How AI Is Quietly Breaking Your Senior Bench

“We’re worried because there are fewer entry-level jobs right now, and in five years, there will be fewer intermediate or senior-level designers. There’s going to be a gap.” That is a working landscape architect, one of 722 interviewed for a new Cornell study presented at the BIG.AI@MIT conference last month. The quote is not about a dystopian future. It is about what the practitioner is watching happen, month by month, in her own firm. The headline narrative on AI and work is about entry-level job loss. That is real, and it matters. But the more consequential story, the one that is almost invisible in quarterly earnings calls, is quieter and slower: the people who would have been senior in five years are not getting trained now. The junior did not lose the job. The junior lost the reps.

What the study actually found

Jose Antonio Guridi and Cristobal Cheyre, researchers at Cornell, spent the last eighteen months studying how landscape architecture firms across North America are adopting generative AI. They did 25 semi-structured interviews, spent time observing operations at a prominent firm, and ran a survey of 722 practitioners. That is one of the largest datasets on AI adoption in a real profession that has been published. Three findings stand out. First, the adoption is uneven in a specific pattern. Juniors are driving it. Seniors are holding the judgment that decides whether the AI output is right. In firms that have not designed for this reversal, the junior uses AI to produce something the senior reviews. The senior edits the output. The original work the junior would have done, the intermediate steps where skill used to form, quietly disappears. Second, most of the adoption is hidden. 73% of the practitioners who use AI at work do not disclose that use to their firm. They use personal devices. They treat restrictive firm policies as an obstacle to route around rather than a signal to stop. The work gets done. The firm does not know how. Third, the firms that are handling this well have all done the same thing: they have made the adoption explicit. Structured workshops. Shared documents. Senior oversight built into the workflow, not as policing, but as a design feature. The distinction between the three patterns is where the story is.

Passive, hidden, explicit

Guridi and Cheyre name three adoption patterns. Each one produces a different organization five years from now. Passive adoption. AI arrives through software updates. The design tool adds a new button. The email client starts suggesting full paragraphs. The research database surfaces AI-generated summaries above the actual sources. Nobody decided. The practitioners absorb the change as background noise. Skill formation is whatever it would have been, minus the steps the software now does automatically. Passive adoption is the modal case. Most organizations are in it right now and do not realize it. Hidden adoption. The firm has a restrictive AI policy. The practitioners need to produce the work anyway. They open ChatGPT on their phones, paste the brief, and keep the output in their personal notes. They know they are not supposed to. They do it because the alternative is not doing the job. The 73% disclosure-gap statistic is this pattern, captured at scale. Hidden adoption looks like conformity from the outside. From the inside, it is an underground apprenticeship running in parallel with the firm’s official one, except the underground one is entirely unsupervised and invisible to every senior person who might intervene. Explicit adoption. The firm has decided, out loud, how AI fits into the work. There are designated workflows where AI is expected. There are designated workflows where AI is not welcome. There are senior reviews built into the AI-assisted paths, not as gates, but as teaching moments. Juniors get exposure to the AI-generated output. They also get exposure to the senior’s reasoning about why the output is right or wrong. This is the only one of the three patterns that preserves apprenticeship.

The mechanism: where the learning moments go

A team of economists at MIT, Yale, and Microsoft, led by Mert Demirer, gave this phenomenon a structural name. They call it AI chains.

An AI chain is a sequence of production steps in which each automated step flows into the next without a human in the middle. Verification happens once, at the end of the chain. The economics are obvious: verification is expensive, so fewer verifications are better. Organizations will push toward longer chains whenever the AI is good enough.

The consequence is that jobs where AI-suitable steps sit next to each other are the jobs where chains form fastest. Research, drafting, and rendering are adjacent. So are summarization, synthesis, and first-pass review. Chain the three together and you have converted what used to be a six-hour junior assignment into a thirty-second prompt and a five-minute senior review. The efficiency gain is real. So is the apprenticeship cost, which does not show up anywhere on the quarterly report.

In landscape architecture, Guridi and Cheyre watched this happen inside the firm they observed. Rendering production used to be the junior’s job. It was slow, iterative, and humbling. You started something, showed it to a senior, received criticism, started again. After two years, you had internalized the senior’s taste. After five years, you had your own.

The rendering step is now in a chain. The junior writes a prompt. The AI produces four variations. The senior picks one and edits it. The junior has watched, but has not done. The internalization does not happen the same way. The taste does not form. One practitioner put it to the researchers this way: “If you’re using something to generate everything, you miss all of these moments to be iterative and review your own work.”

The pattern is not unique to design. In May 2025, Moderna’s Chief People and Digital Technology Officer Tracey Franklin described to the Wall Street Journal a system of more than 3,000 internal GPTs, including a broad HR GPT that routes employee questions to specialized GPTs for performance management, equity, and benefits. Her own description of the workflow: “It’s like your virtual HR, AI agent. It’s what would normally be a junior-level HR analyst type, we’ve now converted into a GPT.” Same chain. Different industry. The intermediate work that an HR analyst would have done on the way to becoming a senior HR partner is gone.

Why executives don’t see it

The reason this pattern is so hard to see at the executive level is structural. It is not a failure of leadership attention. It is a failure of legibility. The metrics you have are the metrics that matter. Revenue per employee. Project cycle time. Client satisfaction. None of these show apprenticeship. All of them might actually improve in the short term when AI chains form, because the outputs ship faster and the staff count drops. The disclosure gap compounds the invisibility. 73% of AI users are hiding the use from the firm. Senior leaders cannot see what they cannot see. The firm’s governance layer is responding to a world where AI use is still occasional. The actual daily reality has moved past that. And the time horizon is precisely the wrong length. Five years is long enough that the consequence is somebody else’s problem, probably the problem of whoever succeeds today’s CEO. Five years is short enough that the seniors who exist today will still exist and can still cover the gap, right up until they retire. We name this pattern “Experience Starvation,” after the term coined by Gartner’s Tori Paulman at last year’s Digital Workplace Summit. Experience starvation is what you get when the workflow around the AI strips out the intermediate work the junior used to do on the way to becoming the senior. The organization continues to function. The talent pipeline quietly thins. Paulman’s framing has a sharp corollary: AI is not taking entry-level jobs. Senior people are.

What the firms getting it right are doing

The explicit-adoption firms in the Guridi study are not slower. They are not abstaining from AI. They have just designed the adoption so that apprenticeship survives. The most teachable pattern in the research is one Paulman calls the Option 3 workflow. It has three moves. The expert builds the template. The senior practitioner, who has the taste, captures her reasoning in a reusable form. The template is the artifact. It encodes the judgment. The rookie executes with AI. The junior runs the template, feeds it the project context, and gets the output. They see the template working. They see where it breaks. They do the adaptation work the template did not cover. The expert reviews the insights. The senior does not edit the output. The senior reviews the judgment the junior exercised when the template was not sufficient. The feedback is on reasoning, not on rendering. The workflow preserves three things at once. The firm gets AI leverage on the routine work. The junior gets exposure to the senior’s reasoning, not just the senior’s output. The senior spends her scarce time on the decisions that only she can make. This is what Guridi and Cheyre observed in the firms that were explicit about their AI adoption. It is not a program. It is a set of working conventions that the senior partners enforced because they had decided, out loud, that training the next generation was part of the firm’s product. The firms that had not made that decision were not using any of this. They were using AI chains that removed the work and the learning together.

