Four in five organizations deploying autonomous capability cut headcount. The cuts did not produce the returns. Here is the AI investment strategy that does.

Roughly four out of five organizations that have piloted or deployed autonomous business capability have reduced their workforce. That number is not the surprising part. The surprising part is what sits next to it: the rate of workforce reduction is nearly the same at organizations reporting strong returns and at organizations reporting modest or negative ones. [SOURCE: Gartner, “Gartner Says Autonomous Business and AI Layoffs May Create Budget Room, but Do Not Deliver Returns”, 2026-05-05, survey of 350 global business executives at organizations with at least $1B revenue, Q3 2025] Read that twice, because it dismantles the most common AI business case in the market. The cuts are not what separates the organizations getting returns from the ones that are not. Both groups cut at about the same rate. Something else is doing the work. Helen Poitevin, a Distinguished VP Analyst at Gartner, put it about as plainly as an analyst firm puts anything: “Workforce reductions may create budget room, but they do not create return.” [SOURCE: Gartner, “Gartner Says Autonomous Business and AI Layoffs May Create Budget Room, but Do Not Deliver Returns”, 2026-05-05]

The Contradiction Is Not Hiding in the Research

It would be satisfying to claim we spotted something the analysts missed. We did not. They named it, out loud, in a press release, and they named the alternative too. Poitevin again: “Organizations that improve ROI are not those that eliminate the need for people, but those that amplify them by aggressively investing more in skills, roles and operating models that allow humans to guide and scale autonomous systems.” [SOURCE: Gartner, “Gartner Says Autonomous Business and AI Layoffs May Create Budget Room, but Do Not Deliver Returns”, 2026-05-05] The same research program had already flagged the tension from the other direction. In the 2026 CEO survey, 80% of CEOs said they expect AI to force a high or medium degree of change to their operational capabilities, and 39% already count AI agents as employees. Those same CEOs rank people as a top capability for organizational resilience, and the survey results simultaneously show a reluctance to hire alongside an expectation that AI agents will serve as a lower-cost workforce. [SOURCE: Gartner, “Gartner Survey Reveals 80% of CEOs Say AI Will Force Operational Capability Overhauls”, 2026-04-23, 2026 Gartner CEO and Senior Business Executive Survey, n=469 global CEOs and senior business executives, fielded March through November 2025] So the contradiction is documented, published, and available to anyone who reads the press release. The research rates people as the top resilience capability and records a reluctance to hire them. Which makes the interesting question a different one. Not “why has nobody said this,” but “why does knowing it change so little?” The advice has been public since May. The spending pattern has not moved.

Cost Is Legible. Capability Is Not.

Here is the honest mechanism, and it is not stupidity. A headcount reduction produces a number you can put in a board deck this quarter. It has a date, a dollar figure, and a name attached to the decision. Investment in skills, role design, and operating models produces a number nobody can isolate. If it works, the organization simply keeps functioning well, which looks like nothing happening. One side of the ledger is instrumented and the other is not. That asymmetry, not a failure of insight, is what keeps the pattern running after the research says it does not work. Leaders are optimizing for the thing they can prove they did. We see this in the rooms we facilitate constantly. The AI investment gets a business case, a steering committee, and a dashboard. The capability investment gets a training budget line and a hope that people will figure it out. Then, six months later, the tools are deployed, the work has not actually changed, and everyone is puzzled about why.

The Capability You Cut Is the One That Governs the System

Gartner’s framing for what autonomous business actually requires is a useful piece of vocabulary: humans to guide, govern, expand, and transition autonomous capability. [SOURCE: Gartner, “Gartner Says Autonomous Business and AI Layoffs May Create Budget Room, but Do Not Deliver Returns”, 2026-05-05] Sit with those four verbs, because every one of them is a judgment activity. To guide a system you have to know what good output looks like. To govern it you have to recognize a bad answer that is confidently phrased. To expand it you have to see which adjacent work is genuinely similar and which only looks similar. To transition to it you have to run a change with people who trust you. None of that is knowledge you can hire in a quarter, and none of it lives in documentation. It is accumulated judgment, and judgment gets built by doing consequential work and being wrong in front of someone more experienced. This is where the cut and the capability collide. The work being automated first is precisely the work that used to build the judgment. The junior analysis, the first-pass draft, the routine review: developmentally rich, individually unremarkable, and the easiest thing in the organization to hand to a model.

Where the Pipeline Narrows, and Where It Does Not

Some of this compression is already measurable. Researchers at the Stanford Digital Economy Lab found that early-career workers aged 22 to 25 in the most AI-exposed occupations have seen a 16% relative decline in employment since generative AI came into wide use, while employment held steady or grew for less exposed workers and for more experienced workers in those same occupations. [SOURCE: Brynjolfsson, E., Chandar, B., Chen, R., “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence”, Stanford Digital Economy Lab, 2025-11-13] Be careful with that finding, though, because the evidence is genuinely mixed and the firm-level studies cut the other way. Research pairing corporate AI vendor spending with workforce data found that the heaviest adopters ran employment about 10.2% higher than companies that had not yet adopted, and that their entry-level share rose by 1.15 percentage points relative to non-adopters. [SOURCE: Simon, L.K., Kharazian, A., Stevens, R., “The Companies Spending the Most on AI Are Also Spending the Most on Humans”, Ramp Economics Lab \+ Revelio Labs, 21,000+ US companies over the 24 months following adoption, 2026-06-30] Those two findings are not actually in conflict, and holding both is what makes this useful. Exposure and adoption are different things. Whether an occupation is the kind of work a model can do is a different question from whether a given company bought the tools, and the companies investing most aggressively in AI appear to be growing rather than shrinking. Which puts the question back inside your own organization, where it belongs. The aggregate does not tell you whether the work you automated this year was draining or developmental. Only you can know that, and most organizations have never asked. When it does happen, nobody decides to break the apprenticeship pipeline. Nobody holds a meeting about it. Each individual choice is locally correct, and the compounded effect is an organization that will be short of senior judgment in three to five years, precisely when its autonomous systems most need governing. The timing is the cruel part, and it is why this failure mode survives. The consequence arrives on a horizon longer than the tenure of the executive who set the pattern in motion. It looks like a talent shortage in 2029 and gets explained as a market condition.

What the Organizations Getting Returns Do Differently

The pattern among organizations that treat capability as infrastructure rather than overhead is consistent, and it is more specific than “invest in people.” They redesign roles before they automate tasks, not after. The sequence is the whole thing. Automating first and then asking what the remaining humans should do produces a job description assembled from leftovers. They decide deliberately which friction to remove and which to keep. Some friction drains people and should be engineered out without ceremony. Some friction develops them, and removing it is how you end up with a fast organization full of people who cannot make a call. Telling those two apart is the actual executive skill of this decade, and it cannot be delegated to a tools decision. They keep consequential work in human hands on purpose, and they say why out loud. A junior person who understands they are being handed hard work in order to develop experiences it completely differently from one who suspects the organization simply has not gotten around to automating it yet. And they instrument the capability side, even crudely. Any measurement of whether judgment is developing beats the current default, which is to measure the cost side precisely and the capability side not at all.

The Number Worth Watching

Gartner’s own longer-range read is that autonomous business becomes a net-positive job creator by 2028 to 2029, on the argument that demographic decline and high-stakes, trust-dependent customer moments keep human talent central to running and scaling these systems. [SOURCE: Gartner, “Gartner Says Autonomous Business and AI Layoffs May Create Budget Room, but Do Not Deliver Returns”, 2026-05-05] If that holds, the organizations cutting hardest right now are not getting ahead of a permanent shift. They are taking a temporary cost credit and paying for it with a capability gap that arrives exactly when demand for that capability returns. The correction does not require slowing AI investment. Nobody needs to be talked out of buying the tools. It requires treating the capability to guide, govern, expand, and transition those systems as the thing that makes the tools worth anything, and funding it like infrastructure rather than like a training line item. The analysts have said it. The next survey will show who was listening. Want to explore what closing this gap looks like inside your organization? Learn more about Voltage Control’s AI transformation work.

The post You Cannot Cut Your Way to an Autonomous Business appeared first on Voltage Control.

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Alyssa Coughlin, Director and Chief of Staff for Autodesk’s Data, AI, and ML Platform organization, about what it actually takes to move a large engineering company from AI-enabled to AI-native. Coughlin describes how bottlenecks have shifted away from writing code toward code review, deployment permissions, and design decisions now that AI has made execution fast and cheap. She walks through concrete changes her org has made, including abandoning two-week Scrum sprints for Kanban flow, moving from PRDs to spec-driven development consumable by both humans and AI, and building shared “knowledge graph” brains to catch duplicated work before it ships. Throughout, she frames change management as a balance of carrot and stick, arguing that engineers aren’t losing their jobs so much as shifting from author to orchestrator, and that managers themselves must stay hands-on with the tools to coach effectively. She closes by describing AI as an amplifier that exposes organizational seams rather than a fix, so the real work is continuously finding and addressing the friction it reveals.

Show Highlights

[00:00:00] Framing The New Friction Series
[00:02:15] Shifting From AI Enabled To AI Native
[00:04:00] Bottlenecks Move Upstream To Reviews And Design
[00:06:45] Balancing Change Management With Carrot And Stick
[00:09:15] Mourning The Coder Identity As Orchestrator
[00:11:30] Ditching Scrum Sprints For Kanban Flow
[00:16:00] Building An Autodesk Brain To Avoid Duplication
[00:19:45] Spec Driven Development As A Living Document
[00:22:15] Roles Blurring Across PM, Engineering, And Design
[00:25:30] Closing Thought: AI As An Amplifier

Links | Resources

Alyssa Coughlin on LinkedIn
Voltage Control

About the Guest

Alyssa Coughlin is Director, Chief of Staff for the Data, AI, and ML Platform organization at Autodesk, a role she describes as spanning operating model design, change management, and helping the organization transition from using AI as a tool to treating it as a genuine working partner. Her background is in project management, holding a PMP credential and years of experience across Scrum, Agile, and Kanban practices before landing in this role. She speaks about leading her organization through practical shifts like moving from Scrum to Kanban, adopting spec-driven development, and building shared knowledge graphs to reduce duplicated work, all while managing the human side of a fast-moving AI transition. She frames her core philosophy as treating AI as an amplifier of both strengths and weaknesses in an organization, and as a partner rather than a replacement for human judgment.

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. Today, I’m with Alyssa Coughlin at Autodesk, where she is the chief of staff for the organization that builds the data and ML platforms and agentic AI infrastructure. Welcome to the show, Alyssa.

Alyssa Coughlin: Thank you so much for having me. I’m excited to be here.

Douglas Ferguson: Yeah, it’s great to have you. It’s been a minute since we spoke. In fact, I think you embarked on a cross country trek, so lots of change, not only the AI, but you’re having change of environment as well.

Alyssa Coughlin: Yeah. Life wasn’t hectic enough, so why not throw in a cross country move?

Douglas Ferguson: There you go. Amazing. So yeah, I’d be curious just for starters, what are some of the things you’ve been noticing as AI has been more and more prevalent in the workforce?

Alyssa Coughlin: Yeah, the shift has been really interesting. What my teams have been primarily focused on recently is transitioning from AI enabled to AI native. And so what I mean from that is AI adoption is really just do people use the tools? That’s where you start looking at token maxing and are they vibe coding? Are they connecting MCPs? Are they doing all the things, versus AI native is making AI part of the process in the workforce holistically. So from beginning to end and kind of redesigning how we work to incorporate AI as a partner and not just as a tool. So that shift has been really interesting because there’s obviously a really big people and process component to that. And so trying to navigate what that looks like in all of its forms end to end is where we’re really focused right now. And it’s interesting. I mean, we call this podcast the New Friction and the friction’s definitely part of that process.

Douglas Ferguson: What friction have you noticed most prevalent or the most maybe difficult to get your hands around?

