The Only Trust Move That Closes the AI Perception Gap

The organizations that are struggling with AI adoption have usually made the same diagnosis: employees are not using the tools enough. The response is predictable. Mandate harder. Add measurement. Tie adoption rates to performance reviews. Enforce through the management layer. Adoption rates go up. The dashboard looks better. And then, quietly, the resistance goes underground. Many employees don’t know if they’ll lose their job to AI. Few feel involved in the decisions about how AI gets deployed in their work. These two realities explain more about the failure of AI mandates than any tool-selection decision or change management curriculum. The organizations that have cracked AI adoption are not the ones that mandated better. They are the ones that involved before they mandated, and that sequence changes everything.

The Mandate Reflex

There is a logic to the mandate approach. Leadership has data showing that AI tools produce significant productivity gains for the people using them deeply. Leadership also has data showing that most employees are not using the tools deeply. The gap between what the tools can do and what employees are actually doing with them is money left on the table. So the reflex is to close that gap through authority. Roll it out. Train everyone. Track the dashboards. If usage is low, mandate. If mandates are not working, mandate with more specificity. What the mandate approach misses is that the resistance is not behavioral. It is psychological. The mechanism is straightforward incentive psychology. Executives who authorized the AI investment have staked their credibility and budget on the claim that AI makes the organization more productive. They need it to be true. Frontline workers who read job-loss headlines every morning need it to not be true, or at least to be less true than leadership claims. Both sides are filtering identical information through fundamentally different personal stakes. No mandate resolves that. You can compel tool use. You cannot compel the psychological safety that makes tool use productive.

What 12% Means

Very few employees feel involved in decisions about how AI gets deployed in their work. That gap is worth holding for a moment. Most of the people who will be expected to use AI transformation tools… are not in the room when those tools are chosen. It is describing how organizations are making decisions. Eighty-eight percent of the people who will be expected to use AI transformation tools, to build their work around them, to advocate for them or resist them through daily action, are not in the room when those tools are chosen. They are handed the outcome of a decision they did not make and told to adopt it. In most organizations, this is not malicious. It is an artifact of how transformation projects are structured. A small group, usually IT and senior leadership, evaluates tools, secures a contract, builds a rollout plan, and then executes that plan on the rest of the organization. Employees are the recipients of the transformation, not the designers of it. This is not a neutral design choice. It is the choice that predicts resistance. When people feel that something is being done to them rather than with them, they comply at the minimum threshold that avoids punishment. They use the tool in ways that satisfy the dashboard while preserving their actual workflow. They wait for the initiative to lose steam, because most do. They interpret mandatory AI adoption as the latest version of a long history of top-down transformations that did not deliver what was promised to the people who had to live inside them.

Shadow Adoption Is the Message

The data point that most organizations misread is their own shadow adoption rate. According to Microsoft and LinkedIn’s 2024 Work Trend Index (a global survey of 31,000 knowledge workers across 31 markets), 75% of knowledge workers are already using generative AI at work, and 78% of AI users are bringing their own AI tools rather than waiting for IT-sanctioned options. They are not waiting for the rollout plan. They are solving their own problems with whatever tools they can reach. (news.microsoft.com, ‘Microsoft and LinkedIn release the 2024 Work Trend Index on the state of AI at work,’ May 8, 2024.) Organizations typically see this as a governance problem to solve. Stop the unauthorized use. Create a sanctioned list. Block the unapproved tools. The shadow-adoption numbers mean most of your workforce has already told you where the AI leverage is. The question is whether you are treating that signal as intelligence or as a compliance problem.

What “Involve” Actually Requires

The word “involve” is doing a lot of work in AI transformation conversations, and most of what it is doing is insufficient. A town hall is not involvement. A feedback survey after the decision is made is not involvement. An optional AI showcase where the tools are already chosen is not involvement. Involvement that changes outcomes happens before the commitment is made. Vizient, a healthcare performance improvement organization, made this concrete. Before designing any AI-augmented roles or workflows, the company built what it calls a Persona-Based Adoption Model: a phased, empathetic approach to mapping what employees actually wanted from AI tools before rolling anything out. (Sam Fredin, Director of AI Strategy & Technology, Vizient, “Vizient’s AI Optimization Strategy Choice Cascade,” LinkedIn, Oct. 21, 2024.) The questions seem almost too simple. But they reframe the entire design problem. Instead of starting with AI capabilities and asking where they fit, Vizient started with human preferences and asked where AI could serve them. The difference in outcomes is structural. When workers feel that the transformation is built around what they said they needed, resistance looks different. They are not defending their turf from an imposition. They are trying to get the implementation right on something they asked for. This is not the same as letting employees veto every AI decision. It is the difference between involving people in problem definition and involving people in solution approval. You can involve people in the first without surrendering authority over the second, and the organizations that make that distinction get dramatically different adoption outcomes. Red Hat’s own account of its AI rollout describes the same dynamic. Engineers choose their own tools from a bounded set of options, treat AI experimentation as bottom-up rather than mandated, and the company frames the result as ‘a capability multiplier,’ not a threat. The result, in Red Hat’s own telling: an ‘explosion of internal experimentation’ and a workforce that is curious about AI possibilities rather than defensive against them.

The Hallways Know Before the Meeting Does

There is a pattern that shows up consistently in organizations struggling with AI adoption. Leadership believes adoption is low because employees need more training or clearer tools. When you talk to the people doing the work, you find something different: pockets of sophisticated AI use, distributed unevenly, often invisible to the management layer above them. The practitioners using AI deeply are generating the practices that could scale. They are also, almost universally, not sharing what they know with the organization. At a Voltage Control executive dinner, Taran Lent, then leading product at Illumia, described a move that illustrates how shadow practice becomes organizational signal. He had written a five-page AI strategy paper with the model prompt pasted directly at the top, making the AI use visible rather than buried. He sent it to his parent company’s CEO with a TL;DR. The response was immediate: “This is what I mean by AI first. You guys get it.” Making the AI use legible made it safe to share. It turned a private practice into a signal the organization could see and build on. Rachel Brown, Managing Director of Innovation at CIBC Global Asset Management, made the complementary observation: a discovery sprint across every team sounds like a way to surface the real picture, but it ends up asking the people who are following the official story rather than the ones doing the work. What you actually need are domain leaders who understand the art of the possible in their specific context. Those people exist in most organizations. They are simply not in the room where the strategy is being set. The involve-before-mandate sequence requires knowing who those people are, which means you need a way to see the hallways before you design the mandate.

The Sequence Is the Prescription

Mandate-then-involve creates the adoption loop most organizations are caught in: announce, train, mandate, measure, discover that measurement is gaming the metric, mandate more specifically, discover deeper resistance. Involve-then-mandate runs the sequence differently. The first move is to ask before you commit. Vizient’s persona-mapping approach is a good starting structure: find out what people want to do more of, what they’d do with more time, and what they’d rather not do at all. The answers tell you where the AI intervention has actual demand, which is where it will stick. The second move is to make shadow adoption visible rather than punishable. The three-quarters of your workforce already using AI without sanction are your best evidence of what the tools are good for in your specific context. Create a channel that makes their discoveries legible without requiring them to confess a policy violation. The policy may need to change before that channel can work. The third move is to frame the transformation as a two-way deal with explicit terms on both sides. What the organization commits to: transparency about what AI will and will not replace, involvement in decisions that change how work is designed, honest communication when the plan changes. What employees commit to: learning in real workflows, sharing what works, raising problems early when the implementation is not delivering. These three moves do not require new tools. They require a different set of decisions about who is in the room before the decisions are made.

What This Looks Like in Your Organization

If most of your workforce does not feel involved in AI decisions, you have not yet done the first move. The dashboard numbers are showing you adoption of the tool, not adoption of the transformation. Those are different things, and only one of them compounds. The leaders who will have functioning AI-enabled organizations in 2028 are the ones who used 2026 to ask the questions before they committed to the answers. The leaders who will be mandating harder in 2028 are the ones who committed first and are still trying to get everyone on board. Involvement is not the slow path. It is the only path that does not end in the loop. Want to explore what this looks like for your organization? Learn more about our AI transformation programs.

