’re not saying you’re threatening my status. They just are resisting because it’s an innate thing and they don’t understand it. And so they may be pushing back in ways that really aren’t important to them, but if you can show them on your diagram, this is what it is and this is how we’re doing it, it gives them the point to have that discussion, “I disagree with this step right here. I should be doing that,” and having that discussion instead of, “I’m not going to do any of it.”
Douglas Ferguson: Yeah, that makes sense. Sometimes people are protecting something and aren’t even sure what they’re protecting. They can’t articulate it, but they feel this innate need to protect. And then when they can see it better, they understand, “Oh, that’s the sliver I need to protect,” they can be more precise.
Kristi James: Exactly. Yeah. And that makes the conflict less tumultuous.
Douglas Ferguson: Yeah, smooths things out, for sure. I love this idea of anytime we can bring in tools that allow us to step in the conflict with more grace and more care, because conflict doesn’t have to be tumultuous, like you say, or fraught. And so anytime we can bring tools to the table that allow us to step into it in a way that feels very human and very intentional without… We make it less scary, less dangerous, we make it more safe.
Kristi James: Exactly. And I’m saying drawing, but if you think you can’t draw, I mean there’s really boxes and arrows. Anyone can do it. Flow charts are a miracle, right? Yeah.
Douglas Ferguson: If this, then that.
Kristi James: Yep. Here’s our decision points, da, da, da. Yeah.
Douglas Ferguson: Exactly. Amazing. Well, Kristi, it was such a lovely chatting with you today. I know we could go on and on here, but we will have to at least pause for now. Just want to say thanks for joining me, and we’ll talk again soon.
Kristi James: All right, thank you so much. This has been fun.
Douglas Ferguson: Thanks for joining me for another episode of the Facilitation Lab Podcast. If you enjoyed the episode, please leave us a review and be sure to subscribe and receive updates when new episodes are released. We love listener tales and invite you to share your facilitation stories. Send them to us on LinkedIn or via email. If you want to know more, head over to our blog where I post weekly articles and resources about facilitation, team dynamics, and collaboration. Voltagecontrol.com.
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]]>There’s a fundamental difference in the psychology between asking it questions to get answers or using it instrumentally to create something. – Joe McLean
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Joe McLean, Group Product Manager for the AI Stream at Miro, who led the overhaul of Miro’s Sidekicks and Flows AI surfaces launched at Canvas 25. Joe traces how his hobbyist love of Eurorack modular synthesizers shaped Flows, arguing that visible, patchable connections reveal what a chat box hides, and that a good tool’s structure can enable rather than constrain creativity. He and Douglas dig into what he calls the “visual trace” – treating an AI agent like a new hire who needs onboarding, check-ins, and a replayable record so a whole team, not just one operator, can trust and build on its work. The conversation covers how cheap execution is reshaping product development, from teams showing up to meetings with working prototypes instead of slide decks, to Miro’s internal VibeLab tool solving the “Git problem” of AI-generated design branches, to the rise of throwaway personal software built for an audience of one. They close on a candid discussion of the switching costs of chat-interface lock-in and Joe’s conviction that the healthiest relationship with AI comes from building things with it, not just asking it questions.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:00:00] Welcoming Joe McLean To The Show
[00:02:30] Synthesizers As Inspiration For Miro Flows
[00:04:30] Why Chat Interfaces Limit AI Thinking
[00:14:17] Agents As New Hires Needing Onboarding
[00:19:30] Showing Up With Working Prototypes Now
[00:36:00] Personal Software Built For One User
[00:42:15] VibeLab And Git Problems For Design
[00:48:49] The Switching Friction Of Chat AI
Joe McLean — LinkedIn
Joe McLean — How modular synthesizers and music software inspired Miro’s Flows (Medium)
Joe McLean — Overfitting and the problem with use cases (Medium / Bootcamp)
Joe McLean — Medium author page
Miro AI Workflows product page
AI Teaming Comes Alive on the Miro Canvas (Voltage Control)
Teaming with AI: Voltage Control and Miro AI (Miro Blog)
Miro AI + Voltage Control
Joe McLean is Group Product Manager for the AI Stream at Miro, based in Berlin, where he led the cross-org launch of Miro’s AI Workflows at Canvas 25, including an overhaul of Miro’s Sidekicks and Flows surfaces. Before that he ran Miro’s Canvas Experience and Sync Collaboration groups, and prior to Miro he spent seven years at Splice, the music creation platform, finishing as Director of Product Management for Creator Tools and Mobile. He started his career as a product designer and front-end engineer at Spongecell and ThoughtWorks, where he facilitated project quickstarts, inceptions, and product visioning exercises. A self-described Eurorack synthesizer enthusiast, he writes occasionally on Medium about design philosophy, including how modular synthesis inspired Miro’s Flows and why designing around narrow use cases produces brittle products.
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear. It moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Joe McLean, group product manager for AI stream at Miro. Welcome to the show, Joe.
Joe McLean: Hey, great to be here.
Douglas Ferguson: Amazing. Really looking forward to this conversation. I have been thinking about this since our chat in Vegas where-
Joe McLean: In Vegas. Yeah.
Douglas Ferguson: … realized our… Well, this is one thing that happened in Vegas that can kind of come out of Vegas, I guess, but we realized we have a mutual love of synthesizers.
Joe McLean: Yes, we did. Not something I actually expected to find wherever we were, that weird restaurant in Vegas. I wasn’t expecting to talk about Eurorack for half an hour.
Douglas Ferguson: I think it was Cabo Wabo, right?
Joe McLean: That’s right. Yeah, that’s right.
Douglas Ferguson: Is that a Van Halen kind of… I don’t know. I didn’t look into it, but I think it might be a Van Halen reference, or I don’t know if he owns it maybe. I don’t know.
Joe McLean: I’m of no help to you in that regard, I’m afraid. Yeah.
Douglas Ferguson: But yeah, a revenue kickoff partner event with a business casual turned into conversations about synthesizers and Bitwig and all sorts of stuff.
Joe McLean: Yeah.
Douglas Ferguson: And I was delighted to hear that Reason and this kind of patchable analog modeling system was inspiration for the flows in Miro.
Joe McLean: Yeah. Yeah. I mean, I think for people who make music that way, I’m a big Eurorack nerd, it kind of changed the way I think about music. And I think part of how I fell in love with it so hard is that it just feels like it has some deep affinity with how my brain works. It’s like you see the connections, you can visualize the flow of information, you hold that in your hands. And when we were thinking about how we could make AI accessible in Miro, how we could bring it into the Canvas, it was just such a natural way for those ideas to come together. I think for people who are still kind of wrapping their head around AI and what it does, it’s such a powerful way to visualize what’s happening when you’re hooking up context and transforming it into something else. And there’s something, again, just in my brain, seeing the wires, connecting it up, it has a physicality to it that I think can be missing when you’re just typing into an empty prompt box.
Douglas Ferguson: Yeah. The other thing that struck me too is how linear a conversation is. And you’ve got this conversation history that’s just stacking and stacking, and it’s just getting all jammed into this giant corpus of context where with the flows you can kind of pick and choose, and even after midway through a conversation, if you will, remove a wire and now you’re kind of constraining that context in a new way.
Joe McLean: Yeah. Yeah, absolutely. This is really interesting to me. I think a lot about how UIs make people think about the technology. And I think that there are pros and cons to the fact that so much of modern AI experiences feel like texting someone. That’s been really powerful to make it accessible to people because anyone has context texting someone. But it also sets a very particular expectation about the type of interaction you’re going to have, the types of things that are going to be possible. There’s not a great way for that interface to reveal what it’s capable of. You see people, a lot of companies and tools are playing around with things like skills and skill discovery. But I still think that at the end of the day, the fundamental idea is that you’re sending someone a message. And that’s only one way to think about what the technology can do. And I think we’re all walking around with this constraint that we’re not really thinking about anymore because it’s just the one that we’ve connected to as the first breakthrough UI. But there’s going to be many, many more. This is just the first generation. I think of the people carrying those giant cell phones in cars in the ’80s or whatever. The iPhone will come. There will be a different way of interacting with the underlying raw power of the technology. And I’m not sure that it’ll always be a text box for every job.
Douglas Ferguson: That also has a way of influencing how people innovate, right? Because if they’ve been painted a certain picture, and that’s their worldview of how the technology works, they’re living at that abstraction layer. They don’t necessarily understand that there’s more that could happen underneath. And that can be very stifling on the innovation side.
Joe McLean: Absolutely. And I think that’s where I feel another fun connection to our shared passion for synthesizers. I think one of the things that I really love about the Eurorack world is that by creating some structure and framework around the way that the pieces connect to each other, you create a space for all these tiny manufacturers to be very creative, but within an established paradigm. And when I compare the explosive creativity of all the hobbyist creators in that space to the world of mass-produced commercial synthesizers, there’s a time and a place for that. And the streamlining of that experience for people who don’t want to get in and tinker onto the hood is great for certain applications and certain situations. But I think there’s something so powerful about showing the wires and letting people get in there and engage with it in a new way. And I think that’s really inspiring from the perspective of thinking about how you build UIs for good tools. This is something that I felt going way back to my days at Splice building music production tools, is that when you’re building tools for expert creators or technical people, I think there’s a lot of freedom to build more complex and sophisticated interfaces because there’s a different expectation for things like information density or workflow complexity, or even how much training someone has in the software to be able to use it. We’re always really interested in bringing that barrier down and making it more accessible. But I don’t necessarily assume that the right answer is always for things to be simple, especially if the task is complex.
Douglas Ferguson: Yeah. And coming back to the Eurorack stuff, I mean, it’s like you get the gamut. You got modules that couldn’t be any more simple, like one knob on them even. And then you’ve got modules that are computer menus. It’s like a bank of 100 algorithms that have their own idiosyncratic settings and stuff. And it’s such a wild world. And to your point, I love the fact that it’s a bit of an ecosystem. You’ve got folks that are building modules that do nothing, maybe shape the control voltage, and they’re not there to make a sound. They’re not doing something in the conventional synthesizer sense. But they’re like, oh, I see what’s going on in this universe of how people are using these tools. This would be a really handy thing to give people just to create a slightly different experience.
Joe McLean: Yeah. And one of the first pieces of advice I ever got when I started playing around with synthesis and building electronics for synthesis is you got to get an oscilloscope. You got to get something that visualizes what’s going on because otherwise it’s just too abstract. You can’t wrap your head around it. That was good advice. And I think it is advice that is applicable these days to AI. So much of debugging an AI system comes from going in and reading the trace, seeing exactly what happened, being able to follow the path, so to speak. I think we’re visual people, and it’s really helpful to have something that you can understand and process in that way. And so yeah, an interesting way of thinking about a lot of these tools we’re building is how do you build an oscilloscope for the AI trace? How do you visualize the 4,000 line pull requests so you can understand what it’s actually doing to your server? That’s obviously something that’s very interesting to us in Miro, creating visualizations of all different kinds of information. And I think a lot about the information throughput of those different formats. Very challenging actually to read lots and lots of text to understand a plan or to understand a deep technical change. And it’s really powerful to have these visualization tools on hand to understand how information is flowing, or how things are changing or transforming. I think that’s quite important.
Douglas Ferguson: Yeah, I feel that. I’ve been using Miro in some ways that have driven me to want even more power in that regard. A great recent example is I built a very complex board for a client, and it involved 100 boards for 100 different locations, and then a synthesis board that rolled them up and doing some analysis, roll-ups per regions and areas. And then so the synthesis board ended up with so many flow connectors that I really wanted some visualization on the connections themselves, or even just having MCP tell me what’s connected and applying some rule sets and saying, hey, are the right regions connected to the right roll-ups? And based on what you announced at Canvas, and how you’re working on these things and thinking about these things, I have a hunch that I’ll be able to do that fairly soon.
Joe McLean: Yeah, for sure. I mean, I’m really interested in that, and also in the ways that you can interchange between the conversational ways of thinking about it and the visual ways of thinking about it. I’ll give you a couple examples. It’s interesting to think about something like a flow or really just any diagram as a representation of agent behavior. One of the things that we’ve been doing as we’ve been trying to describe how some of Miro’s more advanced agentic behavior should operate is we wind up drawing a lot of flow charts. And it’s not deterministic. We’re not building systems that say if this, then that. In many ways, the magic of agents is that they don’t have to operate that way. But you still need to be able to visualize the journey that you hope someone will go on as they accomplish a certain job and think about the prompts that will lead to them going on that sort of journey. You could even think about that visualization as a sort of eval on your expectations of the conversations unfold in a certain way. And so even just being able to visualize a conversation as a flow, or vice versa, to be able to understand a flow as a conversation, is an interesting way to think about what we’re trying to do when we’re designing systems with agentic behavior. But then there’s also other ways of thinking about this. We are very quickly seeing that code-based visualization or mapping out user journeys from behaviors defined in code is a great way of understanding code. Or you create a system entity diagram that maps relationships between the different objects in your system. It’s another thing, same idea from a different perspective. It’s like that information is out there, but it hasn’t been compressed in a visually parsable way. And when you can do that, you can build understanding a lot faster than going through the raw line level information.
Douglas Ferguson: A couple of things that it’s making me think about is how important plan mode is and getting to a understanding with your agent on what they’re going to build before just letting… Because if you get to a solid plan, you can one shot a lot of things these days. And a visual plan’s very powerful. Or even if the plan’s getting really, really complex, being able to visualize it on Miro or anywhere is a huge step up. And then the other thing that came to mind is, you mentioned evals, and I hadn’t thought of this before, but this idea of creating space and room for the team to participate in ongoing eval, to remove drift. Because we can build the best agents and the best systems, but they will drift over time. And so can the agent leave telemetry behind or exhaust behind, it’s visual in nature, so that we can just peek in as a team, or in our ongoing check-ins and work in alignment sessions, just look and see, does this exhaust looked right? Is this staying on track?
Joe McLean: 100%. Yeah. We call this the visual trace in our internal discussions of this. And it’s so important. I think you really just hit the nail on the head. I think this really connects back in a core way to the idea of multiplayer AI. I think if I think about all these interactions we’re having every day, there’s one category of things you touched on that’s so important, like preventing drift and creating a clear plan. This is important even just in one-on-one interactions with agents. I think it’s helpful to think of it as almost from a manager framework. It’s like you’ve got this new, very eager, very smart hire, doesn’t have a ton of context on your organization, or how you want to work, or even what the job is yet because you’ve only given it pretty limited amount of information. What kinds of governance, what kind of one-on-ones, what kind of systems, reviews, would you want to have with that report to make sure that they’re doing a good job? You’re not going to just hire them and check in 90 days later. You’re going to talk to them, you’re going to make sure that they can demonstrate understanding of the task, and they’re doing a good job as they go along. You also understand that they might need a little bit more guidance early on before they know how you do things to be able to stay focused. You’re not tracked in that way. And so I think a lot of those intuitions [inaudible 00:14:17]. But where it gets even more interesting is what you’re talking about when more people get into the mix, it becomes so much more important to have that visual trace so that other people can follow the conversation. It’s one thing to build that understanding one-on-one together with the agent. But if then you can’t replay it at all, you’re going to have problems when you try to go and work with other people. And I’ll say as a practitioner, this is a pain that we’re feeling firsthand every day now. Because people are showing up with ideas, prototypes, in some cases, entire systems that they’ve built with their agents as the agents become more capable. And then it falls over when they need to actually explain even what this thing is, or how it could be incorporated into other people’s understanding. And there’s this interesting challenge where if you run for, let’s say, several days, you build out this concept, it feels fully realized to you. There can be a bucket of cold water when you have to bring that to someone else and explain what it actually is. Because maybe before you would’ve gone through reviews, you would’ve built a shared understanding, maybe you would’ve whiteboarded it together and come up with a picture, and you build that collaborative understanding as you go. But when it’s just done, obviously there’s a magic to that. There’s also a totally new challenge, which is this thing just popped out of the fabric of the universe. And now it’s actually quite complicated and it’s hard to explain to other people. That can be a tremendously frustrating collaborative experience. And when you go that journey alone, it’s hard to come back.
Douglas Ferguson: You’re hitting on one of the core new frictions. I think it’s always been there to a certain degree. AI is just amplifying it in a big way. Because I mean, this is the reason user stories got invented. We want to get alignment, we want to tell a story, and let people really get inside of it and understand something that can be nuanced and complex. And certainly if people haven’t had time to sit with it, walk around with it, have that shower epiphany moment, like you mentioned, it just erupted out of the fabric of the universe. I think this is the type of thing that’s so important to focus our training around versus AI fluency. Because this AI fluency stuff, how to prompt, all these more tactical things, they’re changing so rapidly, it doesn’t matter. By the time they learn it, it’s irrelevant anymore. But if you can teach solid storytelling skills, group synthesis, these types of things that are going to be more and more critical as more and more things just erupt out of the fabric of the universe.
Joe McLean: Yeah. No, I think that’s totally spot on. And you’re right also that keeping up with the bleeding edge of prompting strategies is basically impossible. It’s out of date as soon as you learn it. The best way to build intuition for that is just by doing it anyway. And also, I believe that we’re probably in the waning days of that even being a thing that you need to do. The level of agent understanding has already gotten to the point where you’re probably messing it up more than you’re helping by saying you are an expert product marketer, make no mistakes type stuff. I think of that stuff as being very 2024, 2025 now.
Douglas Ferguson: So the rituals that you’re using to solve for some of this stuff, you talked about people bringing these pretty much finished concepts that are getting created so rapidly. What are some of the ways you’re leaning in to solve for that and help people navigate that moment, either on the sharing side or even the receiving side?
Joe McLean: Yeah. So I mean, for one thing, I’m not going to present this like we have this all figured out. I think we’re very actively in the process of reworking a lot of this to fit the agentic era. Something you touched on, which I really like, is that in some ways a lot of these kind of connection points are more relevant than they’ve ever been. So I don’t think it’s like throw it all out the window or something. I think it’s more about reworking and understanding where the new bottlenecks are located. For one, I think that it’s very rapidly becoming an expectation that you show up with a working something to virtually any meeting where you’re going to talk about something. The value of talking about it before any work happens has gone down dramatically. And I think there’s two big versions of this. One is design prototyping. So show up with an interactive prototype. Something that I’ve felt for a long time, especially when you’re talking about creator tools, is that looking at something at Figma is just not a good way to understand what an experience is going to feel like. It’s all in the fabric between the screens. And there’s no substitute for being able to actually experience it or try it. And you can get so close now to something that feels like what it will feel like. Gives you so much more information. Some cases, we even have designers now building on top of the actual live working product and trying to figure out infrastructure for that. The quality of the conversation you can have earlier on in pipeline is going way up. And so that’s becoming a new expectation. I think that’s also creating a bit of a culture shift on, I think there’s been this belief for a long time that you separate the problem definition and the solution definition stage. I’ll say personally as a practitioner, I’ve never loved that. Because we’re technologists, we’re building software. We have baked in a lot of assumptions about our solution to begin with. And sometimes I feel like we’re doing this deeply artificial thing where we’re all pretending like we’re not going to try to solve it with a software feature or something like that. I think the most interesting thing is the match between a problem and a solution. Have you chosen a solution that is well calibrated to the problem? That’s not to say that they’re the same thing. People can definitely jump to solutions that don’t have a problem to find at all and get into a lot of trouble that way by getting attached. But the interesting thing is always about a problem and a solution match. And if you’re missing one half, it makes it harder to have a high quality conversation. So problem definition, more important than ever. But I think that you can show up now with a solution that matches your problem and have a much higher quality conversation. That’s been one big culture shift.