Alyssa Coughlin: I think an interesting shift we’ve seen twofold when it comes to friction. One is embracing friction. In an AI native environment, friction becomes another metric. It shows you where there might be seams in your processes or your workflows that weren’t really surfaced before. So looking at that friction and figuring out why it exists, how does being AI native play into that and what does this new way of working that we’re learning based on this friction become? And then related to the friction is, as we shift to AI native, one of the really big things we’re seeing is the bottlenecks shift. So not all that long ago manually coding was fairly time intensive. And so now with AI assisted coding, that’s not the bottleneck anymore. It’s shifted further upstream. So we’re seeing it in reviews, we are seeing it in deployments, things as mundane as permissions. All of a sudden, there’s this backlog of PRs that need to be reviewed before they can be pushed to prod. We’re also seeing it on the other end of the spectrum from an XD perspective. So it used to be that XD and coding were moving at similar speeds, but while XD is leveraging an AI native mentality, there’s still a lot of human intervention in that practice right now. And so that’s another new bottleneck that we’re seeing, is that determining what that true user experience should look like takes longer than actually coding that experience. So we’re seeing the bottlenecks and we’re seeing the friction move to different parts of the workflow than where they’ve historically existed.

Douglas Ferguson: I like to say that these frictions were already there, now they’re becoming the breaking point. Or because we’re sending more stuff through a process that was maybe broken to begin with or something we hadn’t spent a lot of time rehearsing or getting good at.

Alyssa Coughlin: Yeah. And I think a really interesting way to look at it is, AI is an amplifier in all aspects. It amplifies the positive, but it also really amplifies the things that maybe aren’t working as well in your workflows. And so it’s really hard to hide anything in the era of AI. You’re moving faster, you’re doing more. And so these seams and these bottlenecks and these frictions just become more apparent.

Douglas Ferguson: And the role as chief of staff, how is it impacting you personally? Are you being asked to help identify some of these problems, address them or facilitate the conversations where it’s getting addressed? How are you showing up to meet this moment?

Alyssa Coughlin: Yeah, it’s all of the above. Part of becoming AI native is helping to identify what processes need to shift and where. But it’s also really rethinking the operating model and our strategies. We need an operating model that is based on spec driven development and working with AI, not as a tool, but as a partner. We affectionately refer to AI as the intern because it’s quite capable of doing many things, but it doesn’t quite have the expertise to make the hard decisions. And so figuring out what does that look like? How do we shift our ways of working? One of the things we’re doing right now is we are transitioning away from Scrum to Kanban because that’s more conducive to an AI native working speed, especially when you can have multiple agents working on multiple repos simultaneously. So a lot of my job has been trying to figure out what this new way of working looks like in an AI native environment. And obviously there’s a huge people component there. So figuring out how to balance the change management, because obviously everything in this AI revolution, evolution, whatever you want to call it, is moving at a breakneck speed. But if you aren’t thoughtful with how you’re pairing that with change management, the human cost of that can be massive. So in my job, I’m trying to balance how we work from a day-to-day basis, what needs to change, what can we keep, and what do we honestly just need to reinvent?

Douglas Ferguson: So you talk about balance and change management, and it sounded like a big piece of that was just trying to prevent overwhelm or maybe even revolt from folks. And so I’m kind of curious, what are some of the principles or tactics that you’re thinking about when you’re looking at balancing that change management?

Alyssa Coughlin: Yeah, I think the core principle of successful change management is buy-in. It’s getting people to move along with you. And in order to do that, it’s been a little bit of a balance between a carrot and a stick. And so by carrot, I mean making people really excited to meet this moment. I mean, this is one of our biggest technological shifts since the invention of the internet. This is going to completely change the entire world, hopefully for the better, but we’re still sorting that part out too. It makes so much more room for innovation and experimentation and creativity. So really trying to get people to hone in on that excitement and that opportunity, but balancing that with the tough love. AI’s here to stay. We work in tech. It’s not going anywhere. If you don’t like it, I’m going to be honest, this might not be the job for you anymore. So trying to make sure people have an honest understanding of the reality of the situation. But that does not necessarily translate to doom and gloom, it translates to opportunity.

Douglas Ferguson: Yeah. And when you think about the cross section of the org, the team, what would you say… Is this resistance prominent or is it a small subset? And follow-up question would be, I’ve seen a wide array through these conversations and dinners and things I’ve been having, a wide array of causes for the resistance. So I’m kind of curious what you’ve been noticing? How prevalent is the resistance and then what are some of the causes that you’ve been seeing?

Alyssa Coughlin: Yeah, I think the prevalence continues to decline. I think at first, I mean, you had tech CEOs who were like, “Oh no, this is going to be apocalyptic. This is going to take everybody’s jobs.” Even this week, Bill Gates is like, “Ah, I don’t know about this.” But I think the more we have understood what working with AI truly means, the more we’re able to realize that it’s not getting rid of jobs, it’s not getting rid of work, it’s changing what that work looks like. And so helping people understand that shift, I think has led to a continual decline of people who aren’t really bought in on the whole AI native transition. So I think that’s one side of it, the people who are like, “AI is going to take my job.” And one of my favorite sayings is, “AI will not take your job, an engineer who’s better at using it will.” So it’s really a matter of do you change? Do you transition? Do you meet this moment? And so that’s the second category of resistance that we do sometimes encounter. And I would say it’s not very frequent, but you do have some people who are just like, “This isn’t what I signed up for. I am an engineer. I’m a coder. I want to put my headphones on and be heads down all day just cranking out lines of code.” And that’s not the job anymore. And so you do have some people who I think are still mourning what being an engineer used to mean, and they’re struggling a little bit to adapt that you’re not a coder anymore, you’re a systems thinker. You’ve moved from the author to the orchestrator. Not everyone wants to be the orchestrator.

Douglas Ferguson: Yep. And a lot of that has to do with the skills they developed over time because to be an orchestrator is a lot more akin to a manager. You got to delegate, you got to evaluate, you got to organize, and you’re not necessarily the one doing the direct work, you’re reviewing the work. And it’s just different skill sets. And engineers have the transferable skills to be able to evaluate good engineering work. In fact, they have to do pull requests quite often, and that’s a key part of the job. That’s just becoming more and more a part of the job. And so I do think there is something to this idea of having this identity loss and even acknowledging the fact that people need to go through that mourning process.

Alyssa Coughlin: They do. And you see that with any change. I’ve heard a lot of people compare this AI revolution to the industrial revolution, and honestly, that scared a lot of people and it made a lot of people think that they were no longer going to be relevant, their jobs weren’t going to matter. We still need just as many, if not more people. It’s what they’re doing that is different. So we have replaced some of that more trivial work with machinery, but that just elevated what the human needs to do. And so this is really similar and it’s not a direct correlation of the progression isn’t human to AI, it’s human with AI, human doing, human directing, human orchestrating, human judging. So the human’s more important than ever because until we have AGI, there’s no reasoning coming from AI. It’s an order taker. And so you still need that human judgment in the

Douglas Ferguson: Loop. Yeah, it’s so true. One example would be your shift to Kanban. So talk about that for a moment. Was Kanban something that you were familiar with prior or is it something new that you had to learn as the team were starting to adopt it?

Alyssa Coughlin: Yeah, my background is in project management and I’m a PMP, so Waterfall, Agile, Kanban, they were all kind of already in my repertoire, but working in tech for the last decade, everything’s obviously been very Scrum heavy. And even though a sprint is traditionally two weeks, in the era of AI native, that’s a long time and that sort of time boxing doesn’t work. And even when you think about the Scrum ceremonies and how you do sprint planning and you have two weeks of work that you’re committing to, and that does not leave a ton of time for experimentation. So it’s not that when we switch to Kanban, Agile’s dead, it’s more the actual practice is shifting. So we’re moving away from a more regimented Scrum ceremony, Scrum timeline, Scrum metrics into we have a prioritized backlog and you pull things out, you work on them. If you get stuck, you put it into a blocked row and it gives us a chance to honestly be more agile and more nimble than we could with Scrum. And with Scrum, you’re committing to story points and these blocks of work where honestly, it might make more sense to just prototype a really small piece of that, learn from it, either keep building or try something new. And so I just don’t feel like Scrum leaves enough room for experimentation. And that’s one of the really big unlocks with being AI native, is you’re able to transition more so away from a very regimented way of working to a way of working that’s a little bit more flexible. So I think in the spirit of Agile, like hypothesize, build, test, learn, adjust, constantly inspect and adapt, this is actually more conducive to that than traditional Scrum.

Douglas Ferguson: I’ve used Kanban for years as a CTO. I was a big fan of it just because it’s nimble. And one of the things I’ve noticed when organizations move from a rigid adoption of Scrum, because some folks have a loose adoption, but especially the ones that have a rigid adoption when they transfer to Kanban, they tend to step more intentionally or more purposely into the Agile principles. Because it’s funny that often the Scrum rituals are preventing people from truly adopting the principles.

Alyssa Coughlin: Right. It’s meant to be a framework, but it really in practice becomes more of a gospel, and people get so stuck in, “This is my job today, these are my story points.” And honestly, it worked in the old way of coding, but in the new way, it’s just better to be nimble. And the more you experiment, the more you learn, the more you correct, the more you’re gaining context. And if you’re doing it correctly, if you are learning with your AI tools, then they’re learning context as well. So it’s really a way to expand the knowledge graphs of both the humans and the AI. It’s a better way of working in this environment.

Douglas Ferguson: Yeah, that’s really interesting. And I’m curious how meta that gets for you. Do you have the AI observing your Kanban board and giving you feedback on how you’re thinking about the roadmap and sequencing of tickets? Is it involved in those steps currently?

Alyssa Coughlin: A little bit. We’re just starting to build some of that out. Because that’s been honestly the most challenging part of shifting to Kanban is losing the traditional Scrum metrics. So how are we measuring the productivity and the efficiency of our organizations without those? Because the thing is, not every experiment’s going to be a winner. And so we’re not necessarily looking for how many things got pushed to prod, token maximizing, it’s so easy to fudge. It doesn’t necessarily translate to business outcomes. So we have been struggling to try to figure out how do we measure success in this new way of working? And one of the things we are doing is, we have created a formula within Jira to help us gauge how well our individual teams are transitioning over to Kanban. And it’s been really interesting because we’ve got the whole gamut. We’ve got some that still really have one foot in the Scrum lane and they’re struggling with this change. They like the predictability, they like the standardization. And then on the other end of the spectrum, we have teams that are fully embracing Kanban and they’re enjoying the kind of Wild West of just pick a story out of the backlog, grab your agent and have at it. And then of course, everything in between. So I think it really comes back to the change management. It’s everywhere, because we are completely changing operating models, we’re completely changing operating rhythms. Every person who would wake up and be like, “I know exactly what my day’s going to look like,” now has to grapple with, “Well, let’s see where it takes me.” Which as a chief of staff, it’s every single day for me. When somebody asked me to describe my job, I’m like, “I don’t know. What day is it?”

Douglas Ferguson: “What day of the week is it?” So one of the things that I find liberating with Kanban was the fact that the measurement was about the cumulative flow diagram. And so we were basically estimating our average lead time. So it’s not necessarily what are we going to get done or planning this two-week chunk, but just on average, it’s taking X days to get something to production right now, and we can easily see that because the system is designed just for the flow of work. The release train is the work, right? And so we can just say, “Hey, it looks like these things are running at about this speed.” And so if we look at the backlog, we would guesstimate this is how long it would take to get through. And even though it feels less predictable because it’s less like we’re putting this chunk of thing on this thing and planning it in a super structured way, it’s almost more predictable in the sense that we have a sense of what the system is doing. Even though it’s a bit more emergent, we have a handle on its throughput. And so even if we rearrange the deck chairs a bit, we know it’s going to flow through.

Alyssa Coughlin: Yeah. And I think in addition to that, the value proposition changes. With Scrum, you don’t really review whether or not something worked until your full two-week sprint is up and you’re in your sprint review. And then that’s where you review it, you get customer feedback, you take a look at whether or not something worked. And so the emphasis within Scrum is really on completion of work versus I think a huge opportunity with Kanban is the emphasis switches to learning. It’s not, “Did I complete X amount of story points in this sprint?” It’s, “Did I learn? Did my agent learn? Was I able to share that learning?” That’s a really big part of it. And that’s another organizational challenge I’m struggling with a little bit, is when you start moving at such a fast speed, identifying and catching silos and duplications becomes a lot harder. And so that’s kind of coming back to where an AI native environment really does expose the seams in your organization. Where are things connected versus where might there be a little bit of a gap? And so that is another consideration with Kanban, is people are just kind of grabbing things and running with them and I love it. Let’s move fast, let’s learn, let’s break things. But at the same time, let’s not build the same thing in three different places.