The post Involve Before You Mandate appeared first on Voltage Control.

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.

Show Highlights

[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

Links | Resources

Jeff Chow on LinkedIn
Voltage Control

About the Guest

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.

Transcript

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.

The post Decisions, Not Doing, Are Now The Bottleneck appeared first on Voltage Control.

The Leadership Move Nobody Is Making on AI

Every AI rollout gets scored the same way: how much friction did it remove. Fewer approval steps. Faster first drafts. Less waiting on someone senior to review the work. The instinct is universal because it is usually right. Most organizational friction is waste, and removing it is the whole point of transformation. Some of that friction was never waste, though. It was training. The lawyer who used to draft the routine contract herself, the analyst who built the first version of the model by hand, the associate who wrote the memo nobody important would read: none of that work existed because it was the best way to produce the artifact. It existed because doing it badly, slowly, under supervision, was how a junior person became a senior one. Remove that work with AI and you have not eliminated inefficiency. You have eliminated the on-ramp. This is not a call to slow down AI adoption. Slowing down protects the wrong thing, the existing shape of the work, not the developmental value inside it. The leadership move that actually holds is different and harder. It is deliberately designing struggle back into the system AI just made frictionless. Picture a junior analyst three years into a role that, five years ago, would have had her building forecasting models from scratch for the first eighteen months. Today AI builds the first version in minutes, correctly, most of the time. She reviews it, approves it, moves on. She is faster than her predecessor ever was at the same tenure. She is also, by every account from the people who manage her, worse at knowing when the model is wrong. Nobody made a decision to let that happen. It happened because nobody made a decision at all.

The instinct to protect is the wrong instinct

When leaders notice that AI is eroding how junior people learn, the reflex is protective. Ring-fence some tasks. Tell the senior team to do things “the old way” some of the time. Add a training program to compensate for what the workflow no longer teaches. None of it works, because it treats a design problem as a scheduling problem. A training program bolted onto a workflow that AI has already hollowed out is not struggle. It is theater. The junior person knows the real version of the task is being done by AI three doors down. Practicing on a sandboxed exercise that nobody actually depends on does not build the same judgment as doing the work when it counts, because judgment is built under real stakes, not simulated politeness. There is a second failure mode that looks like caution but is really nostalgia: refusing to automate anything a junior person currently does, on the theory that all friction is developmental. That is just as costly as removing everything. Most of the friction in most workflows is genuinely waste. Formatting, reformatting, chasing down a source, restating something already said in a different template. AI should eat all of that immediately and completely. The leadership move is not protecting the old workflow and it is not automating everything in sight. It is building a new workflow that has real stakes and is still safe enough to fail inside.

Lever one: build the practice environment, not the training deck

The clearest version of this shift already exists in the market: the GenAI simulator, a realistic, high-stakes practice environment where the cost of a bad decision is contained but the decision itself is not simplified. We wrote about this shift in depth here, and the data is not subtle. Bank of America uses simulators to train financial advisors on the hardest conversations they will have with clients, the ones carrying real emotional and financial weight, before those advisors are in a room with real money on the line. \SOURCE: Bank of America, The Academy – [https://careers.bankofamerica.com/en-us/career-development/the-academy\] What makes a simulator different from a training module is that it preserves the actual difficulty of the judgment call while removing the actual cost of getting it wrong. A compliance officer practicing on a simulated ambiguous disclosure is still wrestling with ambiguity. A new manager rehearsing a layoff conversation with a simulated employee is still managing real discomfort, real hesitation, real second-guessing. AI can generate the finished artifact instantly. It cannot generate the discomfort of deciding under uncertainty, and that discomfort is the entire training mechanism. Skip it and you have skipped the part that actually builds the skill. Most simulators in production today are company-built, not vendor-bought, and that is not a gap to wait out. It is a signal that this capability has to be designed on purpose rather than procured off a shelf. If your organization has not identified at least one high-stakes judgment call worth building a practice environment for in the next twelve months, that is the first gap to close, and it is worth closing before the senior people who currently hold that judgment start leaving. Picking which judgment call to simulate first is not a hard problem once you frame it correctly. Ask where a bad call currently costs the most, in dollars, trust, or time, and ask who currently only learns that judgment by making the mistake live, in front of a real client or a real dataset. That intersection, high stakes and currently learned the hard way, is the shortlist. Most organizations have two or three candidates on it, not twenty, which is exactly why this is buildable in a year rather than a decade

Lever two: redesign the role before you deploy the automation

Most automation decisions get made at the task level. Can AI write this memo. Can AI draft this contract clause. Can AI build this model. The right question sits one level up: if AI does this task, what does the person who used to do it now do instead, and does that new thing still build judgment, or did the promotion happen in title only. This is a redesign question, not a tooling question, and most organizations skip it entirely. They automate the task and assume the person “moves up the stack” without ever specifying what moving up the stack actually requires them to practice. The associate who no longer drafts the memo needs a genuinely new developmental task, something like reviewing three AI-generated drafts critically and defending, in front of someone senior, which one is right and why. Without that explicit reassignment, the associate does not move up the stack. They just stop practicing. Do this redesign work before the automation ships, not after someone notices the bench thinning out. Naming the replacement developmental task, in writing, as part of the rollout plan, is the actual substance of the redesign. Everything else, the tool selection, the pilot, the rollout timeline, is just removing a step. The step you removed has to be replaced with something that still teaches, or the organization has quietly traded a training pipeline for a productivity metric. This is also where facilitation earns its place in the AI conversation, and not as a soft add-on. Deciding which developmental tasks survive automation is a genuine disagreement waiting to happen: the people who benefit from moving fast and the people who are responsible for who exists in the role five years from now rarely agree on the trade instinctively. Someone has to structure that conversation on purpose, in the room, before the rollout, or it never happens and the default answer becomes whatever is fastest to ship. “Review and defend” is a useful default for what the replacement task looks like, but it only works if the defense is real. That means the junior person does not just initial the AI draft. They have to be able to explain, out loud, to someone who will push back, why this version and not one of the other two the model could have produced. If nobody ever pushes back, the review step degrades into the same rubber stamp the memo used to be, just with less writing involved.

Lever three: apply a real test, not a feeling

The test that separates friction worth keeping from friction worth removing is simple to state and hard to apply: does this friction develop the person doing it, or does it just drain them. Formatting a document by hand develops nobody. Deciding what argument the document should make, under real constraints, with real consequences for getting it wrong, develops everyone who has to do it. AI should eat the first kind of friction completely and immediately. The second kind is the one leaders need to protect, redesign around, and in some cases deliberately reintroduce. Applying that test requires actually walking the workflow, task by task, and asking who is currently doing the developmental version of each step and whether AI just quietly took it from them without anyone deciding that on purpose. Most leaders have not done this walk. It takes an afternoon with the team that actually does the work, not a quarter with a consulting deck, and it is the single highest-leverage hour available to anyone worried about the bench five years out. The output of that afternoon should be a short, specific list: which tasks stay fully automated because the friction there was never developmental, which tasks get a redesigned developmental replacement, and which one or two tasks get deliberately protected from automation for now because nothing has been designed yet to replace what they teach. That third category should be small and it should have an expiration date. Protecting a task indefinitely is the nostalgia failure mode again, just moving slower.

What this looks like in the room

None of this happens by memo. It happens in a room with the people who actually do the work, walking the real workflow step by step, naming out loud where the struggle currently lives and what it currently builds. That conversation surfaces disagreement fast: the senior person who says “that’s just busywork” and the junior person who says “that’s the only place I ever get real feedback” are describing the same task from two different vantage points, and both of them are right about their own experience. Leaders who skip that conversation and redesign the workflow from a whiteboard alone consistently guess wrong about which friction is developmental. The people doing the task know. The redesign only works if someone facilitates that disagreement into a shared answer instead of letting the loudest voice or the fastest deadline decide by default. The senior person in that room also needs to hear something uncomfortable: the busywork they are relieved to hand off might be the exact thing that made them good at their job. That is not an argument for keeping it exactly as it was. It is an argument for taking seriously what it built before deciding it is safe to remove.