Douglas Ferguson: That’s really cool. I love that. I mean, for years we’ve been preaching the importance of prototyping and bringing visual representations of what we’re trying to build, whether that’s a product feature or even a slide deck, or anything we’re trying to do as a team. If we can visualize it and get on the same page conceptually, it beats just having meeting after meeting after meeting. In fact, I think Dude’s Law is a prototype is worth 1,000 meetings. Back to your point about the product problem fit or the solution problem fit, I think of it like a lock and a key. And I love your framing on that, because I’ve had a bit of an aversion to the folks that are so religiously just like, we got to stay in the zone for a fixed amount of time or whatever. Because I’ve always naturally vacillated between the two because I had to go tinker and then come back to the problem. Do I understand the problem enough? And kind of swinging back and forth. And so I love this kind of version you’re presenting. I think it’s even more important that people lean in in that way now that it’s so easy to explore different solutions. It’s not costly to do so.
Joe McLean: Yeah. And I think that also gets to another topic that’s quite interesting to me in this space, which is I think we’re biased to think human scale as to what exploring the solution space could actually look like. You have this agent, you talk to it like it’s a person, so you think about it like a designer and you collaborate it with it the way that you would collaborate with a designer. So maybe you have Opus or maybe now even Fable and Claude Design make you a beautiful UI or something like that, or a slide deck or something like that. But there’s also this very interesting possibility where maybe you have a lightweight model make you 20, and then you choose the one you like the most. And I think that there’s this kind of old guard double diamond kind of thinking where it’s like a lot of those ways of thinking are actually still super relevant. There’s value in exploring a lot of bad ideas and making things very cheap and throwaway. So I don’t assume that the answer is always going to be that you’re going to use the most powerful model to do the most amazing thing. There are other strategies to explore the possibility space. And this is another way where I think the tool has really biased us. You mentioned before that flows can branch out in different directions. That’s one of the things I love about flows. And I think there will be more tools that emerge to start to take advantage of lighter weight exploration across a broader space. On the nerdier end of this spectrum, I’ve been super interested with Karpathy’s AutoResearch and the idea that LLMs can supervise the training of future LLMs. Obviously a very hot topic these days. But the general architecture of just setting up a bunch of parameters that could be optimized, and then making the runs and the experiments really inexpensive, something that can run in five minutes, not over a multi-day billion core GPU mega optimization run, but just like a lightweight experiment. I think a lot of exciting things are going to come from applying that type of thinking to other things.
Douglas Ferguson: Yeah. I keep pointing back to the early 2000s when e-commerce and the web was first showing up in a real significant way. In the late ’90s, I had an email address, but it’s like, okay, yeah, there’s some bulletin boards, blah, blah, blah. But once eBay and Amazon, some of these various juggernauts started to really figure it out and how it works, and Google replaced Yahoo, there was a seismic shift in how people came online and how they behaved. And so I think there’s still a lot of models that have yet to be developed. And to your point, whether that’s rapidly training small models that are fit for purpose, or even moving beyond this chat biased approach, what doors start to open in the next five, 10 years that are even hard to predict right now?
Joe McLean: Yeah. That’s one of my favorite topics to think about. And I think I would sum a lot of that up is how does your mode of interfacing with technology and the implications of that shape the ideas that you’re even capable of having? Bringing it back around to Eurorack, one of the things that I love most about that mode of creativity from the beginning is I made music that I never would’ve made sitting behind a computer or a traditional synthesizer. There’s no difference to the underlying capabilities of the technology. From the minute that we had a DAW, you can create any sound that could ever exist ever, hypothetically. There’s something about the texture and the choices that are made about where the knobs are and what’s connected and what’s easy and what’s hard that kind of creates a grain that makes some ideas more available than others. It’s a kind of compression of the possibility space. And so I think that the same phenomenon is going on right now. The choices that you make, the things that are surface level, even the things that the models are trained on and fine-tuned on, they all point you in a specific direction on top of a technology that is much more general purpose. And so yeah, I love your question. It’s like what new things are going to be possible as we explore that space? I think that’s really the question for software practitioners in the next five years.
Douglas Ferguson: How have you noticed the PDLC changing over time? You mentioned, I don’t know if it’s an official requirement or not, but almost this soft imperative at least to bring finished prototypes and visualizations of your concept to meetings and planning sessions. So that’s definitely a shift. Are there other very mechanical or specific shifts to the PDLC, whether it’s like I’ve seen things like automatic pull request reviews, these sorts of things. What sorts of stuff have y’all adopted in this new era?
Joe McLean: Well, another shift on the design side is that I think, across the industry, the expectation for polish is going up a lot. I was at a Shopify event in New York, and it was really interesting. It was a design event and it was really interesting to hear a lot of their designers talk about how the inexpensiveness of writing code has made it possible to invest a lot more in types of design polish that they never would’ve dreamed of investing in before. I mean, there’s some obvious things, like just all the design tweaks and paper cuts that tend to accumulate. I think every PM is guilty of pushing that stuff down the roadmap in exchange for business critical stuff. And there’s often a lot of really good reasons for doing that. But a lot of that stuff was premised on the idea that it was going to be really expensive actually to get all those things through the pipeline. And when you’re dealing in a very scarcity-minded environment where it’s tough to get those pull requests across the line, makes sense. Doesn’t make sense anymore really. I’ve seen a huge change in the last six months in the number of small tweaks that really up-level the experience that we’ve been able to get across the line. Shopify designers were talking about even investing in 3D animations and motions, things that they wouldn’t have even known how to do in the old world. And so I think that’s going to be a really interesting one to watch. It reminds me a little bit, I’m old enough to remember how the iPhone swept through the design world, and really up-leveled the expectation for design in consumer applications. And I think we’re about to go through a similar thing where all of a sudden teams that were always deprioritizing that work actually find the time for it. And so then that creates a new expectation for what a product experience feels like. People have less tolerance for that kind of jankiness that pervades a lot of applications.
Douglas Ferguson: Yeah. Not only jankiness, but also I would imagine accessibility requirements. Because that almost becomes no effort required to scan and make sure these-
Joe McLean: No excuse.
Douglas Ferguson: Yeah, exactly. It can point out the issues-
Joe McLean: No excuse now.
Douglas Ferguson: … and correct them for you. The other thing, and this is something I was thinking about earlier, was I love using the inference and probabilistic capabilities of the LLM to generate deterministic things. And so this is one of those traps that the bias around the chat interface traps a lot of people. Because if you’re in that mode, it’s hard to step back and go, wait a second, I don’t have to always ask it to do the thing. I could have it build a tool or a script or a command or a system that does the thing. And it’s not that I’m going to Lovable and saying, “Build me this whole product.” It’s like like I just have this one thing that I need done and I want it to be very consistent every time. And so please do that for me in a way that doesn’t consume tokens. And I think that’s going to be a pattern that hopefully more and more people start to employ because, A, we’ll be less dependent on tokens and data centers, and B, it’ll be less likely to create errors, right?
Joe McLean: Yeah. I mean, this kind of skips across several topics that are super interesting to me right now. One is the topic of personal software, basically building for a user of one. I’ve had a lot of fun experimenting around with that. I read, I think it was a blog post that really influenced me in this direction. And the author’s point was, essentially, think about all the overhead in every product that comes from essentially two things. That that piece of software needed to be designed to scale to tens or hundreds of thousands of users, which created complexity around infrastructure, complexity around how the product itself operates. But then also the fact that, because of that, it also had to be designed as a compromise between those 10,000 people. And so much of the software that we interact with on a daily basis is a product of deep, deep compromises that have been made to serve the deployment scale that was honestly needed to justify the VC money that went into that product. And so you kind of create this perpetual flywheel of a certain expectation of the scope and scale of software and how it’s supposed to be distributed. Some of the most interesting things I’ve seen built at Miro were internal tools only, things for our design team to use. And immediately people start thinking like, oh, well maybe we could monetize this. Maybe we could scale this up. But then you absorb all the complexity that comes along with that. You have to make it accessible to an external environment. You have to think about multi-tenancy, you have to think about security, you have to think about monetization. All these layers that start to come in once you start thinking about deploying software at that scale. And so when you don’t have to think about all that, something changes in your mind. Just to give you a little example, I’ve been having a lot of fun as a weekend project, I’ve just been building myself a little tiny personal streaming service just for me. I have a lot of music from my friend’s bands, MP3 collection that I built up in college. And I just built a little app. It was my weekend project with Fable back in the previous weekend where we had it for 48 hours. And I’ve been getting a kick of just having my own little streaming service, my own Apple Music, Spotify that I’ve been carrying around for the last couple of weeks. It works great. It’s really fun. Hardly has anything in it, but it’s mine. And it doesn’t have the complexity of serving hundreds of thousands or millions of albums. It has one user. Doesn’t even need to have a login system because it’s just me. And there was something really profound about building a software that way, thinking that way. Because it makes you account in a new way for all those compromises and frictions that come from the necessary scale. Obviously, not all of these lessons are applicable if you’re working at a B2B SaaS company, but I still think it’s valuable to build a little bit of intuition for where that line is and where that fabric is. And there’s also a connection here, and this is maybe where it does get more relevant to the world of scaled enterprise software, is thinking about what we really mean when we talk about forward deployed engineers. If we’re talking about people going into a company and building an extremely complex custom solution that sits on top of a larger platform, really interesting to think about what that actually represents in terms of the product that’s being offered to that company. It’s software for a user of one in a sense. I mean, maybe they’re a 10,000 person organization, but you don’t need to think about the complexity of taking that one enterprise feature request, and figuring out how to feature flag it and fold it and deploy it at scale. Maybe you can just build a very custom version that never leaves their premises. And so I’m oversimplifying a little bit, but I think that there’s something quite interesting about challenging the boundary of the assumed deployment scale for software.
Douglas Ferguson: But you’re making me think about how the age-old advice for SaaS companies were configurable maybe, but customizable, no. It’s like we don’t want to be in the business of creating unique installs for each client because it’s so hard to provide customer service and documentation varies. But in this age of AI, it would be possible to manage all divergent help documentation and support requests. And also it’s so quick to build. So maybe it opens a door for that as a possibility nowadays that was not even a path, or a path that all the elders advised against, right?
Joe McLean: Yeah. Yeah, yeah, absolutely. Well, and I think it’s interesting just how linearly, I don’t even know if linearly is the right word, it’s interesting to think how directly, let’s say, interesting to think how directly that stems from the shift in economics of the cost of producing software. The idea was always that you would make this very expensive thing, and then you get all your money back on the infinite scalability of building and deploying and distributing multiple copies of that thing. And it really flips the entire equation all of a sudden if it becomes very cheap and inexpensive to create that thing. Because now the level of customization you can achieve is a lot higher. And it only works if it gets a lot easier to build it. And I think we’re only feeling the very early ripple effects of that shift. At the extreme, you can even think of software that’s created on demand, just-in-time software, voidware, some really out there ideas.
Douglas Ferguson: Well, I wrote down emergent UI, because this is the concept I’ve been thinking about for a bit as you were talking about some of the concepts earlier around personal software. I actually experienced this in some of the stuff I was working on with my agents that I built in Claude Code. And one of my agents is a marketing agent, and the marketing agent has access to all of our marketing tools. And one of the things I was able to do is stitch together the data in ways that I’ve never seen it visualized. Sure, maybe I could have done it with Looker or something, but it was too expensive to go do all that stuff based on the value that I might get from my size company. But my agent was able to do it. And I didn’t even ask for this little UI or interface. It just decided that was the best way for Douglas to consume this information. And now I had this light bulb moment that that’s the future, where products and also chats and other AI interface and experiences are going to be delivering these interfaces that were never predetermined. And it won’t be a full-blown app. It’ll just be a little moment or a little widget that says, hey, this will be an easy way for Douglas to communicate with me right now.
Joe McLean: Yeah. This is one of the most fascinating topics in the world of software development to me right now, what the future of this particular thing is going to be. Going back to the previous topic of behavior shifts, we had a very similar shift that happened with our data team. So I feel like there’s certain groups within the organization that are always trying to get people to read their reports. Our user researchers, the data folks, they get all these amazing insights. And then I think sometimes in just the organizational information environment, it takes more time and attention. You have to give it a lot of focus to be able to understand some of those insights. And so a lot of folks on the data team started distributing these interactive data experiences, where it wasn’t just a report, you could see it and filter it different ways. It was almost like a mini dashboard that had been created for a very specific data set. Again, to your point, not configured on top of Looker, not super heavy, but just one-off for this one data pull that they did. And that was a light bulb moment for me too. I’m like, oh, this HTML embed that’s going in the Miro board now or whatever, that’s a different kind of thing. I don’t even really know what to call that. It’s like a page, it’s a document, it’s a piece of software, it’s an app, it’s a tool. It’s somewhere in the middle of all those. It’s an interactive artifact. And I think this goes all the way back to things we were talking about earlier in this conversation about visualization and communication of information. Now more important than ever, I would argue, and now also very inexpensive. So a data scientist or an analyst on your team who you never would’ve invested a week of a front-end engineer’s time to build an interactive data representation for a report, they can spin that up in a minute, and it makes the information that much easier to understand. And so I think we’re feeling the acceleration in building right now. I don’t think we’re totally feeling the acceleration yet in the ability to communicate information. And it goes back to something you said earlier, which I love, about thinking about storytelling, communication, the new skills that we need to be building. I think that has a deep relationship with the velocity. So as things start to move faster and faster, you need to be able to communicate more and more faster and faster, and that’s going to require new types of artifacts. And so all those kind of merged together for me. I’m thinking about the future of information sharing, especially in large organizations, how that’s going to work.
Douglas Ferguson: Yeah. And when I heard that I can now embed HTML files on the Miro board, that was exciting for me for this very purpose because of my agents building these little HTML applets or whatever we call them. And Miro can be a place where I can share those with the team. That’s amazing. Because emailing these things around is like an ice pick to the brain. And so I actually built a little thing into my agentic harness dashboard where the team can go view them. Much nicer to have a shared Miro board that can be updated on the fly and not have to build your own agentic dashboard. But coming back to the internal tools, I want to get your thoughts on this because this is something that came up as we were chatting. It was like a light bulb for me, that the impacts that this is going to have on design ops and product ops and probably even DevOps, right? Because those teams are very limited by the tools that they had time to build or the tools that are available in the market. And if they can now build more internal tools faster that help the teams do their jobs, I think that’s going to be an interesting space to see explode across organizations.
Joe McLean: Yeah, absolutely. To give some specific examples of tooling that we’ve been building, we have something internally at Miro called VibeLab that a few designers on the team built. It’s super cool. Basically what happened is they were all vibe coding interactive prototypes, and two problems emerged really quickly. First of all, you start to have Git problems with your design. So you’re like, okay, what does it mean to be on the branch that has the new stuff that the team is thinking about? How real is that yet? Has that been merged to main, and how we’re thinking about the future of the design? That has now diverged from the core product experience. So you’ve got your core product experience, you’ve got Mauricio’s thing, you’ve got Tilo’s thing. They kind of go in different directions. How do we merge our designs back together? That was the first problem. Second problem is we’re building all these prototypes. And like you said, I’m trying to email people things. I’m deploying it to Vercel, and I don’t know how to give you a link to it. That’s also insecure. So stuff is just floating around on people’s personal Vercel accounts. All this to say we built VibeLab, the designers internally, we worked on this to solve a lot of those problems. You could think of it as a highly specialized internal only version of some of the same things that Replit and Lovable are doing, but much more focused on allowing people to very easily play in their own sandboxes with Claude Code and just push something up to [inaudible 00:42:15]. So automating away the deployment problems basically, but still staying very flexible in the local environment, which also makes it a lot easier to bring ideas back together. Because then that problem becomes as simple as just pointing Claude to a different branch or a different repo and saying like… them together. So the combination of solving the deployment problem and keeping the working version very light has allowed our design team to move way faster. But then new bottlenecks emerge. So just to give a couple examples, hard to gather feedback on those prototypes. And so we built tools that take the prototypes and print screenshots back into Miro boards so you can annotate them with comments. And I’ve heard of a lot of designers building systems for that sort of thing. Another funny issue, we’re getting ready for the marketing event in May, and at some point folks from the brand and product marketing teams reach across, and they’re like, “We need all the Figma files for the landing page.” And we’re like, “We’re very sorry, but there are none.” We built all of the designs for everything we launched in code. It was built in some cases on top of the live product. And so going back to what you were saying about design ops and tooling, all of that stuff is getting rewired in real time across our organization. New tools are being built. And I think what’s really interesting about a lot of that, going back to personal software, is that I don’t think we would buy an off-the-shelf solution for a lot of these problems. So much of it is tailored to our internal process, our internal stack. I think that’s going to be a common story for internal tools where teams are more and more interested in building their own things. It’s also the maintenance costs are not as big of a problem there. If you’re going to build and ship a feature to your customers, you have a different maintenance obligation than something that you can deprecate internally. It still creates friction, but it’s not quite as severe.
Douglas Ferguson: Easy to go from version two to version three with crazy idiosyncratic changes when it’s an internal tool versus when it’s global. I’m curious, in working in this way, custom tooling, it’s not in Figma anymore. It sounded like Miro was playing a role in some of that with the commenting and stuff. And so these tools are interoperating with Miro. I’m curious how much innovation has that driven to the Canvas because you needed to have access to a certain thing or a new feature or a new doorway for connectivity or what have you. Has it driven much innovation on that side?
Joe McLean: Yeah, I would say on several different fronts. I mean, we use Miro for everything at Miro, maybe unsurprising, but I don’t know if folks realize totally how deep it goes. We use it for an everything. We use it for docs, tables. So much of our organization’s information is there. And as a consequence, the connectivity to external systems is extremely important for us because there’s a lot of valuable information that lives there. We also use it as a system of record for many things. And so as we were building some of the new stuff that we launched at Canvas 26 this year, we all felt a step change internally in our own utility for the tool when we started getting connectors hooked up. So it’s like, okay, well now I can take this big long Slack thread, and I can turn it into a collaborative workspace for the team. Or I can automatically take all those tickets from the team’s Jira board and drop them into Miro and start working with them and reprioritizing. And so because we’re so reliant on it as a tool, the connectivity to the outside pieces helped us a lot in terms of getting our work done. That’d be the first thing I would say. We were pushed to create more interoperability simply because of our own needs for the tool [inaudible 00:46:05] would be obviously quite useful for other people. One of the pieces that I’m paying the most attention to right now, even just in my own work, the initiatives I’m trying to drive at Miro right now, is a lot about our ability to read and write fluidly from Canvas. I think we had to jump through a lot more hoops to be able to do that. We’ve created complex layers of business logic to translate LLM output into board objects. As the models are advancing, we’re finding that the models can work more and more directly [inaudible 00:46:38] Canvas, but there’s still a translation layer needed to be able to get LLM information into board information and vice versa. And so I think one of the key innovation areas we’re pushing forward on now is trying to make that interchange as seamless as possible. Because we’re seeing that as the AI can see more of the board and manipulate the board almost as if it’s a file, we’re able to build much more powerful experiences than we could even a year ago. So that’s one of the main places we’re pushing right now.