Douglas Ferguson: Yeah, absolutely. Yeah. I wonder how much of that are you anticipating that agents might help with? For instance, scanning for duplicate work, or de-duplicate even some of the things in the backlog to even get ahead of it or even just finding issues with stuff that, because we talked about the review cycle getting so heavy and that creating such a burden on the team. So I’m just wondering about some of these kind of adjacencies even though it’s not necessarily a step in the product development lifecycle, but it’s the tooling that helps make things keeps things from falling apart, if you will.

Alyssa Coughlin: Yeah. And that’s really where context and knowledge graphs are so important. And it’s not just the technical documentation, it’s also the organizational knowledge, so much of which lives in either disparate disconnected locations or it just lives in people’s heads. And so the more we can pour that knowledge into context and knowledge graphs that AI can read, AI can turn into semantic search, the more we can help mitigate some of these circumstances. So one of the things we are doing in my organization is each group is creating their own brain as we call it. And so every organization is expected to develop some form of a knowledge graph, which can then be connected to all of the other organizations’ knowledge graphs and really create an Autodesk brain for us. And so obviously it’s down the line, we’re still working on just even gathering that knowledge and connecting the right MCPs and figuring out what that looks like. But eventually, ideally the AI can help us catch some of these things before we go too far down the road. And then we realize in some sort of review or QBR that we built the same thing twice.

Douglas Ferguson: Yeah, I’m super curious about these kind of. It’s almost like AI DevOps or developer experience kind of stuff. How are we investing in the infrastructure surrounding the experience of building the software? And because it’s not just about site reliability and how we deploy and get stuff live, but it’s also to your point, how are we identifying that there’s not duplicate tickets in the backlog or that one developer built a feature, but the AI decided to be super thorough, so it superseded three other things in the backlog because hey, it was there, it did it, it figured it out, and how do we discover that stuff and patch it? And it’s like I’m even experiencing that to some degree on my own. I built a harness and I have some agentic employees that are helping augment the team and support them on a lot of laborious, tedious kind of things or even stitch together stuff that was just really difficult to do because there was multiple systems involved. So one of the things it does is, as we’re building stuff, the agents will identify things that maybe we decided to set aside for later or that an idea that we came up with that we said, “Let’s just not focus on that now.” And so my backlog just grows and grows. And so every now and then I’m looking at, I’m chatting with the agents about what we might pull from the backlog. And so much of it’s been superseded because we just fixed it along the way while addressing some other thing. And sometimes the agent didn’t even call it out. I’m noticing in a pull request or we’re even noticing it when I go into planning cycle. So I think there’s a million ways to solve it, whether you have some sort of Damon that’s scanning the system constantly for these things or even a rule set in your planning phase that’s saying, “Hey, let’s double check that nothing’s been released to production or to the dev environment that actually addresses this issue.”

Alyssa Coughlin: Totally. And I think the most important thing about that entire point is the significance of the human in the loop. AI is just a worker bee, it’s going to keep going forward. It’s not necessarily stopping to think about is this the right thing? And so that’s really where the human expertise shines. And when you’re working with AI, telling it what to do is just as important as telling it what not to do. These are your processes, these are your checks, these are your guardrails, here’s your harness. All of that requires a human making the call. There’s human judgment and that is really what powers all AI development. Another thing that we’ve been doing is we’ve been requiring everyone to move towards spec driven development. So instead of just writing a PRD for a human, we’ve transitioned to writing specifications so that it is consumable by both humans and AI. And it’s more about bringing AI into the loop, treating AI as a partner and a member of the team and learning how to work with it versus just treating it as a tool. But yeah, completely to your point, the AI is just going to run a muck if there’s not a person in there to give it some coaching and some guidelines.

Douglas Ferguson: Absolutely. And not necessarily in a bad way, you just might not get efficient business outcomes. Because you hear all these horror stories around, “Oh, AI broke into hugging phase.” Or AI went and did this thing that was unasked for. And even in the most innocuous ways, it might be, to your point, spinning up duplicate work or doing things that are redundant. So I think it behooves us. And this is where people mourn the loss of the craft. I think the craft is just shifting. How are we being thoughtful and intentional about how we deploy these tools in ways that don’t consume tokens egregiously and our efficient use of them? And it’s not super simple. It’s not always like sometimes reaching for the cheaper model is a more expensive route to go.

Alyssa Coughlin: Yeah. And I think that’s been a really interesting industry trend, is everybody is really moving towards those open weight models because faster’s not always better right now. Just because you’re working with AI and it can build five things in the amount of time it used to take you to build one, well, if all five of them are wrong, have you still accomplished any business outcomes?

Douglas Ferguson: Right.

Alyssa Coughlin: Faster isn’t always better. And so again, that’s where that human expertise comes from. And so I think when people have this existential mindset, I think they’re really missing that component. And I think they’re missing a really exciting opportunity in that the scope boundaries of positions are really shifting and melding during this era of becoming AI native. For example, I’ve got product managers who are coding prototypes now as requirements. So they’re kind of teetering into that engineering section a little bit, and engineering is teetering a little bit more into kind of a people and operations perspective because they are that orchestrator now. And then you have XD is now kind of a little bit of everybody’s job because everybody needs to be thinking about that end user experience and that customer value. And so what it means to be an engineer, what it means to be a product manager, what it means to be a designer, I think all of that’s going to shift. And I think having a mindset of this is opportunistic versus existential, it’s going to be really critical in people succeeding in this environment.

Douglas Ferguson: Have you gotten to the point where role definitions have started to officially change or it’s still in this kind of liminal space where behaviors are shifting, but we haven’t necessarily documented it in a role or title shift or a responsibility, maybe not documented yet? I’m kind of curious where things are on the journey so far.

Alyssa Coughlin: Yeah. Well, we’re a large enterprise, so actually changing people’s job descriptions in the formal HR way has absolutely not taken place. However, the ways of working, everybody uses AI, everybody is a creator. That is very much in place. As far as what is the scope boundary for product management versus engineering, we haven’t necessarily formally defined that. Because I think we’re still figuring it out. Right now we’re still trying to learn what does our business model look like with AI? And then I think from there we can back into, okay, what roles are needed to support this model? So there’s definitely a spirit of experimentation. There’s definitely product management getting in there and kind of vibe coding something versus trying to explain it in a PRD. But we haven’t gotten to the point yet where we are formally shifting those roles. We joke that these big companies are big ships and they don’t turn quickly.

Douglas Ferguson: Well, also it kind of gets back to this idea of experimentation versus exploitation. And if you want to be innovative, you got to stay in this experimental mindset. And titles and definition and specificity is more about optimization. That’s when you’re in the exploitation phase. We’re starting to exploit the knowledge and the innovation that we learned and landed on. And so I think prematurely defining those things would be a hindrance, because it’s harder to adapt once we’ve locked in new titles or new definitions.

Alyssa Coughlin: Yeah. I think the biggest shift we’re seeing right now is actually with managers. Our expectations for them are shifting quite a bit in that all managers, nobody is just a people manager anymore. Our managers are expected to also be part of the team, to be technical experts as well, and to be able to lead the team, but to also be able to contribute. Because down the line when we eventually have these teams of people and agents, a manager’s going to have to be able to manage both. And so having them jump into the work and jump into the technology is really important. It’s also requiring a shift in their mindset and how they manage and that it’s not just about outputs anymore, it’s about outcomes. So again, coming back to token maxing is a really easy way to fake productivity. It’s not just how many things did you put out there? You’re really coaching your team and rating your team based on what business outcomes did they unlock? And to make that a reality, you do have to leave room for experimentation and you have to leave room for failure. I think freedom to fail, honestly, reducing the fear of failure. It’s something that really thrives in the startup world and it’s not as readily embraced in large companies. But I think that’s a really big transition we’re seeing as well, is it’s okay to try something, that’s kind of the whole point of this rapid fire Kanban way of working, is it’s not a failure because you still learned something even if it didn’t work. And learning is how we really want to measure success going forward.

Douglas Ferguson: Yeah, I hear that and I think of two things that I’ve seen across clients and just listening and paying attention to where folks are at these days. And one is, this managers have to get comfortable with these tools if they’re going to coach through the use of them, because if you think back to the days of coding with punch cards, if that’s all you know, then using modern abstracted languages through a keyboard, it’s going to be very difficult to manage a team doing that work if all you know is punch cards. And so I think managers need to upskill and be comfortable. And it’s not just upskilling, it’s really a behavioral paradigm shift. So learning the new ways of leaning into these tools and working in the ways that these tools can open up for you is really important for folks to experience firsthand. And then second, I think that it’s really critical that managers understand the new competencies that are critical for people to survive in this era, in this moment. For instance, we already talked about you’re shifting to the orchestrator. And so if engineers need to learn how to delegate because now they’re orchestrating, or if they need to get better at eval, the manager cluing in on those things and knowing where the got yous are and knowing what’s uncomfortable and use the word friction again, understand and diagnose the friction, they’re going to be a lot better at coaching their team through those moments too.

Alyssa Coughlin: Yeah, absolutely. I mean, I know a lot of companies are starting to creep up on Q4. And so when we think about giving feedback to your teams, how can you accurately do that if you no longer understand their work? And that aspect of being able to be an efficient manager and mentor, being hands-on is really important. And the other benefit is not only are they learning the tools, but they’re working with their team’s technology firsthand, which again allows everyone to think through that end user perspective. Call it eat your own dog food, drink your own champagne, whichever trope you prefer. But it gives them a chance to really experience what it’s like to be a member of their team and what it’s like to consume the technology they’re creating.

Douglas Ferguson: Absolutely. A few things I wanted to come back to, you mentioned the shift towards spec driven development, and you also mentioned that product managers are creating basically vibe coded prototypes. Are you seeing that the prototypes become part of the spec that is given to the LLM or what’s that kind of ritual or the shape that these specs are taking?

Alyssa Coughlin: Sometimes yes. We’re still very early in the journey and there’s definitely a change management aspect to it. I had one PM make a really great prototype that did end up going into production, but engineering originally kind of ruffled their feathers at how dare you. So coming into that, everybody has an opportunity to be more AI is an amplifier mentality. We’re still learning the best way to incorporate spec driven development into our existing development workflows. We don’t want to throw the baby out with the bath water. There’s a lot of good processes and tools already in place that we want to learn how to weave AI into. And I think what’s important as well when you’re going through these processes is slapping AI on everything is not being AI native. That doesn’t fix your problems. It’s really inspecting what does your workflow look like? What does your operating model look like? What are tasks or areas that you could leverage AI in and then free up your human bandwidth and capital to focus on more challenging topics that require that human judgment? So we’re still navigating best practices and we’re still learning, but it seems to be, it’s becoming pretty well embraced, and it’s becoming the operating norm across our organization. So yes and no. Some of it’s gone into prod, but we’re still learning and that’s kind of part of the fun of this adventure.

Douglas Ferguson: Yeah. I ask a question out of curiosity because I found that even if the prototype is throwaway, it tends to be really valuable during the spec process. I’ve always been a big fan of visual specifications. If we can show what we imagine the product looking like before we build it it’s a lot easier to build it because then we can start poking holes on edge cases or where are we going to need to put an error handling or what if someone’s name is really long? It might push out of the space. You can start asking a lot of these questions that are a lot harder to ask when you just read about something. And so I’ve found taking the visual spec or prototype or different mock-ups or even customer research plus some technical maybe architectural things and requirements and constraints, putting it all together and letting the AI have access to that, super powerful when we start putting together the plan for how we’re going to build.

Alyssa Coughlin: Yeah, definitely. And I come back to if you learned something, it wasn’t a failure. And that’s such an important mindset shift when it comes to AI and when it comes to experimentation and innovation, you don’t have to get it right for it to be valuable. You could have learned exactly what not to do or you could have learned where you had a gap that you want to fix in the next iteration. And that’s the really cool thing about specs too versus requirements, is they’re kind of living documents. They’re always evolving. You’re always adding to them as you learn more. And so they’re valuable not only because they are consumable by both humans and agents, but they learn.

Douglas Ferguson: Yeah, absolutely. I love this evolving living document concepts. It also comes back to the knowledge graph you were talking about where if teams have knowledge graphs, departments have them, orgs have them, it gives us a lot more power to use these tools in a more deeper fashion. I think a lot of organizations that are kind of handcuffed are ones where the agents don’t have access to the knowledge they need, to the tacit information that are in the heads of the humans. And if we don’t unlock that stuff, if people are hoarding that stuff because they’re afraid, it’s really going to be more limiting than anything. And it doesn’t bode well for the individuals that are doing that, because they’re only delaying the inevitable because the more that we can share and safely share and understand what’s safe to share, the more that we can understand how these things work, the better guardrails we have in place, all that stuff. So yeah, I totally agree. As we start to head toward wrapping up, you mentioned quite a few changes to the nature of the work, this adopting spec driven development, shifting to Kanban. Anything else shifting or changing as it relates to the product development life cycle and how folks are putting the software together?