The stakes are five years out, which is why almost nobody is doing this

Nothing about eroded apprenticeship shows up in this quarter’s numbers. Output goes up, cost goes down, and the dashboard looks great. The cost shows up later, when the senior people who have been quietly absorbing junior work retire or move on, and the organization discovers it has no one who actually built the judgment to replace them. By then the fix takes years, not an afternoon. That lag is exactly why this requires deliberate leadership action instead of waiting for the market to fix it on its own. Nobody gets punished this year for skipping the redesign. Somebody gets punished badly in year five, and the people making today’s automation decisions are rarely the ones who will answer for that later. Design the struggle back in now, while it is still cheap. The friction AI removed was never the point. The friction leaders choose to keep on purpose is.

The post Design the Struggle Back In appeared first on Voltage Control.

The self-inflicted talent crisis hiding in your AI productivity gains

Here is what the productivity dashboards don’t show: every time a senior developer uses AI to write the code she would have delegated to a junior, that junior role doesn’t get eliminated in a dramatic announcement. It just stops getting backfilled. Every time a principal consultant uses AI to produce the first-pass analysis a second-year associate would have sweated through, there’s no reorganization memo. Just a slightly smaller entry class next year. This is not AI taking jobs. This is experts taking junior jobs with AI assistance. Tori Paulman, a Gartner analyst, named this in early 2026: experience starvation. “When experts use AI,” she said, “they’re able to do a lot more work. And so what happens is we see what we call experience starvation, which is that now there’s nothing easy for people to cut their teeth on.”

The mechanism is subtle. The consequences are not. And most organizations won’t see the damage until it’s too late to reverse it.

The numbers are already moving

The labor market data has become hard to ignore. Erik Brynjolfsson and colleagues at Stanford’s Digital Economy Lab analyzed ADP payroll data (ADP processes payroll for more than 25 million U.S. workers, though the study’s actual working sample is 3.5 to 5 million workers per month after data-quality restrictions) and found that early-career employment in the most AI-exposed occupations declined 16 percent, controlling for firm-level shocks, since late 2022\. Developer employment among workers aged 22 to 25 is down nearly 20 percent from its peak in late 2022\.

These aren’t layoff numbers. They’re quietly empty desks. Yale School of Management’s Chief Executive Leadership Institute put it directly: “The biggest impact of Agentic AI on jobs will not be the layoffs we can see. It will be the opportunities that never materialize.” Deloitte’s 2025 Global Human Capital Trends survey, drawing on nearly 10,000 business and HR leaders across 93 countries, found that 66 percent of managers already report their recent hires are not fully prepared for their roles. That figure was in motion before AI became the dominant rationale for junior hiring cuts. Now those two trends are compounding. And PwC’s 2026 AI Jobs Barometer, which analyzed more than a billion job postings, identified a second form of the same problem. Entry-level positions in AI-exposed occupations are now seven times more likely to demand skills historically associated with experienced workers. The floor of what counts as “entry-level” has risen sharply, while traditional entry-level openings shrank 10 percent. The ladder hasn’t been removed. The rungs have. The result: recent graduate unemployment has climbed to nearly 6 percent, rising approximately twice as fast as the overall workforce since 2022, and underemployment for recent graduates sits at 42.5 percent, per the New York Fed’s Labor Market for Recent College Graduates series.

The unit of analysis is wrong

The standard case for eliminating junior roles goes like this: AI can produce better output faster than a junior employee. The economics are straightforward. This framing measures the wrong thing. Junior employees contribute to organizations primarily through their development, not their current output. The slide decks, the data cleaning, the first-draft analyses – these matter less than what they are producing in the person doing them. The work is training. The output is a byproduct. Linda Argote’s decades of organizational learning research established something most executives don’t treat as a serious operational risk: knowledge in organizations is not permanent. It is actively subject to “organizational forgetting” through employee turnover, decaying social networks, and broken pipelines. If knowledge were cumulative and stable, disrupting junior pipelines would hurt individuals but leave organizations intact. Because organizational knowledge decays when the pipeline that maintains it breaks, experience starvation is a threat to institutional capability, not just to individual career paths. This is what Paulman was pointing at with her concept of discernment: the skill of evaluating GenAI output by verifying its accuracy and judging its relevance and usefulness for the task at hand, the fourth of four essential GenAI skills she names for every worker.

Discernment is the accumulated ability to assess AI outputs. It requires having been wrong. It requires having produced something confident and incorrect and having someone with more experience show you why. It requires enough edge cases that you know when the plausible answer is the dangerous one. AI can generate plausible content at scale. Discernment determines whether to trust it. And you cannot build discernment by watching AI do work. You build it by doing work, failing, and adjusting under the supervision of someone who has already made those mistakes. When a senior engineer uses AI to produce the code a junior would have written, she produces the code. The junior doesn’t develop the discernment. The organization looks more productive in the short run and more fragile in the medium one. Ethan Mollick of Wharton made the same point in a recent New York Times roundtable on the AI workforce. “Field experience is often crucial to evaluating work you didn’t create yourself, whether it comes from humans or A.I.,” he said. A senior person can glance at a draft, a contract, or a block of code and know in seconds whether it was produced by an expert or an idiot. “If you have no experience, you can’t do those things.” The mechanism that built that experience has a name and a long track record. “We had this great technique, which was apprenticeship,” Mollick said. “It’s worked for 4,000 years.” A junior does the grunt work, a senior assesses it, both learn, everyone gets paid. “And that all collapsed” the moment AI started doing the grunt work instead.

The time delay is the trap

The insidious feature of experience starvation is the lag between cause and consequence. Stop hiring entry-level talent today, and your organization does not immediately become less capable. Your senior people are still there. They are, in fact, more productive than ever. The metrics look fine. A Harvard Business School and Revelio Labs study of 62 million workers across 285,000 firms found that junior employment at AI-adopting companies declined 7.7% within six quarters of significant AI adoption, while senior employment was virtually unchanged. On the surface, the organization is stable. Beneath the surface, the pipeline has stopped flowing. When the seniors retire, or leave, or move to other organizations, the people who should have been ready to replace them aren’t there. The ones who are there have thinner experience than anyone anticipated. They have used AI to produce output. They have not been wrong in the ways that build judgment. Gartner’s own research points to where this lands. Gartner predicts that by 2027, half of companies that attributed headcount reductions to AI will rehire staff to perform similar functions, often under different job titles. Companies will discover they eliminated roles that contained more judgment-critical work than their headcount analysis suggested. Klarna is already in this cycle. The company eliminated roughly 700 customer service positions for an AI assistant its CEO said handled 75 percent of customer interactions; by early 2025, satisfaction had dropped and Klarna was rehiring for the roles it had cut.

The talent pipeline, once drained, doesn’t refill quickly. Kaelyn Lowmaster, a director analyst in Gartner’s HR practice, put it plainly: “If you’re not developing people in-house, you might have talent pipelines internally that dry up.”

Meanwhile, a Gartner survey of 110 heads of HR found that 22 percent of CHROs report at least one business leader in their organization has already stopped hiring for entry-level roles because of AI automation (Gartner, 4Q25, published July 27, 2026). Not a future risk. Something that’s already begun. Not in the future. Now.

The leaky pipe

Experience starvation is not happening in isolation. It is one of four simultaneous forces pressuring the same pipeline. Skills atrophy compounds the problem. When AI handles the foundational tasks, people stop exercising the skills those tasks built. Consider a useful image: decide to stop walking and use a scooter everywhere. Thirty days later, try to walk. The muscles have atrophied. The organizations removing junior work from their workflows are, in many cases, also removing the regular exercise that keeps senior judgment sharp. Labor scarcity adds a third pressure. The World Economic Forum’s 2025 Future of Jobs Report projects that 59 percent of the global workforce needs brand new skills within the next two to three years, with 19 percent requiring actual role changes. . This is not a future scenario. Organizations are already navigating a talent market where the supply of experienced workers is constrained. And then there is the reskilling gap itself. Companies that have cut junior pipelines for efficiency will find, in several years, that there is no internal cohort to reskill for the roles AI is creating. Those roles go unfilled or get staffed expensively from outside, with workers who carry none of the organization’s institutional context.