What to do this month

Three moves that do not require a transformation program. Make disclosure safe. The 73% who hide AI use are not malicious. They are responding to incentives you set. If the penalty for disclosing AI use is higher than the penalty for hiding it, you will get hiding. Change the incentive. A one-line policy update (“we encourage AI use in designated workflows; here is how to propose a new one”) can move the whole distribution. You cannot design around a pattern you cannot see. Route some work through juniors even when AI could do it. Not all of it. Some. The criterion is whether the work teaches something the junior needs to know in five years. If the answer is yes, the junior does it. The efficiency loss is the training budget, reclassified. You are already paying for training; now you are spending it on practice instead of on certificates. Audit your senior bench replacement rate. Not headcount. Replacement rate. For every senior who will retire or exit in the next five years, who is on track to replace them? If the answer is “unclear,” you have the gap already. The only question is whether you find out now, when you can still do something, or in three years, when your best seniors are announcing and the bench is empty. None of these require new hires. None require new tools. They require the decision to design for apprenticeship at a moment when every incentive is telling you to optimize it away.

What is at stake

The five-year gap is not a forecast. It is a trajectory measurement. The apprenticeship loss is happening now. The consequence is scheduled to arrive in 2030\. The organizations that will have the senior bench they need in 2030 are the ones that decided, in 2026, that apprenticeship was a design problem. They built Option 3 workflows. They made disclosure safe. They kept routing work through the junior even when the AI was right there and faster. The organizations that will have the gap in 2030 are not doing anything wrong, exactly. They are optimizing for the metrics they have. The metrics they have do not measure apprenticeship. Apprenticeship erodes silently. By the time it shows up as a capability gap, the people who could have been trained have moved on to firms that trained them. The juniors are not losing their jobs. They are losing the work that would have made them senior. That is a different problem, and it hides better, and it bills later.

Ready to close the gap?

If your organization is watching AI chains form and is not sure whether apprenticeship is surviving, three places to go deeper. Talk to us. We help leadership teams design the workflows that keep AI leverage without losing the learning cycles. Learn more Our pillar page lays out why apprenticeship loss is one of the new frictions AI has relocated into the center of your organization. Build the capability. Our facilitation certification teaches the skills senior leaders need to run Option 3 workflows at scale.

Frequently Asked Questions

Is AI taking entry-level jobs?

The headline narrative says yes, but the more consequential pattern is different. AI is enabling senior workers to do entry-level work themselves, which removes the on-ramps for skill development. The junior role often still exists; the work that used to fill it has been compressed into AI chains. The Cornell study of 722 practitioners shows this pattern clearly. The junior did not lose the job. The junior lost the reps.

What is experience starvation in AI adoption?

Experience starvation is a term coined by Gartner’s Tori Paulman to describe the systematic removal of the low-stakes, high-repetition work that builds professional judgment. When AI handles the steps where skill used to form, the junior misses the iterative cycles that produce taste. The organization keeps shipping. The talent pipeline quietly thins. By the time the gap shows up, the people who could have been trained are five years past the moment when training mattered.

How does AI break the apprenticeship model?

AI chains the production steps where junior workers used to learn. Research, drafting, rendering, review: each one used to be a discrete moment where a junior practiced and a senior critiqued. When those steps chain together, the junior writes a prompt and the senior edits the final output. The intermediate work, where taste forms, disappears. Most organizations have not noticed because their metrics do not measure apprenticeship. Cycle time and revenue per employee actually improve in the short term.

What is the Option 3 workflow for AI in the workplace?

The Option 3 workflow, also from Paulman’s research, has three moves: the expert builds a reusable template that encodes her judgment, the rookie executes the template with AI on real project context, and the expert reviews not the rendered output but the reasoning the rookie applied when the template was insufficient. It preserves AI leverage on routine work while giving juniors exposure to senior reasoning. It is the only workflow pattern in the Cornell research that survives apprenticeship.

How do you protect your talent pipeline from AI-driven erosion?

Three moves: make AI disclosure safe so you can see what is actually happening (the Cornell data shows 73% of users hide their AI use from their employers); route some work through juniors even when AI could do it, with the criterion being whether the work teaches something the junior needs in five years; and audit your senior bench replacement rate, not headcount but replacement rate, so you know where the pipeline is actually broken before it shows up as a capability gap.

The post The Five-Year Gap appeared first on Voltage Control.

Something strange is happening inside organizations that have invested heavily in AI. Leadership reports productivity gains. Dashboards show adoption metrics trending upward. The transformation appears to be working. And then you talk to the people doing the work. Gartner’s 2025 AI in the Enterprise Survey found that executives are four times more likely to report high AI productivity gains. Individual contributors are five times more likely to say AI made no difference at all. This is not a minor variation in optimism. These are two fundamentally different versions of reality operating inside the same organization, at the same time, about the same technology. That gap is the single most important thing standing between your AI investment and actual transformation. Not the model. Not the license. Not the training curriculum. The fact that the people making AI strategy decisions and the people living with those decisions are not even looking at the same picture.

Two Realities, One Organization

The numbers tell a story that most AI strategies are not designed to hear. 78% of employees do not know whether they will lose their job to AI. Only 12% feel involved in decisions about how AI gets deployed in their work. And only 14% of leaders believe their employees are effectively using the AI tools they have already been given. Sit with that combination for a moment. Leadership is looking at underutilization and concluding the workforce needs more training, better tools, clearer mandates. The workforce is looking at the same AI rollout and wondering whether the whole point is to make them redundant. Both sides are interpreting the same set of facts. Neither is wrong, exactly. But they are operating from different starting assumptions, and those assumptions are shaping behavior in ways that no amount of change management communication can override. When employees believe they are being replaced, they do not experiment with new tools. They protect their territory. They withhold the institutional knowledge that makes AI implementations actually work. They comply with the minimum requirements of training programs and then return to their existing workflows. The dashboards still show adoption because the licenses are being opened. But the transformation is not happening.

The Psychology Nobody Is Talking About

Most analysis of the exec/IC gap treats it as an information problem. Leadership has data that employees do not. Or a communication problem. The message is not getting through. Or a training problem. Employees just need more hands-on time with the tools. Tori Paulman, the Gartner analyst who authored the perception gap research, offers a different and more uncomfortable explanation. The gap is not informational. It is psychological. Executives authorized the AI investment. In many cases, they championed it to their boards. They have staked credibility and budget on the claim that AI will make the organization more productive, more competitive, more efficient. They have cognitive skin in the game. The investment has to be working, because if it is not, that reflects on the decision to make it. Frontline workers live in a different psychological reality. They read the headlines about displacement. They watch colleagues get reassigned or laid off.

They hear the word “transformation” and parse it, correctly, as a word that means someone’s job is about to change in ways they did not choose. They have cognitive skin in the game too, but the stakes point in the opposite direction. AI cannot possibly be as good as leadership claims, because if it is, the implications for their own role are terrifying. This is cognitive dissonance operating at organizational scale. Not ignorance. Not resistance to change. Two groups of people filtering identical information through fundamentally different personal stakes, and arriving at conclusions that are perfectly rational given their respective positions. No training program resolves cognitive dissonance. No town hall presentation bridges a gap that is rooted in what people need to believe in order to feel safe.

The biggest threat to your AI strategy is not the technology. It is that your executives and employees are looking at the same data and seeing completely different things.

The Evidence Is Piling Up

The perception gap is not a theory. It is showing up in behavioral data, not just surveys. Anthropic’s Economic Index, built on over one million real conversations with Claude, found that experienced AI users, those with six or more months of regular use, have a measurably higher success rate in their interactions. The gap is not trivial. It is the difference between using AI as a basic task executor and using it as a genuine thought partner for strategy, planning, and complex problem-solving. That finding maps directly onto the perception divide.