Douglas Ferguson: I’m excited about that because I’ve been just blown away by what I’ve been able to do just with the API and my agents. And the API is limited. There’s certain format that it doesn’t have access to. It can’t unlock and lock things and can’t connect flows and stuff. But the amount of stuff that I was able to do saves so much time just getting in there and doing tedious stuff. So I’m excited to see where that goes. But one last question before we go to wrap. One of the things I hear from folks often as we’re working with them on their AI strategy and helping them think about multiplayer AI and starting to have conversations around how they set up their tooling and what makes sense for them long-term. One point of pushback I get is that, yes, I see that it’s more powerful if we can use AI as a team, but it’s almost like they’re addicted to their chat interface that they’ve been on for three years now and has learned so much about them. There’s memory there. They’re worried about losing, it’s almost like breaking up with a girlfriend or boyfriend. It’s like, hey, they know so much about me. This might be better over here, but I don’t know if I want to start all over again. This kind of switching friction. I’m curious your thoughts on that.
Joe McLean: Yeah. I mean, this is a really key topic I think. And it’ll be interesting to see to what extent companies are able to use that as leverage for retention on their products. I think you could argue on one hand that it’s an extremely powerful tool for that. On the other hand, [inaudible 00:48:49] will be huge pressure to start building export tools for that context for exactly that reason. I think there also may be a difference in how personal software, consumer software versus enterprise software, works in this regard. I would be extremely nervous if I were a CTO, was thinking about a vendor, an LLM vendor, Anthropic or OpenAI, capturing my entire organization’s memory with no way to get that information out in a way that would be allowed in platform switching order. And I think you’ll start hearing more and more conversation about this for exactly this reason. I felt this pain myself. One interesting way I felt it is I’m starting to really feel the friction of the segmentation between my personal AI world and my professional AI world. And before Miro paid for subscriptions for the various services, very early on when I was playing, I spent a ton of time at these tools. I have significant history built up in both Claude and ChatGPT. I’ve built strategies to cross-share context between the two of them. There’s a lot of that stuff that I don’t want in my professional LLM world for a variety of reasons, like personal stuff. But then I have the problem of all that context being missing doing my work. And so I am really starting to feel the contours of what my work AI knows versus my personal AI knows. And I think where it really gets wild is I get different results with that. I get a different kind of collaboration from my personal Claude versus my Miro Claude. And I think it’ll be really interesting to watch that trajectory forward, see what happens. Will there be eventually strategies, I’m sure that they’re aware of this problem, will they build strategies to have data security within the AI’s context from what it knows about you so that it has manners in a way to know the right things at the right time? Or are there reasons to keep those hard segmented for data security reasons? Imagine, again, like a CTO or a CIO being very nervous about their employees’ personal AI usage getting anywhere near company information. I am much more liberal in the connectors of data sources I hook up to my personal AI if I [inaudible 00:51:18] AI. And so a lot of really interesting questions ahead of something that we do as humans really naturally where we know how to keep those bullets separate most of the time, hasn’t really translated into the AI realm.
Douglas Ferguson: Yeah, compartmentalization.
Joe McLean: So not a direct answer, but yeah. Yeah. Yeah, exactly. Yeah.
Douglas Ferguson: When I switched to Claude, my ChatGPT became personal at that point. My strategy previously was to use ChatGPT projects if I was cooking or something. So then my cooking stuff didn’t pollute any of my other stuff. But now ChatGPT, I just kind of use that as my lightweight personal stuff. But yeah, I mean, I think that’s the big takeaway. Right now you have to develop your own personal strategies, and hopefully the large frontier models will start thinking about strategies for export and how to solve for that long term. But as we wrap here, I want to give you an opportunity to leave our listeners with a final thought.
Joe McLean: Oh man, put me up on a soapbox here.
Douglas Ferguson: I know. I didn’t warn you ahead of time. I usually warn folks. But I think we were talking too much about the Bouldering Project.
Joe McLean: Yeah, yeah. I think something I’d like to say, I’ve had the good fortune in my career to build tools that people use to be creative. I think that AI is the most amazing creative tool that we’ve ever been handed as humanity. I think incredible things will be created with this technology. I think there’s a dark path too, and I really encourage anyone who hears this, anyone who’s interested in this to build something. I think there’s a fundamental difference in the psychology between asking it questions to get answers or using it instrumentally to create something. And I think the healthy version of our relationship with this technology lies down the second path. And so I think I assume that many people in your audience are product builders and are already of this mindset, but I’ve seen over and over again people’s aha moment with this technology coming from building something that they weren’t able to build before. And I think that that represents a happier future for our relationship with AI.
Douglas Ferguson: Amazing. Thank you so much, Joe. It was a pleasure chatting as always, and look forward to our next one.
Joe McLean: Yeah, it was great to see you. Take care.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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]]>“Great companies, you’re never deciding between a great idea and a bad idea. You’re looking at 20 great ideas that all have merit and you can do three.” – Taran Lent
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Taran Lent, Chief Technology Officer at Illumia, about how his organization moved from individual AI experimentation to enterprise-wide capability. Lent describes building an “enablement task force” that deliberately avoided becoming a governing bottleneck, instead creating conditions for employees to play, learn, and share what worked, before this year’s push to elevate personal AI habits into shared team and company-wide skills, standards, and vetted tools. He talks about using AI as a contrarian thought partner and even having it grade his own interviewing and meeting behavior, while cautioning that transcripts alone miss tone and body language. The conversation also covers the governance side of scaling AI, including a stakeholder review process for new tools and skills, the four-to-six-week approval timeline for new vendors, and guardrails to prevent incidents like an unauthenticated internal dashboard. Lent connects this to Illumia’s recent merger of Transact and CBORD, crediting a shared “humble, hungry, smart” culture for making the integration smoother, and closes by arguing that judgment, creativity, and human discernment remain the differentiators as AI adoption accelerates.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:00:00] Introducing Taran Lent And New Friction
[00:02:30] AI Loses Its Social Stigma
[00:06:00] Using AI As A Contrarian Thought Partner
[00:09:30] Having AI Grade His Own Interviews
[00:14:00] Building An AI Enablement Task Force
[00:20:30] Zach Kass On AI Working Invisibly
[00:23:30] Traffic, Tragedy Of The Commons, And Self-Driving Cars
[00:29:00] Vetting And Governing New AI Tools
[00:34:30] AI-Assisted Code Reaches 90 Percent
Taran Lent — LinkedIn
Illumia — LinkedIn
Voltage Control
Taran Lent is Chief Technology Officer at Illumia, the company formed from the 2024 merger of Transact and CBORD. He describes himself as an engineer by training who has spent his career building and scaling technology companies, and at Illumia he leads engineering along with security and compliance responsibilities. Lent talks about designing tailored interviews and using AI as a feedback partner on his own leadership behavior, and about steering his organization’s shift from individual AI use to shared, enterprise-wide AI skills and tooling. He frames the recent merger as a test of culture, emphasizing that Illumia looks for people who are “humble, hungry, and smart.”
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align and how to move forward when the path isn’t clear. That friction, the human side of change is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Taran Lent, Chief Technology Officer at Illumia. Taran is an accomplished entrepreneurial leader with a proven track record in building and scaling technology companies throughout his career. Welcome to the show, Taran.
Taran Lent: Hey, it’s great to see you, Douglas. Thanks so much for the opportunity. Looking forward to the conversation and what we might learn from each other today.
Douglas Ferguson: Agreed. I always enjoy our chats. Excited about diving into this one. So I guess to start off, we’ve spoken about this topic quite a bit. You’ve attended one of our executive dinners and have attended the mastermind that we do monthly as well. And I’m curious, since you know the topic well, what news emerging for you since we last spoke? I know that you’re experimenting with a lot and trying new things. What have you encountered most recently in this world of AI and working alongside AI and the frictions that come along with it?
Taran Lent: It’s a great question. Well, it’s obviously a rapidly evolving space. The visual I have in my head is a snowball rolling down a hill and with each turn, it’s picking up more speed and more surface area and it’s getting bigger and it’s becoming kind of a juggernaut. But I just think it’s such an exciting time to be involved in technology. Anytime you have generational emerging technologies that kind of change paradigms the way you think, it’s just fun to have that in your career. Before I get into the details of it, I think it’s worth noting that humans have a terrible track record historically anytime new technologies come along predicting what it means and how it’s all going to play out. I don’t think with electricity, with computers, with the internet, if you kind of go back, a lot of people were wrong about how things were going to evolve and turn out. And I think you have people that underestimate emerging technologies. I give people that overestimate it. And I think it’s okay by the way. It’s okay not to know the answers, but what we’re trying to do and what I’m trying to do personally is just stay on the edge of the learning curve, play and experiment a lot, learn from other people, share stories. And if you do that with technology, whether it’s AI or something else, your chances of ending up in a good place are better than not is my opinion of that. To me, I think the biggest thing, the biggest thing I’ve noticed is maybe just social. There’s no stigma of any kind when people use AI. I just noticed that when people use AI, it’s perfectly okay to say, “I’m using AI.” I think there was a period where people would use AI and present it as non-AI work. And now people are using AI out in the open and the public and in a very genuous way. It’s like, “Hey, let’s bring AI into this and help it brainstorm, help us do critical thinking, help us automate.” And so I think everybody that I’m interacting with, you included, it’s becoming more of an AI first. “Hey, let’s make sure we’re leveraging AI to help us do whatever we’re doing at a higher level.” Right.
Douglas Ferguson: Yeah. I was just at an educational conference recently and one of the tables stood up as part of the debrief and said, “We don’t care if it’s AI generated, we care if it’s true.” I though that was so profound in its simplicity because it’s like, let’s not put a stigma on the AI itself, but let’s hold ourselves accountable to what matters.
Taran Lent: One of the things that I’ve had an experience with, I would say early in my career, my superpower was my ability to write and structure information or be persuasive or make a pitch. And when AI first came out, I felt like my superpower had been neutralized because now I was really good at writing a five or 10-pager or a strategic document, and then suddenly AI, everybody can now generate a lot of content. And then I was reflecting on how I use AI to do my own writing. I’m not sure it’s actually any faster at this point, but I think it’s better ’cause I’m still providing a lot of content and a lot of context. I don’t worry so much about structuring and organizing it, but I’m using AI as a contrarian partner. “Hey, what are all the things I’m not thinking about? Rate this on a scale of one to 10. How could I make this a 10? What could I be wrong about? Is this concise enough?” And by the time I provide all my context and I do all my think about it from other angles and I iterate on it and then I review it and then make sure it’s in my own voice and my own style, it might be about the same amount of time I was doing before, but the quality of when I put it out there I would argue is better. I spend more time not on the creation of the content, but on making sure the quality of the message and the information’s really good. And I think the same is true for my developers, the people that are coding. So I think there’s an analogy between writing human prose and then writing code. What AI I think helps you do a lot is really iterate faster and have more cycles so that the end result in theory … And look, there’s obviously lots of cases where it does absolutely accelerate, but the only outcome is not speed. There are other outcomes in terms of quality, completeness. I think to your point, being right and being correct or truthful are important outcomes too.
Douglas Ferguson: Yeah, I can resonate with that higher quality. And it’s interesting too, it’s like, what you’re describing is maybe even runs deeper than quality because I would argue that the stuff you were creating before was probably high quality, but you might’ve overlooked something or you didn’t have time to uncover one little thing that might bite you down the road or you share it and someone asks a question about, “Hey, what about this corner case?” And you’re like, “Oh, I should have caught that.” And I’m finding I am able to include more things like that. I’m able to iterate more and maybe it’s more expansive and more thoughtful and inclusive of other ideas and perspectives.
Taran Lent: I think the inclusivity is really good. I mean, overwhelmingly for me, I love using AI as a critical though partner and as a devil’s advocate. And I’ll give you a couple of examples in my own situation and others, but there’s oftentimes friction between just since we run a product and technology company between product management and engineering, right. Because product management’s trying to say, “This is what we want to build and this is a saving,” and then providing inputs into engineering. And oftentimes I hear things like, “Well, you’re not operating the right level of detail,” or, “This story’s not clear,” or … And I’m like, “What a gift it is to a product manager if you’re providing an input to engineering before you actually share it to say, “Here are the personas of the different people on my other side.” Review what I’m about to provide from their perspective and say, “Hey, is this complete? Is there anything that would make it better? Is there something more I could provide? Is there additional research that I should do to make this great?” And so it’s such a safe place to critique your own thinking and your own work. And like you said, the inclusivity to think about … I absolutely do that with my stuff. I have a boss, that’s the CEO. I have a board of directors. I have leaders in other departments. And by the way, and I have different relationships with these people. Some are really harmonious and peer-to-peer. Some might have more dynamics to them. And so I love that, screen my communications through that and say, “Hey, thinking about these personas and thinking about the nature of our relationships, is this message going to be successful? Is it going to be effective? And what could I do to make it better?” But that’s just scratching the surface of what AI can do, but it’s an interesting launching point of we all work with it in different ways and I’m definitely not a person that takes the first output and said, “Hey, let’s take that to the bank.” To me, it’s all about iteration, A/B testing, alternative thinking. And so I think that’s been very valuable for me. And when I talk to other people about how I use it, I kind of encourage them, “Hey, are you …” I’ll give you one more example, Doug, before you … When I do interviews, I do a lot of interviewing. A big part of my job is recruiting talent to the company. I find AI is a great tool to help me design a really tailored interview for that person so I’ve already taken into account the resume, the job description. And so I use it to help design really tailored interviews. But what I find when I am done, I don’t have it grade just the candidate, I have a grade me. “Hey, how did I do as an interviewer? Did I conduct a good interview? Did I give them lots of space to answer the questions without prompting? Did I delve two and three levels deeper? Did I hit on all the questions that were important?” And I’ll be honest, the first few times it was some tough love. It’s like, “Hey, you’re not doing very good interviews.” I was like, “Okay, well, I need to …” And that was great. I love that I got that feedback and it’s kind of a feedback loop that’s making me think about, “Okay, what do I need to do different?” Right.
Douglas Ferguson: Yeah, I was going to ask about that actually. I love that you brought a story about reflection and feedback because when you were talking about the screening of messaging for peers and other individuals inside the organization that you had to collaborate with and work alongside, and as you said, some are more harmonious than others. So I was curious, have you used it at all for reflection or after the fact feedback maybe from email exchanges or meeting transcripts, like, how the meeting transpired, how you could have shown up differently?
Taran Lent: 100%. I have a bunch of skills and prompts that basically, for any meeting where I think I didn’t do a great job or I felt like maybe the dynamics weren’t right, I’ll go back and say, “Hey, evaluate.” Especially in my role, one of the things I worry about is I, because I have a high level role and the power of the post, I really work hard to make sure I speak last and give lots of space for people to do it. And so I kind of do it as a way of accountability, “Hey, did I …” I don’t want to be known as a person that necessarily dominated the meeting or the conversation or interjected my position too early because I could create bias for how other people think or what they say. So I really like that idea of everybody can … And it’s not necessarily meetings that I’m in. Sometimes there are meetings I hear about that I wasn’t at where there’s some conflict or some issue or some decision that people have different takes on what happened. And it’s a really fast, efficient way for me to get a sense of, “Hey, how did that meeting go? And was there anything from a cultural perspective that was not great that I need to know about and address and coach?” Right.
Douglas Ferguson: Yeah, much more efficient than trying to sit everyone down individually and try to figure out what was going on if you can go back to the source.
Taran Lent: You do need to be careful though, because that’s transcripts and body language still matters, tone still matters. There’s lots of other things that play into that. You have to be careful not to take just texts on a page and a transcript and an AI. As I get further and further, the question people are asking is what does this all mean for humans and what do humans bring to the table that’s special? And I still come back to judgment, creativity, resourcefulness. Those are always going to be valuable I think in our society and in business. And I think you have to remember, AI is probabilistic. It’s trained on what’s already happened and what’s already known. And so there’s still a lot of room for humans to use their creativity and the power of our minds to forge things that have never happened before. And AI can help us do that, but I think humans are still just innately so good at that part of creativity and invention.
Douglas Ferguson: Yeah. I’m curious, how have you been encouraging your organization to lean in to the human parts and to embrace AI and use it in ways that are going to benefit the business?
Taran Lent: So first of all, I’ll provide some context. If I wound the clock back, I’ll say two years, two and a half years ago, I would say that we were not where we needed to be. I would say we were kind of behind the curve, and for reasons that were good. As you know, we were going through a transaction and selling the business and Roper acquired a company and then they merged us with another company. So it consumed a lot of capacity just to work through those mechanics. And I think as a side effect of that, we weren’t leaning into AI as much. We woke up one day and said, “Okay, hey, we’re not where we want to be.” So I looked at last year. Last year was the year where we really enabled. So the first thing we did is we created what we call the enablement task force and the naming of that was intentional. We didn’t want it to be a governing board. We didn’t want it to be a central bottleneck. We said, “Hey, look, we want to create greenhouse conditions where people can start to play, learn, experiment, apply, and then share with each other what’s working and what’s not.” And so the task force was pretty big. We’re a thousand person company. The task force was 40, 50 people. And we started really the whole focus of the conversation is, “What can we do to grease the skids to make it easier for people to get started on their personal journey?” And that included things like creating, you know, getting an updated AI policy, making it very clear, “Hey, here are all the tools that are already approved that you can use.” We looked at our process for how we review new software requests and we could get new tools approved more quickly while still keeping true our security and compliance obligations. We started creating resources and opportunities for people to share. So one of the things we did is at our company all hands, we always showcase at least one or two AI showcases that we think that’d be interesting to the whole company. But I think that word enablement was really the key piece. We said, “Hey, let’s help people get started.” There’s money and licenses for people to have one or more AI tools. So we did enterprise deals with Anthropic and with ChatGPT. We’re a Microsoft shop, so we enabled the Copilot M365 for employees, which is amazing ’cause that has access to the Microsoft Graph, OneNote and Teams and Outlook and so forth. So last year I would say was the year of, “Hey, get everybody in the game.” And by the way, I lead engineering at the company, so the developers are a whole nother story. We forget that developers created this technology. They’ve been playing with it for many years. And so the developers are in a different place in this maturity curve ’cause they’ve been … Developers don’t like to do work that they find tedious. And so they were extraordinarily resourceful at automating things that we don’t want to do. And I’ll come back to that point in a second. But this year is more about, “Okay, hey, it’s not good enough to be using AI at a personal level anymore. We need to really elevate it to team and enterprise. Like, how do we create skills that can be shared across the company? How do we make sure people have access to prompts and standards and defaults that really represent our company and our principles, our values, our thinking, our branding?” So this year the emphasis is really on elevating it beyond team. And so it’s no longer okay to have something that you just do yourself. And there’s an expectation is if it’s really providing that value, how do you bottle that up and make it available to the whole team? I want to come back to the point that I made earlier is, and this is my advice to anybody doing this, success is contagious and success is kind of compounding. And the number one best way, and this would apply for any role, any function, any department is, and I use this quote all the time, “Just look for high toil, low joy work, work that can be automated or where AI can provide assistance or leverage.” And anytime you free up capacity so people can work on other things or more strategic work or more fulfilling work, those use cases just build momentum 100%. So we still are … And we’re on the hunt for that internally in our org. So we call it reducing the drag. Anywhere where we have friction, toil, slowness, we now surface that and say, “Okay, let’s just go back and reimagine how can we leverage all these technologies we have to make that better?” And then in almost every case, we can find a better way to do it. And we’re trying to do the same thing in our products for our clients. All of our clients are being asked to do more with less and tighter budgets. And the only way you can do that is leveraging technology to help you have leverage and to force multiply people. And AI of course is a great technology for that. And so we’re just looking for opportunities where we can help people do different work, more strategic work, or free up capacity. Even if it’s just five to 10 hours a week across an organization per person, that’s huge.