When this argument doesn’t hold

It would be intellectually dishonest not to name the counter-argument. Not all junior work develops judgment. NBER research found that meaningful AI employment effects are concentrated in a minority of firms: more than 90 percent of executives report no measurable AI impact on their own firm’s employment, a self-reported figure rather than a directly measured outcome. Experience starvation is a risk concentrated in organizations that are actively automating at scale, not a universal condition. More precisely: routine, codifiable work that requires no discernment can often be safely automated without talent pipeline cost. The question is not whether AI can do a task. The question is whether doing that task developed the judgment the organization needs five years from now. For a significant share of knowledge work, the answer is yes. The path from junior lawyer to senior associate goes through the research memos that got marked up. The path from analyst to vice president goes through the spreadsheets where you were wrong and got corrected by someone who’d seen it before. The path from junior engineer to staff engineer goes through the bugs you introduced and the debugging you had to do to find them. Remove those experiences and you don’t just save money. You shorten the path that would have made the next generation of senior people. The organizations that will get this wrong are the ones optimizing on output efficiency without asking what each category of junior work was producing in the people doing it.

Redesigning for developmental friction

The answer is not “don’t use AI.” It is designing deliberately for what Paulman calls developmental friction: preserving the work that builds capability even when AI could handle it. Paulman’s “Option 3” workflow is the practical starting point. Option 1 is the expert training the rookie directly, which is developmental but slow. Option 2 is the expert using AI to do the work, which is fast but eliminates the development entirely. Option 3 is the expert building the prompt or template, the junior executing the work with AI assistance, and the expert reviewing the insights and providing coaching. Nobody produces output the old way. The junior gets exposure to the decision-making layer and the feedback loop they need to build discernment. This is how Vizient approached role redesign before deploying AI into their workflows. They asked their workers: what do you want to do? What would you do with more time? What work do you hate? They built the new role design around the answers. Human-centered design applied to AI transformation produced something different from pure efficiency logic. The emerging category of GenAI simulators offers another pattern, particularly for roles where doing genuine work carries too much risk for learning purposes. Bank of America built a conversation simulator for financial advisors to practice high-stakes conversations before handling real calls. Hiscox Insurance used a GenAI simulator for certification training and found an 85 percent improvement in skills and a 75 percent reduction in certification failures. The simulator creates the difficulty, the error, the correction. It provides developmental friction without production risk. None of this is as efficient as having the senior person use AI to do everything. But efficiency is not the right metric when the thing being produced is judgment.

What’s at stake

The organizations that get this right will have a compounding advantage that doesn’t show up in any quarterly metric. Five years from now, they will have a cohort of senior people who built their judgment through real work at lower stakes, who developed discernment through being wrong and being corrected, who carry institutional context because they were in the room when decisions were made. The organizations that optimized junior roles away to fund AI productivity will face a different reckoning. Not dramatically, and not soon. The pipeline fails quietly, and over time. You notice it when the seniors turn over and the people behind them are thinner than expected. By then, rebuilding is expensive and the institutional knowledge you assumed could be documented turns out to be harder to reconstruct than it looked from the outside. Tracey Franklin, Moderna’s Chief People and Digital Technology Officer, described converting what was “normally a junior-level HR analyst type” into a GPT. In the same month, Moderna cut 10 percent of its digital technology headcount. That equation balances on a spreadsheet. What it doesn’t calculate is what those junior analysts would have become by 2029.

Talent pipelines don’t collapse loudly. They dry up slowly, and quietly, and the cost only becomes visible when you need what they used to produce. Voltage Control helps organizations design for both the speed that AI enables and the human capability that only judgment-building friction develops. If you’re navigating this tradeoff, we’d like to talk.

The post Experience Starvation appeared first on Voltage Control.

Why Nobody Gets Fired for Destroying Opportunity

The data that should reframe every AI budget conversation came out this month. Gartner surveyed 350 global executives at organizations with more than a billion dollars in annual revenue, all of them running or piloting what Gartner calls autonomous business capabilities. Eighty percent have cut their workforce. Those cuts are not producing returns. Workforce reduction rates are nearly identical between organizations reporting high ROI from AI and organizations reporting low or negative ROI. Cutting is not the mechanism of value creation. It is the mechanism of budget release. Budget room is not return. And yet nobody is getting fired for missing the return. They are getting credit for the cut. That is the accountability gap.

The Credit Trap

AI transformation has a measurement problem, and it is not subtle. When an organization uses AI to automate processes and reduces headcount, there is an immediate, legible, auditable number: cost savings. That number shows up in the quarterly report. It gets attributed to the AI initiative. It earns the sponsoring executive a line in the board presentation. What does not show up is what was destroyed in the process. Three separate research efforts put numbers around the problem. MIT Project NANDA’s 2025 study found that ninety-five percent of enterprise AI pilots deliver zero measurable financial returns within six months. This number is striking

Only 28 percent of AI use cases in infrastructure and operations fully succeed and meet ROI expectations. Grant Thornton’s 2026 AI Impact Survey found that seventy-eight percent of business executives lack confidence they could pass an independent AI governance audit within 90 days. These are not statistics from organizations at the fringe of AI adoption. These are the median outcomes across organizations large enough to be writing the checks. If this were any other capital allocation category, we would call it a crisis. We would ask who was accountable. We would want to know, with specificity, what went wrong. Instead, we are giving executives credit for headcount reductions and calling it AI leadership. The problem is structural, not behavioral. Organizations have optimized their measurement systems for legibility, and layoffs are legible. You can count them, report them, attribute them to a program, and show them on a slide. The opportunity that gets destroyed in the process is not legible. You cannot count what you failed to build.

Why Nobody Gets Fired

The accountability gap exists because opportunity destruction and cost savings operate on fundamentally different timelines. When you cut 30 positions, that number is immediate, auditable, and attributable. When you cut the team responsible for governing your AI systems, or reduce the people closest to the actual work who could have guided how those systems improve, the destruction does not appear on any dashboard. It appears six months later as stalled AI performance. It appears twelve months later as a system that was never adapted to the evolving needs of the business. It appears two years later as a talent base that no longer has the organizational knowledge to govern the systems that replaced them. By that point, the executive who made the cut has moved to a different role, or the organization has attributed the shortfall to new external factors, or both. The connection between the original decision and the downstream damage is no longer visible. This is not bad faith. It is a natural consequence of how organizations measure. Output accounting tracks what you produce or eliminate in a given period. It is efficient, legible, and compatible with quarterly reporting cycles. Outcome accounting tracks whether those outputs are producing the results you actually wanted. It is harder to measure, harder to attribute, and incompatible with the timeline on which most executive careers are evaluated. AI transformation is suffering from a forced adoption of output accounting applied to a problem that requires outcome accounting. You measure the cut. You do not measure whether the cut advanced the mission. And because nobody is measuring the mission, nobody is accountable for it.

What Gets Destroyed

Here is what makes this problem structural rather than merely behavioral: the cuts that look best under output accounting are often the ones that destroy the most value under outcome accounting. Gartner’s May 2026 human-amplified business research identifies the capabilities that determine whether an organization can sustain and expand AI performance over time. These are the people who give AI context. The people who govern how automated decisions get made. The people who adapt systems as the work evolves. The people who understand both the domain and the technology well enough to catch errors the system cannot catch for itself. These are not the roles that survive efficiency-focused headcount reduction programs. They are rarely the roles with the clearest ROI justification in a traditional cost model. Their value is mostly upstream: they prevent failures before those failures become visible, they improve systems before those systems cause problems at scale, and they build the organizational knowledge that allows technology to be used at increasing levels of sophistication over time. Consider what actually happens when an organization deploys AI to handle a function and simultaneously reduces the team that was doing that function. The AI begins operating. It does what it was trained to do. It also makes errors that the people who were just let go would have caught, because those people understood the edge cases, the organizational context, and the exceptions the system was never taught to handle. The AI does not get better on its own. It gets better when humans guide it, correct it, expand its scope, and translate domain knowledge into system improvements. Cut those humans, and you freeze the system’s capability at whatever level it was at when the cuts happened. This is opportunity destruction. It does not appear in the budget variance report. It appears in the AI initiative that was supposed to transform the business but, three years later, still does the same thing it did at launch.