The people who have had enough sustained exposure to move past the anxiety and into genuine fluency are extracting compounding value. The people who are still in the “comply with the training but don’t actually trust it” phase are getting almost nothing. And they are interpreting that lack of value as confirmation that AI does not work, which reinforces the very behavior that prevents them from discovering that it does. Meanwhile, 75% of knowledge workers are already using AI in some form, often through unsanctioned shadow tools their organizations do not know about. The workforce is not anti-AI. They are anti-being-replaced-by-AI. When they choose the tool themselves, on their own terms, for problems they define, adoption is not a problem. When the tool is handed to them by the same leadership team discussing “efficiency gains” and “headcount optimization,” every interaction carries the weight of an existential question.

Why Communication Strategies Fail

The default organizational response to the perception gap is communication. Town halls. FAQ documents. Executive memos about the exciting future of AI. Internal newsletters with success stories and productivity metrics. This approach fails for a specific reason: it treats the gap as an information deficit when the actual problem is a trust deficit. Consider what a typical AI communication strategy sounds like from the frontline perspective. Leadership says: “AI is going to transform how we work. It will make you more productive. It will free you up for higher-value activities.” The employee hears: “We are changing your job. We have already decided. You were not consulted. The framing assumes this is good for you.

If you disagree, you are resistant to change.” The more polished the communication, the wider the gap becomes. Because polished messaging signals that the narrative has been constructed, and constructed narratives are exactly what people distrust when their livelihood is on the line. The organizations that actually close the perception gap do not communicate their way across it. They involve people in the decisions before there is anything to communicate.

What Closing the Gap Actually Looks Like

The organizations making real progress share a pattern that looks nothing like a communication strategy. They start by asking different questions. Not “how do we get employees to adopt AI?” but “what work do you want to do? What work do you hate? Where do you lose time to tasks that do not require your judgment?” Vizient, the healthcare performance improvement company, took this approach before deploying any AI tools. They built personas and playbooks around what their workforce actually wanted their roles to become. The result was not just higher adoption. It was genuine ownership. People adopted the tools because the tools were designed around their preferences, not imposed despite them. This is not a soft approach. It is structurally different from the standard deployment model. The standard model decides what AI will do, then tells the workforce. The alternative decides with the workforce what problems are worth solving, then selects tools accordingly. The difference in adoption, trust, and sustained behavior change is not marginal. It is categorical. The practical moves are specific: Involve employees in identifying which tasks AI should augment.

Not as a feedback exercise after the strategy is set, but as a design input before it begins. When people participate in defining how their work changes, the perception gap closes because the gap was never about information. It was about agency. Make leadership’s AI use visible. One of the strongest findings from practitioners working on AI adoption is that visible leadership modeling, leaders demonstrating their own AI workflows, their own struggles, their own learning curve, does more for adoption than any training program. When a VP shares how they used AI to prepare for a board meeting and what it got wrong, that single act of vulnerability communicates more than a hundred slides about the future of work. Create reflection loops, not just training sessions. The research on AI fluency is clear: people who verbalize what they learned, who connect the AI use case to their own work out loud, retain and apply the skill. People who sit through a demo and return to their desk forget 50% within a day and 90% within a week.

The difference is not the content. It is whether the person had to think about what it means for them, specifically. Stop using the word “transformation” without naming what stays the same. The perception gap is partly a fear gap, and fear responds to specificity. When leadership can articulate not just what is changing but what is not, what roles are protected, what skills remain essential, what institutional knowledge becomes more valuable rather than less, the anxiety that drives the gap begins to lose its grip.

The Real Stakes

The perception gap is not just an adoption problem. It is a strategy problem. Organizations making AI investment decisions based on executive perception of success are allocating resources against a version of reality that their frontline workforce does not share and may be actively undermining. The dashboards say adoption is at 80%. The actual behavior says adoption is performative. The training metrics say 500 employees completed the AI certification. The workflow data says those 500 employees are still doing their jobs the same way they did six months ago. Every week this gap persists, it compounds. Executives become more confident in a narrative that is increasingly disconnected from operational reality. Employees become more entrenched in protective behaviors that prevent the very transformation leadership is measuring. And the organization loses the one thing that makes AI adoption work: the institutional knowledge, contextual judgment, and domain expertise that only comes from a workforce that is genuinely engaged rather than performatively compliant.

This is not a technology problem. It is not even a change management problem, at least not in the way most organizations practice change management. It is a trust problem that lives in the gap between what leadership believes is happening and what the workforce experiences every day. The organizations that close this gap will not just have better AI adoption metrics. They will have something far more valuable: a workforce that is actively participating in its own transformation rather than bracing against it. And in a world where human consensus is becoming the primary constraint on organizational speed, that difference is the difference between an AI strategy that works and one that just looks like it does. Want to close the perception gap in your organization? Let’s talk about how our facilitated sessions can surface the real barriers to AI adoption and build the trust that no training program can manufacture.

Frequently Asked Questions

What is the AI perception gap?

The AI perception gap is the measurable divide between how executives experience AI in their organization and how individual contributors experience it. Gartner’s 2025 data shows executives are four times more likely to report high productivity gains; ICs are five times more likely to say AI made no difference. The gap is not a misunderstanding. It is two groups of people filtering the same data through different stakes and arriving at incompatible conclusions.

Why do executives and employees see AI so differently?

Because they have different cognitive skin in the game. Executives authorized the AI investment and have to believe it is working. Frontline workers see the displacement headlines and have to believe it is not as transformative as leadership claims. Both responses are rational given the position. The gap is psychological, not informational, which is why communication strategies fail to close it.

How do you close the AI perception gap?

Not through better communication. Communication treats the gap as an information deficit when the real problem is a trust deficit. The organizations closing it involve workers in the decisions before there is anything to communicate. They ask which tasks people actually want AI to help with, design around those answers, and build visible leadership modeling and reflection loops into the rollout. The mechanism is agency, not messaging.

What does the perception gap mean for AI strategy?

It means most AI strategy decisions are being made against a version of reality the frontline workforce does not share and may be actively undermining. Adoption metrics look fine because licenses are being opened. Real behavior tells a different story. Strategy that does not close the gap optimizes for the wrong picture and underdelivers on the investment.

Is the AI perception gap getting worse?

Yes, on current trajectories. Every week the gap persists, executives become more confident in a narrative that is increasingly disconnected from operational reality, while employees become more entrenched in protective behaviors that prevent the transformation leadership is measuring. The compounding effect is the reason the gap shows up as a strategy problem, not just an adoption problem.

The post The Perception Gap appeared first on Voltage Control.

In the inaugural episode of New Friction, host Douglas Ferguson and Erik Skogsberg explore how AI has shifted organizational friction from execution to decision-making and alignment. While AI accelerates production, it magnifies existing dysfunctions when teams lack collaborative habits. They introduce the concept of “multiplayer AI”—moving beyond individual productivity gains toward team-level collaboration. The conversation emphasizes that facilitation, judgment, and organizational health are now the critical differentiators. Practical takeaways include assessing whether your organization operates in “single player” or “multiplayer” AI mode and intentionally slowing down at key decision points to maximize human impact.

Show Highlights

[00:01:36] AI’s Impact on Teams
[00:03:04] The Three Lanes of AI
[00:06:45] The AI Skills Gap
[00:10:00] AI Scales the Chaos
[00:14:57] Organizational Health vs. Operational Excellence
[00:22:18] Benefits of “Multiplayer” AI Habits
[00:32:58] Concrete Takeaways for Listeners

Links | Resources

Erik on LinkedIn

About the Guest

Erik Skogsberg is an educator, designer, researcher, and human-centered change agent. He is VP at Voltage Control, a facilitation-led transformation consultancy that helps leaders and organizations elevate their ways of working to unlock their full collaborative potential. He has worked with leaders, innovators, and creatives at organizations including Google, Microsoft, Nike, NASA, Lockheed Martin, USSOCOM, Atlassian, Stanford, the World Bank, and the Carnegie Foundation.