Douglas Ferguson: Yeah. It’s also making me think this idea of reducing the drag, reducing toil can actually move folks into a place where the work becomes more enjoyable because they’re removing the things that they like doing the least. And so that has an opportunity to maybe improve morale.
Taran Lent: One of the most inspirational things I’ve heard in the last year, we had Zach Kass, one of the founders of OpenAI, just an incredible thought leader. And he’s thinking about this technology on a humanity level. And he was our keynote speaker at our annual client conference this year. And he was talking about what a problem it is when children in particular are spending all this time on their devices and their screens. And there’s lots of science and research around that. But he said one of his hopes for AI for humanity is, “That technology has the potential to work silently invisibly behind the scenes to make our lives better so we spend less time on screens. ‘Cause AI can be assisting us in doing work and doing things so that we can spend more time with in-person interactions versus having to work through [inaudible 00:21:05]. And so maybe there’s a future where technology’s just kind of persistent behind the scenes, making our lives easier, better, and it’s freeing us up to do more of the stuff that’s really human, right.” So I really encourage people to check out his writing and his vision of that. But if that were true, that would be a pretty amazing outcome of the technology for us as a civilization.
Douglas Ferguson: Yeah. And a great example is driving a car. Our cars are becoming more and more intelligent. The artificial intelligence that’s baked into a car is very invisible to us, but we enjoy the benefits when it’s able to adjust the lanes or alert us that we might be falling asleep or all of these things. We’re not sitting there laboring over or thinking about it, but it’s right there when we need it and it can save the day quite often.
Taran Lent: 100%. It’s a great analogy. Adaptive cruise control to me in Houston traffic is like, I love it.
Douglas Ferguson: Yeah.
Taran Lent: I was an engineering major in college and I actually got to work on a traffic study project at one point. And what a lot of people don’t know is our highway systems in most cases could support 10 times more traffic if people just drove sensibly.
Douglas Ferguson: Yeah.
Taran Lent: You maintain spacing, we’re in the correct lane for what’s your next move is, collaborated with each other. It’s incredible. There’s a phenomenon called tragedy of the commons. And when you have an individual or many individuals acting in your own self-interest, you unwittingly degrade the system to everybody’s despair. And the other thing’s interesting about traffic, by the way, this is a fascinating thing, but in a traffic jam, it takes only one car, one driver to unblock a traffic jam. Not if there’s an accident, but just if one car just backs up and provides a lot of space around it and that will unblock a traffic jam in most cases in 10 minutes. So think about if you had cars that were maintaining spacing, allowing other cars to do what they need to do. There’s one, I think we would find that traffic would become less of a problem. And then the safety aspects of that, I’m sure you would have fewer. There’s just no way that’s not our future. There’s just no way that that’s not going to be a part of our future, right.
Douglas Ferguson: Yeah, absolutely. And also now we’re getting into self-driving car land, but I’ve long been dreaming of a world where, yes, the self-driving cars are more respectful and traffic becomes less of an issue, less traffic accidents. And we don’t have to fill our urban areas with parking garages ’cause the car can just go home or go grocery shopping or whatever.
Taran Lent: Yeah. 100%. One thing that’s kind of a corollary to that is I think human tolerance for bad software is going to be a thing. We are not going to tolerate bad software, bad design, bad workflow, because it’s going to get increasingly easier if an experience is not good or not efficient or not at the standard. AI is very good at analyzing that and comparing it to all the other code repos out there and best practices. And I just think there’s going to be very low tolerance for software that’s not well-designed, that’s not well-crafted and works well. And if you’re honest with yourself and you kind of step back, there’s a lot of bad software in the world. And you look at the app store and there’s no reason to have an app of any scale that’s below three stars, but there’s many of those. And I think expectations and the standard is going to get higher and higher and higher and it’s going to be easier to meet that standard. And I think that’s going to be great for almost every realm of life where we have technology and software. The expectation’s going to be very high. And if you just think about, look at my kids, my kids, we have this instant gratification society, but their expectation when they order something, whether it’s DoorDash, Uber or Amazon, when they order it and when they’re going to get it and the level of convenience that comes with that, it’s actually relatively new in the last few years and it certainly didn’t exist when we were growing up. And that just is an example of people are just going to have different expectations over time with how this technology works.
Douglas Ferguson: I wanted to come back to your point around this year being about the move beyond the individual and thinking about these kind of team and organizational use cases. And I’m curious from your perspective, what’s the pathway that you’re taking to get there and any early wins or what are you noticing?
Taran Lent: Almost everything that we do, you do have to think about it from a guardrail perspective and compliance. We sell enterprise software. Our customers depend on us for mission-critical solutions and in some cases their data. And so everything we’re doing has to meet that bar. And so as an example, that means … I’ll take skills as an example, right. People were individually building skills that were very useful for them or maybe they’re a really localized team, but there are absolutely skills that are valuable that could be shared across the whole enterprise. And so just as a simple one, we’re rebranding to Illumia and we have the Illumia branding skill that knows our design language, knows our colors, our fonts, our imagery, everything. And so we built a skill where any document you might be working on, a presentation, a document, a letter, you can have the skill, check it for brand compliance and put that in there. And so that’s obviously a use case that was a no-brainer for us. But even skills have risk. And so we had to put in place the foundation of, hey, if somebody creates a skill, whether it’s internal or external, and it’s going to be shared across the enterprise, what policies and process need to be in place so that we can review those, make sure that they’re okay and approve them and get them published and do that in a way that’s safe. And so that’s an example. And I get requests almost every day for some new something that somebody wants to try out. It could be a plugin, it could be this or that. And honestly, our old way of reviewing requests for these things wasn’t fast enough and it wasn’t modern enough to deal with this new world. And so that’s an example where to get to the next level, we had to say, “Okay, how do we think about this and how can we support this at scale? And how can we anticipate the volume of requests we’re going to see? And how can we get these things decided or approved? Or at least if it’s not approved, a decision with, hey, we can’t do this right now for these reasons.” And so that’s an example of for our company, we had to think about those things so that we could have an operating environment where these things can happen and employees can build these things and share them with each other, right.
Douglas Ferguson: Yeah. So I’m curious, and I would imagine a lot of listeners would be curious ’cause folks are either actively designing similar processes or re-imagining existing ones. Who’s responsible for that review process? Is it a group, an individual? And is it the same process for external tools as it is for an internal skill? How does that work and even get communicated out that something’s approved?
Taran Lent: Right. We have a stakeholder group because everybody adds some value. There’s architects involved, there’s security analysts involved, there’s IT people involved because there’s a lot of things that we want everybody to evaluate from their perspective. But at the end of the day, you have to answer questions like, “Who’s the company that created this? Is it a credible, legitimate company? Are they doing business with other Fortune 500 companies? Do they have a security trust center? Do they have any compliance or assurances? Do they have an AI position? Are they clear about whether you can turn this on or off? Do they use your data to train their models?” And so what’s good is there’s good frameworks for this, but you want to know who created the tool, how they’re supporting it, what the service levels are behind it. Have they given thought to what the potential of abuses of the tool or software could be? And then with AI, you have to think about, “Hey, what happens if it does go wrong? What if it does give you the wrong answer and gives you bad advice or hallucinates? How do you confirm whether it did or didn’t? What telemetry do you have in place to track over time and then how would you retrain it to get it back on track?” And so those are the types of issues you need to think about. One of the things that I think is quite interesting is ’cause Claude’s obviously, and Anthropic are moving really, really fast and they introduced, to people who don’t know about it, a bunch of plugins that I think are amazing. They have this operations plugin and it’s got all sorts of things that are related to operations of a company and one of it’s kind of risk analysis. So they have their own risk analysis skill. And what’s interesting is that when you use the risk analysis skill on some of the new features Claude’s coming out with, it’s quite honest. It’s like, this probably isn’t really ready for primetime yet for a company at our scale. Companies are all different. We’re a public company at scale and so there’s a different standard that we operate to, which might be different than a startup. But that’s even an example of, like, even you could even leverage AI to help you with your risk analysis and to think through, “Okay, what are all the vectors here that could be exploited or abused or cause unintended consequence?” And just so you can have your eyes open when you’re making decisions. And by the way, the goal is never for risk to be zero, just so … As the CTO and I also am responsible for security and compliance, the goal is never for risk to be zero, it’s just to be informed and have your eyes open and the benefits have to outweigh their risks. And you got to say, “Hey, I think this is worth it.” Right.
Douglas Ferguson: Yeah.
Taran Lent: Or, “Hey, mitigation’s in place,” or you can put other measures in place to make it manageable.
Douglas Ferguson: And what’s your typical turn time on approvals now that things are moving so fast?
Taran Lent: So what I would say is if it’s something new, if it’s a company or vendor we’ve never ever worked with before, and the party we’re working with internally is well-informed on what the process is, it’s usually four to six weeks ’cause there’s contracts, there’s usually redlining and there’s some …
Douglas Ferguson: Okay. Yeah, yeah.
Taran Lent: And now if it’s already an existing vendor and something we’ve been doing business with and they’re introducing some new AI capability to plug in or tool, we can kind of fast track that. But one of the things we’re trying to do too is how can you do some provisional approvals? How do you have different tiers based on risk? But what I tell people is yes, and by the way, people complain about the four to six weeks and I understand that. But what’s great is somebody’s got to make the argument and kind of push it through. But after that four to six weeks, then it’s approved and we can use it for the next decade. And so in relative timescale. And look, at the end of the day, our customers depend on us to be thoughtful and to be smart and to be safe. And for me, that’s part of the value proposition you get from our company is that you can trust us with your mission-critical processes and you can trust us with your innovation and we’re going to help you. And by the way, our customers are hospitals and healthcare and universities. They tend to be relatively conservative about these things. They’re absolutely trying to protect. There’s HIPAA and FERPA and all these things. But that’s an opportunity for us to be a thought leader and to help them create frameworks and structure for how you can safely adopt these technologies and apply them to your use cases.
Douglas Ferguson: What’s your approach to sharing skills with the broader organization? The reason I’m asking, I’m really curious because I see a lot of folks checking these things into Git, but then that requires anyone who wants access to kills to also have access to Git. So then there’s challenges there.
Taran Lent: Yeah. Well look, and the other thing too is people need to understand that the real value of the skills too is they’re going to evolve over time. So the first version that you put in is probably not the best and it’s not the last. And so the question is more, I think open source software is a great model to look to ’cause … So for example, we’re really into the Silicon Valley Product Group, product operating model at our company. And we had somebody create the SVPG product operating model skill so you can have it evaluate things. Well, that was just one person. We’ve got a lot of people that are well-trained on this and have points of view. And so the question is how do you create a space where people can review the skill, can add to it, you can add it, collaborate it, evolve it. Ultimately you do need version control. And so Git is a good way to do it. Not everybody has that skillset. So that’s where that board comes into play is, “Hey, get us your feedback on the skill however you want.” And then so long as we have people who help you can get it published and manage version control, there’s a way to do that. But look, I think you do need version control. You do need to have reviews to make sure, “Hey, is the quality there? Is there anything there that may not be in alignment with what we’re trying to get done?” And by the way, kind of a related thing to that, one of the things that’s happened in our company that’s unique is for 20 years, all the developers were in my department and we established process and standards, things like, “Here’s your annual secure coding, here’s how our pipelines work. These are the security and vulnerability scans that are going to get run on code when you check it in. When you download third-party software, it has to come from a repo that we’ve verified it’s coming from a legitimate source.” There’s all these controls around these things. And so my teams are expert at working this way. Suddenly now other departments are hiring developers or builders or creators, right, and they’re creating things and then wanting to publish them and share them, but it’s outside of my org, so they’re not necessarily subject to … And so the question is how do you enable that? ‘Cause I certainly don’t want to stand in the way of that. That’s just where we’re going. But how do you create sandboxes and process where people who are not engineering the products that we deliver to our clients, these are a lot of times our internal productivity tools. How do we put them in a position where they can build things and then release them, but we’re confident it’s … So just as an example of, I think you said you wanted me to talk about some failures too.
Douglas Ferguson: Yeah.
Taran Lent: I’ve seen people build solutions that had access to confidential information that we wouldn’t want our competitors to have, and they published a dashboard or whatever that had no authentication. It was open to the public. You could take the link and put it in incognito and I’m like, “Well, that’s an example of something you absolutely can’t do.” So then the question is how do I create it so that it’s really easy for someone that may not have that skillset to publish their app and have it behind their SSO authentication, and they don’t have to reinvent that wheel, but we can create a place where they can publish that, that you only can get to it if you’re an authenticated person that should have access to it? Point though there is, like, everybody’s going to start building. We’re going to see an explosion of builders and creators. And if you can anticipate that, how do you make sure you set up an environment where you can teach them what they need to know and you can make it easy for them to do what they’re trying to do leveraging these new capabilities?
Douglas Ferguson: And what sorts of guardrails and sandboxes might we create so that-
Taran Lent: 100%. Right.
Douglas Ferguson: … they can play and not worry about creating harm? So there’s a couple of things I wanted to hit on before we run out of time. One is I’m curious what sorts of shifts and impacts you’ve seen on your product development life cycle. Have there been things that you’ve just straight up removed or completely changed or things that you’ve tweaked to support these new ways of working and bringing AI into these moments?
Taran Lent: So I’ll start with talking about engineering and how it’s working there. But if we went back two years ago and you looked at it, probably less than 20% of our code was being AI assisted. If you look today, it’s more like 90 to 95% of the code that we’re putting out is AI assisted. That could be AI generated, it could be AI reviewed, it could be agentically-created code, but that just shows you the natural adoption curve within the engineers. We’re actually seeing, if we actually look today, it’s different based on the tech stack you’re working on, but if it’s a modern tech stack, we’re absolutely seeing 20, 25, 30% productivity gains on throughput. On legacy code, that may not be as standardized and AI models aren’t as trained on, we’re not seeing that kind of gain. And then there’s a few areas where we’ve seen orders of magnitude productivity. But where I’m actually most excited about is not on the engineering productivity. I think on the upfront product discovery, validation, research, prototyping with clients, I think that’s going to be where we see this extraordinary leverage and gains. And you mentioned it earlier, but product managers used to do surveys and advisory boards and all these different ways to get feedback. Now you can just go talk to clients either in person or over the phone, you can observe them while you can capture these transcripts and you run those transcripts through a bunch of AI, you can get insights about your products, problems they have, feedback for services, support. And to me, I think just the tools available to product managers to research, to uncover insights, to prototype, to get early feedback to fail and learn, it almost makes you want to go back into a product management career. I just think it’s going to be so fun for people. And I do think there’s going to be a convergence of, ’cause historically there’s a designer and a product manager and a tech lead. And I think there’s a category person that I think is going to rule the world. The technical person that has design sensibility and good business product acumen, I think in some cases I can converge all the way down to one person using AI in a really resourceful way or maybe two people doing that. But I think, ’cause if you can get validated work that’s really clear and then you provide that to an engineering team, they’re going to go wicked fast, much faster than they’ve gone historically.
Douglas Ferguson: Yeah. And I think the amount of information you can process, not only interviews that I’ve conducted or my team’s conducted, but also what about all the customer service calls and all the sales calls? And there’s so much that could go into learning and extracting insights.
Taran Lent: It’s the best form of feedback. It’s the best form of feedback. There’s no doubt about it. And innovation a lot of time is about seeing patterns that other people don’t see or seeing insights, you know, and people talk about looking around the corner. AI can absolutely help you look around the corner if you have enough data with enough signals and enough patterns, right.
Douglas Ferguson: Yeah. And then there comes the new friction, which is the discernment on which pattern matters and where we’re going to invest our dollars.
Taran Lent: Well, that’s never easy. And I tell people all the time, “Great companies, you’re never deciding between a great idea and a bad idea. You’re looking at 20 great ideas that all have merit and you can do three.” And that’s where we get back to the humanity of it. Like, yes, there’s data and yes, there’s science, but there’s art to everything and human judgment, human discretion, human intuition still has a role to play in this. And we all know them. There’s people that we work with that just are right a lot. They’ve got really, whatever it is, their life experience and everything they’ve read and how their brain works and how they connect dots. And that’s always going to be valuable in this world. And so people that operate that way, AI is only going to make them more impactful, more effective. That’s my view.
Douglas Ferguson: Absolutely. My last question, I know y’all just went through a merger and so you’re in an environment where you’ve got two different cultures, which sometimes can be vastly different and sometimes can be very similar, but rarely are identical. And we’re in this moment where people are asked to show up and work different. So there’s this transition in our ways of working and how we’re using these tools that are frankly evolving daily. And then you’ve also got two different cultures that are coming together. So to me, when I think about that, there’s some extra layers of complexity. I’m curious what you’ve noticed and has that been a smooth ride or is it kind of figure it out as you go? What can you share about that?
Taran Lent: It’s a great question. The culture to me is the X factor. Most companies have intelligent people, but it’s the culture that I think wins the day. I think we’re relatively fortunate the cultures of the two companies we’re putting together. So Transact and CBORD are coming together at Illumia. Because both companies were very purpose and mission-driven … At the end, look, what we do, we help colleges use technology to operate more efficiently and elevate their end user experience for students, parents, faculty, staff. The shorter way to say that is we use technology to make college even cooler than it already is, right. And then in healthcare, we provide solutions around helping hospitals and healthcare environments provide world-class food services. And we think about it when you’re in a patient in a hospital healing from something, food is medicine and food is care. And it might be the one bright spot in the day. And so our technology helps make sure patients get the right food based on their doctor’s orders and also that nurses, doctors, family are well-nourished when they’re in a stressful setting. And because we’re mission-driven like that, I think our cultures were more similar than they weren’t and we were actually quite eager to learn from each other like, “Hey, how are you doing this? How are you doing that?” But what we look for, I mean, we look for people that are humble, hungry and smart. So humble means you care about the team more than yourself and you put team first. Hungry means you’re competitive and you want to win for your clients, you want to beat the competition. And smart means you’re not only intellectually smart, but you’re EQ smart in terms of the culture and the dynamics. But those things matter. And so I think those people that kind of fit that criteria are people that tend to be more adaptable and willing to be self-learners. And look, what AI demands of all of us is that you lean in and you try, you experiment, you fail, you learn. And if you do that, you’re going to be fine. I tell people all the time, “Look, if you show up and you put the team first, you work hard, you’re learning, you’re experimenting, you’re keeping your skills sharp, you should not worry about the future for yourself or your career. If you’re resisting or you’re not willing to learn, you’re not willing to change, that might be a tough road for you, so …” But people have a choice. And for me, this is the most fun I’ve had my whole career just ’cause it’s just so exciting. How lucky are we that this is happening during our careers and that we can … And just put yourself in my shoes. If I can drive productivity in my teams, if I can accomplish the same thing with smaller teams, that means I can just self-fund more ideas and more innovation. It just means the dollars go further and I don’t need to go ask for money or resources. I can free up capacity and go be in control of my own destiny. So as a tech leader, that’s amazing.
