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
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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.

The post Why Miro’s AI Bets On The Canvas, Not Chat appeared first on Voltage Control.

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.

The post Scaling AI From Personal Habit To Company Capability appeared first on Voltage Control.

Which Friction to Remove, and Which to Design Back In

For most of the last two decades, removing friction was the whole job. Every extra click, every approval step, every handoff that made a customer wait was waste, and the work of good leadership was to find it and delete it. We built entire disciplines around it. Lean. Six Sigma. Growth. Conversion-rate optimization. Design thinking, in its most reductive form, became a hunt for anything that slowed the user down. The companies that got smoothest fastest won, and they deserved to. AI has now made friction removal nearly free. Whatever obstacle is left in a workflow, there is a tool that will dissolve it this quarter. The drafting step, the research step, the first-pass review, the scheduling back-and-forth, the synthesis of twelve documents into one. The reconciliation, the summary, the first draft of nearly anything. Gone, or going. And that is the trap. When removing friction costs almost nothing, the temptation is to remove all of it. But not all friction is waste. Some of it was the only thing developing your people. Some of it was the only thing keeping a human close enough to the work to notice when something was about to go wrong. Strip that out along with the rest, and you get an organization that runs beautifully right up until the moment it needs judgment it no longer has. The discipline leaders need now is not friction removal. That skill is commoditized; the tools do it for you. The new discipline is friction discernment: the ability to tell the difference between the friction that drains people and the friction that develops them. Remove the first kind without mercy. Design the second kind back in on purpose. Everything else in the new friction follows from getting this one distinction right.

The two kinds of friction

Draining friction is friction that costs effort and returns nothing. The approval that exists because someone got burned in 2014 and no one has revisited it since. The status meeting that could have been a sentence. The reformatting of a report from one template into another. The manual data pull that a script does in a second. The form that asks for information the system already has. This friction does not build skill, protect quality, or surface insight. It just taxes attention and demoralizes the people subjected to it. AI should eat all of it, and you should let it. There is no virtue in preserving busywork, and no one should confuse what follows with nostalgia for it. Developmental friction is different. It costs effort and returns capability. The junior analyst who has to build the model by hand the first ten times, and only then earns the right to have a tool build it, because now they can tell when the tool is wrong. The reviewer is forced to articulate why an output is flawed before they are allowed to reject it. The team that has to argue its way to a decision instead of accepting the first plausible recommendation that appears on the screen. The new hire who sits in on the hard customer call instead of reading the AI summary afterward. This friction is slow. It feels like waste in the quarterly numbers. And it is the entire mechanism by which expertise, judgment, and trust get built. Here is what makes discernment hard, and why it is a discipline rather than a checklist. The two kinds of friction look identical on a process map. Both are steps that slow things down. Both show up in a time-and-motion study as cost. You cannot tell which is which by measuring duration, because duration is not the variable that matters. You can only tell by asking what the friction is producing. A leader who optimizes purely for speed has no way to see the difference, and will remove both with equal enthusiasm.

Why we are getting this wrong right now

The error is not stupidity. It is a structural asymmetry in what leaders can see. Efficiency is legible. It shows up in the dashboard the week after you automate something: fewer hours, lower cost, faster cycle time, a clean line that goes the right direction. You can put it in a board deck. You can attach your name to it. Judgment loss is illegible. It shows up nowhere, for a long time. It hides inside the year-over-year improvement metrics and the reduced headcount and the deliverables that ship faster and look clean, right up until a situation arrives that needs taste, or context, or the ability to know what is not in the data. By then the people who would have caught it have either atrophied the capability or never built it at all. JoAnna Vanderhoef gave this hidden cost a name: capability debt, the widening gap between an organization’s apparent efficiency and its actual adaptive capacity. Like technical debt, it accumulates quietly and charges interest later. Unlike technical debt, most organizations are not even tracking it. They are removing developmental friction at speed, booking the efficiency, and treating the judgment that disappears as if it were free. It is not free. It is borrowed, and the loan comes due on the worst possible day.

The evidence that friction can be load-bearing

This is not a motivational point. It is measurable. In a controlled study presented at the BIG.AI@MIT conference this year, Renee Gosline’s MIT team gave people cognitive tasks with AI assistance. In one condition, the AI made a recommendation and the person accepted or rejected it. In the other, the person first had to articulate their own reasoning, or predict what the AI’s reasoning was, before deciding. That single step took about thirty seconds. It measurably reduced over-reliance on the AI and preserved the person’s own critical thinking. Thirty seconds of deliberate friction kept the human’s judgment intact. Remove it, and the judgment quietly erodes until the day the AI is confidently wrong and no one in the room has kept the muscle to notice. The mechanism behind where this damage concentrates was formalized by a team of researchers at MIT, Yale, and Microsoft led by Mert Demirer. They studied what they call AI chains: sequences of work steps where the automatable steps are contiguous, so a human only has to verify the final output. The economic incentive is to keep extending the chain until the marginal cost of an error overwhelms the saved verification. The jobs that automate fastest are the ones where AI-suitable steps cluster together. Those are also, and this is the part that matters, the jobs where learning loops used to live. The junior who once did the research, drafted the slides, and watched a senior edit them loses three apprenticeship cycles per deliverable when the whole chain collapses into one automated unit. The work still gets done. The person stops getting made. So the friction you are tempted to remove fastest, the long contiguous chain, is frequently the exact friction that was developing your bench. Efficiency and capability erosion are not opposing forces you can balance. In the most automatable workflows, they are the same move

The test

Before you remove a piece of friction, run it through three questions. They take a minute, and they are the discipline in practice. First: what is this friction producing? If the honest answer is nothing, it is draining friction. Remove it without hesitation. If the answer is a skill, a judgment, a relationship, or a moment where someone learns to catch what a system would miss, you are looking at developmental friction, and removal has a hidden cost you need to price. Second: who was getting developed here, and where will they get developed instead? Most automation quietly deletes an apprenticeship without anyone deciding to. If you cannot name where the replacement reps come from, you are not saving time. You are borrowing capability from your future bench, and the interest rate is high. Third: what happens on a bad day? Efficiency holds until something breaks, and then recovery runs on the slack and the judgment you preserved, not the slack and judgment you optimized away. If removing this friction means no human is left who could step in when the system is confidently wrong, the friction was load-bearing, and you are about to knock out a wall. Run a concrete case through it. A team proposes to fully automate the first draft of every client proposal. Question one: what is the drafting producing? Not just a document. It is where account managers learn the client’s business well enough to defend the recommendation in the room. Question two: if the AI drafts them all, where do new account managers build that fluency? No one has an answer. Question three: when a client pushes back hard in a meeting, who has internalized the reasoning well enough to respond? The honest run-through does not say “never automate this.” It says automate the formatting and the boilerplate, and keep new managers writing the core argument by hand until they have earned the shortcut. That is friction discernment producing a different, better decision than “remove it” or “keep it.”

The moves

Discernment becomes real when it changes what leaders actually do. Three moves follow directly. Stop automating contiguous chains to the end without asking what skill the chain was building. The most automatable workflows are exactly where capability debt compounds fastest, because they are where whole apprenticeships used to live. Automate them deliberately, and keep a human in the loop where the learning was, not only where the legal liability is. The liability checkpoint protects the company this quarter. The learning checkpoint protects it in five years. Start designing developmental friction on purpose. Route a deliberate fraction of automatable work to humans anyway, so the capability stays alive. Require a thirty-second reasoning step before anyone accepts an AI output on a decision that matters. Run novelty drills, where work that could be automated is occasionally done by hand to keep the skill warm. Sample AI outputs not for quality assurance but for drift. Bring in someone who has not been close to a pipeline to ask whether it is still doing the right thing. None of these are productivity moves. All of them are capability moves, and the point is not to make the system slower. The point is to keep it teachable. Change what you celebrate. When a team automates forty percent of someone’s job, the reflex is to bank the savings and move on. The better move, which we have watched work, is to make the freed capacity a deliberate conversation: what harder, more developmental, more human work does this person now get to do? Organizations that celebrate only efficiency teach their people that the goal is to automate themselves toward the exit. Organizations that celebrate the redeployment teach them that AI is how they grow into more valuable work. Friction discernment is not anti-efficiency. It is efficiency pointed at the right target.