Prior to Voltage Control, Erik co-founded Michigan State University’s Hub for Innovation in Learning and Technology, an internal design consultancy focused on learning experience design, innovation, and organizational transformation. Drawing on his background as an educator and researcher, he brought human-centered design and systems thinking to some of higher education’s most complex organizational challenges.

Erik designs and leads immersive, research-backed programs that help leaders navigate complexity, build alignment, and move from misalignment to momentum — building facilitation as a craft, a calling, and a catalyst for change.

He holds a Ph.D. in Curriculum, Instruction, and Teacher Education from Michigan State University, an M.A.T. in Secondary English Education from Brown University, and a B.A. in English Literature from Western Washington University.

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, 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, Erik Skogsberg, vice president at Voltage Control, where he leads strategic design and delivery of immersive research-backed programs that transform facilitation into a craft, a calling, and a catalyst for change. Looking forward to the chat with you today, Erik.

Erik Skogsberg :
Yeah, absolutely. Absolutely. So first time. I think it’s been a while since I’ve been on the podcast.

Douglas Ferguson:
Yeah. And this is the first episode of New Friction, so first of many firsts.

Erik Skogsberg :
Cool, cool, cool, cool, cool.

Douglas Ferguson:
So what have you been seeing in the rooms you’ve been in lately?

Erik Skogsberg :
Well, I mean, as AI is the talk in a lot of spaces, certainly seeing a lot of teams, a lot of leaders asking about how do we use this strategically? It’s out there, but we aren’t necessarily seeing the ROI that we’ve hoped for. And it’s really still caught in pockets of just individuals. I’ve been able to speed up my individual work, but what does that mean for my team?
And so a lot of questions there about how do we get this to be more collaborative? How do we integrate what I’m seeing individually in terms of productivity into the team? And also too, seeing some folks call out like, wow, this is actually just magnifying dysfunctions that I thought we were going to be able to fix. Because some team members, or even individuals, are creating a lot of new work product and then trying to bring it back in the team and meeting some big frictions there in making that happen. So ripe space for us to certainly lean in, and really having me thinking a lot about really core tenets of facilitation and collaboration that we’ve been working on even before AI was in the scene in these ways.

Douglas Ferguson:
Yeah. I think important to step back and think about what do we mean when we’re talking about AI? Because a lot of times folks will hear AI, and it’ll conjure up any number of things. And that’ll vary depending on role and perspective within an organization.
I think I’d like to boil it down into three lanes. There’s like we have a really important critical business challenge we need to solve. And these new computer techniques that are labeled AI can help solve those challenges. So whether it’s computer vision or machine learning, or any number of AI related algorithms that have gotten more and more sophisticated lately, can help solve those. And those are typically custom models, very fine-tuned, built specifically for that challenge for that company. So that’s one lane, and lots of people doing lots of work there.
Another lane is how do we embed AI into our products to directly impact the customer experience. And a lot of that stuff these days is being built on the foundational LLM models. So through the OpenAI APIs or through the APIs over at Microsoft or even Anthropic. And then also you can write custom models for that stuff too. But the difference is it’s being embedded into our products that we’re delivering to customers.
Now, the third category, which is really the category where we operate is around how do we 10X teams? How do we use AI to really level up and as a force multiplier for our organization and our people? And that naturally is very human. As we’re thinking about how our roles might be impacted, how we’re rethinking the way the organization might work and function and be structured, that can certainly create a lot of uncertainty and fear for folks. And so that’s a conversation that needs to happen. And also as an organization, if we’re going to design this stuff well, we need to be well versed in what’s possible when we empower humans with these tools. So it’s a naturally facilitation forward task to approach this work and to help people navigate this change.

Erik Skogsberg :
Yeah, absolutely. I’m glad you put a finer point and some clarity there on those lanes, because I think that’s certainly another thing I’m seeing. You sent me that, what was it? A PwC commercial, the AI, AI, AI.

Douglas Ferguson:
AI, AI, AI, AI, AI, AI, AI.

Erik Skogsberg :
It’s just everywhere, but people aren’t clear as to what they mean and/or don’t know about those multiple lanes. And so then the company, the team, the organization suffers because it’s all mashed together, and then the human pieces are lost. Or one person means in this way, another person means in another. It really gets back to, again, some old lessons here around clarity, around clarity of communication, clarity of purpose.
And that’s such an important thing for us to be bringing in. What are we hoping to accomplish? And oftentimes those lanes that you laid out are operating in the same organizational space. And so at what points do they need to intersect? At what point should they remain a part in service of the work that oftentimes we’re coming in to do, which is really about maximizing the human and thus organizational potential there?

Douglas Ferguson:
Yeah. And I want to double tap on that idea of what they don’t know. And it really gets at this issue that I call the skills gap. And when you look at historically most technological transformations and advancements, this sort of thing happens. Some people get really good and proficient at it, others lag behind.
The thing about this movement is that AI is moving so incredibly fast. There’s a lot to keep up with. And the people that are starting to adopt it and go deep with it can move at lightning speed compared to others. And so the ones that don’t know and are following behind are falling even further behind, and they also are afraid to admit it. It’s really frightening to step up and say, “I don’t know.” And so this is where leaning into this very human-forward approach, this facilitated approach, giving people frameworks, even maturity curves where they can say, “This is where I’m at, and these are the steps that I can take the move forward.” Really powerful.
Another thing is something we’ve talked about a lot around this multiplayer mode that’s so critical for operating a team at 10X in an AI forward world. And the multiplayer capacity allows everyone to follow along and move together in progress. So while you have a variety of levels of adoption, the awareness is shared and the discovery is happening in real time across the team, and there’s a ton of value in that.

Erik Skogsberg :
No, absolutely. And I think even just being able to articulate that for folks can be reassuring, can provide some common language. And then to your point there, here’s how to bridge that delta. It’s not impossible. There are some great now models and moves that have a long history because a lot of it does come back to what we have regularly done when it comes to helping teams collaborate better together. It’s just some new inputs and outputs. And having AI as a toolmate, as Gartner frames it, as a part of that equation there.
And I think the other thing that you pointed out that’s important to double click on as well is the speed piece. I think this is both at a pace that we haven’t necessarily seen before and also has allowed certain individuals or organizations to excel at a pace that we haven’t necessarily seen before. And so as individuals and organizations are seeing this, they want to jump in because they don’t want to be left behind. But without that clarity, without that framework, it can actually end up being worse. Because it’ll magnify the dysfunctions that were already there versus digging into unearthing, dealing with what may have been the issue in the first place, AI or not. It just actually magnifies that dysfunction.

Douglas Ferguson:
Yeah, I would like to say it scales the chaos. And I think it’s really interesting just to step back to the thesis too here. What we’re saying is AI is making execution nearly free, so you can build the thing in 10 minutes. What’s most critical now?
And ultimately it comes down to decision making, discernment, and consensus, like deciding what to build, building the right thing. Because putting more of the wrong thing out in the world is certainly not going to solve any problems. I mean, you look at all of the innovation techniques, the design sprint, startup kind of incubators, all of that was helping them to get to product market fit. Now, this matters in companies as well. If we’re executing a new project for employee onboarding, if we’re launching new products for our clients, or if we’re creating a new legal review process, it doesn’t matter what we’re doing, we need to execute it correctly and we need to put the right things out there.
So if we’re just speeding up creation of the wrong things, or we’re just moving faster through a broken process, it’s just scaling those things much more quickly. And so as it becomes cheaper to get things out the door, we need to really be focusing on are we creating the right things and how are we creating the right environments for those decisions to happen smoothly? Because if we’re generating tons of things faster, more and more things are piling up to make decisions about.