Douglas Ferguson: Yes, totally agree. And as we come to a wrap here, I want to give you an opportunity to leave our listeners with a final thought.
Taran Lent: I’ll go back to what I said at the beginning. None of us really know where this is all going and that’s okay. So don’t pretend. I think there’s a whole bunch of people that are overestimating what this means for us. There’s a lot of people underestimating and I just would encourage people to just experiment, to play, to have conversations like this one, to learn, to share. There’s so much information out there, but I think we need thought leaders that are going to use this technology … All technologies can be used for good or for bad, so if you’re in this industry, be a force for good. Help steer this in the right direction. Be engaged and be active. And I think if we have enough people do that, we’re going to see a really amazing future that we’re all proud to be part of.
Douglas Ferguson: I agree. It’s been great chatting with you, Taran. I’m looking forward to catching you again soon.
Taran Lent: Yeah. Thanks again for the opportunity and you take care. We’ll be in touch.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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]]>“You can’t force people to change. They will change when they want to, in general.” – Sarah B. Nelson
In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Sarah B. Nelson, Distinguished Designer at Kyndryl and co-founder of Kyndryl Vital, about why AI’s promise to remove friction is actually surfacing the human dynamics organizations have always avoided facing. They unpack how a single word like trust splinters into distinct concerns — model accuracy, data use, organizational credibility — and why treating human in the loop as a rubber-stamp step risks disengagement and stripped-out meaning. Nelson draws on the NeuroLeadership Institute’s SCARF model to explain why AI rollouts stall on status, certainty, autonomy, relatedness, and fairness rather than on the technology itself, and shares stories spanning cybersecurity burnout, Holacracy at Zappos, and the extraction economics behind AI training data. The conversation keeps returning to her insistence on designing with people rather than at or for them, and on imagination as the resource most at risk of being engineered out of enterprises chasing speed. She closes with a Buckminster Fuller line she keeps returning to: that people are called to be architects of the future, not victims of it.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:01:46] Why 95 Percent Of AI Initiatives Fail
[00:09:20] Trust Is Behavior Over Time
[00:12:23] Human In The Loop Versus On The Loop
[00:16:03] Psychological Safety And Cybersecurity Burnout
[00:25:59] The SCARF Model And Organizational Autonomy
[00:30:27] Lessons From Holacracy And Flat Organizations
[00:35:40] Building New Rituals For Working With AI
[00:42:38] Imagination As The Friction Worth Keeping
[00:47:48] Architects Of The Future Not Victims
Sarah B. Nelson on LinkedIn
Voltage Control
Sarah B. Nelson is a Distinguished Designer and co-founder of Kyndryl Vital, Kyndryl’s co-creation and experience design service, and hosts Kyndryl’s The Progress Report podcast. She has spent her career at the intersection of human-centered design and enterprise transformation, including roles at IBM and PepsiCo, building the methodologies and communities that moved organizations toward shared futures even on shaky ground. She previously joined Douglas Ferguson on Episode 42 of the Voltage Control podcast, “Healing the Collaboration Pain Point.” A classically trained violinist whose first computer was an IBM 360 terminal, Nelson describes her current focus as figuring out what comes next for design — new applications, new methods, and the new organizations that support them.
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change is what this series is about. Each episode I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Sarah B. Nelson, Distinguished Designer at Kyndryl where she specializes in emerging human-centered design practices. She’s also the host of Kyndryl’s The Progress Report Podcast. Welcome to the show, Sarah.
Sarah B. Nelson: Hey, hi. Happy to be here.
Douglas Ferguson: Yeah, it’s great to be in conversation with you. I always enjoy our conversations and I think today will be no difference.
Sarah B. Nelson: Excellent. Yeah, same.
Douglas Ferguson: Yeah, let’s start off with this reframe that I’ve been considering. It’s like designers have always been the people in the room arguing for friction, the user research, the design phase. Let’s slow down and understand the human first. Now that we’re operating in a world where AI’s whole pitch is removing friction, are you the resistance or has design just become redefined?
Sarah B. Nelson: I think it’s still a yes. We’re in a transition period and transitions aren’t… It’s not like flipping a switch. I think there’s a sort of dawning realization that I see on a pretty regular basis when we look at the statistics around the number of AI initiatives that fail, and it’s like 95%, which I always say is like, well, 5% succeed. I don’t say that as to be Pollyanna, but I like that that number is a lot smaller because it’s sort of saying that there’s something going on, more than one or two things going on about why these things are failing. Some of it… And generally, at least my bias is that most problems, the root cause of most problems is something to do in people dynamics. And I think that’s a lot of what design does, is it is that sort of pause to ask what’s going on. And I think, I hate to say it like this, but sometimes lessons have to be learned the hard way. And so people, you can see it, they’re investing the dot strategies. They’re like, “We got to do this, but then our data’s not set up and then none of our people want to use it, and some people are even sabotaging it.” There are all of these signals that are much louder than, “We have this persona, or we talk to 20 people, or trust us, bro. We stand for the user.” I think there’s some really clear stuff coming up and then the friction becomes everyone’s problem. It’s not just designers waving a flag. And I think that’s where it becomes really interesting when people start to realize that experience is everyone’s responsibility and everyone’s problem, because the technology itself does not… If you put technology first, it doesn’t fix it. Because I feel like a lot of times people go, like technology, money, process, people. And it’s exactly the opposite. It’s people into process into money into technology. Technology kind of comes at the end. By money, I mean how does the economics of the solutions work as well?
Douglas Ferguson: Yeah. And it’s funny, a lot of the people that I see starting to realize the orders flip, they typically end up putting the process first, as they’re trying to flip it and invert it. And it’s like, no, no, no, you really need to start with the people. And to your point, it needs to be ubiquitous. We can’t just have, just like where heads of innovation or little innovation groups never really actually worked. When design is something only one group is doing or thinking about, we’re going to be fraught with issues because the fact of the matter is every part of the organization is getting disrupted by AI, and we all had to be approaching this from a design problem, from a systemic kind of lens so that we’re not just, to your point, throwing a technology solution at it and hoping it works.
Sarah B. Nelson: Yeah. I mean what’s interesting is that in some of the projects that we’re working on, we’re rethinking roles. I mean, obviously I have distinguished designer, but I think the question of, what does it mean? I mean, so my definition of designer is every human… Designing is what we do as humans. And some people are professionally trained to do that. But everyone is designing. So there’s a lot of skills that need to go in behind that. But what’s kind of interesting right now is the conversations, even in this deep in the technical work, you hear the words trust thrown around a lot, transparency, explainability, accuracy, because we’re not calling it hallucinations anymore, but I just say lying on the part of the model. But all of these things that become this soup of, at the heart of it, understanding why people are hesitant to use it. So if it’s inaccurate, why should I trust it? What’s it doing with my data? Why should I trust it? If it’s sucking up to me all the time, why should I trust it? And then am I training people? Am I training it to do my job? All of those very fundamental pieces that are rocking people at their very, very core. But it’s interesting because I see that language in even the most technical conversations. Sometimes the solution is then technical because the next step doesn’t necessarily always say, “Well, what is it that humans are experiencing?” It’s like, “Well, how do we ensure more accuracy?” And there’s this next level of digging into what the next level problem is. I think that’s going to come next as people are starting to make these workflows and test them and seeing what works as well.
Douglas Ferguson: I think you’re right. And in addition to drilling into the knock-on effects, the second and third order, we also need to step back and even consider what we mean by these words because there’s many facets of trust as we walk into this moment and into the future. The word trust comes up a ton. Very similarly, trust and governance are two that come up a bunch and they can mean a lot of different things. And we had to be careful when we throw the words around without getting a layer deeper and really book-ending and compartmentalizing it. What are we concerned with in this moment? What are the outcomes we’re trying to design to? And what kind of environment are we trying to create? Because a great example of this is, you touched on one facet of trust, which is like, is it giving me reliable answers? And this came up in Buffalo when we were working on the Empire AI Summit, and it was a table of librarians. They said, “I don’t care if it’s AI generated, I care if it’s true.” So it’s just like this trust and this like, is it true? Is it accurate? But then there’s also so many other types of trust that come up in these conversations, but another one that’s surprising, just to give you an example, is trust in the organization.
Sarah B. Nelson: Yes, that’s what I was just going to say. Yeah.
Douglas Ferguson: Yeah. Orgs are constantly reorging or they’re just trying to respond to the market and what’s developing around the capabilities of AI and how things are getting reshaped. The message might change a ton and that can be very, especially if an org hasn’t necessarily put people first in the past. It’s a double-edged sword because now you’re like, “Well, I don’t trust your past behavior, so what does this even mean now?” So it just almost adds kerosene to that fire.
Sarah B. Nelson: Yeah, that’s what I was exactly thinking. I think there’s so much foundational work. I was trying to think about… So I think trust is behavior over time. Are you doing something? Are you showing up in a consistent way? I mean, in some ways you can trust a lot of businesses’ behavior because they will always put shareholder first. On one level, you can trust that you know that that’s how they’re going to behave. But there’s this fundamental, I think I 100% agree with you that, if you haven’t demonstrated that you put people first to now, why should I believe you that you would do that? And I think that’s where I just get really interested in how people respond to that. There’s this idea in relationship systems coaching called rank and revenge, which I love. So the idea of rank and revenge is that people respond when they don’t have power often by rank, their power is in revenge. And they can be little small things. They can be overt revenges. They can be little small things, like I’m five minutes late to the meeting. Or, oh, I ate your lunch. I don’t think that that necessarily is one, but I don’t know, it could be. But I guess it’s been really fascinated by this idea of, or what’s happening in some places where people are putting in false data or taking the company data and polluting it into public models. How can they respond when they feel like they have no power? Oh, and we were at the same thing. We were at the Gardner Workplace thing and they talked a lot about this. Trust was huge and experience was huge. And one of the things I really took away from that was how do you engage people in the design of their own future? I mean, I’ve always been a participatory design person. I really think you should be designing with, not at or for. And I think the question is how do we engage people in this, the design of the systems they’re going to use so that they see themselves in it and it actually solves their needs? I was then run up into the, how do you do that at scale? Blah, blah, blah, blah, blah. But you can’t force people to change. They will change when they want to, in general.
Douglas Ferguson: That’s right. I remember early on in one of our conferences, maybe our second or third conference, and we were holding a workshop on facilitation. It was a little two-day session, kind of a introductory thing. And someone raised their hand at some point and say, “Well, when people aren’t doing the thing I need to do, how do I make them do it?” In regard to some activity or something.
Sarah B. Nelson: Bad question.
Douglas Ferguson: Yeah, it was like a laugh out loud moment because I was like, “Well, you don’t make people do anything. It’s all about creating the conditions where they want to do it. They’re enrolled, they’re enticed, they’re invited.” And I liked what you were saying about a participatory design and how important it is. And there is a question around how to scale it, but if you take a systems approach, anything’s possible one step at a time. And I think that in this age, one way to rethink it is, we talk a lot about human in the loop, but if we rephrase that to human on the loop, we start to think about something that people have a little bit more agency. They’re not just a cog. I’m not just on this assembly line moving my little piece. It’s like I’m observing the loop. I’m noticing patterns. I’m maybe evolving the loop. I don’t necessarily have to be a stage gate. I can be a more authorship, ownership agency.
Sarah B. Nelson: Yeah. I’ve been thinking a lot about that because I’m seeing diagrams, I’m seeing well-intentioned people designing these processes and they take the human in the loop and that’s the acknowledgement that not everything’s going to be accurate. But that’s one of the concerns I have is that it starts to put people in a spot where they’re button pushers, or I worry about not challenging people, giving people meaning. And I worry about it for a couple of reasons. There’s the humans need meaning. They need to matter. And that’s I think a huge part of what work brings us actually. But the second part is disengagement. So you start, you put in these stage gates and people can just, they become rote or they don’t feel like they matter so they just get approved without really looking at them. Or you just become used to the machine giving you information being like, “I trust it. I trust it. I trust it.” Even if it’s not giving you that. So I like the idea of changing that relationship on the loop. One of the things we’ve been talking about is how do you have… I can’t think of the word for it right now, is when something is like… A decision is made that is unfair in some way or incorrect. How as a human can you intervene in a system where human maybe wasn’t designed originally to intervene? So I don’t know if restitution, I can’t think of what the word is.
Douglas Ferguson: Well, it’s reminded me of in the lean manufacturing, it’s the Andon cord where Toyota had a whole line. It was literally the cord that people could reach up and pull. So anybody, regardless of rank or position or whatever role they were performing, could shut the entire assembly line down. Yeah, so what are these ripcords or these kind of eject halt all progress because we’ve noticed something? I think that’s going to be super critical in the systems we build that are truly agentic and cross-functional.
Sarah B. Nelson: Yeah. What you just put me in mind of is around safety. And there’s something about in that assembly line that there’s actual… I’m sure they can pull it for quality reasons, but they can pull it for physical safety reasons as well. And it’s interesting because safety is a huge part of what… One of the big concerns around AI as well, but it’s almost in some ways more abstract. It could be real. I mean obviously if you’re doing in physical applications, yes. But I think about this, what is safety and how do you notice safety in that way? I don’t know, but there’s something, this might be completely off the topic, I don’t know. So let me try first. I did this podcast at Kyndryl. One of the ones that I really enjoyed is a strange word for it, but it was about PTSD in cybersecurity professionals. And a lot of cybersecurity professionals and CISOs and people like that, they have one of the highest burnout rates of any profession, including frontline nurses. And one of the reasons is that they sit in a perpetual state of threat. They can’t see threat. So if you’re in a battlefield, there are bombs going off or you have a sense of threat, but the threat ends. Let’s assume you make it out, the threat ends, you go back somewhere. Now people do PTSD as things will trigger it. But in security world, there’s a sense that there are people who are intruding. You cannot see them. You’ll never see them coming. You’re trying to do your best. And what happens, is your body never lets it go. So it’s a different kind of this perpetual stress and they’re often in their homes. So their homes are where this stress is. So this is a guy who works with them in different processes to help folks relieve themselves the PTSD, particularly after intrusions. But it just strikes me that there’s these different notions of what safety is, and that we don’t know exactly what the impact would be on workers and maybe even on seemingly safe kinds of applications. So I don’t know if that-
Douglas Ferguson: No, I mean it’s interesting. It brings up a whole new definition of psychological safety. Not only, like the Amy Edmondson’s, do I feel comfortable speaking up? But is this safe to my psyche? Is this going to be neurosis-inducing or it’s going to cause issues if we’re using it in these ways? It certainly hasn’t been studied yet. There’s lots of folks that say that our test scores are failing because of how much computers are used in education now and it’s impacting actual deep learning. I don’t know. I think the jury might still be out on that a little bit. There’s some people that are very passionate about it and they have evidence and research, but we certainly don’t have research yet on how AI is impacting our brains at that level and not any longitudinal ones for sure.
Sarah B. Nelson: Yeah, not longitudinal. I think there’s also the other parts of what’s happening in AI. And I’m thinking about the extraction in the global south, that a lot of AI, the models are being trained by people in areas that are economically challenged areas. They get paid very little to see often very traumatic information. And so that’s kind of in the system. And I think about, so companies have given them those kinds of things to do. And they’re like, okay, there are these humans, we need humans. So these are humans in the loop, but they’re going to do the stuff that we won’t want the Westerners to look at. We don’t want it to even show up for… So we work on these models already that have been cleaned by humans. And then I think in solution land, we have to be thinking about that whole human in it. And I think obviously the ethics of how these models are developed and the large tech companies and how they’re doing that. And then thinking about how we’re setting up employees and where are we dehumanizing them? Well, technically keeping the human in the loop as well. And I think just to your point, I think we just don’t know where some of these things are going to really do. Just know that pushing buttons all day long is not… Pushing buttons that doesn’t have a sense of connection or all of that.
Douglas Ferguson: Talking about dehumanizing, I ran into someone the other day while walking my dog and I hadn’t seen them in a long time. I was just chatting about things, and they’re a bit out of this space. They work kind of tech-adjacent. They have a white collar job, but they’re not living in AI. They’re not building products. And they’re a little bit outside of the spaces you and I occupy day-to-day, but it’s still hitting them. And it’s really fascinating because she’s very disgruntled about, there’s a specific project that leadership was pushing through and she’s like, “We’ve been telling them for months, if not years, that this is important. And now because the AI is saying it’s important, now they’re prioritizing it.” And it’s kind of dehumanizing because it’s like, “Wait, now that this machine that often gets things wrong is saying this, you’re going to believe it, but you didn’t believe us?” And so I think we have to be careful, even if it is helping us see the world a little differently as leaders, we have to think about how our actions that are influenced by these machines are getting perceived by those around us.
Sarah B. Nelson: Yeah. So those are these kinds of, for instance, leaders constantly make decisions without… And I mean it all, good intent, bad intent can make decisions without really being aware of impact, or just thoughtlessly. So there is more of a need for attention to what’s happening on the ground. And again, it goes back… There’s a fundamental, I can’t quite put my finger on it. There’s just this fundamental mistrust of other humans. I don’t know. I mean, if I get all wax, I’m so intellectual, I keep thinking about Turnerism and the history of, at least American business that Peter Drucker was one of the first people that said, “Hey, thought workers are not assembly line workers, you need to manage them differently.” But that even in the world of business education and all of that is that we’re not that far off of the ’50s, ’60s belief that business is an assembly line. And I remember actually there was a company I had joined and I went through the orientation. It was like, “This is how the business works.” And the entire business was about getting product to market and getting money for that. And it was every single thing in the business was doing that. Now it was manufacturing, so it was about that. And then they kind of just tucked… Design and innovation and marketing were almost literally tucked at the sides of it. And it was a moment that I had a realization, it’s like, the way that I think about what we’re doing and what my role is, and what my role is in the company is very, very different than everyone else’s. And it was the first time I saw it very laid out, as my job is to, are we working on the right thing? Are we serving the people? Are we developing new sources of value by doing that? There’s all these assumptions in there, but it is not about doing that in literally an efficient way, in the way that the rest of the business is clearly measured on. So that just becomes like… Do you know what I’m saying?
Douglas Ferguson: Yeah, it reminds me of just innovation functions. Innovation’s not meant to be efficient. It’s meant to uncover the next big opportunity. And then operationalizing is when you think about bringing in efficiency. And I think a lot of folks don’t necessarily set their strategy accordingly. If we’re in an innovation cycle, we should not be trying to optimize and make things as efficient as possible, but oftentimes that’s the posture. And I think that’s another good point, is making sure that we as leaders identify good postures for how we want to leverage AI. So it’s not just, “Hey, everyone’s doing it. We got to jump on or we’re going to get left behind.” It’s like while those fears and anxieties might be rooted in truth, we’re not going to be successful unless we step back and say, “Well, to what extent? What is the remit? Why do we want to use this stuff? What kind of outcome is it going to drive for us?” And that allows us to get beyond this intoxicating speed in which it can generate things.
Sarah B. Nelson: Yeah. It’s interesting too, because I keep thinking when I’m listening to you say that, this we got to do it or we’re going to lose in the market. This comes back to these really fundamental leadership things, that people will get on board if they know why something is happening. There’s, what is the SCARF model from the NeuroLeadership Institute?
Douglas Ferguson: Oh yeah.