Why this is the leadership job now

When execution was expensive, leadership’s job was to clear the path: remove the blocker, approve the budget, unstick the review cycle. That job is mostly done, and the leaders still doing only that are optimizing a bottleneck that has already moved. The new job is friction discernment, and it cannot be delegated to a tool, because it is precisely the judgment about which judgments to keep. It is the one decision the AI cannot make for you, because the AI’s entire bias is toward removing friction, and the question in front of you is when not to. The organizations that get this right will look slower for a few quarters and less impressive in the efficiency reports. They will also still have, when the situation changes, the people who can do the work the model cannot. The organizations that remove every obstacle they can afford will discover, on the worst possible day, that they removed the ones holding the building up. Stop removing every obstacle. Learn to tell the difference. Remove the friction that drains your people. Design the friction that develops them. That is the discipline, and everything else in the new friction follows from it.

The post The Friction Discernment Test appeared first on Voltage Control.

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Sarah B. Nelson, Distinguished Designer at Kyndryl and co-founder of Kyndryl Vital, about why AI’s promise to remove friction is actually surfacing the human dynamics organizations have always avoided facing. They unpack how a single word like trust splinters into distinct concerns — model accuracy, data use, organizational credibility — and why treating human in the loop as a rubber-stamp step risks disengagement and stripped-out meaning. Nelson draws on the NeuroLeadership Institute’s SCARF model to explain why AI rollouts stall on status, certainty, autonomy, relatedness, and fairness rather than on the technology itself, and shares stories spanning cybersecurity burnout, Holacracy at Zappos, and the extraction economics behind AI training data. The conversation keeps returning to her insistence on designing with people rather than at or for them, and on imagination as the resource most at risk of being engineered out of enterprises chasing speed. She closes with a Buckminster Fuller line she keeps returning to: that people are called to be architects of the future, not victims of it.

Show Highlights

[00:01:46] Why 95 Percent Of AI Initiatives Fail
[00:09:20] Trust Is Behavior Over Time
[00:12:23] Human In The Loop Versus On The Loop
[00:16:03] Psychological Safety And Cybersecurity Burnout
[00:25:59] The SCARF Model And Organizational Autonomy
[00:30:27] Lessons From Holacracy And Flat Organizations
[00:35:40] Building New Rituals For Working With AI
[00:42:38] Imagination As The Friction Worth Keeping
[00:47:48] Architects Of The Future Not Victims

Links | Resources

Sarah B. Nelson on LinkedIn
Voltage Control

About the Guest

Sarah B. Nelson is a Distinguished Designer and co-founder of Kyndryl Vital, Kyndryl’s co-creation and experience design service, and hosts Kyndryl’s The Progress Report podcast. She has spent her career at the intersection of human-centered design and enterprise transformation, including roles at IBM and PepsiCo, building the methodologies and communities that moved organizations toward shared futures even on shaky ground. She previously joined Douglas Ferguson on Episode 42 of the Voltage Control podcast, “Healing the Collaboration Pain Point.” A classically trained violinist whose first computer was an IBM 360 terminal, Nelson describes her current focus as figuring out what comes next for design — new applications, new methods, and the new organizations that support them.

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, Sarah B. Nelson, Distinguished Designer at Kyndryl where she specializes in emerging human-centered design practices. She’s also the host of Kyndryl’s The Progress Report Podcast. Welcome to the show, Sarah.

Sarah B. Nelson: Hey, hi. Happy to be here.

Douglas Ferguson: Yeah, it’s great to be in conversation with you. I always enjoy our conversations and I think today will be no difference.

Sarah B. Nelson: Excellent. Yeah, same.

Douglas Ferguson: Yeah, let’s start off with this reframe that I’ve been considering. It’s like designers have always been the people in the room arguing for friction, the user research, the design phase. Let’s slow down and understand the human first. Now that we’re operating in a world where AI’s whole pitch is removing friction, are you the resistance or has design just become redefined?

Sarah B. Nelson: I think it’s still a yes. We’re in a transition period and transitions aren’t… It’s not like flipping a switch. I think there’s a sort of dawning realization that I see on a pretty regular basis when we look at the statistics around the number of AI initiatives that fail, and it’s like 95%, which I always say is like, well, 5% succeed. I don’t say that as to be Pollyanna, but I like that that number is a lot smaller because it’s sort of saying that there’s something going on, more than one or two things going on about why these things are failing. Some of it… And generally, at least my bias is that most problems, the root cause of most problems is something to do in people dynamics. And I think that’s a lot of what design does, is it is that sort of pause to ask what’s going on. And I think, I hate to say it like this, but sometimes lessons have to be learned the hard way. And so people, you can see it, they’re investing the dot strategies. They’re like, “We got to do this, but then our data’s not set up and then none of our people want to use it, and some people are even sabotaging it.” There are all of these signals that are much louder than, “We have this persona, or we talk to 20 people, or trust us, bro. We stand for the user.” I think there’s some really clear stuff coming up and then the friction becomes everyone’s problem. It’s not just designers waving a flag. And I think that’s where it becomes really interesting when people start to realize that experience is everyone’s responsibility and everyone’s problem, because the technology itself does not… If you put technology first, it doesn’t fix it. Because I feel like a lot of times people go, like technology, money, process, people. And it’s exactly the opposite. It’s people into process into money into technology. Technology kind of comes at the end. By money, I mean how does the economics of the solutions work as well?

Douglas Ferguson: Yeah. And it’s funny, a lot of the people that I see starting to realize the orders flip, they typically end up putting the process first, as they’re trying to flip it and invert it. And it’s like, no, no, no, you really need to start with the people. And to your point, it needs to be ubiquitous. We can’t just have, just like where heads of innovation or little innovation groups never really actually worked. When design is something only one group is doing or thinking about, we’re going to be fraught with issues because the fact of the matter is every part of the organization is getting disrupted by AI, and we all had to be approaching this from a design problem, from a systemic kind of lens so that we’re not just, to your point, throwing a technology solution at it and hoping it works.

Sarah B. Nelson: Yeah. I mean what’s interesting is that in some of the projects that we’re working on, we’re rethinking roles. I mean, obviously I have distinguished designer, but I think the question of, what does it mean? I mean, so my definition of designer is every human… Designing is what we do as humans. And some people are professionally trained to do that. But everyone is designing. So there’s a lot of skills that need to go in behind that. But what’s kind of interesting right now is the conversations, even in this deep in the technical work, you hear the words trust thrown around a lot, transparency, explainability, accuracy, because we’re not calling it hallucinations anymore, but I just say lying on the part of the model. But all of these things that become this soup of, at the heart of it, understanding why people are hesitant to use it. So if it’s inaccurate, why should I trust it? What’s it doing with my data? Why should I trust it? If it’s sucking up to me all the time, why should I trust it? And then am I training people? Am I training it to do my job? All of those very fundamental pieces that are rocking people at their very, very core. But it’s interesting because I see that language in even the most technical conversations. Sometimes the solution is then technical because the next step doesn’t necessarily always say, “Well, what is it that humans are experiencing?” It’s like, “Well, how do we ensure more accuracy?” And there’s this next level of digging into what the next level problem is. I think that’s going to come next as people are starting to make these workflows and test them and seeing what works as well.

Douglas Ferguson: I think you’re right. And in addition to drilling into the knock-on effects, the second and third order, we also need to step back and even consider what we mean by these words because there’s many facets of trust as we walk into this moment and into the future. The word trust comes up a ton. Very similarly, trust and governance are two that come up a bunch and they can mean a lot of different things. And we had to be careful when we throw the words around without getting a layer deeper and really book-ending and compartmentalizing it. What are we concerned with in this moment? What are the outcomes we’re trying to design to? And what kind of environment are we trying to create? Because a great example of this is, you touched on one facet of trust, which is like, is it giving me reliable answers? And this came up in Buffalo when we were working on the Empire AI Summit, and it was a table of librarians. They said, “I don’t care if it’s AI generated, I care if it’s true.” So it’s just like this trust and this like, is it true? Is it accurate? But then there’s also so many other types of trust that come up in these conversations, but another one that’s surprising, just to give you an example, is trust in the organization.