Erik Skogsberg :
Yeah. Well, I mean, it’s changed the cadence actually in some really cool ways because it allows us to then slow down better at some real key points that we may not have been able to before because, all right, the prototype or a couple different prototypes have been created so fast. Now let’s slow down and really get into the finer points and nuances there of what has been created, what we want to move forward, what we want to iterate on.
And let’s take a look at our prompting. Let’s take a look at what we asked for. Is that what we ultimately want? We can be more purposeful in how we’re building those things into place or creating those. Whereas in the past, there was so much time that had to be given to actually the creation of things, we couldn’t slow down in as purposeful of ways. So in some ways here, this is even better because it invites us to be that much more purposeful at the key points, at the decision points, at the points where we’re making a call as to here’s what we’re going to invest in, here’s what we’re going to move forward with.

Douglas Ferguson:
Yeah. This is making me think about how we often talk about reading the room, or sensing as a facilitator or as a leader, just understanding where we need to place attention, what do we need to attune to, what do we need to address with the group, what are the dynamics that are playing, what’s emerging that requires the most attention and the most care?
And that’s really what we’re getting at here with this new friction thesis is that as we look at our organizations and our enterprises as systems, and you could think of it as an organism, what portions or what subsystems do we need to point our attention to and attend to? And what we’re saying is that the friction is just shifting to new areas and just presenting themselves in new ways. I would argue that nothing’s really changed. It’s inherently still very human.

Erik Skogsberg :
Yeah. Yeah. Well, and also too, again, it allows us to put more of our total attention on this new friction and a friction in ways that has always existed, but maybe hasn’t gotten the attention that it’s due here because we were trying to focus on both. We’ve got to execute and we’ve got to make the decision, and we’re kind of splitting our attention between each. This allows us to really dig in purposefully to the decision making, the consensus, the human side of things as those other pieces are that much quicker and create a new cadence here that allows us to maximize what is very human and is a real benefit of having strong teams and strong decision makers.

Douglas Ferguson:
This is making me think about Lencioni’s differentiation between organizational health and operational excellence. And how typically leaders will focus on trying to drive more operational excellence, whereas organizational health is what actually drives more value, more sustainability, higher quality, these kinds of things.
And I would argue, if you really think about it, operational excellence is around that execution. And so if that is starting to get optimized by the AI, maybe that affords an ability for folks to actually say, “You know what? Lencioni, Voltage Control, all these folks that are focused on facilitation and organizational health were really onto something. Now that we’re not distracted by the operational excellence and the execution, we can focus on what is most critical, has been most critical all along.”
And also I would argue the fact that the execution is getting so cheap and easy, and so commoditized and also just so abundant that not only is the friction shifting, or our attention might shift, I think it’s exacerbating that friction because it’s kind of putting a lot of strain on something that was already somewhat broken, that it was already dysfunctional, but now we can’t ignore the dysfunction. We can’t just operationalize ourselves past it.

Erik Skogsberg :
Agreed. Well, I mean, it provides a real opportunity to fix dysfunctions that, to your point, were always there, and now we have more time and opportunity to focus on them. And that then helps us address what could just be the creation of a lot of slop to then instead focus on purposeful outputs, prototype, execution, content that ultimately gets us to exactly where we need to be.
And I would argue that because we have more time to be in purposeful decision-making, be in purposeful collaboration, be in spaces where we’re addressing those organizational health pieces, then each time we execute, here’s an opportunity for us to level up that organizational health piece. So we’re now able to address those pieces every time we need to produce something again. Here’s another opportunity for us to get healthier and healthier and healthier.

Douglas Ferguson:
Yeah, I love that. It’s like iterative kind of improvement over time. Yeah. Why do you think this isn’t obvious? I think most companies are still operating like execution’s the bottleneck. And part of me thinks that’s a bit of an awareness thing. Also, just being in the replay loop, it’s like Groundhog Day. It’s just like it’s hard to break out of the patterns often for folks. But I’m wondering what you’ve been noticing and if anything comes to mind there.

Erik Skogsberg :
Yeah. I mean, part of it I think gets back to where we started the conversation. Everything’s just happening so quickly, and people are in reactive mode. So when everything’s coming at you, you aren’t necessarily seeing as clearly as you could. Because of that, that limits then awareness and what we can see of the system. I think this does get back to a larger gap in teams’ ability to see the system and to engage in systems level change when it comes down to it, which has always been something that’s important to cultivate and learn. And then also too, I think because of that, there’s a dearth of models for how to do this well. I still think we’re helping and building and have got some great early cases, but at scale, I still think there’s a dearth of models of teams really doing this well. I think that’s still very much in process.
And the more that we can look to cases of how this is going well, we’re a part of creating with our clients right now, I think that’s going to help. But if you can’t, it’s happening fast, because it’s happening fast, you’re not able to see the system that was already a gap, and then there are no models to look at, it’s easy to be lost in that process there. And then just continue to come back to it as an individual and continuing to then magnify the dysfunction that’s already there.

Douglas Ferguson:
Yeah. It reminds me of something I talk about, which is that leaders are currently trying to coach a sport they’ve never played.

Erik Skogsberg :
Yeah, it’s true.

Douglas Ferguson:
Some of them are dabbling in AI, but man, they’re not using it every hour. They’re not building the proficiency and comfort that you really need to be able to really guide folks through what it means to live.

Erik Skogsberg :
And certainly not using it as a team, even if they’re doing it individually lots and lots and lots, that doesn’t, from my experience, translate to then the team level, which is what we’re talking about.

Douglas Ferguson:
Yeah, they’re not building those multiplayer habits.

Erik Skogsberg :
Absolutely. Absolutely.

Douglas Ferguson:
So just to recap before we move into a new section here, it’s like the old friction was execution, the new friction is judgment. And of course, not all friction is a waste. There’s lessons we can learn from things. But we really need to attune to where that friction resides today as leaders and facilitators. And at the end of the day, that means it is leadership and facilitation work. It’s not a tools problem. It’s not about picking the right model or putting together the best adoption plan or creating the best training. It’s really about leadership facilitation and sparking a change.

Erik Skogsberg :
And that makes, if we think back to that idea of cadence, this new friction is a purposeful slowing down in a new space around the decision making. That’s a great opportunity again for leveling up the organization here. So it should be an exciting thing there to lean into.

Douglas Ferguson:
Yeah. So let’s use this moment to pivot into what we’ve noticed specifically, how folks are behaving with some of these changes, when they start to take on these multiplayer habits, when they start to get in and work together in these ways, what kind of benefits or what kind of shifts are we noticing?

Erik Skogsberg :
Yeah. I mean, out of the gate here, I’m thinking about some foundations work we did with a client around the holidays recently. All of a sudden, as people are getting some small reps in, in this case, we tackled an organization-wide question or problem and had teams come together to ideate some potential solutions in partnership with AI, but they did that altogether. And by AI here, I mean, they’re using Miro and using the AI affordances of Miro to ideate some new ideas and then to collate down.
All of a sudden their ability to create together was sped up. They now were able to slow down and say, which ones of these do we like best? And at which points is it best for us to call in AI as a toolmate? At which point is it best for us to turn to our team? It’s one of those things that all of a sudden with those reps happening, people are reflecting in different ways about work practice or work product there, which is really, really cool.
Also too, I’m seeing people being able to, to pieces of our conversation earlier, we just did some recent design sprints. We were much quicker to the creation of the prototype. We were able to create some newer more versions than we would’ve been able to in the past. And we were able to dig into some really important strategic conversations about, well, is this going to be the best way for us to go? And is this the best sort of team makeup even for this? Which wouldn’t have been able to happen before when we were just so focused on creating the prototype and just trying to get that to the end because so much was focused there.
And I just say anecdotally between both of those engagements, having C-suite folks observe or be in the room at points, they were witnessing things that, until seeing the teams working together, hadn’t clicked for them until that moment. And then they wanted to have a different strategic conversation like, how do we do more of this? How do we give people more opportunities to practice here in a way that was an unlock? So those are a couple of things that I’ve been seeing there.