Alyssa Coughlin: I think my biggest takeaway is lean into learning. So instead of going into a project or approaching a team with what’s the plan, rephrase that as what are we trying to learn or what did we deliver versus what did we unlock for our end users? It’s transitioning away from this very black and white, I deployed, I executed, it’s done, to how can I completely evolve and grow and transition as I learn? So it’s much less of a regimented stagnant process. It’s ever evolving. I mean, consider yourself to be one of the specs. You’re learning and you’re growing and you’re connecting new data and making new insights. And my favorite way of thinking of AI is, we have transitioned from a bicycle to a car. So a car can take us the same place as a bicycle can take us and it can get us there much faster. But what’s really fun about the car is it can go so much further to places that we’ve never been able to go before. And I think going in with that learning and that experimentation mindset is more important than any of the tools themselves.

Douglas Ferguson: Yeah. Maybe another way to think about that too is, once you get there, you’ll have a lot more energy to enjoy the place that you got to.

Alyssa Coughlin: Way less sweaty.

Douglas Ferguson: Yes, that’s right. Amazing. Well, as we come to a close here, I’d love to leave you with an opportunity to offer up a final thought to our audience.

Alyssa Coughlin: Yeah. Well, I think my biggest recommendation to everyone is, AI is an amplifier in every capacity and lean into that. Don’t be afraid of it. Don’t be afraid that it’s going to highlight the bad right along with the good. See all of it as an opportunity to figure out where that real friction is, where do you need to focus your energy, and then move on. Once you fix that one place, AI is going to amplify something else that needs to be fixed. And so it’s a really great partner for evolving your people, your technology, your processes. It’s going to show you everything and all you have to do is just respond.

Douglas Ferguson: Amazing. Well, it was a pleasure chatting with you. Thank you for spending some time with us and we’ll chat with you again sometime soon.

Alyssa Coughlin: Always a pleasure. Thanks for having me.

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

The post AI Doesn’t Remove Friction, It Just Moves Upstream appeared first on Voltage Control.

Why Your AI Deliverables Aren’t Creating Alignment

A year ago, a strategy deck took a week to build. A journey map took a workshop, a synthesis sprint, and a designer with strong opinions about Post-its. Today an AI tool can produce a passable version of either in minutes. That should be good news. It is, and it isn’t. When the artifact gets cheap, the artifact stops being the point. What was scarce, the deliverable, is now abundant. What was always scarce, the shared understanding that made the deliverable worth having, is exactly as scarce as it ever was. Jim Kalbach saw this coming from the customer experience side, not the AI side. In the third edition of Mapping Experiences, out this July, he writes that “AI-driven workflows and agents should give us more time to be more human.” He arrives at the same conclusion Voltage Control has been building toward all year: when execution time collapses toward zero, the constraint that matters is no longer producing the work. It’s aligning the people who have to act on it. Picture the scene that plays out in a hundred conference rooms this quarter. A transformation lead needs a customer journey map for a steering committee meeting. Two years ago that meant a week of interviews, a synthesis session, and a designer laying it out in Figma. Today it means a prompt and a coffee break. The map that comes back looks credible. It has swimlanes, pain points, emotional highs and lows, the works. It goes into the deck. The meeting happens. Everyone nods. Nothing changes, because nobody in that room actually built the thing together, and building it together was the part that mattered.

The gap AI just made worse

Long before AI touched a single Miro board, journey mapping had a credibility problem. Kalbach cites Gartner analyst Cassandra Nordlund’s finding that 82 percent of organizations had created a customer journey map, but only 47 percent were using it effectively (Gartner, cited in Mapping Experiences, 3rd ed.). Call it the 82/47 gap: most companies can produce the artifact. Fewer than half know what to do with it once it exists. That gap did not close because AI showed up. It widened, because AI made the easy 82 percent even easier. A team that used to need two days to draft a journey map can now get a first pass in an afternoon. The habit of stopping there, of mistaking a finished-looking document for a finished conversation, gets reinforced instead of interrupted. Polished is not the same as validated, and AI is very good at polished. This is the same trap showing up across every AI transformation program we see, not just customer experience work. Strategy decks, technical architecture diagrams, org redesign proposals, onboarding plans. All of it can now be generated fast enough that “we have a draft” stops meaning “we’ve thought this through together” and starts meaning “the model produced something plausible.” The organizations getting into trouble are not the ones using AI to draft things. They are the ones who stopped noticing the difference between a draft and a decision. Kalbach names the failure mode bluntly. Teams build “wall maps with butterflies and unicorns” that “failed because no one facilitated conversations around them, they were documentation, not dialogue.” That line was true before generative AI existed. It is more dangerous now, because the documentation has never been easier to produce and never looked more finished doing it.

It’s not the map, it’s the mapping

Kalbach’s reframe for the third edition is the sharpest sentence in the book: “it’s not the map, it’s the mapping.” The artifact was never supposed to be the deliverable. It was supposed to be the byproduct of a room full of people building shared understanding together, out loud, in front of each other. This is not a new idea inside Voltage Control. It’s the same argument underneath our own past writing on facilitation, and it’s flattering, if a little strange, to see it reflected back from a different discipline entirely. Kalbach’s book cites our 2024 piece on journey mapping directly, calling Voltage Control “a leading facilitation consultancy” and quoting our line that “this alignment of cross-functional teams around a shared understanding of the user experience is a catalyst for change.” Two people who never coordinated landed on the same conclusion from opposite starting points. That kind of convergence is worth taking seriously. Here’s what makes this interesting for the AI moment specifically. For twenty years, the map and the mapping were bundled. You could not get one without doing the other, because producing a decent map required weeks of interviews, synthesis, and cross-functional buy-in. The bundling did the alignment work almost by accident. Now AI unbundles them. You can have the map in ten minutes and skip the mapping entirely. The organizations that keep winning will be the ones that notice the unbundling and deliberately rebuild the mapping. The rest will ship more polished artifacts to teams that are no more aligned than before, and wonder why nothing changed. Rebuilding the mapping does not require throwing out the AI-generated draft and starting over by hand. It requires changing what the draft is for. Instead of walking into a steering committee meeting with a finished map and asking for sign-off, a facilitator puts the AI-generated draft on the wall at the start of a working session and asks the room to argue with it. Where is this wrong? What did the model miss because it wasn’t in the room for last quarter’s customer complaints? Where does this map contradict what sales just heard on three calls? The AI draft becomes the provocation that gets a cross-functional group talking to each other, not the artifact they file away. The mapping happens in the disagreement, not in the generation.

What IBM already knew

Enterprise Design Thinking, IBM’s internal transformation effort, ran into a version of this problem at massive scale, and Kalbach’s account of it is instructive. IBM did not try to change culture by getting people to believe something different. They changed what people did every day: playbacks, sponsor users, hills as a way to frame outcomes instead of features. The mechanism, not the mindset, is what moved. “You don’t change culture by changing what people believe,” Kalbach writes of IBM’s Enterprise Design Thinking effort. “You change culture by changing what people do.” That is a hard thing to hear if your instinct is to fix AI adoption with a better all-hands or a sharper vision statement. It is a much more useful thing to hear if you’re trying to figure out what to actually change on Monday. Applied to the mapping problem, the fix is not a policy that says “always facilitate a session before finalizing a deliverable.” Policies get skipped under deadline pressure, especially when the AI-generated draft already looks done. The fix is a changed default in how the work gets built. The AI-generated map becomes the opening move in a session, not the closing one. The habit that has to change is what a team does the moment the first draft appears on screen. That is a small design change with a large consequence. It moves the moment of AI use earlier in the process, before the deliverable is treated as settled, instead of later, as a stand-in for the settling. Teams that make this shift stop asking “did we generate a map” and start asking “did the room leave with the same understanding it walked in without.” Those are very different questions, and only one of them AI can answer for you.

The mapmaker becomes the facilitator

Kalbach makes one more claim that lands squarely in Voltage Control’s territory: the person who used to be called a researcher or a designer “needs to become a facilitator,” because “good facilitation feels invisible.” The skill that used to be a specialty is becoming a baseline expectation for anyone who produces shared artifacts for a living. That shift is not limited to UX teams. It is happening to strategists building AI transformation roadmaps, to product managers synthesizing customer feedback, to anyone whose job used to end when the deck was finished. AI collapsed the time it takes to build the deck. It did not collapse the time it takes to get a room of stakeholders to actually agree on what the deck means. That gap has to be filled by someone, and increasingly it is being filled by whoever happens to be holding the AI-generated draft when the meeting starts, whether or not they ever trained for it. “Good facilitation feels invisible” is a harder standard than it sounds. It means noticing who has gone quiet in a session and pulling them back in before the loudest voice in the room becomes the map’s point of view. It means treating disagreement about the AI-generated draft as the useful part of the meeting, not an interruption to get through before lunch. It means knowing when to let the room sit with a hard question instead of rushing to the next slide. None of that shows up in a prompt. All of it determines whether the map that comes out the other side reflects what the business actually knows, or just what one person typed into a chat window at ten the night before. This is the real implication of “it’s not the map, it’s the mapping” for an AI-native organization. The scarce skill was never cartography. It was convening. AI just made that fact impossible to hide behind a good-looking deliverable. The friction this creates is real, and it is worth naming plainly. Most knowledge workers were never trained to run a session. They were trained to produce an output and hand it off. AI just handed them a faster way to produce the output, without handing them the skill of getting a room to actually use it. That mismatch, a workforce equipped to generate and unequipped to convene, is exactly the kind of gap that determines whether an AI transformation program sticks or stalls. It shows up as meetings that end in polite agreement and no behavior change, as roadmaps that get rewritten every quarter because nobody actually committed to the last one, as decks that get more beautiful while decisions get slower.

The scarce skill isn’t creation, it’s convening

None of this argues against using AI to draft the map, the deck, or the plan. Draft it. Draft it fast. The mistake is treating the draft as an ending instead of an opening. A journey map that never gets argued over in a room is not a finished artifact, it’s an unfinished conversation with a nice layout. The organizations that internalize this will start every AI-generated deliverable the same way: as raw material for a facilitated session, not as a substitute for one. They will staff for facilitation the same way they staff for AI tooling, because the two are now inseparable parts of the same workflow. They will train the strategist who used to just build the deck to also run the room where the deck gets argued over, because that skill is no longer optional once the deck itself is nearly free. The organizations that don’t will keep producing beautiful, unused artifacts, and keep being surprised that alignment didn’t follow. That is the actual choice AI transformation programs are making right now, whether they realize it or not. Every budget line that goes toward another generation tool and none toward the capability to convene people around what that tool produces is a bet that the 82/47 gap will somehow close itself. It won’t. It has had years of journey-mapping practice and now generative AI to close on its own, and instead it widened. The 82/47 gap was always a facilitation gap wearing a documentation costume. AI didn’t create that problem. It just removed every excuse for not seeing it clearly. The map was never the deliverable. The mapping still is, and now it’s the only part of the work an AI can’t do for you.

The post It’s Not the Map, It’s the Mapping appeared first on Voltage Control.

The Scoreboard Nobody Updated

Walk into any leadership offsite this year and you will hear the same number celebrated: how fast the team is moving now that AI has entered the workflow. Sprint velocity is up. Time-to-first-draft is down. The deck that used to take a week now takes an afternoon. None of that is false. It is also no longer useful. Execution speed stopped being a scoreboard the moment every competitor got the same acceleration. When everyone’s code writes itself, everyone’s report generates instantly, and everyone’s analysis runs in minutes, speed stops being a differentiator and becomes a floor. You cannot out-execute a market where execution is free. The organizations still tracking execution velocity as their headline metric are measuring the one thing that no longer separates winners from everyone else. Worse, they are measuring it instead of the thing that actually does.