Sarah B. Nelson: Yes, that’s what I was just going to say. Yeah.

Douglas Ferguson: Yeah. Orgs are constantly reorging or they’re just trying to respond to the market and what’s developing around the capabilities of AI and how things are getting reshaped. The message might change a ton and that can be very, especially if an org hasn’t necessarily put people first in the past. It’s a double-edged sword because now you’re like, “Well, I don’t trust your past behavior, so what does this even mean now?” So it just almost adds kerosene to that fire.

Sarah B. Nelson: Yeah, that’s what I was exactly thinking. I think there’s so much foundational work. I was trying to think about… So I think trust is behavior over time. Are you doing something? Are you showing up in a consistent way? I mean, in some ways you can trust a lot of businesses’ behavior because they will always put shareholder first. On one level, you can trust that you know that that’s how they’re going to behave. But there’s this fundamental, I think I 100% agree with you that, if you haven’t demonstrated that you put people first to now, why should I believe you that you would do that? And I think that’s where I just get really interested in how people respond to that. There’s this idea in relationship systems coaching called rank and revenge, which I love. So the idea of rank and revenge is that people respond when they don’t have power often by rank, their power is in revenge. And they can be little small things. They can be overt revenges. They can be little small things, like I’m five minutes late to the meeting. Or, oh, I ate your lunch. I don’t think that that necessarily is one, but I don’t know, it could be. But I guess it’s been really fascinated by this idea of, or what’s happening in some places where people are putting in false data or taking the company data and polluting it into public models. How can they respond when they feel like they have no power? Oh, and we were at the same thing. We were at the Gardner Workplace thing and they talked a lot about this. Trust was huge and experience was huge. And one of the things I really took away from that was how do you engage people in the design of their own future? I mean, I’ve always been a participatory design person. I really think you should be designing with, not at or for. And I think the question is how do we engage people in this, the design of the systems they’re going to use so that they see themselves in it and it actually solves their needs? I was then run up into the, how do you do that at scale? Blah, blah, blah, blah, blah. But you can’t force people to change. They will change when they want to, in general.

Douglas Ferguson: That’s right. I remember early on in one of our conferences, maybe our second or third conference, and we were holding a workshop on facilitation. It was a little two-day session, kind of a introductory thing. And someone raised their hand at some point and say, “Well, when people aren’t doing the thing I need to do, how do I make them do it?” In regard to some activity or something.

Sarah B. Nelson: Bad question.

Douglas Ferguson: Yeah, it was like a laugh out loud moment because I was like, “Well, you don’t make people do anything. It’s all about creating the conditions where they want to do it. They’re enrolled, they’re enticed, they’re invited.” And I liked what you were saying about a participatory design and how important it is. And there is a question around how to scale it, but if you take a systems approach, anything’s possible one step at a time. And I think that in this age, one way to rethink it is, we talk a lot about human in the loop, but if we rephrase that to human on the loop, we start to think about something that people have a little bit more agency. They’re not just a cog. I’m not just on this assembly line moving my little piece. It’s like I’m observing the loop. I’m noticing patterns. I’m maybe evolving the loop. I don’t necessarily have to be a stage gate. I can be a more authorship, ownership agency.

Sarah B. Nelson: Yeah. I’ve been thinking a lot about that because I’m seeing diagrams, I’m seeing well-intentioned people designing these processes and they take the human in the loop and that’s the acknowledgement that not everything’s going to be accurate. But that’s one of the concerns I have is that it starts to put people in a spot where they’re button pushers, or I worry about not challenging people, giving people meaning. And I worry about it for a couple of reasons. There’s the humans need meaning. They need to matter. And that’s I think a huge part of what work brings us actually. But the second part is disengagement. So you start, you put in these stage gates and people can just, they become rote or they don’t feel like they matter so they just get approved without really looking at them. Or you just become used to the machine giving you information being like, “I trust it. I trust it. I trust it.” Even if it’s not giving you that. So I like the idea of changing that relationship on the loop. One of the things we’ve been talking about is how do you have… I can’t think of the word for it right now, is when something is like… A decision is made that is unfair in some way or incorrect. How as a human can you intervene in a system where human maybe wasn’t designed originally to intervene? So I don’t know if restitution, I can’t think of what the word is.

Douglas Ferguson: Well, it’s reminded me of in the lean manufacturing, it’s the Andon cord where Toyota had a whole line. It was literally the cord that people could reach up and pull. So anybody, regardless of rank or position or whatever role they were performing, could shut the entire assembly line down. Yeah, so what are these ripcords or these kind of eject halt all progress because we’ve noticed something? I think that’s going to be super critical in the systems we build that are truly agentic and cross-functional.

Sarah B. Nelson: Yeah. What you just put me in mind of is around safety. And there’s something about in that assembly line that there’s actual… I’m sure they can pull it for quality reasons, but they can pull it for physical safety reasons as well. And it’s interesting because safety is a huge part of what… One of the big concerns around AI as well, but it’s almost in some ways more abstract. It could be real. I mean obviously if you’re doing in physical applications, yes. But I think about this, what is safety and how do you notice safety in that way? I don’t know, but there’s something, this might be completely off the topic, I don’t know. So let me try first. I did this podcast at Kyndryl. One of the ones that I really enjoyed is a strange word for it, but it was about PTSD in cybersecurity professionals. And a lot of cybersecurity professionals and CISOs and people like that, they have one of the highest burnout rates of any profession, including frontline nurses. And one of the reasons is that they sit in a perpetual state of threat. They can’t see threat. So if you’re in a battlefield, there are bombs going off or you have a sense of threat, but the threat ends. Let’s assume you make it out, the threat ends, you go back somewhere. Now people do PTSD as things will trigger it. But in security world, there’s a sense that there are people who are intruding. You cannot see them. You’ll never see them coming. You’re trying to do your best. And what happens, is your body never lets it go. So it’s a different kind of this perpetual stress and they’re often in their homes. So their homes are where this stress is. So this is a guy who works with them in different processes to help folks relieve themselves the PTSD, particularly after intrusions. But it just strikes me that there’s these different notions of what safety is, and that we don’t know exactly what the impact would be on workers and maybe even on seemingly safe kinds of applications. So I don’t know if that-

Douglas Ferguson: No, I mean it’s interesting. It brings up a whole new definition of psychological safety. Not only, like the Amy Edmondson’s, do I feel comfortable speaking up? But is this safe to my psyche? Is this going to be neurosis-inducing or it’s going to cause issues if we’re using it in these ways? It certainly hasn’t been studied yet. There’s lots of folks that say that our test scores are failing because of how much computers are used in education now and it’s impacting actual deep learning. I don’t know. I think the jury might still be out on that a little bit. There’s some people that are very passionate about it and they have evidence and research, but we certainly don’t have research yet on how AI is impacting our brains at that level and not any longitudinal ones for sure.

Sarah B. Nelson: Yeah, not longitudinal. I think there’s also the other parts of what’s happening in AI. And I’m thinking about the extraction in the global south, that a lot of AI, the models are being trained by people in areas that are economically challenged areas. They get paid very little to see often very traumatic information. And so that’s kind of in the system. And I think about, so companies have given them those kinds of things to do. And they’re like, okay, there are these humans, we need humans. So these are humans in the loop, but they’re going to do the stuff that we won’t want the Westerners to look at. We don’t want it to even show up for… So we work on these models already that have been cleaned by humans. And then I think in solution land, we have to be thinking about that whole human in it. And I think obviously the ethics of how these models are developed and the large tech companies and how they’re doing that. And then thinking about how we’re setting up employees and where are we dehumanizing them? Well, technically keeping the human in the loop as well. And I think just to your point, I think we just don’t know where some of these things are going to really do. Just know that pushing buttons all day long is not… Pushing buttons that doesn’t have a sense of connection or all of that.