Douglas Ferguson:
For those listening that might want a quick diagnostic, what’s something that they can watch out for? I’m trying to think about the folks that have come to us, what they were noticing before they came to us, or what we were noticing, helping them notice that might be good signals for them to pop up and say, “Oh, now’s the time, this is happening here. Let’s do something.”

Erik Skogsberg :
Well, I mean, back to, it’s not a huge mystery here, we were mentioning earlier this difference between single and multiplayer AI. I mean, I think that’s a good gut check question for folks to ask right now. If we were to step back and look at my company, look at my organization, are the majority of folks just using this individually? Are they even there yet? Because I know in some companies they’re just trying to push, let’s get this out in people’s hands. All right.
So would you say your company, your teams, are mainly in the single player mode, individuals using this for productivity gains? Are you seeing or experiencing any moments where teams are using this well together? Are you seeing any signals in this popping up across the company? I think those are some good initial gut checks there. And certainly for folks, and we’ll talk about this a little bit more, can put a finer point on that in some upcoming events we’ll be doing, webinar upcoming where we’ll share some of our maturity models and how we get people thinking about that.
But just as a gut check, we mentioned single versus multiplayer AI. Is your company mainly in single player mode? Are you seeing some multiplayer? How easy is that to shift there? I mean, I think that’s a good gut check out of the gate because you’re not going to see ROI at the organizational level, at the systems level, if all of that AI investment and practice is just in individuals. It’s got to get into teams, and to get to the organization, then teams ladder up to the organization.

Douglas Ferguson:
Yeah. And I would say certainly when folks are just coming to terms with this kind of multiplayer capacity, getting the teams doing this together, that’s step one. But then getting that cross-functional, cross-team collaboration piece, such a power up, and these are orders of magnitude. The getting the team together is 10X. And then you 10X from there when you can draw different groups together because what was previously impossible to integrate is now a feasible task to get folks working across these boundaries in ways they weren’t before. And I think that it’s really missing for a lot of folks. And that’s part of the fun right now is helping people realize what’s possible and kind of guiding them through that.

Erik Skogsberg :
Yeah. And it’s amazing how quickly things lock in, or light bulbs come on, as we’re with folks guiding them in workshops. We’ve got some collaborative AI labs that we do. We’ve got AI executive studio that we do. Just little moments where people can start to experience this as a team in real time, visualize this in Miro, seeing things happen, how all of a sudden things start to lock in. Oh, okay, that’s interesting. Now this might be possible. Or, huh, this conversation wasn’t possible because we couldn’t see everything together before. Huh, that now allows us to shift this piece over here. And it really starts to lock in pretty quickly there in terms of certainly seeing potential, seeing some small wins, and small practices on the way to what might be a larger transformation.

Douglas Ferguson:
Leadership instinct is something that’s always really fascinating to me because I think some leaders just have intuition, and that drives quite a lot. And if someone’s a naturally gifted facilitative leader, they’re probably tuning into quite a bit of that. But this is a moment where I’m noticing that intuition isn’t always kicking in. And I think it’s just a sheer paradigm shift that’s happening. But that’s something I’ve just been noticing, and that’s why these experiential moments we’re creating are so powerful because we’re helping them experience the paradigm shift so their instincts can then take root. Because they’re there already. They just need that new framing. So I’m just curious how leadership instinct is showing up in the interactions you’ve been having, how that’s driving the way people are thinking about this, and fostering better alignment across teams on their usage of AI, and what the future could look like as the org evolves.

Erik Skogsberg :
Well, I mean, I think what I’m seeing, and certainly what is open conversations with a lot of the clients we’re working with right now, is there’s a sense that the ways that they’ve been going about AI adoption, decision making, the team-based side of things, isn’t working. So they see that and they want to address that.
Also too, I’m seeing, hearing more folks recommitting to the power of facilitation. It’s like, oh yeah, this thing that we know you guys do really well and has been so important all along, we need you even more at this point because of how broken we’re seeing things. And so I’m seeing folks noticing and raising their hand there and wanting to invest in purposeful ways, which is great.
Also too, I’m seeing both for these individual leaders and then other leaders across teams, more investment in leveling up the ability to guide decisions differently, and to really resource what it takes for this. And I would say too, a recommitment to practice and playing and prototyping. Let’s try this together as a team, and what are we learning from that? And then let’s codify it. I’m thinking of a client, a large tech client we’re working with right now, that they’re now recreating some key playbooks and codifying things that have shifted now using Miro blueprints and templates so that these shifts in practice are now memorialized in some really important ways around rituals that needed to be addressed before. And now this gave them the opportunity to do so.
And these leaders are resourcing that and prioritizing that for teams. And really taking the time to step back and say, “Hey, we’re going to, instead of just focusing on the execution and outputs here, here’s what’s just as, if not more, important for us to make sure we have our rituals right.”

Douglas Ferguson:
Yeah. And as we come to the close, I think it’d be fun to reflect back on a few key concrete takeaways for folks and maybe even some prompts and things to consider as they reflect on this episode and go about their work in the coming days and weeks. And one of the things that I’d encourage folks to do is, before your next AI assisted deliverable, before you ship that thing, think about how you would’ve approached that prior to AI, who you would’ve conferred with, who’s going to have to receive this AI generated thing? Could you loop them into the process before wrapping your interactions with the AI? Could you create ways where they can give you feedback on how you’re thinking about assembling this thing? Could you shape it together through a screen share or through a multiplayer Miro board where AI is coming into a joint meeting or group gathering?
Also, I would say if you’re thinking about who’s going to receive this AI generated asset, could you even have another AI pose that individual and give you the feedback they would give you? So then you’re already taking one rev past how you think that individual is going to respond and critique this. So you’re ultimately saving time by making sure you’re catching the early revisions and early pieces.
And so really thinking about how we’re being more conscious. It’s not just about the speed. The speed can help us get to higher quality. It can help us do more reps before we even pass it off for more human review. So how can we model the humans that we have around us to make sure we’re taking their thoughts and needs and perspectives into consideration before we even share it with them? And then also how are we bringing folks into collaborative moments so that we’re doing collaborative prompting and collaborative review of these intermediary assets before we polish it and get something to where we produce it.

Erik Skogsberg :
Yeah, absolutely. I mean, I think that dovetails nicely with a couple of things that I have written down here as I was listening to you. Part of this gets back to just a quick gut check assessment. If you were to step back and quick gut check of your company, your organization, are you mainly single player AI or are you multiplayer AI, or are there pockets? What’s the picture right now? If you were to just do a quick gut check assessment, of that assessment, what places might you shift into some more multiplayer and potentially cross-team approaches? Even if you don’t know how to do it, where might that be an improvement?
And then back to our cadence piece from earlier, if something that you used to do before is now much quicker, then where might you slow down in places that you would’ve rushed before, for the ends that might get you and the organization to a better place? And increasingly too, as we think about where might I now slow down, I’d add too, where might I slow down and re-anchor in the talents of the humans on our teams? Where might we maximize their impact and their input here at a place that we may have had to rush before? So if I’m speeding up over here, where might I slow down over on this part of things?