The Measurement Problem

The governance gap compounds this. Grant Thornton’s 2026 AI Impact Survey found that seventy-eight percent of executives cannot pass an independent AI governance audit within 90 days. This number is striking, and it is also, in some ways, the wrong metric to obsess over. Six months is not long enough for an AI initiative to transform the operating model of a complex organization. The organizations using six-month evaluation windows are setting up a measurement system that will always find AI wanting, because they are measuring a transformation initiative with an efficiency-improvement timeline. The deeper problem is that most organizations have not defined what they are building toward. They have defined what they are eliminating. The pilot documents tell you how many positions will be displaced, what the projected cost savings are, and when the payback period is expected to occur. They do not tell you what the business will be able to do in three years that it cannot do today, what human capabilities will be required to govern and expand those systems, or how the organization will develop the expertise that allows AI to operate at increasing levels of sophistication. Without that definition, outcome accounting is impossible. You cannot measure progress toward a destination you have not defined. And without outcome accounting, the accountability gap persists. Executives continue to get credit for cuts, and nobody is accountable for the opportunity quietly destroyed along the way. The governance gap compounds this. Seventy-eight percent of executives cannot pass an independent AI governance audit within 90 days. Most organizations are running AI systems without clear accountability for how decisions get made, how errors get caught, or how the system gets improved when it produces bad outcomes. The financial accountability gap is mirrored by an operational governance gap.

What Human-Amplified Business Actually Requires

Gartner’s prescription runs counter to the prevailing logic of AI-driven workforce reduction. They call it human-amplified business: investing in the skills, roles, and operating models that let people guide, govern, expand, and transition autonomous systems. That is an investment argument, not a reduction argument. The organizations reporting genuine ROI from AI are not the ones that made the deepest cuts. They are the ones that built governance before they built scale. They prepared their workforce before they demanded returns. They had the discipline to stop programs that were not working, which requires having people in place who can evaluate what working actually looks like. In practice, human-amplified business looks like something specific. It looks like retaining and developing the people who understand both the domain and the data well enough to direct AI outputs. It looks like building new roles: not just people who use AI tools, but people who can evaluate system performance over time, identify drift, make judgment calls the system cannot make, and adapt processes as the technology changes. It looks like treating organizational knowledge as a strategic asset that needs active investment, not a cost to be rationalized away. The research also clarifies what happens when organizations skip this. The 95 percent failure rate on six-month ROI. The stalled governance. The talent loss. The organizations that lose their ability to govern AI systems also lose their ability to improve them. That is not a technology problem. That is an organizational design problem.

What Accountability Actually Looks Like

There is a practical version of this, and it starts before the first cut is made. Before reducing headcount in any AI-adjacent function, an organization should be able to answer three questions with specificity. What is this organization trying to be able to do in three years that it cannot do today? Which human capabilities are required to govern, expand, and adapt the AI systems that will support that future state? Are the people being reduced essential to those capabilities? If the answer to that third question is yes, the cut is destroying opportunity. The budget room it creates is real. The opportunity cost is also real. Both belong in the analysis, and both should be presented to whoever is approving the reduction. This is not an argument against efficiency. It is an argument for measuring efficiency correctly. Output accounting tells you what you cut. Outcome accounting tells you what you built and what you destroyed. Organizations that refuse to do both will continue optimizing for the metric that makes this quarter look good at the expense of what they are trying to become. The practical implication is a different kind of board presentation. Not “we reduced X positions and saved Y dollars through AI.” But “we reduced X positions, which freed Y dollars. Of that, we reinvested Z percent in the human capabilities required to govern and expand the systems that replaced those positions. Our outcome metrics for this initiative are A, B, and C. In twelve months, we will show you whether we hit them.” That presentation is harder to make. It is also the only one that closes the accountability gap.

The Stakes

Every executive running an AI transformation program is making an implicit choice between output accounting and outcome accounting. Most are not aware they are making it. Clara Shih, the former Salesforce and Meta AI executive, named the choice plainly in a recent New York Times roundtable on the AI workforce. “The key thing about A.I. agents is that they all have a goal,” she said. “And it depends on who deploys it, because whoever deploys it gets to set the goal. Maybe the goals of A.I. so far haven’t been aligned with the goals of regular people. But that’s a choice we can make.” The accountability gap is what opens up when no one names that choice as a choice. The ones who are aware ask different questions. Not “how many positions can this eliminate?” but “which human capabilities are irreplaceable in an AI-augmented operating model?” Not “what is the six-month payback period?” but “what does our AI governance look like in year three?” Not “how do we capture the cost savings?” but “how do we build the organizational competency that lets us capture value at increasing scale?” The accountability gap will close eventually. It will close when the organizations that optimized for cuts run out of runway and have to reckon with what they built versus what they destroyed. It will close when investors and boards start asking about AI governance with the same rigor they apply to AI investment. It would be more useful if it closed before either of those things happens. The question worth asking now is whether your measurement system would catch opportunity destruction before it becomes irreversible. If the answer is no, that is the governance gap worth closing first, before the next round of AI-driven workforce reductions. Voltage Control works with executive teams building the organizational structures and human capabilities required to run AI transformation at scale. If this is the conversation you are trying to have inside your organization, we can help you start it.

The post The Accountability Gap appeared first on Voltage Control.

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.

Show Highlights

[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

Links | Resources

Facilitation Lab Community
Voltage Control

About the Guest

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.

Transcript

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.

The post Drawing Your Way Through Conflict and Change appeared first on Voltage Control.

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.

Show Highlights

[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

Links | Resources

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

About the Guest

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.

Transcript

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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Facilitation Is the Core Leadership Competency for the AI Era

Walk into any leadership offsite and watch what the room is designed around. It is almost always an execution exercise. How do we build faster? How do we reduce cycle time? How do we ship more? That was the right question for twenty years. It is the wrong question now. AI has fundamentally changed what is worth optimizing. The execution layer, the part of your organization that turns decisions into output, is being automated at a pace that makes traditional throughput bottlenecks look like legacy concerns. Code writes itself. Reports generate in minutes. Analytical tasks that once anchored quarterly planning cycles now take an afternoon. The constraint that used to define leadership’s job is dissolving. What replaces it is not a technical problem. It is a human one. When execution collapses as the bottleneck, the new speed limit is human consensus: the time it takes for your leadership team to align on the right direction, navigate the competing priorities beneath the surface agreement, and move with enough shared conviction to act rather than stall. That is a facilitation problem. And facilitation is about to become the core leadership competency for the AI era.

The Old Job Description Is Done

For most of the history of modern management, the logic of leadership authority made sense on its face. The person who produced the best work earned the right to guide others doing it. The most technically excellent individual became the team lead. Execution quality was the primary credential. That model worked when execution was the constraint. If you were best at doing the work, you were also the most credible guide for how it should be scaled and improved. The leader’s value was embedded in their ability to produce and direct production. AI is ending that logic. A single expert, amplified by AI, can now match the output of a team. The question organizations face is no longer how to produce more. It is how to align on what to produce, and why, with the speed and fidelity that determines whether the production was worth anything at all. That requires a different skill set. Not production. Orchestration. Not executing better than everyone else in the room, but helping everyone in the room think and decide together well enough that their collective output is worth more than the sum of its parts. This is the leadership job that AI is creating. Most organizations are not yet building for it.