What Speed Used to Buy You

For most of the last century, execution speed was a reasonable proxy for organizational health. A team that shipped fast was usually a team that had already done the hard work: they knew what to build, they agreed on why, and they had cleared the internal friction that slows weaker teams down. Speed was the visible tip of a mostly invisible alignment. That correlation is what broke. AI decoupled speed from alignment. A team can now produce fast output with zero internal agreement about direction. Six people can each generate a confident, polished strategy document in an afternoon, and the fact that all six documents exist quickly tells you nothing about whether the team agrees on which one is right. If anything, it hides the disagreement, because everything on the page looks finished. This is the trap. Leaders keep watching the speed dial because it used to mean something, and it is still the easiest number on the dashboard. But a rising speed metric in an AI-accelerated org is no longer evidence of health. It is evidence that people are producing. It says nothing about whether they are producing the right thing, together, on purpose. Miro CEO Andrey Khusid named the mechanism from the vendor side at Miro’s Canvas 26 keynote: “Six people, six AI copilots, six confident, polished, completely divergent strategies.” Nobody is slow. Nobody looks unaligned. The speed dial reads great right up until the moment those six plans collide in a room and someone realizes the team was never actually agreed on anything. It was just producing quickly in six different directions at once.

Alignment Velocity: The Metric That Actually Differentiates

Here is the replacement metric, and it deserves a name because it deserves to be tracked with the same discipline leaders once reserved for sprint velocity: alignment velocity. Not how fast your team executes. How fast your team reaches genuine consensus and commits to a direction. Genuine consensus is the operative phrase. Not a meeting that ended in a vote. Not a document that got a thumbs-up in Slack. Genuine consensus is the point where the people who disagreed loudest can now articulate the decision in their own words, without translation, and act on it without a second private conversation about what was really decided. Alignment velocity asks a different question than execution velocity does. Execution velocity asks: how fast can we produce? Alignment velocity asks: how fast can we agree on what is worth producing, and how fast does that agreement actually stick? The organizations that win the next decade will not be the ones who moved fastest. They will be the ones who redirected their measurement, their meetings, and their leadership attention from the first question to the second. This is not a soft metric dressed up to sound rigorous. It is the metric that predicts everything execution velocity used to promise: fewer reversals, less rework, faster real-world delivery, because the team is not quietly building on a foundation half of them never actually bought into.

Polished Is Not Validated

The clearest recent articulation of why this matters came from an unlikely source: a tooling vendor’s own product keynote. At Miro’s Canvas 26 event this spring, Miro’s Kendra Wilkins, Product Director for AI Prototyping, named the exact failure mode organizations are walking into: “AI made it incredibly easy for something to look finished… But polished does not mean validated.” Her sharper line cuts even closer to the bone: “You’re building a hundred wrong things quickly, and you don’t even know it.” That is the mechanism. Before AI, the slowness of production was an accidental safeguard. It took long enough to build something that misalignment usually surfaced before the thing shipped. Someone would ask a clarifying question in week two. A stakeholder would push back before the deck was finished. The delay was annoying, but it was also a checkpoint, and checkpoints are where validation happens. Remove the delay and you remove the checkpoint. You do not just save time. You lose the moment where someone would have said, wait, are we actually agreed on this. Wilkins named the consequence directly: “That buffer is completely gone. Because while you’re planning, your competitor is shipping.” This is exactly why alignment velocity has to be measured on purpose now. It used to happen for free, as a side effect of how slow everything was. Nothing forces it to happen anymore. If you are not tracking it, you are almost certainly not doing it, no matter how fast your team looks on paper.

Where This Shows Up First

You will not see this in the projects everyone agrees are important. You will see it in the quiet ones: the mid-tier product line, the internal tooling decision, the vendor pick that nobody thought was worth a full alignment process because it “obviously” had one right answer. A product team splits a roadmap question across three sub-teams. Each sub-team uses AI to model options fast, and each converges on a confident recommendation within a day. On paper, that looks like extraordinary velocity: what used to take a quarter of workshops now takes seventy-two hours. Leadership rolls the three recommendations up expecting to rubber-stamp a synthesis. Instead they get three plans that quietly assume three different answers to the same unresolved question about what the product is actually for. Nobody argued about it, because nobody realized it was still unresolved. AI gave each sub-team the confidence to skip the conversation that used to force the disagreement into the open. The rework that follows does not show up as a rework metric. It shows up as a quarter of “replanning,” which is what a low alignment velocity looks like when nobody is measuring it.

Why Leaders Keep Scoring the Wrong Thing

If alignment velocity is the metric that matters, why does almost no leadership team track it? Because speed is easy to count and alignment is hard to see. Execution speed shows up in a dashboard automatically. Tickets closed, drafts produced, cycle time, all generated as a byproduct of the tools people already use. Alignment velocity requires someone to actually watch how a decision gets made: who spoke, who stayed quiet, whether the quiet ones agreed or just gave up arguing, whether the decision that got announced on Friday is the same decision people are executing against three weeks later. There is also a career incentive at work. A leader who reports rising execution speed looks decisive and modern. A leader who reports “we slowed down to make sure everyone actually agreed” sounds, to an impatient board, like an excuse. The visible metric rewards the behavior that looks like progress. The metric that predicts real progress requires the discipline to look past the appearance of speed and ask what is actually underneath it. This is not a call to slow down for its own sake. Slow is not the goal any more than fast is the enemy. The goal is a scoreboard that measures the thing that determines whether your organization’s speed is pointed anywhere useful.

How to Actually Measure Alignment Velocity

Alignment velocity is trackable, and it does not require new software. It requires leaders to ask three questions with the same rigor they once applied to sprint retros. Start with commitment lag: the gap between when a decision is announced and when the people affected by it can restate it accurately, in their own words, without checking with each other first. A short gap means real alignment. A long gap, even with a fast-looking decision date, means the announcement outran the agreement. Track the reversal rate next, meaning how often a “final” decision gets quietly relitigated, walked back, or silently ignored within a month of being made. A high reversal rate is not a sign that your team moves fast and adapts. It is a sign that the original alignment was never real, and the org is now paying twice for one decision. Finally, watch when dissent surfaces. Teams with high alignment velocity surface disagreement before a decision is finalized, in the room, out loud, where it can actually change the outcome. Teams with low alignment velocity surface the same disagreement after the fact, in side conversations, in the hallway, in the version of the meeting that happens once the real meeting has ended. The volume of dissent is not the signal. The timing is. None of these require a dashboard. They require a leader willing to sit in the room and notice what execution velocity metrics were never built to catch. This cannot live only at the top. The VP who owns the roadmap sees speed. The people closest to a decision see whether it stuck. Alignment velocity has to be tracked by whoever is closest to where the decision actually gets used, then rolled up, the same way you would never trust a single director to self-report their own team’s execution speed without a shared definition underneath it.

What’s at Stake

The organizations that keep optimizing for execution speed will get exactly what they are measuring: more output, produced faster, with no better odds that it was the right output. They will ship more polished work than ever, and quietly rebuild more of it than ever, because nothing in their scoreboard was ever designed to catch the difference between agreement and appearance. The organizations that shift to alignment velocity will look slower on the old dashboard and faster on the one that actually matters. Fewer reversals. Less rework disguised as iteration. Decisions that survive contact with the people who have to execute them, because those people were actually part of making them. The difference will not show up in this quarter’s output report. It will show up in how many of this year’s “finished” projects are still standing next year, and how many of them quietly had to be rebuilt because nobody checked whether the room actually agreed before it started moving fast.

Where to Start This Week

Do not wait for a new dashboard to start measuring this. Pull the last three decisions your leadership team called “final” this quarter. For each one, ask the three people closest to executing it to restate the decision in their own words, without conferring first. Where the restatements diverge, you have just found your real commitment lag, weeks or months after the fact, which is exactly the delay you are trying to shrink going forward. Then pick your next contentious decision and change one thing: build in a deliberate pause before you call it final, long enough for someone to voice the disagreement they are quietly sitting on. That pause is not the opposite of speed. It is the only way to find out whether the speed you already have is pointed anywhere real. Stop reporting how fast your team executes. Start reporting how fast your team actually agrees, and how well that agreement holds. That is the number that was always supposed to predict the other one.

The post Stop Optimizing for Speed appeared first on Voltage Control.

The Only Trust Move That Closes the AI Perception Gap

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

The Mandate Reflex

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

What 12% Means

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

Shadow Adoption Is the Message

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

What “Involve” Actually Requires

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

The Hallways Know Before the Meeting Does

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

The Sequence Is the Prescription

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

What This Looks Like in Your Organization

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

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

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Jeff Chow, Chief Product and Technology Officer at Miro, about where friction is showing up inside product teams as AI reshapes how work gets done. Jeff describes how PMs prototyping in high fidelity, designers coding, and engineers orchestrating agents are collapsing old role boundaries, and argues the underlying tensions — who owns a decision, who gets paged at 2am, how a designer receives feedback — aren’t new, just amplified. Much of the conversation centers on decision-making as the real bottleneck: Jeff makes the case that 10x individual output doesn’t translate into 10x business results unless organizations can also accelerate cross-functional alignment and cascade decisions with their underlying context, not just their conclusions. He and Douglas dig into Miro’s bet that a shared visual canvas — carrying both artifacts and a decision log of why choices were made — is the connective layer that keeps AI-assisted work and human teams moving together instead of fragmenting into isolated single-player sessions. The two also trade observations on viral AI adoption patterns inside organizations, the psychology of design crits applied to AI-generated work, and the early signs of “ephemeral UI” and custom, vibe-coded widgets reshaping what software teams expect to build for themselves.

Show Highlights

[00:00:00] Introducing New Friction With Jeff Chow
[00:02:15] Where Friction Is Showing Up At Miro
[00:05:45] Guarding Against Dehumanizing AI Mandates
[00:10:30] Lo-Fi Versus Hi-Fi Prototyping Debate
[00:15:20] Visual Specs Replacing Product Requirement Docs
[00:18:40] Chasing Magic Moments Of Invention
[00:22:10] Decision Bottlenecks And Cascading Alignment
[00:26:30] The Canvas As A Shared Context Layer
[00:31:00] Multiplayer AI Adoption Across Departments
[00:36:15] Ephemeral UI And Custom Widgets

Links | Resources

Jeff Chow on LinkedIn
Voltage Control

About the Guest

Jeff Chow is the Chief Product and Technology Officer at Miro, where he oversees a visual collaboration platform used by tens of millions of people to align on strategy, product development, and decision-making. He describes himself as a “recovering founder” and a self-professed “ways of working nerd,” with a career spent building customer-centric digital products and leading teams through periods of disruption. Before Miro, he held senior product and executive leadership roles, including CEO and CPO at InVision, and product leadership positions at companies focused on travel and consumer technology. Jeff is especially focused on how organizations make and cascade decisions at scale, and on using a shared visual canvas to keep human teams and AI working from the same context rather than fragmenting into isolated, single-player workflows.

Transcript

Douglas Ferguson: Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear, it moved, and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about. I’d like to introduce you to my conversation partner today, Jeff Chow, chief product and technology officer at Miro. Welcome to the show, Jeff.

Jeff Chow: Hey, Douglas. Great to be here.

Douglas Ferguson: Yeah, thanks for coming and really looking forward to the conversation. And this is the New Friction series, and we’re talking about how AI is reshaping roles and the ways that organizations work and how the friction is shifting and appearing in different places. So I’d love to just start there. What are you noticing at Miro as you’re building the products of the future, as we’re thinking about how AI impacts work and as you’re building these tools, what are you noticing friction-wise? Where are folks struggling or what’s showing up as new problems to solve as we think about the product development life cycle?

Jeff Chow: Yeah, I mean, first and foremost, probably my favorite topic. I’m a little bit of a ways of working nerd, and anytime there’s disruption, I’m always like, “Ooh, what is this going to do?” And not being dramatic, I think we all would all agree this is likely the mother of all disruptions in terms of challenging ways of working. So always great for that mindset. Yeah, I mean, I think it’s a very interesting thing for Miro because we both have a product that is driving some transformation of how teams work, and we ourselves are going through that transformation. So it gets very meta within Miro. I think the standard ones that you’re seeing around friction is starting to present itself. And it’s like product managers are now prototyping, designers are now coding, developers have product mindsets, they have their orchestrating agents as opposed to coding themselves. And so I think there’s that just fundamental skills shift is driving a rethinking of the PDLC. And then maybe I think from a pure friction perspective, I see that that’s actually introducing very interesting tensions maybe we’ve always seen. It’s like how does a designer and a product manager align when the product manager creates a high fidelity workable prototype to begin with? There’s a lot of psychology that gets into that around how do you actually align? If a designer can check in front-end code, what does that mean around a lot of the sensitivities around code management, SLAs, who actually gets paged in the middle of the night? And so there’s always a lot of those things. But again, I think there’s also the glass half full, which is like, it’s worth the friction because it’s a really interesting time to drive impact for an organization.