Douglas Ferguson:
That’s well said, Erik. And before we come to a close, I’d like to just give listeners a little bit of a perspective on what’s coming in the rest of the series. And I’d say just be on the lookout for more conversations like this. I will be inviting critical thinkers, clients, people in the thick of these challenges. So who is it that is experiencing this new friction, and what does it look like from the inside? What experiments are they running? What have they tried? Where are they getting gains? What worked, what didn’t work? What are they curious about? What are they hoping to try next? So some real lived experience and some real honest assessments of where things are at and where we’re hoping things go.
And at the end of the day, like you were saying earlier, there’s a deficit of proven models and ways to do this, like best practices. But I’d almost argue that, in this new world we’re in, it’s a little bit of a fallacy because things are moving so fast anyway, we have to figure out what works and we have to keep experimenting. We’re more and more in a world governed by complexity law. And so we have to constantly probe and experiment.
And so I think the thing is is we have to be willing to reinvent all the time. And so the folks we’re inviting on are folks that are in these moments of reinvention, folks that are champions of reinvention, and getting good at it. They may not be experts, they might not be researchers, but they’re brave, experimental, and curious and smart folks that are making their path through. And I think that’s how we all learn together when we share from those points.
And so I would say if someone wants to be on the show, reach out. We’d love to chat and see if you’d be a good fit. And then otherwise, just stay tuned for more of these series and looking forward to having more conversations like this.

Erik Skogsberg :
Yeah, absolutely. I’m excited, just in our conversations around this, who’s going to be in the podcast upcoming. Some cool voices conversation from a variety of areas. And to your point there, really leaning into the reinvention and timeliness as we go week to week, how that conversation will shift, and we’ve got a great lineup of folks who will join us.

Douglas Ferguson:
Yeah. And ultimately, when we say new friction, there’s no one singular friction. And to my earlier point, it’s evolving. And so we’re going to name these frictions with our guests and with these conversations we’re having. And we’re going to name them one by one as they evolve and as they present themselves. And we’ll be exploring things like trust and governance, speed and identity, the new apprenticeship model. What does that look like?
And so I would say if any of this hits for you, you’re curious, tune into a future episode, or sign up for something new that we’ve got coming that we’re hosting, the New Friction Webinar where we’re going to list out more framework and more opinions. We’ve just been chatting about the landscape here and sharing some perspective in order to set up the thesis around the podcast and what’s coming with other guests.
But the New Friction Webinar that’s coming up on, I believe it’s June 11th. And I would encourage folks to sign up for that. And I broke the rule of not making this evergreen. So you might be listening to this after June 11th. But there’ll be a recording. You can check it out in the show notes to find the event or the recording. And we’re going to do more of these. It won’t just be a one and done. So no matter when you listen, go check it out in the show notes, or check out our website, sign up. We’d love for you to join us and see what we have to say.

Erik Skogsberg :
Cool.

Douglas Ferguson:
Thanks for joining, Erik.

Erik Skogsberg :
Absolutely. Always a pleasure.

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.
So, I’m really excited to be exploring this in partnership with people who want to go on this journey with me. And it’s just a very personal offering that I am making in this chapter in my life.

The post Is AI Really Eliminating Friction? appeared first on Voltage Control.

In 2017 I gave a talk at Facilitation Lab on language models in facilitation. Most people in the room had not heard of GPT. The thesis I put on the screen was simple: the technology side would outrun every organization’s ability to absorb it, and the human side would become the bottleneck. Around the same time I was advising Kungfu.ai with Stephen. His bet was on building AI. Mine was on building the human capability that would have to grow around it. I founded Voltage Control to make that second bet, and I have made it every year since. Eight years later the thesis is no longer abstract. Organizations are not choosing between using AI and not using it. They are being pulled into two different futures, and the middle ground is disappearing. One future is deep tech: organizations that have built genuine infrastructure, deployed agents at scale, automated workflows end to end, and are operating with AI as a core organizational capability, not a feature. The other future is deep human: organizations that have recognized that the hard part was never the technology, that it was always the people, the trust, the identity questions, the way groups make decisions under uncertainty. They are investing in the human capability that makes any technology productive. The space between these two positions is collapsing. You can see it in the data now. I have been waiting for the research to catch up to the pattern. It has.

The Evidence of the Split

Gartner’s Digital Workplace Summit this year surfaced a finding that should land harder than it has: executives are four times more likely to report high AI productivity gains from their AI investments, while individual contributors are five times more likely to say AI made no difference to their work. That is not a technology gap. The tools are the same. The gap is in how the technology is being experienced, and it maps almost perfectly onto the structure of most enterprise AI programs: leadership makes the decision, licenses get purchased, individuals get trained once and then largely ignored, and the two groups live in completely different realities about what is happening. Four out of five employees believe their organization is trying to replace them with AI. Only 12% feel involved in the decisions about how AI gets used in their work. 78% do not know whether they will lose their job to AI. These are not the stats of an organization that is integrating AI. These are the stats of an organization that has deployed AI at its leadership layer and left the rest of the workforce in the dark. And when 14% of leaders believe employees are effectively using the tools they have been given, the people making deployment decisions and the people living with the consequences are not operating from the same reality. This is what the bifurcation looks like from the inside.

What Is Actually Happening

The technology is not the problem. The technology, in most enterprise contexts, is working. 72% of IT leaders say Copilot users struggle to integrate it into their daily routine, but the failure mode there is not the tool. It is the design of how adoption happens. The World Economic Forum projects that 59% of the workforce needs brand new skills in the next two to three years. Gartner estimates that 32 million jobs will be transformed per year due to AI, and that managing this transformation requires 20 times more organizational effort than managing job losses. The effort ratio is 20 to 1. That 20:1 figure is the one that should reorient every AI strategy. Organizations are allocating budget and attention as though this were a technology problem, when the data says it is primarily an organizational problem. The work of transformation is not writing code or buying software. It is the human work: the alignment conversations, the role redesigns, the trust-building, the change management, the process of getting a 40,000-person organization to operate differently. That work does not scale through typical training programs. Without application and practice, half of what people learn from a one-time training session is gone within 24 hours. 90% is gone within six days. Learning decay does not care how good the content was.

Two Organizations, Same Technology

The split is easier to see in examples than in statistics. At Gartner’s Digital Workplace Summit, Ivanti presented their approach to internal AI transformation. They built a centralized AI platform called Ivy and created AI pods, cross-functional environments where subject matter experts, senior DBAs, network engineers, storage specialists, rotate through and imbue AI models with their domain expertise. The output is what Gartner called “cybernetic teammates”: AI agents that carry the actual knowledge and judgment of specific senior practitioners, available to everyone in the organization, not just to the people who happen to sit near the expert. They surfaced approximately 700 AI use cases this way. The mechanism was not a training program. It was a structured process for capturing and distributing human expertise at scale. In Manchester, University NHS Foundation Trust deployed Microsoft Dragon Copilot to give doctors back something they had been losing: full attention on the patient in front of them. The voice AI handles transcription and note-taking in real time. The doctor reviews, edits, and approves. The consultation, the actual human work, is now uninterrupted. Manchester’s Chief Executive has estimated that at full rollout, the trust could see up to 250,000 additional patients per year. That number is a projection, not a measured result, and it depends on redesigning scheduling, staffing, and workflow to convert freed-up minutes into actual appointments. The technology is the easy part. The organizational redesign is the work. These two cases look different on the surface. One is an IT infrastructure vendor restructuring how expertise flows across their organization. The other is a hospital trust giving clinicians room to be clinicians. But they are both illustrations of the same underlying logic: AI works when it is designed around what humans do best, not when it is deployed as a replacement for the conversation about what that even means.