Douglas Ferguson: Talking about dehumanizing, I ran into someone the other day while walking my dog and I hadn’t seen them in a long time. I was just chatting about things, and they’re a bit out of this space. They work kind of tech-adjacent. They have a white collar job, but they’re not living in AI. They’re not building products. And they’re a little bit outside of the spaces you and I occupy day-to-day, but it’s still hitting them. And it’s really fascinating because she’s very disgruntled about, there’s a specific project that leadership was pushing through and she’s like, “We’ve been telling them for months, if not years, that this is important. And now because the AI is saying it’s important, now they’re prioritizing it.” And it’s kind of dehumanizing because it’s like, “Wait, now that this machine that often gets things wrong is saying this, you’re going to believe it, but you didn’t believe us?” And so I think we have to be careful, even if it is helping us see the world a little differently as leaders, we have to think about how our actions that are influenced by these machines are getting perceived by those around us.

Sarah B. Nelson: Yeah. So those are these kinds of, for instance, leaders constantly make decisions without… And I mean it all, good intent, bad intent can make decisions without really being aware of impact, or just thoughtlessly. So there is more of a need for attention to what’s happening on the ground. And again, it goes back… There’s a fundamental, I can’t quite put my finger on it. There’s just this fundamental mistrust of other humans. I don’t know. I mean, if I get all wax, I’m so intellectual, I keep thinking about Turnerism and the history of, at least American business that Peter Drucker was one of the first people that said, “Hey, thought workers are not assembly line workers, you need to manage them differently.” But that even in the world of business education and all of that is that we’re not that far off of the ’50s, ’60s belief that business is an assembly line. And I remember actually there was a company I had joined and I went through the orientation. It was like, “This is how the business works.” And the entire business was about getting product to market and getting money for that. And it was every single thing in the business was doing that. Now it was manufacturing, so it was about that. And then they kind of just tucked… Design and innovation and marketing were almost literally tucked at the sides of it. And it was a moment that I had a realization, it’s like, the way that I think about what we’re doing and what my role is, and what my role is in the company is very, very different than everyone else’s. And it was the first time I saw it very laid out, as my job is to, are we working on the right thing? Are we serving the people? Are we developing new sources of value by doing that? There’s all these assumptions in there, but it is not about doing that in literally an efficient way, in the way that the rest of the business is clearly measured on. So that just becomes like… Do you know what I’m saying?

Douglas Ferguson: Yeah, it reminds me of just innovation functions. Innovation’s not meant to be efficient. It’s meant to uncover the next big opportunity. And then operationalizing is when you think about bringing in efficiency. And I think a lot of folks don’t necessarily set their strategy accordingly. If we’re in an innovation cycle, we should not be trying to optimize and make things as efficient as possible, but oftentimes that’s the posture. And I think that’s another good point, is making sure that we as leaders identify good postures for how we want to leverage AI. So it’s not just, “Hey, everyone’s doing it. We got to jump on or we’re going to get left behind.” It’s like while those fears and anxieties might be rooted in truth, we’re not going to be successful unless we step back and say, “Well, to what extent? What is the remit? Why do we want to use this stuff? What kind of outcome is it going to drive for us?” And that allows us to get beyond this intoxicating speed in which it can generate things.

Sarah B. Nelson: Yeah. It’s interesting too, because I keep thinking when I’m listening to you say that, this we got to do it or we’re going to lose in the market. This comes back to these really fundamental leadership things, that people will get on board if they know why something is happening. There’s, what is the SCARF model from the NeuroLeadership Institute?

Douglas Ferguson: Oh yeah.

Sarah B. Nelson: They talk about when people are threatened, it’s like status… I can’t remember all the ones, but I do remember fairness as one of them. And that for people, if they understand why decisions are made, they’re more likely to accept them. And a lot of the resistance comes from when they don’t know why they were made and it feels like it’s being imposed on top of them. So there’s that kind of, remember the basics of human dynamics of leadership. And I think there’s so much noise in the world. We have organizations that financially benefit from scaring everyone ahead of their IPOs.

Douglas Ferguson: That’s right.

Sarah B. Nelson: And you can see Sam Altman backing off. “Oh, it’s not going to take all the jobs.” It’s like, oh, because the message doesn’t work for you now. So I think people are starting to pay attention to that. But I did want to go back. I saw this morning, Alan Kay from Apple, from the ’80s, if any of the listeners don’t know who he is, he was one of the major folks in Apple in middle ’80s. And he was giving this talk and he was talking about, if you’re digging a hole and you’re looking for gold and you get down three feet and there’s no gold, you have two choices, is that you can dig faster or you can acknowledge you might be digging in the wrong place. And what he was saying was that American business just digs faster. It’s like, “We got to dig faster, get more people in here to dig more. There’s gold down there someplace.” And so I think a little bit about that discipline of going right back to what you said in the beginning, where can you introduce friction, asking people to slow down in order to just say, “Are we doing the right thing?” And then how can we do it better?

Douglas Ferguson: Yeah. And there’s alternatives to digging faster. Is there better instrumentation that might help us know if we’re digging in the right spot, et cetera. And I want to come back to the SCARF just for listeners that may have not have run into it. Status, certainty, autonomy, relatedness, and fairness. And I think the reason I wanted to come back to it is because autonomy is a really interesting thing and certainty are two really interesting things right now, because certainty is something that feels very elusive right now. And I think as leaders, we can acknowledge the fact that there’s a lot that’s uncertain, but what can we make certain? Because if there’s anything that we can make certain, whether that’s our point of view, our posture, the direction we want to go, the strategy we’re going to take, there’s so much unpredictability right now. Anything that we can make more certain and predictable and knowable is going to make the organization more calm, more supportive, more aligned, more understanding. Autonomy is another interesting one because people have this sense of losing autonomy in this new agentic AI-driven world and how they imagine it will even become less and less autonomy. And that’s very frightening for folks. And I think that’s really wrapped up into the identity and I’m going to lose my job and all these things. And what we can do is that if we start to really step back, and this is really why it’s so critical that folks need to adopt multiplayer team-based AI habits versus before they go to the full systemic agentic cross-functional use cases. Because the more that we can map the playing field, understand how we want to use AI together and understand the potential and align that with our vision, and we get to the shared perspective together, then we could start to understand where our autonomy can reside and then we can be very autonomous. People don’t feel autonomous when it feels like their autonomy’s being threatened in all these ways. But if they understand, if they look back and look at the system and go, “Oh, I shouldn’t have autonomy there because of X, Y, and Z, the system is going to function better if I’m not autonomous over here. But look, these are the places where I’m autonomous.” But when people don’t see the system and they don’t see it all mapped out, they don’t even understand where their autonomy resides and then it feels like they have none.

Sarah B. Nelson: Then it feels like they have none. That’s interesting because I always think constraints will set you free, but that clarity of where… Because I think about autonomy a lot as this ownership. I mean, just before AI, just that question of where do I get to make decisions? But I think that I’m just probably just very much emphasizing what you’re saying, but that making clarity of roles, clarity of decision making, all of that discipline that, honestly most corporations struggle with anyway, because we know that those things, when you have clarity of roles, when you have clear goals, when you have good communication and you have a leader who shows up shoulder to shoulder, you have all of those things, then people start to rise to the occasion because you’ve taken a ton of noise out of the system. And I don’t have any… And this is maybe just me complaining, but I don’t have any solution for it, but I just never really understand why speed to somewhere always trumps the just like, “Let’s just put the bricks in place in order for us to be able to go faster.” Because we know that if you do that, you go slower to go faster. The process is… Every time I’ve ever done that it’s like, “Oh yeah, I trust that process.” But I think most people, it’s risky. I don’t know what that’s about, but it’s too hard maybe? I do remember this Zappos, what was it called? Holacracy, this organizational model Holacracy. Does that sound familiar?

Douglas Ferguson: Oh yeah, for sure.

Sarah B. Nelson: It was a guy, came from the agile world and he was thinking about organizations as operating platforms. So the Holacracy was like the operating system for an organization. And then the idea was you didn’t have managers anymore, or a leader, you had a constitution and there were certain kinds of rules of engagement around all kinds of things. And it included things like rules of decision-making, rules of ownership, how certain kinds of meetings were conducted. And Zappos was the largest adoption of it. But one of the things that’s interesting is that we’re so ingrained on these kind of hierarchical ways of doing things, which actually turn out to be easier than trying to do this sort of super flat organization so everyone’s excited, “Oh my gosh, no more managers. I can do what I want.” The work becomes so much harder because now the decision-making is collective and there’s tons of models in the world, like Quakers and things who have collective decision making, but that is not a quick process. That is a slow process. So it’s interesting to me those kinds of the organizational systems and beliefs that people have, and how that then impacts the work that comes out of it.