Douglas Ferguson: Yeah. I mean, folks that enjoy organizational design, it’s a perfect moment to really reflect and think about, well, what are the impacts here and how do we really put folks in positions and give them context so that they can be successful?

Jeff Chow: Yeah, 100%. And I would actually just maybe one other thing to say is this is kind of what we’ve always wanted. And I would say the opportunity, and maybe that’s sometimes when we get into the minutia of the pain points, we can kind of lose the plot and we have to stick to that, which is the, isn’t it great that teams are able to co-create even more together? Isn’t it great that we can actually reduce the doing of the work so we can think more about the highest impact work? And how do we harvest that more? And my job as a leader in org is to try to keep the change to a glass half full take as opposed to a glass half empty take.

Douglas Ferguson: Yeah, and I agree. And the thing I’ve noticed that sure you have folks that are, let’s say, resistant that that goes with any kind of change, and it’s just attending to that and understanding the values that are driving people’s needs. The thing that I think that’s unique in this moment that we had to attend to as leaders is if people are feeling dehumanized by this stuff. A great example is I ran into my neighbor not long ago and she actually works in public sector and they were starting to adopt some AI stuff and her work. And it was a project that the team had been advocating for a while and the leaders finally said, “Oh yeah, we should do this because the AI told us to do it.” And so that’s very dehumanizing because they’ve been advocating, let’s do this, let’s do this. And then now the AI is saying doing it so the leaders are on board. And so we have to watch out for those things. We really want people to feel empowered and-

Jeff Chow: Yeah. I will say though, it’s really interesting because of, I actually don’t think anything is new around the friction. It’s just amplified. It’s like as a leader, if a team gets a mandate, say we wanted to change a strategic direction or we wanted to shift our product priorities, if the team goes to their team and says, “Hey, we’re redoing this because Jeff said so,” they absolutely would be demoralized and whatever. And it’s no different from AI saying that. And I just think that that’s what we’re feeling. There’s nothing new about product managers coming with a high-fidelity wireframe or clickable prototype and a designer rolling their eyes.

Douglas Ferguson: Yeah, just more of them can do it.

Jeff Chow: More of them can do it. I used to be that person. I would use keynote because I’m old and back in my day I would use magic move and keynote and I’d have these amazing transitions and I’d have a mobile skin and I’d be like, “Hey guys, I have an idea.” And they’re like, “Oh, here we go.” And so it’s not new, it’s just scaled. And I think so that means things that we should have probably addressed in the past. It’s like, well, how do you get a designer to yes and that process? And how do you get Jeff with the really beautiful keynote to be willing to kind of boogie and expand their thinking as a way to communicate, not just like, can you please build this? That’s literally human nature since the dawn of time.

Douglas Ferguson: Yeah, that’s fascinating. I was also thinking about when you have PMs checking in code, I love your analogy or your point around who’s going to get paged in the middle of night, but it ultimately comes down to the rigor of the process and are we following the standards? And I think that gets a lot more difficult when you have folks that aren’t completely trained in those things or aren’t thinking about those concerns because they’re dispositionally oriented somewhere else. And so I’m just kind of curious, what are y’all noticing there as far as how to open it up so that you can support those kinds of things? Because it is valuable if a PM can just change a typo, why pull in a ticket for a developer to have context switching just to do that work? But at the same time, how do we have the guardrails to keep things smooth?

Jeff Chow: Yeah. We’re going through that journey right now, and I would say we by no means have nailed it, but ultimately I think it’s about getting people to be willing to say, “I can see how this could be really great on the other side.” And then it is a deep, deep, deep amount of empathy. You might not have had to actually have the discussion around who holds the bag, but guess what? Engineers talk about that all the time. Another team contributing to your code base because we should have platform level thinking. Again, not new. Again, I’m a PM by trade, so I can make jokes about PMs. But having a PM with that attitude of how hard could it be to. And then all of a sudden that permeates. Of course, engineers are going to be like this, and then they’re going to be like, “Let me tell you all the bad things that could happen,” even if it’s just a text change. “Have you thought about localization? Ah.” Stuff like that. So I think there’s a little bit of, there’s a really dynamic change if people feel like they’re in it together. If they’re being told, “Not going to work,” and things like that. So we’re going through that. So one is getting everyone to accept that, is this a problem we want to solve together? Yes. Intuitively, designers from the dawn of time have created these amazing experiences that have been air quotes approved. And then by the time it hits production, it looks nothing like it. Now you actually can have a designer iterate 25 times until they feel like they’re really happy with what they can do. And that’s an amazing opportunity. And that frees up the other front end engineer to do some amazing things as well. So I think the opportunity is huge, and I think it’s really plays to the strengths of each individual. So the change management is worth the journey.

Douglas Ferguson: I think about prototyping culture, and the reason it’s so powerful is that you can share your ideas quickly and get feedback and correct them before you’ve really fallen in love with them and they’re too resistant to change. And so I wonder if there’s a flip side here with things being so high fidelity and people being able to iterate so much by themselves, is there a risk of getting into this false sense of confidence before you even share it with anyone that you’ve fallen in love with it and so you don’t want to change it?

Jeff Chow: We talk about this all the time. We have our own prototyping product. We integrate with all the prototyping products. And a lot of our users at Miro are of the kind of UX experience design, concepting and validation. So it’s really a big topic. And I’ll tell you where we’ve gone, one, there’s no hard and fast rule. And so I think there’s two paths. One is tried and true practice, start lo-fi. It keeps the barrier there and even create design systems where it’s intentionally lo-fi so that when you’re sharing it, you’re visually communicating, it is at the right fidelity. That’s a really powerful one. There’s also one which is what we’ve seen a lot is the visual communication of the art of the possible, telegraphing what is possible. Actually, high fidelity experiences really help, especially for what we would call brownfield development, existing work, you’re trying to try something out. And it’s actually inspired higher levels of ambition from designers too, by the way. It’s like designers, product managers, even engineers, they have this idea in their head that will solve a strategic problem that as a business we’re trying to solve. I’ve seen more invention coming out of this, and that’s amazing. And so on that track, what we’ve decided is, okay, what are we trying to avoid? Well, that first high fidelity prototype is more a visual storytelling mechanism than it is like, I want to check this. It’s like if we accept that, then what’s the next step that would be bad? Probably treating it like a design crit. All of a sudden people are just throwing rocks and you’re like, “That’s horrible.” So we’re trying to create these kind of collaborative techniques that invites ideation because that’s the step. The step is like, okay, maybe you need to diverge and converge, but you started a great conversation. And so if you treat it like that, then we have these capabilities like variations. It’s like, okay. And our ways of working is like, okay, you share a prototype, you tell your story of what it’s trying to solve. But when people give feedback, especially around the design, we first start with variations to get truly divergent ideas of solving the same problem. And then people are now reacting to what they like and don’t like against a sample size of three or four. And it becomes less personal. It’s not a crit. It’s just actually more of a open kind of ideation session. And I think that’s one way of maybe not reverting to just the lo-fi to high-fi. It’s more of like, okay, what’s the new way of what I call boogying together and creating that experience? And to us, it’s all psychology. It’s like if a product manager comes with a prototype and you’re like, “I want to build this,” then the designer’s like, “Okay, I’m going to critique. Why is there a sixth button? That’s crazy.” And you’ve lost the plot. You’re not actually talking about the value you’re trying to communicate. You’re just nitpicking the pixels.

Douglas Ferguson: Yeah. And also, I think it’s fascinating if folks sit with the opportunity or the challenge or the problem that it’s solving, and they critique that. It’s like, “What is this prototype telling us about the questions we’re trying to ask? And how satisfactory is it at answering those questions?” And to your point, are there variants, other ways to get at that problem? Because the prototype in a way can help us understand a challenge or problem or opportunity better. That might be the value it brings to us. It’s this genius spark of innovation that we’re getting from this developer that now the whole team can start, to your point, boogieing on it and coming up with other variations and things. And the thing that I’ve found about the high fidelity, there’s two things I really love. It’s the folks that really have trouble thinking or communicating in visuals. Then they can write their specifications. They can sit there and describe it all day long or even just speak into the AI and prompt it and then generate these great visuals and it democratizes that ability to communicate in visuals. And the more things are common and similar and familiar, then we’re not critiquing the differences in style or presentation. It’s the merits of the idea.

Jeff Chow: Yeah. I’ll say 100% agreed. I think it’s truly democratizing work in a way that I find amazing. The other way we think about it, and I think this is along your lines, is there’s a little bit of work theater to writing a product requirements doc. You have a kickoff and you’re like. Because most of the time people have an idea in their head and then they try to reverse engineer the how to the why and the what. And then they’re being very academic about it. And so we’re sort of like, “That’s cool.” And you have the classic founder, and I’m a recovering founder myself, you’re always painting in the air being like, “What if we did this?” And then you rely on the product team to abstract the requirements from that and all this other stuff. I think there’s something really relieving about just saying, “You know what? Share the how.” And it’s actually a visual spec. It’s like, great. And as a team, we’ll abstract the requirements from that. We’ll talk about this. Because it’s a real human, a natural human behavior of when you have an idea, you kind of know exactly a little bit of a thing. And so you might as well just share it. We don’t have to go through the work theater of it. And then we can align on what are the moving parts, what are the principles, what are the et ceteras? And even when you say that, you have a visual example of what the principles might be. So it’s just way easier for people to align. Whereas the other approach, which is the classic PDLC, is you start with the product requirements doc, you then wait a couple of weeks to get your first design. Engineers don’t actually have a visual proxy. They’re architecting in the void. It just feels like looking back, it just feels very antiquated at this point.

Douglas Ferguson: Yeah. So it’s definitely more fun, more explorative. And it reminds me of this awesome innovation concept of, and it goes like this, the main impediment to innovation is your first good idea. You get to something good that works and it’s hard to get that out of your head. But the system you’re describing where it’s like you just, okay, there’s the how, let’s just use AI to just manifest that quickly, and then let’s decompose it together from the materialized endpoint.

Jeff Chow: Yeah, that’s right. That’s right. And I think that’s for us, I think there’s this kind of north star, the how becomes immediately launched and we’re all fine. And that might be the case for certain JIRA ticket level improvements, if you will. But I do think keeping the actual prototype as not the design handoff, but as the visual communication vehicle, it’s a pretty good mental model to get teams to understand, okay, I get it. It used to be pros. Now it’s like, okay, it’s a way to symbolically have that. And then I think the change management, the friction, if you will, is we just have to be honest with ourselves. Probably that product manager does want to ship it. And probably a designer’s having trouble with that. They want the ones who created the first version. And how do you handle that level and create a great collaborative environment despite that? And that’s where I go back to, that has always been the friction. So we have to solve it anyways.

Douglas Ferguson: Yeah, I mean it comes down to identities and how folks think of themselves and what their responsibilities are. And if we can break those things down and reimagine them, then I think it creates a lot more possibility.

Jeff Chow: Yeah. I’m sure you’ve seen this in the past, but there’s nothing more magical than when somebody creates an idea that is so cool, that is so amazing that the front end engineer can’t shake it out of their mind and they would just want to build it. And the team just kind of rallies. And because there’s oftentimes not that, those magic moments, that’s when magic happens. Somebody does something that unlocks everything and then the teams happen. I’ve for my entire career have been chasing that endorphin hit. It’s always fun. When that magic happens, business trajectory changes, all this. It’s true invention. And the reason I get so excited about this stuff is this has actually now democratized that. It’s like, okay, anyone can do that. And we at Miro have seen these kind of magic moments where somebody’s just like, it’s usually a very introverted junior or somebody. And they’re like, “Hey guys, I had an idea.” And they share it, they record a talk track on it. And then you can just see people like, “Oh my God, yes, let’s do that.” And all of a sudden the energy shifts. And I think that’s, especially if I look at the companies we talk to, they’ve built for decades an optimization culture. And now they’re sitting in the face of like, “Oh wait, it’s pretty competitive right now, pretty existential. The only way out of this is innovating your way out of it.” And I do think that’s the starting place here. It’s like, okay, there’s some really great things. But I do also think it’s not about displacing. AI is not displacing the team. It’s just giving you a stronger, sharper mission and a sharper why on what you’re doing and clarity of purpose. It’s like, okay, I could run 25 experiments on sizes of this button, or I can actually do something that actually changes the trajectory of this organization. And I think that’s really exciting.