Sarah B. Nelson: They talk about when people are threatened, it’s like status… I can’t remember all the ones, but I do remember fairness as one of them. And that for people, if they understand why decisions are made, they’re more likely to accept them. And a lot of the resistance comes from when they don’t know why they were made and it feels like it’s being imposed on top of them. So there’s that kind of, remember the basics of human dynamics of leadership. And I think there’s so much noise in the world. We have organizations that financially benefit from scaring everyone ahead of their IPOs.
Douglas Ferguson: That’s right.
Sarah B. Nelson: And you can see Sam Altman backing off. “Oh, it’s not going to take all the jobs.” It’s like, oh, because the message doesn’t work for you now. So I think people are starting to pay attention to that. But I did want to go back. I saw this morning, Alan Kay from Apple, from the ’80s, if any of the listeners don’t know who he is, he was one of the major folks in Apple in middle ’80s. And he was giving this talk and he was talking about, if you’re digging a hole and you’re looking for gold and you get down three feet and there’s no gold, you have two choices, is that you can dig faster or you can acknowledge you might be digging in the wrong place. And what he was saying was that American business just digs faster. It’s like, “We got to dig faster, get more people in here to dig more. There’s gold down there someplace.” And so I think a little bit about that discipline of going right back to what you said in the beginning, where can you introduce friction, asking people to slow down in order to just say, “Are we doing the right thing?” And then how can we do it better?
Douglas Ferguson: Yeah. And there’s alternatives to digging faster. Is there better instrumentation that might help us know if we’re digging in the right spot, et cetera. And I want to come back to the SCARF just for listeners that may have not have run into it. Status, certainty, autonomy, relatedness, and fairness. And I think the reason I wanted to come back to it is because autonomy is a really interesting thing and certainty are two really interesting things right now, because certainty is something that feels very elusive right now. And I think as leaders, we can acknowledge the fact that there’s a lot that’s uncertain, but what can we make certain? Because if there’s anything that we can make certain, whether that’s our point of view, our posture, the direction we want to go, the strategy we’re going to take, there’s so much unpredictability right now. Anything that we can make more certain and predictable and knowable is going to make the organization more calm, more supportive, more aligned, more understanding. Autonomy is another interesting one because people have this sense of losing autonomy in this new agentic AI-driven world and how they imagine it will even become less and less autonomy. And that’s very frightening for folks. And I think that’s really wrapped up into the identity and I’m going to lose my job and all these things. And what we can do is that if we start to really step back, and this is really why it’s so critical that folks need to adopt multiplayer team-based AI habits versus before they go to the full systemic agentic cross-functional use cases. Because the more that we can map the playing field, understand how we want to use AI together and understand the potential and align that with our vision, and we get to the shared perspective together, then we could start to understand where our autonomy can reside and then we can be very autonomous. People don’t feel autonomous when it feels like their autonomy’s being threatened in all these ways. But if they understand, if they look back and look at the system and go, “Oh, I shouldn’t have autonomy there because of X, Y, and Z, the system is going to function better if I’m not autonomous over here. But look, these are the places where I’m autonomous.” But when people don’t see the system and they don’t see it all mapped out, they don’t even understand where their autonomy resides and then it feels like they have none.
Sarah B. Nelson: Then it feels like they have none. That’s interesting because I always think constraints will set you free, but that clarity of where… Because I think about autonomy a lot as this ownership. I mean, just before AI, just that question of where do I get to make decisions? But I think that I’m just probably just very much emphasizing what you’re saying, but that making clarity of roles, clarity of decision making, all of that discipline that, honestly most corporations struggle with anyway, because we know that those things, when you have clarity of roles, when you have clear goals, when you have good communication and you have a leader who shows up shoulder to shoulder, you have all of those things, then people start to rise to the occasion because you’ve taken a ton of noise out of the system. And I don’t have any… And this is maybe just me complaining, but I don’t have any solution for it, but I just never really understand why speed to somewhere always trumps the just like, “Let’s just put the bricks in place in order for us to be able to go faster.” Because we know that if you do that, you go slower to go faster. The process is… Every time I’ve ever done that it’s like, “Oh yeah, I trust that process.” But I think most people, it’s risky. I don’t know what that’s about, but it’s too hard maybe? I do remember this Zappos, what was it called? Holacracy, this organizational model Holacracy. Does that sound familiar?
Douglas Ferguson: Oh yeah, for sure.
Sarah B. Nelson: It was a guy, came from the agile world and he was thinking about organizations as operating platforms. So the Holacracy was like the operating system for an organization. And then the idea was you didn’t have managers anymore, or a leader, you had a constitution and there were certain kinds of rules of engagement around all kinds of things. And it included things like rules of decision-making, rules of ownership, how certain kinds of meetings were conducted. And Zappos was the largest adoption of it. But one of the things that’s interesting is that we’re so ingrained on these kind of hierarchical ways of doing things, which actually turn out to be easier than trying to do this sort of super flat organization so everyone’s excited, “Oh my gosh, no more managers. I can do what I want.” The work becomes so much harder because now the decision-making is collective and there’s tons of models in the world, like Quakers and things who have collective decision making, but that is not a quick process. That is a slow process. So it’s interesting to me those kinds of the organizational systems and beliefs that people have, and how that then impacts the work that comes out of it.
Douglas Ferguson: Yeah. I think also too, there’s some rhetoric around flat structure and whatnot, but a lot of it is about cost-cutting and savings, not actually trying to build a culture that’s resilient to that. And often I found it’s not just about an unwillingness to go slow, to your point, a lot of the process is low, but it’s an unwillingness to attend to the process that’s necessary to operate in that way. And it’s just a matter of like, “Hey, we want to remove the middle managers or we’re cutting costs or whatever without being attentive to how the organization… What does the system need to look like to support that?”
Sarah B. Nelson: Yeah. I think with AI, the emphasis right now is like, “Oh great, cost efficiency.” First of all, we already know that consumption… With what’s happening with consumption, cost is actually probably not going to be the driving force around this. And to me, it’s like you have to be more creative about thinking, like what can you do? If you take the drudgery out and you take the high production things that are highly manual and you take that out, what does that enable you to do? So to me, it’s about this sort of identify, I don’t want to be businessy, the new sources of value, new things that become possible because you’re no longer consumed with that. In design, over the last few years, there’s been all these small technology advancements that I’ve gotten weepy about multiple times. The first one was the Post-it note, the 3M Post-it note app, that would let you capture Post-it notes and break them up and bring them into whatever program you were using. And then you start to get them with OCR attached to them. And I actually did get a little teary the first time I used that 3M app. And then the next time was that now I’ve got these things into Miro and I just highlighted all of them and asked it to sort it and see what it saw. And what would’ve been a three-day job or a two-day… Because after every workshop, we would sit and type them all in and then we would hand analyze them. And there’s something very valuable… I’m going to put an asterisk there because there’s something really valuable about that too. But there was this other part which was like, this got me pretty close to where I need to be and it did it so now I can focus on where is the unusual insights in here? So then my asterisk is sometimes the unusual insights come from doing the manual work.
Douglas Ferguson: Well, here’s something to think about. This is a really important point and it’s come up a couple times in some of the events we’ve hosted and the work we’re doing to try to understand where we’re headed with this stuff. And one of the new frictions that comes about is, now that the AI is doing a lot of the grunt work and the analysis and things, now then what we might’ve gotten through osmosis by just taking notes or doing the things that we would’ve had to do to be prepared for this all the post-event work or whatever it is, insert your problem. But the ways that you were showing up and the little rituals you had adopted prepared you to then do the final project to be ready for the presentation. And so an example was a designer had adopted a tool that could basically record user interviews, did a bunch of synthesis, did a bunch of analysis, generated an amazing report, but he had to study the report to be able to present it. Normally by the time you’re done making the report, you don’t even need to practice it. It was like you know it in and out, you just present it. Which was interesting, because I wanted to reframe that whole question he was posing because he was saying it’s actually shifting the work to where we need to study the presentation. But I said, actually, this is a design problem. This is analyzing the friction problem and saying, “Hey, how do I need to change my rituals and how I show up in the first place to maybe make it easier? So it’s not about cramming for some presentation. It’s about how I’m using this new tool to learn in a new way versus having to then take its answers at the very end and cram.” So I don’t know, I’m really fascinated by, we can’t just take our old ways of showing up, our old rituals and just jam these tools in. We really need to step back and say, “How are they materially changing how we need to show up?”
Sarah B. Nelson: Yeah, I 100% agree. The words that keep coming up for me are data intimacy. I don’t know. It popped into my head one day. I’m sure somebody smarter than me said it someplace, but there’s that idea of how well you know something. I mean, for me, when we would do mental models of complex workflows, I know that workflow. I could still talk about it because I have visceral stories that we captured from people, and spending time really manually with that data. So I know a lot. There was some stuff that was like… The flip is, is that sometimes you spend a lot of time on it and you only get to the stuff everybody would know anyway. So you don’t get any place in particular. But one thing that, there was someone, I listen to millions of things, but was talking about when you need to really learn something and change your thinking, you need to make yourself go slow and pull out the book, sit with the book, read the book, and munge at that information because that is when your brain is making connections. And so it’s sort of knowing about when you need to summarize something and when you need to actually spend time with it. And I think everyone’s kind of going like, “Oh my gosh, we can summarize everything.” And I think it’s, to your point, finding what are new things we need to do to make sure we retain the things that are really meaningful and useful.
Douglas Ferguson: Yeah. And also even if we’re using to summarize, what are the signals that we should identify ahead of time so that when we see them, we know to slow down, to do the deeper look, to say, “Hey, there’s something new to learn here.” Because frankly, people are using this stuff all the time to create rapid synthesis, to do a lot of grunt work, to use that other word. I think we have to invent some new signals, some new ways of looking at this stuff so that we know when, hey, this is a moment to dive a little deeper, to ask some other questions.
Sarah B. Nelson: Yeah, interesting. I think because it’s also thinking about the recipients of this. So recipients of reports, it’s always like… That’s often the thing is they read them, it’s hard to internalize, they get some information out of it. It’s not internalized. So there’s also probably the question of, and now we’ve got both sides not internalizing it, but maybe this is also the opportunity to have both sides do some more internalizing too. It goes from reports to ways of using the information from the reports in some way, that that’s how we experience the outputs. I don’t know. I’m just thinking off the top of my head.
Douglas Ferguson: Yeah, no, I love that. And also it makes me think too, we need to be intentional about how we break the cycles because when it’s my agent sending your agent an email, then your agent replying to that email, at which point is something real happening versus things just getting thrown around. It reminds me of this cartoon, it’s like self-driving cars were starting to become a conversation some years back and the cartoon was these two cars, and one of them kind of looked like a police car and the policeman’s standing out in front of the first car and he’s saying, “Does your car know why my car pulled you over?”
Sarah B. Nelson: Yes, that.
Douglas Ferguson: Yes, I think we need to… That’s something to contemplate as we’re building these systems, right?
Sarah B. Nelson: Yeah. Yeah, for sure. Yeah, for sure. I think maybe that’s just one of the most important things is just being able to… You’ve got to check yourself for when you go into automatic. I mean, I think about this a lot. I’m working on something and I’m like, am I conditioning myself to just go ask, have a conversation with Claude about it, where I would’ve talked to a human about it, or I would’ve gone an written about it and then evaluated it myself. So I’m thinking about those things, like where now I have to have some interventions on myself about when, no, actually you need to go back to what you know how to do, which is, you need to write about this or draw about it, or do some other mode that isn’t having a chat with something that may just be blowing sunshine up your butt. You know what I mean?
Douglas Ferguson: Yeah, yeah.
Sarah B. Nelson: And there are times that I sometimes think, am I actually getting… I don’t want to lose the muscles, but am I kind of in a reflection anyway? Am I already in a mirrored room? And so maybe working on my own and writing, I could probably do the same or better anyway. So it’s an interesting… I guess the main thing is to really stay self-reflective. I feel like that’s the name of the game right now. It’s like what’s happening? What’s happening to me? What’s happening to others around me? Is this better work or worse work or different work, or, yeah.
Douglas Ferguson: Yeah, I think that’s why we feel that this framing around friction’s important because are we taking note of where the friction points are? Which ones are good friction? So we’re intentionally slowing down, which ones that we might want to repair or lean into to redesign around. And so it’s introducing a little slowness, a little contemplation, reflection, back to what you were saying. But yeah, I think we just have to stay aware and attuned versus just falling into this kind of automated soup.
Sarah B. Nelson: Yeah.
Douglas Ferguson: So five years out, what’s the friction we’ll wish we hadn’t removed?
Sarah B. Nelson: Five years out. It’s hard to even imagine five years out. If things go wrong, it’s imagination. I actually think it’s time for imagination. That would be the thing I think would be the worst thing that we would lose because imagination is the thing that, I think that is something that humans uniquely do. I think, okay, whatever, never say never, but I think it’s like we’ll have all of this information and we have all these possibilities, but if people can’t think of creative ways to use it or doing like what we’re doing in this moment, of like, what does the future look like? What could it be? We don’t ask those questions anymore. I mean, I don’t know what we’re doing. I think I just imagine this sort of spiraling or flatness, or things don’t change or… I don’t know, but imagination to me feels like a keystone.
Douglas Ferguson: Yeah. It’s interesting too that you reach for imagination as an example of friction. And I think it is something that a lot of organizations try to lubricate out of the system. It’s like, “Hey, let’s not stop and worry about that. Who needs daydreaming or whatever? You need to be more professional.”
Sarah B. Nelson: That’s for children and artists, and they’re all silly people. Yeah, absolutely, because it’s amorphous, it’s threatening, it feels like guessing. It goes against this sort of belief that we can rationalize everything out. We can put it all on the spreadsheets and add it up and organize it, and make diagrams about it. It’s much harder. It’s much harder because it’s more subjective. There’s a lot more risk involved in all kinds of ways. But none of this exists without someone imagining it. I mean, some of it obviously comes out of needs, but even that, it’s the, like what is the problem we need to solve here? I mean, it’s like dumb stuff. How do I make it easier to light my candles? It’s like somebody said, “Oh, that’s imagination too.” So I guess that’s the thing that I think is the most precious thing to hold onto.
Douglas Ferguson: Yeah. It’s easy to note that, imagine has image in it and it’s conceptually bound to this idea of visualizing things and sketching and drawing and having vision. And I think that’s very strategic, and it’s unfortunate that’s not part of how most people define and capture strategy. And I would argue if you look at a… In fact, we just did a webinar last week and I talked about how we all know a photo’s worth a thousand words, and then [inaudible 00:45:23] Law said that a prototype’s worth a thousand meetings. Well, I’m now saying that a visual specification is worth a thousand prompts because text prompts are linear. And if we visually build up and imagine together what the future could be, the AI is going to be a lot more aligned with how we’re imagining and perceiving the future. And I think that’s a beautiful way to think about working with these tools when we start to work collaboratively.
Sarah B. Nelson: Yes. Yeah. I’ve been really impressed with how some of these models are dealing with visuals. I don’t mean creating them, I mean being able to interpret them in all kinds of ways. I’ve actually given it paintings of mine and I’ve been shocked at the critique I’ve gotten back from it.
Douglas Ferguson: Yeah. You mentioned stopping the sketch versus consulting with the AI. Have you experimented with sketching first and then given the AI the sketch?
Sarah B. Nelson: No, but I’m going to.
Douglas Ferguson: It’s pretty fun. In fact, it’s really fun to do a really loose sketch where you’re just like, you’re not even worrying about how understandable it is. It’s not for any other human’s consumption. So you can just flow and go wherever you want to go and then get in a conversation with it, and it’s really fun because you’re unlocking parts of your brain that maybe wouldn’t have just gone into language. Then it’s really good at being able to extract things. It is a fun use case.
Sarah B. Nelson: All right. Well, because I actually have a diagram. I was like, I need to go get a big giant piece of paper and actually draw this whole system out. And the idea that I don’t have to have it for another person but myself and AI is like, that’s awesome because it takes so much work.
Douglas Ferguson: Yeah, exactly. And also I’ve found too sometimes that it’s not quite a critique, it’s almost like just a dialogue around, hey, what’s here? What are we emerging? It’s kind of almost emergent meaning that can be fun to extract with it. Because it’s really, at the end of the day, I’m nudging and prompting, and it’s just reflecting back some things. So it’s almost like a fun way of trying to drill deeper than I might’ve gone on my own self-reflection.
Sarah B. Nelson: Yeah. Oh, interesting. Okay. Well, I have a project for this afternoon then.
Douglas Ferguson: Fun, fun.
Sarah B. Nelson: Nice. Nice.
Douglas Ferguson: So I think this is a good time to maybe hit the pause on this conversation. So I want to invite you to leave our listeners with a final thought.
Sarah B. Nelson: Yeah. So the thing that’s been just rattling around in my brain on a daily basis is this quote from Buckminster Fuller, which is, “We’re called to be architects of the future, not victims of it.” And it’s hit me really hard because I think that’s the hope that I have for all of us, is that we do actually have autonomy. We do actually have the ability to use our imaginations to bring a new future in. We’re not locked into the one that’s being sold to us right now. And by attending to the moments that we’re in and building towards the thing we actually want it to be, I think we have a lot of power to do that. So that would be what I would encourage people, is to look for the power that you have to bring the future that you believe needs to happen into life.
Douglas Ferguson: Incredible. Well, thanks for joining me, Sarah. It’s been a lovely conversation. Looking forward to our next.
Sarah B. Nelson: Awesome. Thank you so much. Great conversation.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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]]>“Until you fundamentally rebuild the SDLC for agents, you are not going to get the kinds of ROI that you’d expect out of these systems.” – Peter Bell
In this episode of the New Friction podcast, host Douglas Ferguson speaks with Peter Bell, founder of Gather.dev and author of the forthcoming O’Reilly book Scaling AI Adoption in Engineering. Bell draws on his work running invite-only peer communities for senior engineering leaders to diagnose why most organizations stall out in AI pilot mode rather than achieving meaningful transformation. The conversation maps three distinct patterns of engineer resistance—skeptics burned by early models, craft-focused developers who resist the shift toward managing agents, and those with principled objections to AI—and offers concrete tactics for reaching each group. Bell and Ferguson explore how AI amplifies existing organizational health: strong DevOps practices compound upward while process debt scales its dysfunction. They examine the mandate trap, measurement via token usage as a diagnostic rather than a performance metric, and the non-negotiable role of psychological safety in any serious adoption effort. The episode closes with Bell’s call for engineering leaders to build hands-on with current models, arguing that firsthand intuition—not secondhand reports from a VP of AI—is what this transition demands.
This episode is part of the Facilitation Lab Podcast. See all episodes
[00:00:00] Introduction to New Friction
[00:01:30] Peter Bell on Agentic AI and His O’Reilly Book
[00:04:30] Three Patterns of Engineer Resistance
[00:09:00] Encoding Craft and Taste Into AI Quality
[00:14:30] Delegation Skills and the Agent-Manager Mindset
[00:19:00] Harness Engineers and Developer Experience
[00:26:30] Psychological Safety and Blameless Postmortems
[00:33:00] The Mandate Trap and Measuring Adoption
[00:42:00] Token Economics and Rebuilding the SDLC
Peter Bell on LinkedIn
Gather.dev
Scaling AI Adoption in Engineering (O’Reilly)
The Future of the Engineering Org (Substack)
Voltage Control
Peter Bell is the founder of Gather.dev, an invite-only peer community for senior engineering leaders navigating what he calls the agentic transition. He is writing Scaling AI Adoption in Engineering: A Leader’s Guide to Driving Alignment, Adoption and Impact for O’Reilly, and publishes The Future of the Engineering Org on Substack. Bell previously served as SVP of Engineering at General Assembly and built Flatiron School’s engineering team to 50 people in 18 months. He hosts the O’Reilly CTO Hour and facilitates CNCF Executive Summits at KubeCon, and is actively building his own multi-agent orchestration systems—keeping him well outside pure pundit territory.
Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Peter Bell. Welcome to the show, Peter.
Peter Bell: Douglas, thank you so much for having me. I’m excited to be here and to kind of have this conversation.
Douglas Ferguson: Absolutely. And we’ve been in conversation a lot over the years and it’s been a lot of fun recently diving back into this new era of hosting conversations around what matters for leaders.
Peter Bell: Absolutely. Well, I’ve really been getting into how agentic AI is transforming the SDLC, the software development lifecycle. I’m writing a book for O’Reilly called Scaling AI Adoption and Engineering, which is like the hard part’s like, “I got 300 humans. How do I get them to work and be different?” And then, I’ve also been doing a lot of builder work myself. I’ve built my own harness and dreaming systems and orchestrators and tools like that. And it’s been really fascinating just to see the impact of this both on a personal level and also at the larger organizations where I’m discussing with the CTOs how this is impacting their teams and their priorities.
Douglas Ferguson: Yeah. I’ve been along on that ride. I’m building some of my own stuff and we’ve been comparing notes. And personally, I’ve found it illuminating to dream about the future a bit more, being able to get hands-on and making some things that are useful for me, and then I can more easily see how this is impacting organizations.
Peter Bell: One of the really interesting things has been fundamentally the model’s changed in mid-November of last year. You got like four, five, you got… I’m forgetting now, the version of Codex. It was at 5.2, 5.3. And what’s really interesting is I’ve found that there is a absolute divide between two types of engineer and leaders. There are the people who have taken a day to go build anything in Claude Code or something similar since then, and there are people who haven’t. The people who haven’t are still like, “Man, I’ve got a VP of AI. They’ll take care of this stuff or my platform team will figure it out.” And the ones who have done are like, “Wait, we need to call an all hands. We are not going to be writing code in a year and we need to ready the organization for our new future.” So I feel like if you’re not doing this, this is one of the few transitions as a leader where you can’t just manage and lead through it because your intuitions are going to be wrong.
Douglas Ferguson: Yeah, that’s an important point. It’s funny, not only have I seen it from leaders, but I’ve also seen leaders really struggling to bring some of their engineers or just any talent along because the talent, the individual has made an impression of what AI is based on earlier versions of the model. You were kind of touching on that a little bit, but I think it’s really important to really sit there for a moment because it’s an important thing to wrestle when you’ve got folks that have made a determination about what a thing is, but it’s evolved tenfold over.
Peter Bell: Well, I feel like there are three broad patterns of pushback and we’re seeing this in everything from juniors to some of the most senior engineers, product leaders across the board. One is, “I tried it last summer and it sucks. I can do faster.” So they just need to try it again. That’s manageable. Another is, “I understand that this works, but this is now not the job I want. I love being the person who goes on Stack Overflow and figures out just where the semicolon should be. I love the crossword puzzle nature of designing an elegant API. I don’t want to be babysitting a bunch of agents.” That’s hard because the job has changed and artisanal software development, I would be surprised if that’s a real profession for most people in the field a small number of years from now. And then, the third is, “AI is bad. It’s going to world apocalypse, destroy the population, destroy the planet, climate impact.” And there are lots of very valid reasons to have an issue with AI. At the same time, it’s here. I don’t expect it to be going away anytime soon. And so the question is as a leader and with responsibility to shareholders, you need to help the people within your team to build the outcomes you need, and not everyone’s going to self-select into that population.
Douglas Ferguson: Yeah. I’d love to dive into those three a little bit more deeply. And maybe let’s start with the I-want-to-be-a-coder category. That’s just my quick little name there for that. But I’ve seen two versions of this. And so I’d be curious if you’ve seen other layers or variants as well, but this notion of people being in love with the craft and being passionate about the craft. And the interesting thing about them is that you can actually use that as a value or as something to lean into to help them explore and experiment with using AI because you can say, “Well, how can we evolve the craft? What does the craft look like when we bring in these new tools? And if the craft is really about quality, what are the principles baked into the craft rather than the practices?” You know?
Peter Bell: Mm-hmm.
Douglas Ferguson: This comes back to the agile days when say they were doing agile because they were doing story points or user stories, but they weren’t really practicing the values or living up to the principles. And so it’s like helping people distill down their craft into principles that then they can apply in the AI world. I think that’s really fascinating.
Peter Bell: I’ve seen a couple of approaches that you’ve absolutely nailed it. One approach is the, “Oh, you don’t believe AI code is any good.” Great. What I want you to do is review all of it and tell us how bad it sucks. And that effectively, firstly, it means you don’t ship crappy AI-generated slop, which is wonderful. And secondly, it means that you get a training corpus that means that the AI stops generating slop in general if you implemented it well and manage your context and deterministic quality gates and adversarial multi-step reviews. I mean, you need to engineer this correctly, but what it allows you to do is it allows that person to say, “AI code sucks,” and for the business to get better by then being precise about how and where it sucks, which then improves the quality of generation. And then, I think the other point, the one that you doubled down on, which I love, is this idea of saying, “We’re effectively moving up another layer.” It’s we can now take infinite pains for these things. You really think that we need to have thoughtfully orthogonally designed names. We need to think about like Eric Evans, domain-driven designs and ubiquitous languages and rich domain boundaries. We can do all that stuff, but rather than doing it, what we’re going to do is teach an agent to care about it and then ensure that we have a review step to make sure that the quality of our architecture is better than we’d probably have time to ship if we’re doing it manually. So yeah, it’s absolutely true that we get to encode taste, which is actually an amazing way to improve the quality, not just of the software you write, but of all the software that is then written by the factory that you’re helping to train.
Douglas Ferguson: Yeah. I mean, there’s kind of no end to the depth that you can go into if you really explore with these new capabilities unlocked from a perspective of craft because you look at test-driven development, that’s something that everyone kind of aspired to, but how many people actually did it? This is a thing that where you can instantaneously have tests if you have well-defined boundaries and well-understood contracts and you can get them for free now. You don’t have to spend the time. And I think it really could put us in a position where we’re being more thoughtful, more mindful about our design processes and things that we might have spent more time designing if we had the time in the past. And also kind of pulling on that thread more, something I’ve seen be really effective for organizations is where if folks are really pushing back and saying like, “I can write better code,” or, “I don’t trust this thing,” have it start doing the code reviews. Ideally in an autonomous fashion, sure, still have human code reviews, but have the humans look at what the AI is discovering as far as the bugs and issues and concerns. And when you’ve got senior engineers where the AI is discovering bugs in their code, it starts chipping on the ego a little bit and they start to realize, “Wow, this thing is actually pointing out things I should have noticed,” and they start to give it a little bit more credit.
Peter Bell: Absolutely. And I think it goes both ways. I think that you get value from the humans reviewing the AI code because it improves as long as you are capturing the context, capturing the transcripts, and feeding them into a well-designed context management system. And then, absolutely, I think we’re going… It’s bizarre, but it feels a little bit like self-driving cars in a couple of ways. In the one way, it’s really hard to get a hundred percent of the way there. Anyone who’s like, “Dude, I just switched on Claude Code and now we’re generating production-ready code,” is either doesn’t know what production-ready code is or hasn’t looked at the code they’re generating. You can absolutely one-shot or few-shot stuff, but it’s a real engineering effort at least today to ensure that it is of the quality and maintainability you’d want. So it’s hard to get all the way. But the other thing is eventually, people are going to think 20 years from now, the idea like, “Wait, Granddad, they used to let humans drive? I mean, how did that work? Didn’t they get drunk and look at their cell phones and kill people?” Isn’t it much safer to use Waymos?” And I feel like we’re going to have exactly the same with writing code by hand, which is, “So wait a minute, in a mythos-level environment where you can get CVEs coming in and they maybe need to be patched in 15 minutes before autonomous agents are basically scanning, seeing what your tool stack is and exploiting a close to zero day, humans can’t patch CVEs in under five minutes, 24 hours a day, but agents can.”
Douglas Ferguson: Yeah. Yeah. That’s pretty soon going to be zero hours.
Peter Bell: Right. I mean, you really need to get to that point where to the extent that you are using third-party open-source tools, which I do still believe bring value, the downside is there’s lots more value in exploiting them. The upside is there are lots more people trying to ensure that they’re not exploitable. So there’s the trade-offs, but to the extent you’re using that, you need to have a dark factory in place by I’m going to say sometime next year for most of your key production code. And if you’re not working on that today, you’re going to be in trouble in Q4 when somebody asks why your competitors are going five times as fast and you’re like, “We’re investing. Give us nine months, we’ll catch up to their velocity.”
Douglas Ferguson: Yeah, your story about the self-driving cars and it makes me think about how we used to write programs on punch cards. And what was that transition like? If you were really into how to provide instructions to a computer via punch cards, your future wasn’t very bright. In retrospect, it seems kind of absurd to think, “Oh, I’m going to hang onto this punch card thing because that’s the way it’s done. And that’s my identity as someone who uses these things.” But I think at the time, there might have been folks that surely felt like, “This is not really programming.”
Peter Bell: Well, and it’s because technologies don’t start off perfectly. And it’s just when we moved from assembler to higher level languages, there were absolutely professional software developments who were like, “Huh, these automated compilers are fine, but then they’re going to work well on a 16-kilobyte memory system on an embedded system.” Or, “I want to double-check that they’re using the registers effectively so that they’re adding and storing in the appropriate register because I think I can do better than that.” And for a period of time, they were right. Today, I’d be pretty hard-pressed to think of anybody who’s literally hand coding hexadecimal to add two registers together to get business outcomes.
Douglas Ferguson: Yeah, and with the compiler optimizations, you’d be hard-pressed to find someone who was better at it.
Peter Bell: And that’s the point. It went from it sucked for almost everyone to. It was good enough for some people to it, was good enough for most people, but there were range cases where it didn’t work to there was no good reason for a human to be doing that. And I think that we’re going to see that in terms of encoding in C or Python or Rust or whatever it is you use to program. The only difference is I think the timeframe is probably going to be compressed given how quick all of these kind of sigmoid curves are kind of stacking on top of each other because so many people are working so hard to improve everything from the underlying silicon all the way up to the harnesses and the context that we wrap the models with.
Douglas Ferguson: Yeah, and also, it’s a reinforcing loop. So the advances in AI create advances in the other areas, which creates more advancement. Advancement advances advancement.
Peter Bell: And the crazy part is like we could stop now. I mean, if we were just to say like, “Okay, 4.8 is good enough, 5.5’s good enough,” there’s so much overhang just-
Douglas Ferguson: Oh yeah.
Peter Bell: … with the current state-of-the-art models, we could became busy for the next decade. And I hear that they’re not stopping. So my case is it’s going to get even better.
Douglas Ferguson: That’s my stance as well. Now, the other piece, because I said there were at least two things that came to mind when I thought about subdividing this I-want-to-be-a-coder perspective. And this one is about pushback on the need to be a manager in this world of working with agents. And so some folks opt to go down the management track, others opt to go down the principal engineer track or something similar, depending on what the terminology is at your organization. And this agentic workforce is pushing everyone into this kind of management model. You can’t just do it yourself. You have to be able to delegate, you have to be able to review work from others. And I think you and I were not that long ago talking about how it somewhat feels like having an army of interns that you’re managing, right?
Peter Bell: It’s really interesting because I think that you’re absolutely right, and the perfect first-level analogy for this is hiring. And I think it’s why a bunch of honestly old ex-technical people like me are having the best time in our lives because like, “Wait a minute, now we can actually ship exactly what we want and bring our understanding of systems design and engineering rigor and good practices, but also shape, merge that with the fact we spent 30 years asking humans to engage and build things for us.” And we’re going to get… It’s not quite the same as managing humans. Actually, you’re not going to need a sick day because your [inaudible 00:15:21] died. That doesn’t happen to Opus. But on the other hand, a lot of the delegation patterns and a lot of the patterns about, “How can I use generalized language patterns?”, the models are good enough now that I’ve got my harness to the point where I spend at least 20% of my time saying, “What would be three ways that you could economically and token-efficiently improve the thing you’ve just shipped?” And I will review the answers, but more often than not, I’ll be like, “Yeah, go with number two.” And so I’m not even proposing what they should do. I’m simply doing what I would do with a very smart… And by this time they start to feel like a junior to senior engineer, not an intern, which is, “Tell me what would make this better for the definition of better that I give you and what would be a token-efficient way of shipping that this week?”
Douglas Ferguson: You know, I think to that point, acknowledging the fact that these delegation skills, these managed tracking, prioritization, all of these skills are going to be super critical in the future and making sure that we spend time upskilling and investing and ensuring that our individual contributors are ready to start taking on those duties. And also, that’s something to even look for when we’re hiring new folks. Do they have those skills? Do they have an innate ability to do some of these things or they kind of wired that way to begin with?
Peter Bell: I think you’re right. This is fundamentally going to change not only the interview process, which I think was already broken in terms of managing the flood of inbound resumes and the validation of competence and fit process. I think that’s going to change, but it’s also going to change fundamentally what we’re looking for. And I think we’re moving towards this world where in R&D, you’re broadly going to have platform and feature as you do now, but the way it’s going to look is you’re going to have what’s effectively harness engineers who are primarily thinking about, “How can I capture more with… You know what? How can I create a step where effectively an instance of something that looks very much like Kent Beck meets Martin Fowler looks through our code and says whether it’s good or bad? How can I extract those insights, capture them in a context-efficient way and help agents to generate a rubric for it and then manage the pipeline for managing that?” So that’s going to be people who are effectively either on the platform team or are embedded in streamlined or feature teams. And then, I think the feature engineers are going to look much more like MTS, like we’re already seeing this member or technical staff where we don’t have front end, backend product engineering design so much as people who are deeply understanding the customer problems and trying to frame experiments and features designed to deliver value to those customers using whatever combination of product and coding front end and backend is required.
Douglas Ferguson: Yeah. The piece you were talking about there around the harness builders or maintainers made me think a bit about DevOps because there was a certain group of organizations that treated DevOps more as a developer experience, especially if you’re thinking about developer experience of internal developers. And then, there were some folks that thought of it more like site reliability or the cloud version of sysadmins, but the organizations that were thinking more around how are we making it more streamlined and more enjoyable to do work as a developer, as an engineer, I think you think about that role, that definition of DevOps and it very much is this kind of universe of how are we building the harness? How does it make for a great developer experience and provide all the tools and functionality we need to excel and create basically agentic teammates?
Peter Bell: Exactly. And I think we’re going to see that there’s two components to this. And the companies that already have some kind of DevOps platform org will be in the best place. And as you called out, if they happen to have already renamed a subset of that DevEx, or developer experience, they’re killing it because they’re thinking about the right things, which is, “How can we help stream align teams, feature teams who are shipping the stuff our customers want? How can we make them stars? How can we make them succeed better? And I think that what you’re going to see is that the platform team’s going to own most of the harness design and management. But I could also imagine at least in an intermediate period for the next couple years, 18 to 36 months and maybe ongoing, you’re probably also going to have embedded harness engineers or DevEx engineers within each team because what you’re going to see is, well, turns out that a good harness, a good set of steps in a pipeline for throwaway React code for an internal admin dashboard is probably different from the level of quality and the type and definition of quality you have for the stuff you use to build your customers every day.
Douglas Ferguson: Mm-hmm.
Peter Bell: And so you’re going to find that different teams working on different projects will require different subsets of… You’re still going to have the same basics, orchestrator, context management, but the details of the rubrics and the steps and the validations are going to be very different across your org depending upon what people are building and how much it matters.
Douglas Ferguson: Yeah. Security and uptime guarantees are totally different when you’re looking at internal tools versus external as well.
Peter Bell: Yeah. Or even similar, so I was speaking with Rob Zuber, the CTO at CircleCI, and he’s like, “Look, it would suck if our admin dashboard went down for an hour. We don’t want to do that. But if we can’t run continuous integration runs for an hour, we’re going to be getting phone calls. They’re two different things.” And so you have to look at the blast radius of the changes you’re making.
Douglas Ferguson: Yeah, that’s an interesting point around even as we’re experimenting with AI and what ways we might leverage it because there’s some obvious use cases around, “Oh, we can have it review pull requests, or we can have it sit here and help generate code,” but there’s tons of nascent opportunities we haven’t pinned down or identified and that’s going to require a lot of experimentation. But there’s a lot of folks in organizations that are afraid to experiment because they don’t know what the consequences are. They haven’t been given the latitude. It hasn’t been spelled out. And I think being very clear where the no-fly zones are and where there’s rife opportunity for experimentation gives people a bit more confidence when they do fly an experiment so they can avoid those no-fly zones but lean in certain areas.
Peter Bell: A lot of this is the context. A couple of this separate things I’d say. The first thing I’d say is don’t expect a hundred percent adoption. That’s not a realistic goal. I was speaking with Angie Jones, who did this transformation off the last year at Block, Jack’s company, before she moved onto the Agentic AI Foundation, and she was like, “We looked for 3 to 5% of people. That was our number, 3 to 5% of the engineers. And they were spending evenings and weekends, they’d installed Gas Town on their personal computers. They were like doing all the things, and we unblocked them and we elevated them.” Although the one interesting thing she also said is she said, “You know what? We also made sure that we picked them from all of our core teams across the company so that rather than just saying, ‘Huh,’ it worked for that admin dashboard or a little bit of app modernization, but it wouldn’t work for hard engineering problems, they weren’t given that chance.” The good news with that is it meant that they created an org-wide transformation very, very quickly. But to give you an idea of the level of executive support that required, that basically meant Angie had somebody from legal and security in her team seconded to her. And it was kind of along the lines of if they couldn’t either approve or reliably say, “No, no, we can’t do that because model hosted in China, probably not a good idea from a security perspective,” if they didn’t have a clear red line they could show or couldn’t approve a tool in a small number of days, they would just pull the lever. And it’s like, “Okay, we’re going to stop everything. Do we need to get Jack in the room?” And because of that, they had such strong executive support they could get things done. I’ve been talking with other companies where they still, “The CISO’s just approved Copilot recently for 10% of the engineers,” and I’m like, “They’re going to get exactly the outcomes you’d expect from that level of support.”
Douglas Ferguson: Yeah. You know, the varying levels of support is an important issue you just pointed out. There’s also even lack of understanding around what governance is. And when you’ve got different folks in the org thinking different things and expecting different things and there’s a lack of alignment there, and then there’s not great governance being provided, you kind of get this perfect storm of like everyone being kind frozen and afraid to do anything because they don’t quite understand. So not only does it take good, solid governance, but great communication as well to make sure that people understand what that means and how to apply it.