Douglas Ferguson: Yeah. I think also too, there’s some rhetoric around flat structure and whatnot, but a lot of it is about cost-cutting and savings, not actually trying to build a culture that’s resilient to that. And often I found it’s not just about an unwillingness to go slow, to your point, a lot of the process is low, but it’s an unwillingness to attend to the process that’s necessary to operate in that way. And it’s just a matter of like, “Hey, we want to remove the middle managers or we’re cutting costs or whatever without being attentive to how the organization… What does the system need to look like to support that?”

Sarah B. Nelson: Yeah. I think with AI, the emphasis right now is like, “Oh great, cost efficiency.” First of all, we already know that consumption… With what’s happening with consumption, cost is actually probably not going to be the driving force around this. And to me, it’s like you have to be more creative about thinking, like what can you do? If you take the drudgery out and you take the high production things that are highly manual and you take that out, what does that enable you to do? So to me, it’s about this sort of identify, I don’t want to be businessy, the new sources of value, new things that become possible because you’re no longer consumed with that. In design, over the last few years, there’s been all these small technology advancements that I’ve gotten weepy about multiple times. The first one was the Post-it note, the 3M Post-it note app, that would let you capture Post-it notes and break them up and bring them into whatever program you were using. And then you start to get them with OCR attached to them. And I actually did get a little teary the first time I used that 3M app. And then the next time was that now I’ve got these things into Miro and I just highlighted all of them and asked it to sort it and see what it saw. And what would’ve been a three-day job or a two-day… Because after every workshop, we would sit and type them all in and then we would hand analyze them. And there’s something very valuable… I’m going to put an asterisk there because there’s something really valuable about that too. But there was this other part which was like, this got me pretty close to where I need to be and it did it so now I can focus on where is the unusual insights in here? So then my asterisk is sometimes the unusual insights come from doing the manual work.

Douglas Ferguson: Well, here’s something to think about. This is a really important point and it’s come up a couple times in some of the events we’ve hosted and the work we’re doing to try to understand where we’re headed with this stuff. And one of the new frictions that comes about is, now that the AI is doing a lot of the grunt work and the analysis and things, now then what we might’ve gotten through osmosis by just taking notes or doing the things that we would’ve had to do to be prepared for this all the post-event work or whatever it is, insert your problem. But the ways that you were showing up and the little rituals you had adopted prepared you to then do the final project to be ready for the presentation. And so an example was a designer had adopted a tool that could basically record user interviews, did a bunch of synthesis, did a bunch of analysis, generated an amazing report, but he had to study the report to be able to present it. Normally by the time you’re done making the report, you don’t even need to practice it. It was like you know it in and out, you just present it. Which was interesting, because I wanted to reframe that whole question he was posing because he was saying it’s actually shifting the work to where we need to study the presentation. But I said, actually, this is a design problem. This is analyzing the friction problem and saying, “Hey, how do I need to change my rituals and how I show up in the first place to maybe make it easier? So it’s not about cramming for some presentation. It’s about how I’m using this new tool to learn in a new way versus having to then take its answers at the very end and cram.” So I don’t know, I’m really fascinated by, we can’t just take our old ways of showing up, our old rituals and just jam these tools in. We really need to step back and say, “How are they materially changing how we need to show up?”

Sarah B. Nelson: Yeah, I 100% agree. The words that keep coming up for me are data intimacy. I don’t know. It popped into my head one day. I’m sure somebody smarter than me said it someplace, but there’s that idea of how well you know something. I mean, for me, when we would do mental models of complex workflows, I know that workflow. I could still talk about it because I have visceral stories that we captured from people, and spending time really manually with that data. So I know a lot. There was some stuff that was like… The flip is, is that sometimes you spend a lot of time on it and you only get to the stuff everybody would know anyway. So you don’t get any place in particular. But one thing that, there was someone, I listen to millions of things, but was talking about when you need to really learn something and change your thinking, you need to make yourself go slow and pull out the book, sit with the book, read the book, and munge at that information because that is when your brain is making connections. And so it’s sort of knowing about when you need to summarize something and when you need to actually spend time with it. And I think everyone’s kind of going like, “Oh my gosh, we can summarize everything.” And I think it’s, to your point, finding what are new things we need to do to make sure we retain the things that are really meaningful and useful.

Douglas Ferguson: Yeah. And also even if we’re using to summarize, what are the signals that we should identify ahead of time so that when we see them, we know to slow down, to do the deeper look, to say, “Hey, there’s something new to learn here.” Because frankly, people are using this stuff all the time to create rapid synthesis, to do a lot of grunt work, to use that other word. I think we have to invent some new signals, some new ways of looking at this stuff so that we know when, hey, this is a moment to dive a little deeper, to ask some other questions.

Sarah B. Nelson: Yeah, interesting. I think because it’s also thinking about the recipients of this. So recipients of reports, it’s always like… That’s often the thing is they read them, it’s hard to internalize, they get some information out of it. It’s not internalized. So there’s also probably the question of, and now we’ve got both sides not internalizing it, but maybe this is also the opportunity to have both sides do some more internalizing too. It goes from reports to ways of using the information from the reports in some way, that that’s how we experience the outputs. I don’t know. I’m just thinking off the top of my head.

Douglas Ferguson: Yeah, no, I love that. And also it makes me think too, we need to be intentional about how we break the cycles because when it’s my agent sending your agent an email, then your agent replying to that email, at which point is something real happening versus things just getting thrown around. It reminds me of this cartoon, it’s like self-driving cars were starting to become a conversation some years back and the cartoon was these two cars, and one of them kind of looked like a police car and the policeman’s standing out in front of the first car and he’s saying, “Does your car know why my car pulled you over?”

Sarah B. Nelson: Yes, that.

Douglas Ferguson: Yes, I think we need to… That’s something to contemplate as we’re building these systems, right?

Sarah B. Nelson: Yeah. Yeah, for sure. Yeah, for sure. I think maybe that’s just one of the most important things is just being able to… You’ve got to check yourself for when you go into automatic. I mean, I think about this a lot. I’m working on something and I’m like, am I conditioning myself to just go ask, have a conversation with Claude about it, where I would’ve talked to a human about it, or I would’ve gone an written about it and then evaluated it myself. So I’m thinking about those things, like where now I have to have some interventions on myself about when, no, actually you need to go back to what you know how to do, which is, you need to write about this or draw about it, or do some other mode that isn’t having a chat with something that may just be blowing sunshine up your butt. You know what I mean?

Douglas Ferguson: Yeah, yeah.

Sarah B. Nelson: And there are times that I sometimes think, am I actually getting… I don’t want to lose the muscles, but am I kind of in a reflection anyway? Am I already in a mirrored room? And so maybe working on my own and writing, I could probably do the same or better anyway. So it’s an interesting… I guess the main thing is to really stay self-reflective. I feel like that’s the name of the game right now. It’s like what’s happening? What’s happening to me? What’s happening to others around me? Is this better work or worse work or different work, or, yeah.

Douglas Ferguson: Yeah, I think that’s why we feel that this framing around friction’s important because are we taking note of where the friction points are? Which ones are good friction? So we’re intentionally slowing down, which ones that we might want to repair or lean into to redesign around. And so it’s introducing a little slowness, a little contemplation, reflection, back to what you were saying. But yeah, I think we just have to stay aware and attuned versus just falling into this kind of automated soup.

Sarah B. Nelson: Yeah.

Douglas Ferguson: So five years out, what’s the friction we’ll wish we hadn’t removed?

Sarah B. Nelson: Five years out. It’s hard to even imagine five years out. If things go wrong, it’s imagination. I actually think it’s time for imagination. That would be the thing I think would be the worst thing that we would lose because imagination is the thing that, I think that is something that humans uniquely do. I think, okay, whatever, never say never, but I think it’s like we’ll have all of this information and we have all these possibilities, but if people can’t think of creative ways to use it or doing like what we’re doing in this moment, of like, what does the future look like? What could it be? We don’t ask those questions anymore. I mean, I don’t know what we’re doing. I think I just imagine this sort of spiraling or flatness, or things don’t change or… I don’t know, but imagination to me feels like a keystone.