The Conductor

Joe Mariano, Senior Director Analyst / Senior Principal Analyst at Gartner, reached for a metaphor that cuts through the abstraction: digital-workplace leaders in the AI era are conductors. [Gartner, “Digital-Workplace Leaders as Conductors” (session 11e), presented at the Gartner Digital Workplace Summit 2026.] A conductor does not play every instrument. A conductor ensures proficiency across the ensemble, maintains it through rehearsal, and governs what the orchestra plays. The output is shaped not by executing the work but by designing the conditions under which the ensemble can perform at its highest level. This is a precise description of what leadership must become. The conductor’s value is not in personal output. It is in coordinating the output of everyone else toward something coherent. The conductor reads the room, feels where the ensemble is drifting, and intervenes at the level that produces the most lift. A gesture here, a structural choice before the performance begins. When something is off, the conductor does not pick up an instrument and play the missing part. The conductor adjusts the conditions until the ensemble can play it correctly. That maps directly to what leadership now requires. When execution is cheap, the leader who can produce the most output holds less competitive advantage than the leader who can align the most people around the right output, fast. The bottleneck has shifted from execution to alignment. And alignment is created through facilitation. Most organizations still have leadership development programs built on the soloist model and leadership cultures that reward individual performance. That mismatch is going to become expensive.

What Facilitation Actually Means

The word carries freight that works against it. Facilitation sounds like running meetings. It sounds like sticky notes and breakout rooms and a practitioner’s voice saying “let’s hold space for that.” That narrow version exists, and it is more valuable than most organizations acknowledge. But it is not the full picture. Facilitation, in the sense that matters for this moment, is the practice of helping groups think together, decide together, and build the shared judgment that no single person could hold alone. It is the ability to frame a decision so clearly that everyone in the room is solving the same problem rather than five parallel versions of it. It is the ability to surface the real disagreement beneath the surface-level debate, because what sounds like a tactical argument is usually a values conflict in disguise. The leader who can name that distinction is the leader who can actually resolve it. It is synthesis rather than compromise. Synthesis generates something from competing perspectives that neither perspective could have produced alone. Compromise averages them into mediocrity that satisfies no one completely. Most organizations default to compromise when they intend synthesis, and cannot tell the difference until the outcome disappoints. It is reading power and motivation in a room: who is not speaking and why, when silence signals skepticism versus deference, when to push for resolution and when to let the productive tension keep working. When to ask one more question before allowing the group to move on. It is governance: knowing which decisions are worth the room’s collective attention, which problems require human judgment and which can be delegated to the model, which questions will only get harder if avoided now. That is the conductor’s highest-value work, and it is irreducibly human. None of this is soft. It is a technical practice with learnable methods, teachable frameworks, and measurable results. And it is the practice that will determine your organization’s real velocity.

Every Archetype Is a Different Facilitation Challenge

The facilitation imperative becomes specific when you recognize that AI is affecting different members of your workforce in fundamentally different ways, and each creates a distinct leadership challenge. Gartner’s workforce research maps workers across two axes: how much accumulated experience their role requires, and how much of that experience they have actually built. [Gartner, “Workforce Archetype Matrix” (session 14b), presented at the Gartner Digital Workplace Summit 2026.] Four archetypes emerge, each with different dynamics as AI accelerates. Experts hold deep domain knowledge. AI amplifies their output dramatically. The productivity gain is real and visible. The risk beneath it is concentration: Experts now absorb tasks that used to require teams, which means they also absorb the developmental opportunities that used to build the next generation. They become single points of failure wrapped in a productivity halo, and they often become them before anyone notices. Getting Experts to slow down, surface their decision heuristics, and transfer the discernment layer rather than just the procedures requires deliberate facilitation. It does not happen without a structured process designed specifically to extract what they know and make it available to others. Proteges are in complex roles but have not yet built the experience those roles require. AI creates a paradox for them. It appears to compress the path to competence, but simultaneously removes the developmental work that builds real judgment. The junior tasks they would have used to cut their teeth are absorbed by AI-augmented Experts above them. Gartner’s Tori Paulman named the mechanism directly at this year’s Digital Workplace Summit: AI is not taking entry-level jobs. Experts are. [Gartner, “AI Is Not Taking Entry-Level Jobs” (sessions 12a and 14b), presented at the Gartner Digital Workplace Summit 2026.] The facilitation challenge with Proteges is creating deliberate learning conditions in an environment that is actively optimizing those conditions away, and convincing leadership that this is worth the apparent inefficiency. Stewards are experienced practitioners whose routine work is being automated most directly. They hold institutional memory that cannot be automated, even as their current tasks increasingly can be. The facilitation challenge is transitioning them from executing routine work to governing the AI that does it, in a way that honors rather than diminishes what they have built over years. That transition is emotionally charged work. It cannot be handled with a memo. Each archetype creates a distinct consensus problem. Experts need to agree to slow down for knowledge transfer. Proteges need to be heard about what they need to learn. Stewards need real involvement in redesigning their own roles, not just notification after the decisions are made. The conductor who treats all three as the same audience will lose all three.

Taran Lent, CTO of Illumia, the higher-ed and healthcare technology company formed from the merger of Transact and CBORD, faced a version of this problem as soon as AI tools started spreading through his engineering org. Employees were experimenting individually, but that individual fluency wasn’t turning into anything the company could rely on or govern. The risk wasn’t too little AI adoption. It was adoption with no shape to it: skills and tools scattered across teams, no clear owner, no way to catch a problem before it became an incident.

Lent’s redesign started with decision rights, not tooling. He built an enablement task force explicitly designed to avoid becoming a governing bottleneck. Its job was to let people play, learn, and share what worked, rather than approve every experiment before it happened. Experimentation without any gate eventually meets reality, though, so alongside it he stood up a stakeholder review process for new AI tools and skills, a four-to-six-week approval timeline for new vendors, and guardrails built specifically to prevent incidents like an unauthenticated internal dashboard slipping into production. The task force owned the early “should we” conversation. The review process owned the “how do we roll this out safely” conversation once something was ready to scale. Two decisions, two owners, both explicit from the start.

The outcome Lent points to is not a single number. He credits a shared “humble, hungry, smart” culture, carried through this governance structure, with making the integration of Transact and CBORD into Illumia smoother than it might have been. The task force gave people room to build real fluency with AI. The review timeline and guardrails gave leadership a way to say yes quickly without finding out about a security gap after the fact. Skip the guardrails and you get the dashboard incident. Skip the permission and you rebuild the bottleneck the whole redesign was meant to remove.

The Move: Redesign How You Decide

Taran’s redesign points to a repeatable practice, with three components that matter most.

Decision rights need to be explicit before the conflict forces the issue. Most organizations only discover gaps in decision authority when two teams have already built conflicting work. AI accelerates this failure mode because execution is faster and misalignment surfaces sooner, often after significant effort has been spent in the wrong direction. The move is to map, in advance, who owns each category of decision, who is consulted, and what happens when the owners disagree. This is not administrative overhead. It is the infrastructure that enables fast alignment rather than repeated negotiation. Dissent protocols need to be designed in, not wished for. Most leadership cultures say they want honest disagreement and actually reward the performance of consensus. If the people in your room do not feel safe saying “I think this is wrong,” the disagreement does not disappear. It migrates to work, where correcting it is expensive. Build structures that invite dissent before decisions are finalized: pre-mortems that force articulation of what could fail, consent rounds that distinguish “I fully agree” from “I can live with this,” structured space for quieter perspectives before the dominant framing sets. These are not trust-fall exercises. They are engineering work on your decision-making process. Facilitation approach needs to match the archetype composition of the room. A session with Experts navigating a knowledge-transfer challenge needs a different design than a cross-functional session where Stewards are working through a role transition. The conductor reads who is in the room and what structure will surface the best of their collective thinking. This is diagnostic work, not template application, and it is a skill that can be learned and built deliberately. We’ve seen leadership teams cut their decision-making time by 40 to 60 percent. Not from faster tools. From fewer cycles. When groups make decisions with enough shared understanding to actually commit to them, they do not spend the following quarter revisiting the same direction. The alignment cost gets paid once, up front, through better process design. The alternative is paying it repeatedly through rework, and the bill compounds.