Douglas Ferguson: Yeah, it’s definitely shaky ground for folks because it’s just kind of uncertain how roles are going to shift. But I’m also of the camp that I don’t think we’re going to see massive job loss, but we’re definitely going to see massive job redefinition.

Jeff Chow: Yeah. Yeah, for sure. I think about all the roles blurring 100% is changing. I think a lot about product operations and every product ops leader I talk to both has a little bit of an existential feel to it, but also an excitement because it’s like, okay, I didn’t necessarily like pushing teams to follow processes versus making sure that we are actually creating something that can scale an organization and deliver impact. And so I think these kind of shifts are pretty deep and exciting if you make it that way.

Douglas Ferguson: One piece of friction that we see a lot, and I’m curious what you’ve noticed, middle managers especially, or just anyone that’s in a position to receive output from others are getting overwhelmed because it’s so easy to generate output. So the reviewers, the people that have to evaluate and review things are just getting hammered with so much content and stuff to review. I mean, luckily the tools allow us to review things faster, but ultimately if we want human eyes on things, that is a bottleneck right now.

Jeff Chow: For sure. I mean, I think you can’t get 10X business outcome with just 10X individual velocity. And a lot of that is around accelerated decision-making. And it could be reviews, it could be decisions, it could be alignment. A lot of the times it’s where we see the bottleneck is the cross-functional alignment, like strategic decisions, et cetera. And so we see that as a huge opportunity. And when we think about when you’re talking about the middle layer, et cetera, I would say the honest truth is a lot of the times the middle layer is more people and process managers. And now’s the time that. And frankly, most of them don’t want to just be that. It’s just like one day we woke up and our ways of working were treated that way. And now it’s like, no, actually we need you to make 10 strategic decisions a week. And how do you do that? And I think that’s fantastic. That is true, the ball moves forward on every layer of the organization. Fantastic. Now, how does that happen? That’s going to be a tough one. And that’s where collaborative decisioning is back to the old bottlenecks are still there, which is if you have a lot of reviews, then you have to do it asynchronously. Very difficult to have asynchronous feedback loops happen at pace to make a decision. If you want to do it synchronously and it’s a really tough problem to solve, how do you empower our team to share it? Or how do you create the right scaffolding of decision options, pros and cons? Because there’s no perfect solution. And oftentimes organizations tend to iterate 93 times to get to the, there must be a silver bullet solution. And guess what? We make money because we have to make decisions that aren’t easy to solve. And so I think that’s just what we have always had to do, and it’s an important opportunity. And we obsess over this at Miro because ultimately we view the canvas as that alignment and decisioning layer and accelerating it. So we’re trying to figure out ways to make it easier to get people to do that.

Douglas Ferguson: Yeah. I’ve been super excited about where Miro’s headed AI-wise and how the Canvas can provide such an interesting alternative to this kind of linear text chat history context. Because I saw a good example of that earlier today in a mastermind I was running where one of the members was sharing some of their clawed workflows. And I was just kind of looking at how they. I was noticing a lot of it was baked into one project and they were shifting between these different sessions and stuff and was just reminded how tough it is for folks to just sit back and think and design how they’re going to manage their context. Where Miro is much more intuitive when you’re looking at the canvas and things are sitting out there and you can just marquee, select whatever you need to go into the context. Or you have to set up flows so it’s visual what’s going where. I think it’s going to be a game changer for folks. The more and more folks that start to work in that way and start to realize how much I’m shaping the context visually and intentionally I would say. Because when you’re in a chat, you have no control. You can’t say, ignore this piece of the context, right? It’s like it’s there.

Jeff Chow: Yeah, for sure. At the end of the day, what’s the gap towards delivering company level impact on the AI transformation is making decisions and cascading those decisions. It’s always been the case, you just have to make so many more faster decisions and you have to pivot more often around just given how fast the world is transforming. And so our point of view is a single shared space with all the context visually where teams can align, they can get the context, AI can help produce that. Even if you started single player, even if you started with say Claude Cowork and you developed a point of view, there still is a gap around how do you socialize, align on that? And then as a team that’s delivering impact that can cascade, how do you make that decision, get really crisp on it? And how do you cascade that throughout the organization? And I think that’s for large enterprise organizations, that’s one of the hardest things. Because you imagine when you make a strategic shift, you then have to socialize it for the next level leaders. Maybe you have to do it all hands, then you have to cascade it, you have to talk about the implications. A month later, you’re still seeing in product review the OG product strategy. And that’s just not fast enough. So we really don’t talk enough around alignment decisioning and operationalizing and cascading those decisions so that everyone understands that without feeling like they were told what to do. I think. And so how do you cascade a strategic why shift? How do organizations at scale be nimble? These have always been a hard problem. And now it’s just the magnifying glass on those friction points are just going to just get amplified.

Douglas Ferguson: Yeah, I think the cascading is an interesting one to think about, especially when you design scaffolding for that to happen. If you’re intentional about how you’re meeting and your rituals, the canvas can provide an interesting place for that to happen almost instantaneously as you think about things flowing. You have to be careful about where you put things and what goes where. And that comes into that intentional scaffolding. The thing that dawned on me not that long ago, because I was doing a workshop with some folks around flows and prototyping and whatnot, and they were kind of mesmerized by it all, but they came back to this concern or this friction around, well, I’ve got a bunch of memory in our corporate ChatGPT setup, or I’ve been using Claude and the memory’s there. And then my thought was like, man, that’s just one node in the entire organization. And the canvas becomes this connective layer and through MCP. And if things are cascading and flowing that way, it’s really powerful. And it’s funny because at the time when they mentioned it, I was like, “I’m going to think about that a little bit more.” But the more I reflected on it’s like, oh, I already have a proof point inside my own company because we meet and do everything in the canvas. So the group decisions are happening there. I’m doing solo work in Claude code. It has access to the team Miro board where the executive meeting is happening. And so it’s constantly peering into that and giving me context on the decisions we made or it’s aware of that stuff. And so it’s not like that you lose it becomes unified.

Jeff Chow: Yeah. And it is not just Miro, I think this is just a big gap around organizational context, includes the process of making a decision. Sometimes I call it the work exhaust. And all of that decision, you can imagine, let’s just take our favorite topic of prototyping. It’s easy to call a process. You have V1 prototype shared by a product manager, you guys ideate, and then you have the aligned prototype at the end. And that is the context that’s shared back to the agents or AI tools. The missing component in the context that you’re talking about is what were those decisions? What were those decisions? Is there a decision log? What’s the true context? The why of those decisions is more rich and important to feed the agents so that when they make the next iteration, you don’t burn tokens by just revisiting the same decisions over and over again. So it’s not actually about the final prototyping output. It’s about the prototyping output plus a decision log that’s clear on why some decisions were made. And so I think those are really clear. Now, beneficial for teams too. It’s like you expect X, you end up with Y, same thing. You need to cascade the why to humans, but you also have to cascade the Y to agents so that they can do their job better as well.

Douglas Ferguson: Yeah, it’s like the context behind the context.

Jeff Chow: That’s right.

Douglas Ferguson: Sure, we made this decision, but why did we make it? And that can help with avoiding the revisionist history or whatever. It’s reminded me of, did you ever see that CIA field guide that they basically distributed and to folks that were embedded in access countries?

Jeff Chow: No.

Douglas Ferguson: Oh, it’s really fascinating. You should check it out. It’s public domain now, and so you can buy it off Amazon. They have printed copies that are like three bucks or whatever. It’s basically corporate sabotage, but when you read it feels like all the dumb stuff that people do in organizations that just break down and create poor work environments. But we were intentionally telling folks to go do that in the companies and enemies. And the funny part is you find it rampant just in companies anyway, but one of the things is revisit every decision.

Jeff Chow: Yeah, exactly. Yeah, let’s really double click into every single one.

Douglas Ferguson: Yeah. Well, maybe we should talk. I know we decided that last week, but what if?

Jeff Chow: Yeah, exactly.

Douglas Ferguson: And so if the AI’s doing that, then we got problems. And so to your point, if they don’t have the context on why we made decisions, they might be pushing back in ways that aren’t helpful.

Jeff Chow: Yeah, for sure.

Douglas Ferguson: So I know y’all are going through a big clawed code, well, maybe more co-work across the org. So I’m curious, as someone who has been overseeing a group that probably uses AI way more than the rest of the org, what’s it been like watching marketing and HR and legal and all the other departments start to lean into co-work? Any epiphanies or observations there?

Jeff Chow: Yeah, I mean first the adoption in the engineering product and design org was very viral. And so mostly cloud code, but also co-work as well. And I think that’s because there were just the themes of, wait, I used to have to negotiate this, now I can just do it myself, and was pretty clear and amazingly sophisticated. And we started building tools on top of tool. We started testing the factory approach for agentic coding and all these other things. So I think right now 95% of the EPD org is already on Claude or Cursor or others, and that’s amazing. The rest of the org has also seen the same kind of virality in, I would say, different ways. I think we’ve seen more, you have to show the specific use case and solution more specifically to then get that light bulb moment versus here’s a sandbox. God speed. But I would say once that’s happened, it’s been just as viral. People have been kind of. And again, it all starts with maybe the low-hanging fruit rote work that just takes up your day and like, oh wait, I can write my weekly update really quickly. I can have these experiences pushing forward. So I think those are the start. Clearly there’s a real value in analytics, and so a lot of value of like, “Hey, I have this question. Traditionally, I’d have to file a ticket to get some dashboard or do something else, and now I could just ask.” And we’re keyed directly into our snowflake, and we have a really good graphs and structures to really give people the ability to ask the nuanced questions to help them self-serve. So these are really powerful things that we’re seeing. And again, it’s just getting more and more and more. And I would say maybe not as much as EPD, but every function has what I would call the AI maker. It’s the kind of viral person that hacked the system to do something that wasn’t planned. And they just can’t help themselves but to share it with everyone, which then gets somebody else to use it and then others. And I love that stuff. That’s probably the most fun when you see an organization get galvanized on something that would save you time, and it organically creates new processes.

Douglas Ferguson: Yeah, I love that. And I think that’s a big selling point for multiplayer AI as well, because that infectiousness, that virality is increased dramatically when folks are together watching other folks use it in novel ways. Because to your point, not everyone can be thrown in the sandbox and just thrive and figure stuff out. Some folks need to be oriented a little more. They need to be sparked a bit more. And in fact, people talk about AI fluency or literacy and training. I think that’s all a waste of time. In fact, there’s some Gartner data that proves it. But if you can spark people, if you can give them that, I don’t know, that X factor of like, “Oh wow, this is amazing.” Help them see where there’s some potential, watch someone else use it, and then boom, they’re along for the ride. And so it reduces that gap between the folks that are really far out and doing amazing stuff and the laggards.

Jeff Chow: Yeah. I would even take that one step further and say when we look at it, we want everyone to be AI fluent, of course, but the multiplayer collaborative impact is maybe that second touchpoint. So say you’re planning a QBR deck for a customer and you’re on the go-to-market team. The one person who could have created that QBR deck maybe with Claude or our sidekicks quickly means that the five other people on the account team who are there can start and obsess less on the doing of the work, aggregating all the data, and do more of the thinking of the work where they’re now in it being like, okay, how can we strategically help our customer? How can we plot a path for them moving forward? And so even the benefits, even if you weren’t the one to click the button or write the prompt, there’s a value in the force multiplier of anybody who got that first move, which is producing say the board in our case of that experience. And so we really obsess internally and externally to look at that. The value is not just, it’s the multiplayer impact of AI. And what’s really interesting now that we’re seeing, and maybe probably every org, is every org starts with everyone just plays a sandbox, just figure it out until you realize, oh my God, the tokens. So what we’re realizing now is the fact that multiplayer collaborative AI where maybe one person does the task, a team converges, and then ultimately a decision or something’s settled and the agents then learned around that context has greatly reduced our token costs, right?

Douglas Ferguson: Yeah, absolutely.

Jeff Chow: Because you’re not doing repetitive work.

Douglas Ferguson: Yeah. The other thing is, it’s making me think about there’s just a lot of power in the design crit process. And essentially we’re bringing that process to every part of the business. Because oftentimes someone will be responsible for doing their thing and it took so long to do it. Maybe there would be some rehearsal, some feedback cycles, whatever. But the fact that we can spend more time, more extended time in that moment together deciding if it’s quality work, tearing it apart, is it good? Is it what we want? There’s so much value in that time spent. And I love that thinking that other parts of the business will start to engage in those behaviors.