Douglas Ferguson: Yeah. It’s interesting too that you reach for imagination as an example of friction. And I think it is something that a lot of organizations try to lubricate out of the system. It’s like, “Hey, let’s not stop and worry about that. Who needs daydreaming or whatever? You need to be more professional.”

Sarah B. Nelson: That’s for children and artists, and they’re all silly people. Yeah, absolutely, because it’s amorphous, it’s threatening, it feels like guessing. It goes against this sort of belief that we can rationalize everything out. We can put it all on the spreadsheets and add it up and organize it, and make diagrams about it. It’s much harder. It’s much harder because it’s more subjective. There’s a lot more risk involved in all kinds of ways. But none of this exists without someone imagining it. I mean, some of it obviously comes out of needs, but even that, it’s the, like what is the problem we need to solve here? I mean, it’s like dumb stuff. How do I make it easier to light my candles? It’s like somebody said, “Oh, that’s imagination too.” So I guess that’s the thing that I think is the most precious thing to hold onto.

Douglas Ferguson: Yeah. It’s easy to note that, imagine has image in it and it’s conceptually bound to this idea of visualizing things and sketching and drawing and having vision. And I think that’s very strategic, and it’s unfortunate that’s not part of how most people define and capture strategy. And I would argue if you look at a… In fact, we just did a webinar last week and I talked about how we all know a photo’s worth a thousand words, and then [inaudible 00:45:23] Law said that a prototype’s worth a thousand meetings. Well, I’m now saying that a visual specification is worth a thousand prompts because text prompts are linear. And if we visually build up and imagine together what the future could be, the AI is going to be a lot more aligned with how we’re imagining and perceiving the future. And I think that’s a beautiful way to think about working with these tools when we start to work collaboratively.

Sarah B. Nelson: Yes. Yeah. I’ve been really impressed with how some of these models are dealing with visuals. I don’t mean creating them, I mean being able to interpret them in all kinds of ways. I’ve actually given it paintings of mine and I’ve been shocked at the critique I’ve gotten back from it.

Douglas Ferguson: Yeah. You mentioned stopping the sketch versus consulting with the AI. Have you experimented with sketching first and then given the AI the sketch?

Sarah B. Nelson: No, but I’m going to.

Douglas Ferguson: It’s pretty fun. In fact, it’s really fun to do a really loose sketch where you’re just like, you’re not even worrying about how understandable it is. It’s not for any other human’s consumption. So you can just flow and go wherever you want to go and then get in a conversation with it, and it’s really fun because you’re unlocking parts of your brain that maybe wouldn’t have just gone into language. Then it’s really good at being able to extract things. It is a fun use case.

Sarah B. Nelson: All right. Well, because I actually have a diagram. I was like, I need to go get a big giant piece of paper and actually draw this whole system out. And the idea that I don’t have to have it for another person but myself and AI is like, that’s awesome because it takes so much work.

Douglas Ferguson: Yeah, exactly. And also I’ve found too sometimes that it’s not quite a critique, it’s almost like just a dialogue around, hey, what’s here? What are we emerging? It’s kind of almost emergent meaning that can be fun to extract with it. Because it’s really, at the end of the day, I’m nudging and prompting, and it’s just reflecting back some things. So it’s almost like a fun way of trying to drill deeper than I might’ve gone on my own self-reflection.

Sarah B. Nelson: Yeah. Oh, interesting. Okay. Well, I have a project for this afternoon then.

Douglas Ferguson: Fun, fun.

Sarah B. Nelson: Nice. Nice.

Douglas Ferguson: So I think this is a good time to maybe hit the pause on this conversation. So I want to invite you to leave our listeners with a final thought.

Sarah B. Nelson: Yeah. So the thing that’s been just rattling around in my brain on a daily basis is this quote from Buckminster Fuller, which is, “We’re called to be architects of the future, not victims of it.” And it’s hit me really hard because I think that’s the hope that I have for all of us, is that we do actually have autonomy. We do actually have the ability to use our imaginations to bring a new future in. We’re not locked into the one that’s being sold to us right now. And by attending to the moments that we’re in and building towards the thing we actually want it to be, I think we have a lot of power to do that. So that would be what I would encourage people, is to look for the power that you have to bring the future that you believe needs to happen into life.

Douglas Ferguson: Incredible. Well, thanks for joining me, Sarah. It’s been a lovely conversation. Looking forward to our next.

Sarah B. Nelson: Awesome. Thank you so much. Great conversation.

Douglas Ferguson: Thanks for listening to New Friction. If you enjoyed this episode, share it with a leader who’s in the middle of this right now. They’ll thank you for it. And if you want to go deeper, we bring leaders together through executive dinners and virtual masterminds. To learn more about our work or to inquire about exclusive executive events, visit voltagecontrol.com. I’m Douglas Ferguson. See you next time.

The post AI Readiness Is A Behavioral Transformation, Not A Technical One appeared first on Voltage Control.

Why Your AI Case Studies Aren’t Working

Your organization has done the work. You have accuracy benchmarks, SLA guarantees, pilot results, case studies with named clients and documented ROI. Your vendor has third-party audits. Your legal team reviewed the data handling. Your IT team certified the security posture. And your employees still are not using it. This is not a failure of evidence. It is a category error. You have been building trustworthiness. You needed to be building trust. These are not the same thing, and conflating them is why most enterprise AI adoption efforts stall at exactly the moment they should be accelerating.

Trustworthiness Is About the System. Trust Is About the Person.

Trustworthiness is what the evidence shows: accuracy rates, compliance certifications, SLAs, pilot results, and audit trails. It is an attribute of the AI system itself. You can measure it, document it, and present it in a deck. Trust is different. Trust is a psychological act that happens inside a person. It is the moment someone decides to rely on something they cannot fully verify. And that decision is not primarily driven by evidence. It is driven by experience, context, identity, and social proof from people they respect. The distinction matters because the interventions are completely different. Loading more evidence into your adoption campaign, another case study, another ROI breakdown, another compliance badge, does not move the needle on trust if the underlying psychological conditions are not met. You are solving for trustworthiness while employees are asking a different question. The question is not “Is this AI trustworthy?” The question is “Do I trust this AI, here, in my role, for this kind of work?”

The Robotaxi Paradox

Here is the pattern that reveals this most clearly. A knowledge worker who hails a robotaxi and lets software navigate them through city traffic at 40 miles per hour is the same person who refuses to accept a Copilot-generated first draft without rewriting it from scratch. Objectively, the stakes do not compare. A robotaxi error could injure them. A hallucinated summary wastes fifteen minutes. But their trust behavior inverts what the evidence would predict. Why? Because the psychological conditions are entirely different. With the robotaxi, the role boundaries are clear. The car drives; they sit. The system is visibly working in real time. Social proof from colleagues who have used it accumulates passively. And critically, their professional credibility is not on the line. If the robotaxi takes an odd turn, they observe it. They do not own it. With Copilot, everything changes. The output lands in their document, under their name, in their domain of expertise. If the summary is wrong and they forward it, that is their error. The AI did not fail. They failed to catch the AI failing. Their reputation as someone who knows their material is at stake in a way it simply is not when they are a passenger. Trustworthiness is similar across both systems, or arguably higher for Copilot given its output transparency and audit trail. Trust diverges completely because the psychological stakes differ. This is not irrational. This is exactly how trust works. The lesson for AI leaders is specific: the trust gap your employees have with enterprise AI is not primarily about the model. It is about the context in which they use it and what failure costs them professionally. An employee who trusts AI to help draft internal updates may not trust the same AI to help draft client recommendations, even if the capability is identical. The context changes the psychological stakes. The psychological stakes change the trust response. Treating both contexts as equivalent, and responding to the skepticism in the second context with more evidence from the first, is the mistake most adoption programs make.