Protect Somewhere for the Freed Time to Go

There is one more design choice the conductor owns, and it is the one most leaders miss. When execution collapses, it gives time back. The question almost nobody asks is where that time goes. Left undirected, it flows straight back into the existing backlog: the same roadmap, the same quarterly pressure, now executed faster. The team becomes a more efficient version of what it already was, generating more output against the same untested assumptions. Jeff Gothelf, who co-authored Lean UX, frames the failure precisely. Most organizations teach their people the AI tools and then send them back to ship more of what was already planned. They taught the team to use a hammer and expected a finished chair. The capability is real, but “capability without permission just gets absorbed by the feature factory.” What is missing is not a skill. It is permission: a protected day to experiment, a small budget that does not require three approvals, an experiment run on real data that the team is explicitly allowed to have fail. This is conductor work because it is a condition only leadership can set. Individual contributors cannot grant themselves the slack or the safety to fail. Those come from the person who governs what the orchestra plays. And the safety itself has to be redesigned for this moment. The old guardrails were built for deterministic tools that did the same thing every time. AI does not, so “safe to fail” has to be defined deliberately for work whose outputs vary, rather than assumed to carry over from the last era. The conductor who frees up execution time and routes all of it back into the backlog has not changed the orchestra’s job. They have only made it play the old score faster.

Why This Compounds

AI tools will evolve. The specific model your organization runs on today will be superseded. The facilitation capability your leaders build, the judgment about how to help groups think and decide together, is transferable across every tool change that follows. This is the argument for treating facilitation as infrastructure rather than as a support function you bring in for offsites. The organizations navigating AI transformation well share a recognizable pattern. Their leaders trust each other enough to be honest about what they do not know. They disagree productively rather than perform agreement. They move together even when not everyone is fully convinced, because they have learned how to build enough shared understanding to act without requiring unanimity. That trust does not come from a workshop. It comes from practicing the conditions that build it, repeatedly, in the actual work. The conductor builds the orchestra through rehearsal. Not by telling the musicians what to play. Mariano’s framing carries a second implication worth holding onto. The conductor also governs what the orchestra plays. In organizational terms, that is the most important leadership judgment of all: which decisions get made, which questions are worth the room’s collective attention, which problems require human judgment and which can be delegated to the model. That governance function is becoming more urgent as AI handles more of the execution work, and it is a job that cannot be automated away. When execution was expensive, leadership cleared the path. Now that execution is cheap and judgment is scarce, leadership’s job is to carry the organization’s judgment capacity forward: design the decisions that matter, surface the dissent that would otherwise stay hidden, ensure that the people who will need a skill later are getting the practice now. That is facilitation in the fullest sense. The organizations making this transition now, while execution still takes some time, are building something that will compound. They are developing the reflexes, the trust structures, and the facilitation capacity that let them move fast together when execution becomes free. The organizations that wait will still be stuck in the same alignment failures they have always had, except now the stakes are higher and the market is moving faster. Your team does not need a better AI tool. It needs a better conductor. Want to explore what this means for your organization? Voltage Control works with leadership teams to build the facilitation capability that AI transformation requires. Let’s talk about what changes when execution is no longer the bottleneck.

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Why AI Output Metrics Mislead and What to Track Instead

Sixteen experienced software developers sat down to work. They had access to the best AI coding tools available. They had been told, reasonably, to expect a 20 to 25 percent productivity boost. When the study ended, they believed, on average, that AI had made them 20 percent faster. They were 19 percent slower. That is from METR’s July 2025 randomized controlled trial, the most rigorous study of AI’s effect on experienced developer productivity published to date. The 39-point gap between what developers believed about their performance and what actually happened is not a rounding error. It is a measurement failure at scale. And it should be the first thing any executive reads before reviewing their organization’s AI productivity numbers.

The Metrics We Default To

Most organizations are measuring the wrong things. Not because their teams are careless, but because the wrong things are easy to count. Tokens consumed. Lines of code generated. Pull requests merged. Tool adoption rates. Story points completed. Time-to-first-response in customer service queues. Meeting transcripts enabled. These are the metrics that appear on AI dashboards across the enterprise right now. Every one of them produces a number. Every one of them trends over time. Every one of them can be presented in a board slide. None of them tell you whether your AI transformation is creating value. This is output accounting. It measures what happened: how much AI was used, how many tasks were touched, and how fast certain activities were completed. It does not measure whether the right things happened, whether the quality of work improved, or whether the organization can now do anything it could not do before. The appeal is understandable. Output metrics are fast, cheap, and unambiguous. In a transformation that feels uncertain and fast-moving, the comfort of a trending dashboard is real. That comfort is the problem.

When Output Metrics Lie

The clearest proof is Klarna. In 2024, Klarna announced that AI had replaced approximately 700 customer service agents. By Q3 2025, the company claimed its AI agent was doing the work of 853 full-time employees and saving $60 million annually. The volume metrics looked excellent: faster resolution times, higher tickets-per-hour throughput, and lower cost-per-interaction. Then, quietly, in early 2026, Klarna began rehiring humans. What the output metrics had not captured was quality deterioration on complex interactions. Customer satisfaction scores on difficult cases had declined. The conversations that mattered most, the ones where customers were frustrated and needed real understanding, were getting worse. The metrics that declared success had optimized for speed in the cases where speed was least important. Klarna’s reversal is not a story about AI failing. It is a story about measurement failing. The organization tracked what was easy to track. The things that were hard to track, judgment quality on complex cases, customer trust in sensitive interactions, brand perception over time, deteriorated while the dashboard numbers climbed. This is not unique to Klarna. It is the predictable outcome of any measurement system that optimizes for outputs rather than outcomes.

The Counter-Signal Nobody Expected

Triumph Financial runs one of the largest payment networks in trucking, moving roughly $18 billion a year to carriers who often wait up to 90 days to get paid by brokers and shippers. In 2023, working with KUNGFU.AI, the company set out to speed up invoice funding with AI. It would have been easy to make speed the headline metric. Triumph didn’t.

Earlier attempts at automation, using rigid, hard-coded rules, had already failed once, rejecting too many good invoices to be useful. So this time, before anyone touted a faster approval time, the team agreed on three specific numbers to watch: disputed invoices, short pays, and write-offs. Not throughput. Not approval speed. The things that would reveal whether the model’s decisions were actually as good as a human’s, not just faster than one. Those numbers were put on a shared dashboard everyone could see, and the model wasn’t scaled up until a staged rollout, moving from historical data testing to a dark launch to a 100-day pilot, showed it held up.

Only after that did the speed numbers get to matter. And they mattered a great deal: invoice approval time fell from an average of 178 minutes to about 10 seconds, with more than $4 billion in invoices now running through the model and over half auto-approved. But the figures Triumph’s CTO Jason Heilig points to first aren’t the speed ones. Short pay is down 57 percent. Chargebacks are down 65 percent. Disputes are down 25 percent. The company didn’t get fast at the expense of getting careless. It got fast because it insisted on being careful first.

That is the inverse of Klarna. Klarna’s dashboard celebrated speed and volume while the quality of the hardest conversations quietly eroded underneath it. Triumph made quality the metric that had to clear the bar before speed was allowed to become the story at all.

What the Dashboard Cannot See

Output metrics fail for a structural reason. They measure what was done. What leaders actually need to know is whether things are getting better. That distinction sounds simple. It produces completely different questions. Counting tokens consumed is easy. Asking whether the judgment underlying those tokens improved is hard. Counting PRs is easy. Asking whether the engineering organization is now capable of work it could not previously do is hard. Counting tool adoption is easy. Asking whether the team’s AI outputs are being accepted, revised, or rejected, and learning from the pattern, is hard. The organizations that stay in output-accounting mode past the early adoption phase are not being prudent. They are deferring accountability. McKinsey’s November 2025 “State of AI” report found that 88 percent of organizations now use AI, but only 6 percent qualify as high performers with measurable EBIT impact. Only 39 percent report any measurable business effect at all. The gap between having AI and benefiting from AI is the gap between output accounting and outcome accounting. High performers, in McKinsey’s data, are 2.8 times more likely to have fundamentally redesigned workflows. That is not a technology finding. It is a measurement finding: the organizations that ask deeper questions build different systems.