Peter Bell: Yeah. And it’s really interesting because I’m all in. I’m all the way AI-pilled. You don’t have to be. I mean, even the book, I’m talking about this idea of pick a lane. It’s like we have a technology adoption lifecycle and there’s going to be no different for this than anything else. And there are valid reasons to be an innovator, an early adopter, early or late majority, maybe a laggard. For example, let’s say you’re in the business of ski resorts, literally you run a bunch of ski resorts. Your biggest business risk isn’t AI. It’s climate change. That’s what you need to deal with. It’s whether or not you’re going to get enough snow. And honestly, if you’re six months or a year late to the party and you’re like, “We’re just going to wait till Microsoft folds it all into 365,” you probably could have made a little more money and shipped a few more features earlier, but who cares? If, however, you are Shopify or like Wix, like a website builder, you probably need to be an innovator or early adopter. Otherwise, you’re probably not going to be here in five years. And so it’s important firstly to pick a lane that is consistent with the business risk and opportunity you have, and then, secondly, to make all of your communications consistent. Otherwise, you get into this… The worst anti-pattern is where the CEO is on NBC telling everyone how AI-pilled you are whilst the CISO is still saying nothing but Copilot.
Douglas Ferguson: Yeah. Yeah, and that kind of gets into this issue we hear time and time again across clients and folks at these executive dinners we’ve been hosting is this issue around trust. And it cuts both ways because you’ve got folks that don’t, and we talked earlier about people that don’t trust the AI. And then, also, you’ve got trust in the organization, and that’s partially because of the phenomenon you’re talking about where CEO’s going on C-SPAN or whatever saying some things, and then might not be in agreement with the CISO, whoever else, but also things are changing so rapidly. The company’s message is going to morph a bit because, “Hey, there’s new things we understand now.” And I think that a lot of individuals are really frustrated by that. And it’s not necessarily a company’s fault, but if we don’t pay attention to that and shape that narrative and make sure that we’re consistent in it and make sure that people understand why it might be evolving, maybe acknowledge, “Yes, we understand we said this last month, but now we know this and so we have to take that into account.” I think it goes a long way to be transparent around the thought process, not just like, “What does everyone need to know?”
Peter Bell: I think that data messaging is always hard in large organization and change management, and this is a huge change management issue. Plus, the fear is, “Wait a second, am I just encoding my tastes so this thing can replace me? Are you going to need the same number of engineers and product managers?” There’s very valid reasons to be scared. And at the end of the day, the transition is happening and the thing that’s most… What’s really interesting to me is, and we saw this in the DORA report like last year, the rich get richer in every dimension. And what I mean by this, if you’ve already got good DevOps practices, it turns out if you’re doing agentic coding but Sally still has to FTP the files to the server, you’re only going to get so much acceleration. There are continuous integration, continuous delivery, test coverage, feature flagging so you can decouple, deploy from release and run experiments in production, whether you’re using Datadog or Honeycomb, like the telemetry that you’ve got the observability data so you know what’s going on in production. All of those are more critical than ever. And the reason I thought of this is the other thing that’s more critical than ever, blameless postmortems, an environment where everyone… If you can’t create psychological safety, nobody’s going to tell you how their job works because otherwise you might replace them with a machine. And nobody’s going to tell you that they’re scared about being replaced by a machine and they’re just going to tell you that Copilot doesn’t work very well, and that’s not going to work for anyone.
Douglas Ferguson: Yeah, I love that point. And I want to come back to your comment, the rich get richer. And I just want to be really succinct there because you can be rich with process or you can be poor with process, and those rich with process will get richer. It will amplify that wealth of process that you have. But if you’re poor and you haven’t invested in process, you’ve got some dysfunction, it’s going to amplify that dysfunction. So not only does Sally FTPing the file over prevent you from really leveraging the agentic workforce to help out in those areas, it’s probably indicative of some other process debt that you have that’s maybe going to get scaled in its own right. And what about the edges of those moves? None of that can be integrated. And so I think that’s the thing we’ve been encouraging people to think about, and that’s what we really mean by new friction is Sally moving the FTP file is going to present itself as serious friction in our ability to become the next-level organization.
Peter Bell: And I feel that the other thing also is it’s investing in your team, not only in terms of don’t expect 60, 80% of your team to jump straight on board. You find the 3% to 5%, the coalition of the willing, the people who are going to spend their evenings and weekends doing this, not because you tell them to, not even because you want them to, but just because what would be more fun? And there’s a certain point in your life as a builder where playing with this stuff is just fun, and that’s great. But then, you need to then build the tools and the trainings and the systems to help at least the 60% in the middle to make that move across, and you’ve got to give people time to win. If you’re like, “Hey, we need you to ship everything, which is still critical, oh, but also take 15 hours a week to go learn this new thing,” that’s not going to work. One way or another, it’s going to break with anybody who has kids or family or commitment or parents to deal with. And so you really you have to give people the time to adopt. And the good news is you actually don’t usually lose velocity, but you need to give them the permission-
Douglas Ferguson: That’s right.
Peter Bell: … to lose velocity for a quarter so that you can speed up in the next quarter.
Douglas Ferguson: Yeah. It comes back to that psychological safety piece you mentioned earlier. You have to make it safe to experiment in a number of ways. It can’t be a side-of-desk project. They have to have reserved and protected time. And then, also, they have to be treated with, I would say, respect and encouragement when things go wrong. To your point, if you miss a deadline because you’re experimenting with AI, well, we need to step back and look and say, “Did we actually learn stuff? Does that mean we’re going to beat the next deadline by 50%? Okay, well, it all comes out in the wash.” But if instead we just have an immediate reaction, that’s bad, we need to be punitive here, then we’re really going to miss the boat. People are going to stop experimenting.
Peter Bell: And I feel like I remember Etsy back in the day, and it’s different, but I think it’s comparable. They used to have this commit-on-day-one policy and they still do, and I think it’s much broader now, but this was maybe 10, 15 years ago. And most people would be like, “Wait, you’re letting somebody who knows nothing about your systems like commit some kind of, even if it’s just a nominal fix to a button, on day one? What if they break things?” And the feedback, the answer from Etsy was, “If your system is so fragile and brittle that somebody with good intentions can break it on their first day at work, you should be building your systems, not putting more gates in place.” And I think that’s the way to think about all of the agentic engineering as well, which is we need to build both the culture of psychological safety and support, but also these deterministic and adversarial gates, these tools to make sure that if somebody does make a mistake, you catch it early and quickly. And it’s unlikely, A, to go to production, and B, to waste two weeks of their time trying to figure out why these prompts don’t work.
Douglas Ferguson: You know, I joined a startup years ago and my first day on the job as CTO, the junior engineer who had just gotten promoted before I came online, they had promoted him from… he had just wrapped up his degree at UT, and so they converted him from intern to a full-time engineer. And it was within my first week and he managed to delete the production database. And, luckily, there were processes in place, we got it recovered, et cetera, et cetera. And I was posting, I can’t remember, it might have been Hacker News or it was somewhere that I posted just like, “Oh my gosh, first week on the job, da, da, da, da, da.” And then, of course, someone commented like, “Don’t let them near production systems anymore.” And my comment was, “I have more confidence in that individual on the production systems now than I do some of the other folks.” Because that experience, watching them go through it and them doing what they could to… A, the fact that they reported it, they didn’t try to hide it, the fact that they were just terrified and they’re going to be walking around on pins on needles anytime they’re on a production machine. And I think that’s the lesson we should learn. It’s like, “Not how do we punish someone, but how do we actually learn from our mistakes?”
Peter Bell: Absolutely. I guess last anecdote for that, so I remember one of the… I think it was the first CTO of the United States, there was a guy who helped to turn around HealthCare.gov, I believe it was, back in the time, I’m thinking Obama days maybe. And what was interesting is he gave this talk at a group I was involved with and he said he couldn’t. So imagine you brought in, this thing is months behind schedule, it’s not working, it’s a piece of junk, and you need to fix it using the same people with no different budget. You can’t change out the team. How do you turn it around? And the first thing he did, he actually kind of seeded it where one of the people stood up and said, “I lost some data from production.” And everyone’s like, “Contractors like Washington, D.C., I mean, this is like, ‘Okay, you’re never going to get another federal contract again.'” And he said, “Great, let’s take a moment. Let’s have a round of applause for that person for being honest and sharing. Great. What did we learn and how can we build processes so that doesn’t happen again?” And that was, he said, the turning point where they could start to build the psychological safety so people were focused on sharing the problems they had so they could fix them rather than hiding them and hoping to run out the clock.
Douglas Ferguson: Yeah. So important, especially in this era of AI where we’re moving so quickly and adopting new things, and creating those environments is so critical.
Peter Bell: Absolutely.
Douglas Ferguson: So I want to switch gears a little bit. You kind of touched on this a bit when you mentioned that in reality the adoption’s about 3% to 5%, and yet we see a lot of organizations with these top-down mandates, and we actually refer to it as the mandate trap. It’s one of the things we’re noticing right now, and we’re trying to coach any of our clients away from any of those behaviors, but I’m curious what you’ve noticed. And specifically when you think about this adoption rate of 3.5%, maybe how to get it up, how do we measure success in this AI world, I guess, is kind of what I’m getting at because that’s how we get past the mandates is being able to measure the process, I think.
Peter Bell: Absolutely. So firstly, I should clarify, I think you’re going to see 3% to 5% of super adopters, and then you’re going to see… I mean, the number, Steve Yegge got into a lot of trouble on Google… on Twitter or by X by saying, “You know, 20% of people are killing it, 60% of people will come along, and 20% will never touch it. And the same’s true at Google and anywhere else.” And first-level round numbers, he’s about right. There’s a few people who are killing it, a bunch of people who are willing to follow along, and a small tail who just have no interest in going. So first thing to do is drop this, not like fire, but don’t focus on the last 20%.
Douglas Ferguson: No.
Peter Bell: What you do is you elevate and you unblock that first 3, 5, 15%, whatever it is. You make sure that they get the tokens they need, the support they need, the resources they need, and you elevate them. Then, you help ask them, “Great, now you’re doing this. How can we do this as an org? Join a council, do lunch and learns. Can we build a small DevEx or platform team that has shared skills and shared resources? How can we get more observability and capabilities within our platform?” So a lot of this is about unblocking and supporting. Another part then is creating a path for the middle, the kind of quiet middle who just want to go home in the evenings, but unopposed to AI, you just need to tell them how to do it. And then, the other piece of this is in addition to that, you need to support these groups in figuring out what problems they have. So you were talking about management and metrics. The success metrics are, honestly, business ones, and you can take proxy metrics as long as you don’t performance-manage them. You learn something by token usage. If somebody’s not blown through a $20 a month plan, they’re probably not using it enough. But if somebody spent 8,000 bucks last month and has spent 6,000 this month and their output increased, they’ve probably improved the efficiency of the levels of the models they’re using. So it’s not that token maxing is good, but it is okay to know how many tokens people are using as a diagnostic to put them into populations which you can then support with adoption in different ways. The true success metrics are pretty straightforward, all right. It’s revenues, it’s customer retention, it’s all the numbers you care about. The challenge becomes that it’s hard to map those to a particular feature deliverable or a particular agent. So I think the main thing to do is the AI token usage and stuff like that, that is a diagnostic to help you to cluster people around common failure patterns of adoption so that you can give them the training and support to learn how to get through, “Oh, that’s the kind of thing we see when somebody’s still on an IDE.” We should teach them how to use skills with agents. That’s the thing we see when somebody’s waiting for one agent 15 minutes at a time, we should show them how to use multiple agents and so on towards moving them towards using a dark factory. And then, the other piece is classic DORA, DX Core 4 space metrics, I think still have a place. They’re not the answer, but things like cycle time, meantime between failures, PR rates, all of those can be gamed, but if there’s no reason to game them, they can be useful diagnostics to help you to see how you appear to be doing.
Douglas Ferguson: Yeah. And it’s really interesting, too, when you think about cohort analysis, you could look at that in a number of ways. You could look at any of our standard metrics like cycle time, a few others you mentioned as it relates to folks that are heavily using AI, barely using it to not using it at all, because then there’s an interesting story to be told there. It’s like, “Well, what kind of outputs are we seeing from these individuals and different teams as well?” The other thing around cohorts that’s fascinating to me is what are we noticing as far as the friction that we might be seeing from each of those cohorts? And because you mentioned looking at, “Well, what signals are indicative of someone still being in the IDE or whatever some of these types of behavior shifts that we’re looking for?” And I think, likewise, if we diagnose where are the sticking points in the organization and what are those indicative of? It’s like, “Hey, if we’re going to be investing in this 20% that’s really leaning in and we’re trying to remove obstacles, well, let’s actually make note of the obstacles they’re running into. And then, how do we codify that into repeatable patterns or better ways of supporting them?”
Peter Bell: Absolutely. And I think we’re seeing that what’s nice is I think that 5 to 15, 20%, what they do is they’re actually as the obstacles they usually run into are self-induced by the company. And I have lots of friends who are CISOs, but like security, compliance, governance, audit, risk, it’s those groups that are designed to keep things the same so we don’t break it all, which is a noble mission, but we need to understand that there’s risk to not changing and support those teams in being enablers and not blockers. And then, once you’ve got that in place, then what they can do is they can… The truth is what they’re doing is hard and it probably is requiring evenings and weekends, but what they can do then is synthesize the good practices, create standard skills libraries, create a standard factory harness and standard adversarial reviews, improve the quality of the observability and the DevOps and CI and CD pipelines, all the things that are going to make it easier for other people on the teams to then kind of join along. And the other thing, it feels to me like I remember when you mentioned TDD earlier, test-driven development, you had to get… Most of us got test infected. We’d read the books, we kind of saw the stuff, it didn’t really make sense. And then, you paired with somebody from like a Pivotal Labs or a Thoughtworks back in the day and you’re like, “Oh, that.” And after two or three hours of pairing, it made perfect sense. And just as you had to get test infected, I think there’s huge value in getting AI infected where a coworker just sits down with you, pairs on a couple of features, and shows you how they’re leveraging skills, how they’re jumping between agents and how they’re building these kind of pipelines so that they can start to trust the quality of code that’s being shipped.
Douglas Ferguson: Yeah, I mean, sure we see the CISO friction all the time like, “Oh, we really want Claud Code, but security has only approved Copilot,” or whatever. And so that certainly is an obstacles we should as leaders be trying to remove if we’ve got folks on the team eager to push things forward in ways that are responsible and secure, then we should pave the way there. But I think the latter half, the stuff you got into, I think is a little less obvious. It’s totally clear if they’re trying to use a tool that’s not available, but what about these models and patterns? Because you can’t go just grab a book off the shelf. You can’t go read about Spotify’s model of how to do this. And so are we creating opportunities to sit with peers and see how they’re each using skills and really look at what is some of that minor friction that they’re running into that’s not maybe apparent or where they’re scratching their head a little bit? A great example, buddy of mine mentioned that someone on his team was… He’s a VP of engineering, and someone on his team had one-shot this piece of code that was like, I don’t… It was like 250,000 lines of code or something insane. And admittedly, the engineer came in and said, “This seems to be working,” but I’m like, “I can’t even fathom how to read this much code. I’m stuck.” And so then, that became a conversation around, “Well, this is some new friction. Look at this thing. It’s brilliant. It seems to work. When we poke it does the things we want it to, but we need to understand this better.” And the thing they came to was, “What if we then use the AI to decompose this into more meaningful, smaller chunks that are easier to read? We’re still get this out the door way faster than we ever would have previously, but let’s induce some slowness here because we want process and we want care and quality. And I think that’s a great example of the types of friction we should be listening out for and helping our teams work through because that’s what’s going to create the models of the future.
Peter Bell: Exactly that. So there’s a guy called Sam Schillace. I first came across him when he was SVP engineering at Box. Now, he’s a deputy CTO at Microsoft. He has helped them to build this tool called Amplify, which it’s like the best harness nobody seems to know anything about. They don’t promote it very much. But what’s interesting is he’s got this Sunday letters from Sam on Substack, and he’s got a couple things that he’s built into his pipeline. One was Cranky Old Engineer, which is basically the salty old engineer like, “That’ll never work under load. You’ve got to ensure that there’s a fallback and you back off your retries against the API or whatever it is.” But now he’s got COS, Cranky Old Sam, which is based on Cranky Old Simplicity as well, which is basically saying, “Hmm,” and it will literally go back to the agent that’s generated a code that’s passing the functional test that’s meeting the performance requirements saying, “Would there be a simpler way to do this?” And proposing unifications and simplifications and simpler ways of solving the same problem. And it turns out that a lot of this is just reprompting and loops. And if you’re willing to burn the tokens, most of the problems the agents solve, you can get other agents to tell them how to fix.
Douglas Ferguson: Yeah, it’s amazing. It’s so fascinating. I mean, and to your point, willing to burn the tokens, I think that’s going to be a conversation that evolve even more so over the next six to 12 months. As we’re seeing the cost of tokens rise, more competition against models, the IPOs are certainly going to influence us because now the market’s going to be a driver and have a voice in the cost of these tokens. So it’s going to be fascinating to look and see how we start to optimize around token consumption and when, where, and why to use them.
Peter Bell: And I just want to throw one thing in there because what happens is every so often people who want AI not to succeed, and I get it, I understand why, will be like, “Oh, token costs are going to become crazy, so we’re just going to hire humans to do it.” If there was one piece of generalized advice I could give is don’t bet against the models. I don’t think that’s a good long-term bet to take. And there’s no question that sanity is going to prevail. Tokenomics is a real thing now just as FinOps is for cloud. We’ve started by, let’s say, we’re just going to run all this Kubernetes stuff in the cloud and it’s going to be perfect. And then, the CFO comes calling like, “Why did our operating cost go up by $12 million last year?” “Oh, we forgot to switch off the… We had this test run and we forgot to switch it off for six months.” That was like a million and a half. And so then you started to bring sanity and improve operational and then the spot versus reserved instances and all the rest. We’re going to do the same here, but here it is you should never use a model to do something that code can do perfectly well. Long running, don’t use supervisor model, use deterministic pipelines. If you’re extracting text from a PDF, have a Python script extract it and just put the text into the model. Don’t burn the tokens on a 4.8. And it’s all of that. I just ran a bunch of evals. I had a bunch of stuff running on Opus that I’ve now downgraded to Sonnet and then to Haiku with evals and test sets and they just auto-tuned the prompts until it would work. So all of this is just a simple engineering problem. We know how to engineer the costs out of stuff. So, yes, token costs are real, and no, don’t believe that somehow magically you’re going to stop this, it won’t.
Douglas Ferguson: So what you’re making me think of is that Chaos Monkey might be coming back but in the age of AI.
Peter Bell: I think it’s going to be so many of the things we’ve seen before coming back just at another level, and it’s going to be really interesting to see how they all play out.
Douglas Ferguson: For sure. Well, as we come to our end here, I want to give you an opportunity to leave our listeners with a final thought.
Peter Bell: Absolutely. I’ll give a two-for-one. The first thing is do it yourself. If you’re a CTO, you shouldn’t be writing production code and blocking that for three months as you’re going to performance review season. But if you’re not spending time building with these models, you won’t get the right intuitions, and that’s the only way to keep up. And second, have a sense as to where we’re going. This isn’t about Copilot, this isn’t about IDE. This isn’t honestly about the kind of interfaces we’re seeing now. We are building systems that will write the software, and our job is to identify the experiments and the verifications and build the toolings to make that work. And understand that until you fundamentally rebuild the SDLC for agents, you are not going to get the kinds of ROI that you’d expect out of these systems.
Douglas Ferguson: Important words. Great to be chatting with you today, Peter, and looking forward to talking again soon.
Peter Bell: Douglas, thank you so much for the invite. So much fun.
Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual Masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.
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