Why More Evidence Backfires

The conventional response to adoption resistance is to produce more evidence of trustworthiness. Refine the accuracy stats. Commission an independent audit. Write up a case study from a similar organization. Schedule a lunch-and-learn to walk through how the model works. This is understandable. It is also almost always wrong. Craig Roth at Gartner’s Digital Workplace Summit named what actually happens: organizations deploy AI rapidly, loading employees with technical information about the system, and create trust deficits precisely because speed and data-loading leave no room for the gradual, experience-based trust-building that works. Speed is a trust deficit. Evidence is not trust. Research on what actually drives psychological trust identifies three factors: perceived ability (can the system do what it claims?), benevolence (does it act in the user’s interest?), and integrity (does it behave consistently and honestly?). Evidence addresses ability. It barely touches benevolence and integrity, which are primarily established through direct experience, not documentation. Worse, detailed technical explanations often activate a risk mindset rather than a trust mindset. Walking through the training data surfaces concerns about bias. Explaining confidence intervals surfaces concerns about accuracy in edge cases. Describing the audit methodology surfaces questions about what the audit did not cover. You have made the system more transparent, which improves trustworthiness. You have also made the failure modes more vivid, which suppresses trust. The information is accurate. The effect is the opposite of what you intended. This is the core tension: the moves that build trustworthiness and the moves that build trust operate through different mechanisms. Most organizations invest heavily in the former and wonder why it does not produce the latter.

What Actually Builds Trust

Trust in AI builds the same way trust in anything builds: through repeated exposure, positive experience, social modeling, and calibrated stakes.

Small starts, visible wins.

The organizations seeing genuine AI traction are not the ones who launched enterprise-wide mandates backed by polished training programs. They ran tight pilots in one team, let people experiment with low-stakes tasks, and let word of mouth carry the initial momentum. When someone uses AI to draft a rough first cut of a weekly update and it saves them an hour, they tell people. That conversation transfers more trust than any case study. You cannot engineer the conversation directly, but you can create the conditions for it: start small, start where the AI clearly succeeds, and give people room to discover it themselves.

Top-down permission, bottom-up testimonials.

Both matter, and they serve different functions. Leadership commitment, when an executive uses AI visibly in their own work and says so, creates permission. It signals that experimentation is safe and that the organization values the output even when it is imperfect. Bottom-up testimonials from actual practitioners, not trainers or IT leads but respected domain experts who talk about specific ways AI helped them, create desire. They answer the question employees are actually asking: “Does this work for someone like me?” Top-down without bottom-up is a mandate. Bottom-up without top-down is shadow AI, happening outside governance, invisible to the organization and to any accumulated trust benefit. You need both.

Sequence use cases to build a track record.

Not all AI use cases carry the same trust-building or trust-destroying potential. A rough first draft on an internal update is low stakes and often succeeds visibly. A client-facing analysis output is high stakes and will be scrutinized in ways that compound skepticism if it fails. The sequencing of early experiences matters enormously. Start where the AI succeeds clearly, with work that is iterative and internal, where recovery from error is easy and the user stays in control. The trust you build in those contexts transfers to harder ones. The distrust from an early public failure also transfers, and it spreads faster.

Address the identity question directly.

This is the piece most adoption strategies miss entirely. Knowledge work AI almost always has professional identity stakes embedded in it. Am I still the expert if the AI writes the first draft? Am I still the analyst if the AI runs the summary? Am I still the strategist if the AI builds the framework? These questions are not irrational objections to be overcome. They are prior to the trustworthiness question. Answering “yes, the model is 94% accurate” does not address “yes, you are still the expert.” The leaders building AI fluency that holds are addressing this directly. Not by reassuring people that their jobs are safe, which most people do not believe, but by making the new shape of expertise visible. Directing a tool well is a skill. Editing a first draft to a high standard is a skill. Knowing when to override the output is a skill. Recognizing what the AI missed requires knowing your domain deeply. The expert who uses AI well is more capable, not less. Making this visible and valued is not an HR exercise. It is a trust-building move.

What to Stop Doing

If you have an adoption problem, the instinct is to add more proof. Resist it. Ask instead what is driving the psychological conditions that make trust difficult. The answers are usually specific. If employees feel AI is happening to them rather than with them, involving them in defining what good output looks like makes a material difference. People trust systems they helped shape more than systems deployed at them. Letting practitioners set the quality bar for what AI-assisted work needs to meet is not a political gesture. It changes how they relate to the system. They will defend the standard they set. If the social modeling in your organization is skeptical, one champion in the right position is worth more than a hundred case studies. Not a trainer. Not an IT lead. A respected domain expert who uses AI openly and talks about what it changed for them. Their credibility transfers to the tool. If you are building the business case around accuracy stats and ROI figures, understand that you are building a trustworthiness case. That case needs to be made to procurement, to legal, to the board. It is not the case employees need. The case employees need is not about whether the AI is reliable. It is about whether they can rely on it, in their context, for their work, in a way that protects their credibility rather than threatening it.

The Real Work

Trustworthiness is table stakes. It gets you through the governance gate and into the pilot. Trust is what gets you to adoption. Adoption is where the value is. The organizations that figure this out are not producing better case studies. They are building different conditions: room to experiment without professional exposure, social proof from real practitioners, early use cases where success is obvious, and an explicit reframing of what expertise looks like when AI is in the room. The ones that do not will keep wondering why employees who nodded through the AI launch presentation still open a blank document and start typing. If you are working through this in your organization and want to talk about where you are stuck, the gap between trustworthiness and trust is usually where the most interesting questions live. Start that conversation here.

The post Trustworthiness Is Not Trust appeared first on Voltage Control.

The design thinking process is a 5-step human-centered framework for solving complex problems: Empathize, Define, Ideate, Prototype, and Test. Each step is designed to reduce assumptions, surface real user needs, and produce solutions that work in practice rather than just on paper.  

79% of companies report that design thinking improves their ideation process, and 71% have seen a significant shift in work culture after adoption, according to the Design Management Institute’s research. The process is not limited to designers: product teams, operations leaders, healthcare administrators, and cross-functional groups working through AI adoption all use it to address high-stakes, human-dependent problems where the right question matters as much as the right answer.  

This guide walks through all five steps in sequence, with practical notes on what each step produces, the most common mistakes practitioners make at each stage, and how the process applies to AI adoption programs where the human-centered foundation matters most.

What is the Design Thinking Process?

Design thinking is a process for creative problem-solving that helps teams move past the first good ideas and discover creative solutions. Rather than a one-shoe-fits-all mindset, the approach encourages a holistic view where uncertainty and ambiguity are welcomed and embraced so a team can consider all sides of a problem. A design mindset can be applied to any organizational challenge, including how to introduce AI into a team without breaking the trust and rhythm that already works.

The method is steeped in a deep belief that the end-user should be at the heart of every decision. The benefit of design thinking is that, through empathy for your customer, employee, or partner, you create products, processes, and adoption strategies that truly help people. That same empathy is what separates AI adoption that lands from AI adoption that stalls.

In this article, we will explore the five-step process that enables teams to come up with impactful solutions to real problems, vetted by the people they intend to serve before they have even been built. These key steps launch you into an innovative and experimental approach, whether you are designing a new product or rolling AI into the daily work of a 200-person team.

Pro-tip: use our Liberating Structures templates to get the most out of the design-thinking process with your team. At Voltage Control we also love to use the Workshop Design Canvas.

The 5-Step Design Thinking Process

1. Empathize 

The first stage of the design process is to develop a deep understanding of the people affected and their unique perspective so you can identify and address the right problem. To do this, design thinkers cast aside assumptions about the problem, the people involved, and the world around them (because assumptions can stifle innovation). This allows them to consider all possibilities about the people they serve and their needs. In 2026, this step is supercharged by AI sentiment analysis, which helps teams process thousands of user interviews and global trends in seconds to find hidden patterns. The team still has to choose what matters.

Typical Activities

• Observations: You go where your users go and see what they care about.
• Qualitative Interviews: You hold one-on-one interviews with a handful of users to understand their attitudes on the topic you are exploring. Asking someone to tell a story about the last time they experienced the problem you are investigating provides a rich description that highlights details you might not have otherwise considered. Use our Interview Observation template to interview someone close to the problem you are working on.
• AI-Enhanced Synthesis: Use large language models to summarize key pain points from massive data sets while preserving the human signal. This is the same pattern we use when running AI readiness assessments inside organizations: machines surface scale, people choose meaning.
• Immersions: Step into the user’s day-to-day so you can feel and experience it.