Jeff Chow: That is such a great way of thinking about it. And it tracks really well because just like a crit or human nature, if you know somebody took two weeks to build this thing, psychologically, you’re a little bit softer with them. If you know that they stubbed it out in an hour with the help of say Claude or Sidekicks, and it looks great, but you’re actually more willing to. And they’re more willing to receive the feedback. You’re more willing to give the candor to get it to a better outcome. And so yeah, I think actually you’re right. The mindset of a crit, and I love crits. They’re so painful, but I love them and everyone comes out way better for it. But I think that you’re right, that’s permeating through the organization. There’s a more welcoming force for that.

Douglas Ferguson: Always invoke Cunningham’s law. In fact, I’m sure I’ve already mentioned it on the podcast many times, but are you familiar with it?

Jeff Chow: No.

Douglas Ferguson: If you want to know the answer to anything, to post a wrong answer on the internet.

Jeff Chow: Yes.

Douglas Ferguson: And so I think of that a lot in the multiplayer AI. It’s one of my favorite ways to use AI in Miro. And it’s that same point you just mentioned is, and it’s not that it took me no time to build it because I used AI. It’s like we just hit a button in Miro. You know what I mean? It just came out of the tool. And so there’s no sense of ownership. And so let’s just beat this. We have a common enemy. Let’s beat this thing up and destroy it until we find the essence of what matters and what’s right.

Jeff Chow: That’s right. That’s right. Yeah, that rapid ideation makes a ton of sense. And you’re right. I think it really lowers the barrier of you can be a little bit more raw, you can be a little bit more honest, and that leads to better iteration cycles.

Douglas Ferguson: There’s two things that you mentioned earlier that I want to double stitch on. One, you mentioned some folks using Cursor, some folks using Claude. And I hadn’t coded in years and a few years back started using Cursor. And then I switched over to Claude code pretty much exclusively. And I noticed that those two surfaces had a bias to me in how I use the AI. And then I would argue Miro even biases me different because it’s like one, you’re in a text space, one you’re in a visual space. I’m curious, given that you have developers using both, do you have a sense of how that impacts development craft or whether you’re in an IDE or on the command line, how that’s impacting how people think about code and how they approach it?

Jeff Chow: Yeah, I think it’s still early days, and I would say it’s probably not necessarily the tool itself, but it’s the mentality that we’re still mining right now. It’s the artisanally crafted engineer that just wants to code versus the ones that are like, “Okay, I need to create my agentic surface where I’m more orchestrating a team of agents to do the work for me and iterating with them.” Both do it just fine. So I think we’re still at that, how do we get people to at least try? And even if you’re skeptical, see what happens. Nothing’s a silver bullet. But if you can get into that mentality, I think there’s a pretty high ceiling to get it to work, and that’s where our investments are. And I think we try to be tool agnostic just because the competitive landscape is, you might think now just because Claude’s the breakout winner and they have amazing traction is just going to be the winner. But I just think the market, if I look at, and I squint and look at the disruptions of the past, cloud, mobile, others, I think that it’s so early days, who knows? So we want to invite our organization to try things, to get that aha moment so that we can learn. There’s no top-down mandate so much as bottoms up discovery.

Douglas Ferguson: Yeah, exactly. That makes a ton of sense. I guess the thing I was kind of hinting at was my experience was being in the ID, I was still putting my stamp on it. I was still reviewing it as like, “Is this Douglas code?” Whereas when I switched to purely agentic, Claude code aside, there’s many tools you can do agentic development with, but it almost felt like I was just reviewing other people’s code. I had a group of interns that I was like, “Is this acceptable?” Which is different than me thinking, “Is this Douglas code?” So I feel like my hypothesis is that that’s going to be the future is less us putting our stamp on it and that’s more reviewing it. Is it acceptable? Can we allow this to be in production?

Jeff Chow: Totally. And by the way, your agents just heard you calling them interns and you’re going to have to watch your back.

Douglas Ferguson: It’s definitely heard that analogy before. We all have room to grow, so they’re happy that I acknowledged that they’re still learning every day.

Jeff Chow: Yeah, a strong performance review with your team.

Douglas Ferguson: Yes. The other thing I wanted to come back to is you mentioned the power of dashboards and you didn’t have to put in a PR and wait for a business analyst to come in and make a thing for you. And one of the phenomenons I’ve been noticing is just the richness of some of these little HTML tools that’ll just build instantaneously for just some random question I asked, which made me start thinking about this concept of ephemeral UI. No one designed or thought about or premeditated the need for this UI to exist. And so it just made me wonder, are we going to start building products that intentionally anticipate ephemeral UI? We just set up the conditions where every user gets the thing they need. I even think your custom widgets is almost an example of this.

Jeff Chow: Yeah, for sure. I think the rise of personalized software, which is just another fun name for customization, et cetera, is a real key. Early days for that. And so I think there’s a really interesting aspect. You think about vibe coding apps. Not everything has to be an app, yet all of a sudden it’s an app. You think about maybe organizational ways of working where people have to retrain themselves every time they get some artifact that’s a little bit different. And so I think there’s back to friction. I think the friction here is just because you can do these types of things and share them doesn’t necessarily mean you should, just given the cognitive change that if you have to collaborate with someone, what does it take? So I think the value is clear. What’s going to happen in the industry is that over time there’s going to be shared permissioning structures. Where do you put it? What’s actually the organizational template so that if you open a product review, there’s not one that sounds like Claude, that sounds like a dramatic reading. Here’s the hook, and stuff like that. And you’re like, oh my God, that’s not your voice, stuff like that. But yeah, I think our approach to the custom solutions is what we call it, is there should be some consistency so teams don’t have to relearn some stuff. In our case, I know how to drop a sticky, I know how to mark things up, I know how to drop a comment, and I know how to share and do permissions. But then the workflows within that to drive some alignment has the potential to be very specific, very tailor-fit, very unique. And so that starts with all of our personal artifacts and how do we get agents to lay them out in an intelligent way that helps people align. But that also means what we have are custom widgets, which is basically a vibe coding platform that adds real-time multiplayer widgets on the canvas. And that’s connected to your data. And I think that means together with existing patterns, with some maybe one new one together, that creates very specific bespoke workflows that organizations could scale, and it truly meets their need. And I think that used to be a really far path away. You’d have to get a developer to create something, you’d have to do something else. It would cost a lot of money, so it’d be prohibitive. And I think that’s another path around speed to decisioning and alignment for us. And then you think about scaling that. Scaling organizations have used our workflows and maybe a department within a large enterprise organization does it. But how do you scale that to 500 teams, a thousand teams, 5,000 teams? These are the sizes of organizations. That’s where these kind of connective solutions really becomes. I’ll tell you, and you know this, there’s not a single PDLC process that’s the same. And everyone says they subscribe to this pattern or not, and then you go into their organization and they’re like, “Oh, sort of.” But we don’t really do it that way. And so I think that’s where we’re seeing real opportunity and honestly great traction with our customers is the, great. Well, we didn’t want to vibe code an end-to-end workflow. We want to use most of what you have, but take us the last mile. Connect the dots for us so we can use it across as our single operating system for product. Great, we got you.

Douglas Ferguson: Yeah, and I’m excited about where some of these customer solutions can go. And I’ve even been chatting with some of my manufacturing pals. We’ve been doing some work there with some lean guys because their rituals and ceremonies are very similar. I’ve studied them. I’m very fascinated. So got some colleagues there. We’ve been collaborating recently, and some of these guys are old school, retired, but still tinkering and speaking and whatnot. And then that reaction’s typically like, “Oh, you got to be in person for an obey, and I don’t see how AI could play a role,” and whatnot. But after a few sessions, the gears start turning and they’re like, “You know, this is the piece that was always problematic. Every group that comes has their way of wanting the world to be seen or they have the format they want to present things in, and AI could translate all that.” I’m like, “Now you’re getting it. Now you’re getting the power of this stuff.” And so I think it’s only going to be a matter of time before we just see more and more impacts across how people are coming together in ways that were just impossible before. It’s just too much contention or it’s just too fraught to really work together in a cohesive way.

Jeff Chow: Yeah. At the end of the day, manufacturing is one of great interest for myself as well as the organization. And a lot of it is because the same patterns that we see everywhere is the truth, which is manufacturing has hundreds of disparate data sources to maintain resources, supply, et cetera. Real visual ways of working like Lean or Baya, Kaizen. But that connected workflow of supply chain management and Lean is always disconnected because it’s so complex. And so there’s this opportunity to smooth even some of the hardest experiences out in a way that makes it more dynamic, faster decisions against some things that are just incredibly difficult. And so a lot of our manufacturing partners, we have mad respect. You jump in there and you talk to them about what they do. And you’re like, oh, that’s not just a Salesforce database in a Jira database. Then it’s some real, real stuff there.

Douglas Ferguson: And even each department has their own level of complexity. So then when you’re integrating across those lanes through the whole value stream, it’s tricky.

Jeff Chow: Yeah, absolutely.

Douglas Ferguson: It looks like we’re running out of time here, so going to have to bring things to a close. But before we do, I want to ask you to leave our listeners with a final thought.

Jeff Chow: No, I think when we started this around both friction and opportunity, I would just say it is such early days. It is very easy. I have existential dread all the time, but I choose to think about where there are opportunities that have always been friction points in the culture of work that has been happening. Where is this an opportunity for us to finally debunk those, those points of friction, those cultural cross-functional inertia moments? And the minute you shift gears to thinking about that, I think the sky parts and the world opens up and you’re like, “Okay, let’s actually solve the things that kind of pissed me off since the dawn of time.” And then it’s like AI becomes this amazing opportunity. And I think that’s probably the best way to ride this wave, because otherwise it’s pretty paralyzing.

Douglas Ferguson: Great words to live by. Amazing. It’s been a great chat, Jeff. We appreciate you coming on, and we’ll talk more soon.

Jeff Chow: All right, thanks for having me, Douglas. Great time.

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

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

The Leadership Move Nobody Is Making on AI

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

The instinct to protect is the wrong instinct

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

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

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

Lever two: redesign the role before you deploy the automation

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

Lever three: apply a real test, not a feeling

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

What this looks like in the room

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

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

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

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

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

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

The numbers are already moving

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

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

The unit of analysis is wrong

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

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

The time delay is the trap

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

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

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

The leaky pipe

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

When this argument doesn’t hold

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

Redesigning for developmental friction

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

What’s at stake

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

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

The post Experience Starvation appeared first on Voltage Control.

Why Nobody Gets Fired for Destroying Opportunity

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

The Credit Trap

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

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

Why Nobody Gets Fired

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

What Gets Destroyed

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

The Measurement Problem

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

What Human-Amplified Business Actually Requires

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

What Accountability Actually Looks Like

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

The Stakes

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

The post The Accountability Gap appeared first on Voltage Control.

In this episode of the Facilitation Lab podcast, host Douglas Ferguson interviews Kristi James, a Lead Change Manager at the World Health Organization’s Health Emergencies Programme in Berlin. Kristi traces her facilitation instincts back to childhood event planning and an early corporate career at DHL, where she discovered that sketching out processes and user journeys helped teams see where they belonged, resolve role confusion, and surface disagreement productively. She describes how a coach, Mary Beth Maines, helped her recognize creativity she had dismissed in herself, and how that shift now shapes the way she draws out scientists and doctors at WHO who are more comfortable debating than co-creating. Kristi walks through concrete practices, from visual cues and whiteboards that anchor group memory to 1-2-4-All exercises that convert passive listeners into active participants, and a recent example of using a user-journey diagram to unstick a stalled budget negotiation. The conversation closes on her core belief that visualizing a process doesn’t just clarify tasks, it gives groups a safer, more concrete way to name resistance and work through conflict together.

Show Highlights

[00:01:40] Drawing Out Prom As A Kid
[00:05:30] Team Baffled By Her Event Mapping
[00:09:00] Realizing Leadership In Her 30s
[00:13:15] Best Presenter Praise From Karen Jones
[00:17:40] Grounding Presentations In Real Stories
[00:21:20] Learning Event Craft With IMG
[00:24:50] Coach Helps Her See Her Creativity
[00:28:30] Diagramming A Stalled Budget Proposal
[00:32:10] Moving Groups From Listening To Doing

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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.

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