Three Questions That Change the Frame

Output metrics answer the question “did we use AI?” The questions leaders actually need are harder and closer to the truth. The first: how many agents do you have running? This measures the scale of actual deployment, not licenses activated or employees who opened a tool. Agents running against real problems are a more honest indicator of organizational AI maturity than any adoption metric. The second: how long can those agents run without human intervention? This is a proxy for quality. An agent that requires correction every five minutes signals weak prompting, poor context engineering, or an immature tool setup. An agent that completes substantive work over hours signals a team that has genuinely learned to work with AI. Autonomy duration is a quality metric dressed as a timing metric. Anthropic’s internal research found that human interventions per Claude Code session fell from 5.4 to 3.3 between August and December 2025\. That four-month trajectory is more revealing than any adoption curve. The third: what novel work are you unlocking that was not feasible before? This is the one that matters most to leaders, investors, and boards. Not “we are shipping the same roadmap 20 percent faster” but “we stood up a customer-segmentation pipeline that had been on the backlog for two years because we could never justify the engineering cost.” Novel work is opportunity creation. It is the metric that corresponds to what organizations are actually hoping for when they invest in AI. None of these three questions are easy to put on a dashboard. That is the feature, not the bug. A metric that is easy to optimize is a metric that will be gamed. Consider what happened in the rooms where CEOs set personal token-consumption targets for their teams: staff ran purposeless prompts to hit the number. Output metrics corrupt the behavior they are meant to measure. Outcome metrics resist that corruption because they are attached to something real.

The Cost the Dashboard Hides

There is a second blind spot, and it sits on the other side of the ledger. The three questions above measure whether the work is getting better. They do not measure what it now costs to deliver it, and that is the other half of any honest ROI. For two decades, software ran on an economic assumption so reliable that most leaders stopped noticing it. The marginal cost of serving one more user was effectively zero. Build the product once, and the ten-thousandth user cost almost nothing more than the thousandth. Engagement was therefore an unalloyed good. More usage meant more value, more retention, more expansion, and almost no additional cost to carry it. AI breaks that assumption. Every interaction now consumes tokens, and tokens cost money in direct proportion to use. Jeff Gothelf, who co-authored Lean UX, put the consequence plainly: your most engaged users can quietly become your least profitable ones. The power user running fifty AI queries a day is the user you celebrated under the old economics and the user who erodes your margin under the new one. The dashboard that shows engagement climbing may also be showing cost climbing faster, and a usage metric will never tell you which. This is why measurement for AI cannot stop at value. It has to track cost-to-serve at the unit level: cost per successful task, gross margin per active user, model cost as a percentage of revenue. These are not finance-team afterthoughts to reconcile at quarter end. They are leading indicators of whether an AI product or workflow stays economically sustainable as it scales. An organization can be creating genuine value, clearing every outcome bar in this piece, and still be quietly building something that gets less profitable with every new power user it celebrates. The discipline is the same one this entire piece argues for. Measure the thing that is hard to see, not the thing that is easy to count. On the value side, that means outcomes over outputs. On the cost side, it means cost per successful result over raw usage volume. A serious AI scorecard holds both, because a transformation that creates value while quietly destroying margin is not a success the dashboard is equipped to catch.

Innovation Accounting: The Closest Precedent

The intellectual framework that comes closest to what is needed already exists. Eric Ries built it for a different context: startups trying to measure progress when traditional indicators, revenue, customers, and profitability, are all effectively zero. His answer was innovation accounting. Instead of revenue, measure validated learning. Instead of units shipped, measure hypothesis tests completed. Build, measure, learn is the loop. The goal is not to produce a big number. The goal is to reduce uncertainty faster than the competition. Nobody has operationalized innovation, accounting for enterprise AI transformation in a published, replicable form. That gap is confirmed across every major measurement research program. DORA has extended its software-delivery metrics toward AI. Anthropic has published a primitives framework that measures how AI is being used at the task level. Accenture and Wharton have built a skills-shift index tracking 150 million professional profiles. None of them answer whether the organization is learning faster, producing better work, or doing things it could not do before. The practitioner community is ahead of the published literature on this. Six consecutive executive dinners across Dallas, Houston, Boston, Boulder, Portland, and Raleigh surfaced innovation accounting independently, without anyone being prompted. In every room, leaders described the same measurement problem and reached for the same frame. In no room did anyone have an operationalized version to point at. That convergence is not a coincidence. It means the field is ready for a framework, and the gap is structural, not a matter of individual companies being slow.

The Objection Worth Taking Seriously

The obvious counter-argument is that outcomes are unmeasurable. That output metrics are at least something, while outcome metrics are a vague ambition. This is a legitimate critique of poorly defined outcome goals. It is not a reason to abandon outcome measurement. Anthropic published the AI Fluency Index in early 2026, analyzing nearly 10,000 human-AI conversations to measure the quality of collaboration, not just the quantity. They identified 24 specific behaviors associated with effective AI use across four dimensions. Quality measurement is not an aspiration. It is an active research program producing real findings. The staged-measurement argument has something to it. Token consumption is a reasonable early proxy when the goal is normalizing AI use across a skeptical organization. Cultural adoption does need to come first. But most large organizations are past that phase now. Adoption is widespread. The question is no longer “will people use this?” It is “are we getting better because of it?” The Goodhart’s Law argument cuts both ways. Yes, any metric will eventually be gamed. That is a reason to rotate metrics deliberately, to pair quantitative measures with qualitative judgment, and to build measurement systems that are harder to optimize against than a single dashboard number. It is not a reason to accept measurements that are actively misleading. Outcomes can be defined. What novel work did your organization do this quarter that was not feasible last quarter? What percentage of AI outputs required human revision, rejection, or acceptance? How has autonomy duration changed over six months? These are measurable. They require judgment to interpret, as all meaningful metrics do. That is not a bug. That is what accountability looks like.

Where to Start

Auditing your current AI measurement against a single question produces clarity quickly: does each metric measure what happened or whether things got better? Tokens consumed, meeting transcripts enabled, PR counts, story points completed: these measure what happened. Replace them with counter-metrics that track quality alongside volume. If PR count goes up, track average PR complexity alongside it. If resolution time goes down, track customer satisfaction on complex interactions alongside it. At one roughly 1,000-person software company, the PR count told one story and average PR size told a different one. Both were necessary to understand what was actually happening. Then introduce the three questions as a leadership review practice. Not a dashboard, but a quarterly conversation: how many agents are running, how long can they run unattended, and what novel work did AI unlock in the last 90 days that was not on the roadmap before? The answers will be uneven and sometimes uncomfortable. That is the point. The organizations that build the discipline now, before their output metrics lock them into the wrong optimization, are the ones that will join the six percent achieving real EBIT impact. Not because outcome accounting is easy, but because it is honest.

The Measurement That Matters

Stanford HAI named this moment the shift from the era of AI evangelism to the era of AI evaluation. The question is not whether AI is transforming your industry. That question is answered. The question is whether your organization is learning from that transformation or simply counting it. Output accounting produces numbers that trend upward while the business-critical questions go unanswered. Klarna had great numbers until it did not. The engineering executive had a disappointing PR count until someone looked at PR size. The METR developers believed they were 20 percent faster until the data showed they were 19 percent slower. The dashboard that looks best right now may be hiding your Klarna moment. Outcome accounting is not optional for leaders who want to know what is actually happening. It is the discipline that makes the difference visible before it becomes irreversible. Want to explore what an outcome-accounting framework looks like for your organization? We work with leadership teams on exactly this question.

The post Measuring What Matters appeared first on Voltage Control.

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.

Show Highlights

[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

Links | Resources

Taran Lent — LinkedIn
Illumia — LinkedIn
Voltage Control

About the Guest

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.”

Transcript

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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