Immersions: Step into your user’s shoes so you can feel and experience their day-to-day.

Tools like empathy maps consolidate the valuable information gleaned from interviews. Empathy maps capture what people do, say, think, and feel in the context of the problem. They help colleagues understand the context and how people experience it.

2. Define

Pull together the information gathered while empathizing. The next step is to define the problem statement clearly. The ideal problem statement is captured from the perspective of human-centered needs rather than business goals. For example, instead of setting a goal to increase signups by 5%, a human-centered target would be to help busy parents provide healthy food for their families. When the problem is AI adoption, the same rule applies: instead of “roll out the AI tool to 200 people,” define “help mid-level managers feel confident enough with AI to use it for the work they already own.”

Based on the frustrations you observed or heard about, generate questions for how you might solve them.

Typical Activities

• Clustering and Themes: There are many ways to do the Define phase, but most include a wall of sticky notes filled with quotes, observations, and ideas from your research. Group and cluster ideas until you find the prevailing themes.
• Problem Statement: Take time to properly articulate the problem statement. Answer the questions: What is the problem? Who has the problem? Where is the problem? Why does it matter?

As you explore empathy data, focus on identifying patterns and problems across a diverse group of people. Gathering information on how people are currently attempting to solve the problem and how they explore alternatives provides clues to underlying root problems.

You cannot solve every problem your users face. Identify the most significant or painful issues to focus on as you move forward.

3. Ideate

Now that the problem is clear, it is time to brainstorm. Today’s teams often use AI co-creators during brainstorming sessions to push past obvious answers and spark concepts that the room would not have reached alone.

Typical Activities

• Brainstorming: Brainstorming is a critical part of the ideation phase. It generates a wide variety of ideas, all aimed at addressing the problem or challenge at hand. It allows the entire team to bring their perspectives, experiences, and insights, fostering diversity and richness in idea generation. Ideas shared can serve as stepping stones to innovative, out-of-the-box solutions.
• Worst Possible Idea: The “Worst Possible Idea” activity may seem counterproductive, but it encourages creativity and eliminates psychological holdups that stall innovative thinking. It allows team members to brainstorm and share their worst ideas without fear of judgment or criticism. Identifying why an idea is the worst can help in understanding the parameters and constraints of a problem.

The ideation stage marks the transition from identifying problems to exploring solutions. It flows between idea generation and evaluation, but it is important that each remains separate.

When it is time to generate ideas, do so quickly without focusing on quality or feasibility. Ideation techniques prioritize quantity over quality so you can move past the first good ideas and find the truly novel ones. Only after you have exhausted idea generation do you move on to evaluate.

The ideation phase is usually a creative and freeing phase because the team has permission to think out-of-the-box before deciding what to prototype.

4. Prototype

It is time to experiment. Through trial and error, your team identifies which of the possible solutions can best solve the identified problem. This typically includes scaled-down versions of a finished product or system, so you can present and get feedback from the people they are intended to serve.

Typical Activities

• Create a Vision Board: This visual representation of ideas, inspirations, and intended outcomes allows team members to envision the desired final product. The vision board is a shared reference point for the whole team. It facilitates communication, aligns understanding, and encourages creative problem-solving.
• Rapid Prototyping: The aim of rapid prototyping is to create low-cost, scaled-down versions of the product or specific features quickly for initial testing. Use paper, sticky notes, cardboard, or digital mockup tools. Use our Take 5 template when you want to collect diverse ideas from the entire room.

With the advent of generative tools, the gap between a paper prototype and a functional mockup has shrunk. It is now possible to use generative design and no-code tools to build interactive models in hours rather than weeks. The same is true for AI adoption pilots: a “prototype” can be a single team running a constrained AI workflow for two weeks before any larger rollout.

The goal is to start with a low-fidelity version of the intended solution and improve it over time based on feedback. Begin with a paper prototype to learn quickly with minimal effort. The prototype should be a realistic representation of the solution that allows you to gain an understanding of what works and what does not. It is changed and updated based on feedback from the Test phase in an iterative process.

5. Test

The prototype is at the center of the final phase as we put all our ideas to the test. The testing phase is part of an interactive cycle. You will have the opportunity to hear from your users again, just as you did in the Empathize phase. User testing is critical to understand how your audience will react to the ideas in your prototype and how desirable that experience will be.

• Observational Testing: Real users interact with the final prototype in a controlled setting while the design team observes their behavior and responses. The goal is not just to confirm whether the solution works as intended but to gain deeper insights into how the user interacts with it, how they approach the problem the product is meant to solve, and where difficulties or confusion arise.
• Iterative Testing: This process uses the results of initial testing to make improvements, and then tests again. Use our 5 Act Interview Cheat Sheet to build the right team for the project.

Testing with real users is essential because everything is ultimately about the people who will use your products. After you collect insights, revisit the problem statement and reflect on how well the prototype is meeting needs and resolving frustrations.

In 2026, teams often perform hybrid testing, combining real-world user interaction with data-driven simulations to predict long-term behavior.

Applying these five steps to AI transformation? The same shape works: empathize with the people whose work is changing, define the human problem before the AI use case, ideate with the team in the room, prototype with one constrained pilot, test with the people doing the work. Read more in Adopting AI-Driven Change Management or explore the AI Transformation Program.

Design Thinking in the Age of AI

The five-step shape has not changed. The work inside each step has. Three shifts matter most for leaders running AI transformations:

• Hyper-iteration: The line between steps is blurrier than ever. Because prototyping is now fast, teams jump between Testing and Empathizing in a single afternoon, creating a live feedback loop that was impossible a few years ago.
• AI as a collaborator, not a tool: AI is now part of the room, not a feature you reach for. From analyzing empathy maps to generating prototype code, AI lets design thinkers focus on high-level strategy and emotional intelligence. The judgment about what is meaningful still belongs to the team.
• People-first adoption: Most AI initiatives fail at the human layer, not the technical layer. Design thinking gives leaders a way to move from “we deployed the tool” to “the team actually uses it.” That is the New Friction we keep seeing across enterprise AI rollouts, and it is the reason design thinking is having a second moment.

Our tools and timelines have evolved. The target has not: meaningful impact through a deep understanding of human needs.

Putting the 5 steps to work.

Design thinking is not a poster on the wall. It is a way of moving through a problem with other people. The teams that get the most out of it have someone in the room whose job is to hold the process so everyone else can hold the problem.

If you are using design thinking to drive AI transformation, our AI Transformation Program is built on the same five-step shape, applied to the specific friction of getting AI adopted across a team or org. If you want your own people to be the ones holding the process, the Voltage Control Facilitation Certification is where leaders learn to do that work.

Need an expert facilitator for your next meeting, gathering, or workshop? Let’s talk

FAQs

• What are the 5 steps of the design thinking process?

The five steps are Empathize, Define, Ideate, Prototype, and Test. Empathize involves research into the people affected by the problem. Define synthesizes that research into a clear problem statement. Ideate generates solution options. Prototype builds a low-fidelity version to test assumptions. Test puts the prototype in front of real users and surfaces what to refine.

• How long does the design thinking process take?

It depends on scope. A focused design sprint can run the full 5-step cycle in 3-5 days. A complex organizational challenge might take 6-12 weeks. The process is iterative rather than linear, so teams often return to earlier steps as they learn more.

• What is the difference between design thinking and agile?

Design thinking is a problem-framing methodology focused on understanding users and defining the right problem. Agile is a delivery methodology for building and shipping solutions iteratively. They are complementary: design thinking informs what to build, agile governs how to build it.

• Can design thinking be applied to AI implementation?

Yes. Design thinking is particularly valuable in AI implementation because AI projects fail most often at the human-adoption stage rather than the technical stage. Empathize and Define help teams identify which problems AI should solve. Prototype and Test help validate AI tools before organization-wide rollout, reducing the risk of adoption failure.

• What is the most important step in design thinking?

Most practitioners cite Empathize as foundational because every subsequent step depends on how accurately you understand the people affected by the problem. Skipping or rushing this step typically produces well-built solutions to the wrong problem. That said, Define is where many teams fail in practice: translating research into a problem statement that is specific enough to generate useful ideas.

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