The Wrong Approach

The organizations going in the wrong direction are not doing obviously foolish things. They are doing reasonable things, badly sequenced. They buy licenses before they understand the work. They run training programs before they have addressed the trust deficit. They announce AI strategies without involving the people those strategies will affect. And then they are surprised when license usage stays flat, when the productivity gains are invisible to the people on the ground, when the AI-fluent individuals they develop become isolated experts rather than multipliers. 56% of CEOs plan to use AI to de-layer middle management within five years. The question is not whether that flattening is coming. It is whether anyone is designing what replaces the development pathways that disappear when it does. Middle management is not just overhead. It is the layer through which expertise gets transferred, context gets communicated, and junior people get the reps that build them into senior people. Remove the layer without replacing the function and you have an experience starvation problem: senior experts absorbing work that used to be the proving ground for the next generation. The pipeline for building bench strength quietly breaks. AI is not taking entry-level jobs. Experts are. That is a subtly different problem that requires a subtly different response.

Where Facilitation Lives

I keep coming back to this: there needs to be a function in organizations that lives at the intersection of all the functional groups. Not IT. Not HR. Not change management as it is currently practiced. A function that understands how groups make decisions under uncertainty, how trust is built and broken, how to create conditions where people can learn through doing rather than just through instruction. That is a facilitation function. And AI does not make it less important. It makes it more important. When you deploy AI at speed, you compress the timeline for every organizational friction. Decisions that used to take weeks get made in hours. Alignment gaps that used to surface slowly become visible immediately. The process problems that were tolerable before, the meetings where nothing gets decided, the strategies that make sense to leadership and mean nothing to the people executing them, those problems do not disappear with AI. They get louder. More inputs and faster inputs can slow alignment down if the process is broken. The organizations getting real value from AI have not solved a technology problem. They have figured out how to have the conversations that the technology makes urgent: about what work means, about who has agency over it, about how expertise flows and gets recognized, about what you are actually trying to do when you say you want to be AI-ready. The dotted line between deep tech and deep human is not a gap to be closed by more tools. It is where the work happens.

The Choice

The bifurcation is not a prediction. It is already underway. The organizations making real investments in both the technology and the human infrastructure to absorb it are pulling ahead. The ones waiting for the technology to prove itself before investing in the organizational side are falling behind, and the gap is compounding. You cannot address the organizational side with the same logic you used to deploy the technology. You cannot train your way to psychological safety. You cannot mandate your way to trust. You cannot run a workshop that solves the identity questions AI raises for the people whose work is changing. What you can do is design environments where those questions get answered through practice, where people learn by doing in conditions that are structured enough to be safe and real enough to matter. Where the facilitation is not an add-on to the AI strategy but the architecture that makes the AI strategy possible. That is the choice. Deep tech alone will get you capability without adoption. Deep human alone will get you culture without leverage. The organizations that understand both, and that have someone whose job it is to hold the space between them, are the ones that will compound the gains. Everything else is just expensive licensing. If you are building AI strategy and finding that the human side keeps creating more problems than the tools solve, let’s talk.

Frequently Asked Questions

What is the difference between deep tech and deep human organizations?

Deep tech organizations have built real AI infrastructure, deployed agents at scale, and treat AI as a core capability rather than a feature. Deep human organizations have recognized that the hard part was never the technology; it was always the trust, identity, and decision-making capacity that lets any technology produce value. The two are not opposed. The bifurcation is happening because most organizations are investing heavily in one side and ignoring the other, and the middle ground is collapsing.

Why is the middle ground collapsing for organizational AI strategy?

Because AI compresses the timeline on every organizational friction. When the technology was slower, organizations could afford to ignore the trust gap, the identity questions, and the decision-rights ambiguity. AI makes those frictions immediate. The 20:1 ratio Gartner reported, that managing AI-driven transformation requires twenty times more organizational effort than managing job losses, is the quantitative version of this collapse. Half-measures stop working.

How do you build organizational trust during AI transformation?

By involving the workforce in how AI reshapes their roles before deployment, not after. Organizations that get this right, like Ivanti’s AI pods or Manchester NHS’s Dragon rollout, are not announcing AI strategy and asking people to comply. They are bringing subject matter experts and frontline workers into the design of how the technology gets used. The mechanism is structural, not communicative; trust comes from agency, not from town halls.

What does “deep human” mean in organizational AI strategy?

Deep human means investing in the human capability that makes any technology productive: facilitation skills, decision rights design, trust-building practices, role redesign, and the developmental experiences that build judgment over time. It is not the soft side of AI strategy. It is the architecture that makes deep tech work. Organizations that go deep tech without deep human get capability without adoption.

Should organizations invest in AI technology or human capability?

Both, in sequence and in proportion. Most organizations are 95% tech investment and 5% human investment, and that ratio is what produces the executive/IC perception gap, the experience starvation pattern, and the 70% IT-leader concern about agent governance. The organizations pulling ahead invest in both at roughly the level the 20:1 effort ratio implies: most of the work is the human work, and treating it as a side-project alongside the technology budget is the failure mode the bifurcation reveals.

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The 2026 Facilitation Lab Summit brought together eight extraordinary facilitators in Austin, Texas for two days of exploration, practice, and connection. The theme was “Edges” — the spaces beyond the familiar where real change, insight, and belonging become possible. Every session this year built on that idea from a different angle: what it means to step into the unknown, to show up fully as yourself, to illuminate others, to read the room, and to hold space for everything people carry when they walk through the door.

The summit provided an invaluable opportunity for facilitators to learn, challenge their assumptions, and grow. We were privileged to hear from a diverse group of speakers who brought their full selves to each session, modeling the very practices they were teaching. From guided meditations and somatic exercises to live facilitation simulations and fire-based frameworks, every session left participants with tools they could use and questions they are still sitting with. Read on for a summary of each workshop delivered at this year’s summit.

Dan Walker

Unlocking Collective Wisdom

In his session “Unlocking Collective Wisdom,” Dan Walker invited participants into a rich conversation about why we facilitate in the first place. Rooted in his foundational belief that the smartest person in the room is the room, Dan guided the group through reflections on collective process, the tension between urgency and long-term change, and how to maintain wellness when the world feels like it is at an edge. Drawing on a personal story of burnout and a transformative conversation with an indigenous elder who offered one word — patience — Dan helped participants explore the difference between the urgent need of now and the generational nature of real change.

The session closed with a practical focus on navigating turbulence in facilitated spaces, surfacing strategies from the room: slow down and let the turbulence be the wisdom that wants to be heard, set clear containers before anyone walks in, and use the Lewis Deep Democracy practice of “finding the no” to honor the full complexity of a group. Participants left with a reminder that discomfort is not danger, and that collective wisdom, given the right conditions, will always find its way through.

Renita Joyce Smith

The Edge of the Room Is the New Center

Renita Joyce Smith’s session challenged facilitators to stop trying to engineer trust with frameworks and checklists, and start asking a more uncomfortable question: how much of you is actually in the room when you facilitate? Opening with her “welcome mat” slide — a belonging-first introduction that traded resume bullet points for honest self-disclosure — Renita modeled the core thesis of her talk: being real enough so the room can be real back. She shared a pivotal story of missing a moment during an executive retreat when a CFO named a trust problem and Renita moved on to the next activity, a choice that defined the rest of her facilitation philosophy.

Through the Hamilton stage metaphor and a three-part framework of notice, name, and invite, Renita gave participants a practical architecture for being present as a full human while still holding the room. The tip exchange that followed surfaced tools from across the group: stuffed elephants for naming the unspeakable, fist-of-five checks done with eyes closed, ten-second pauses, and the reminder that clarity is kindness. Renita’s closing note: we are never finished becoming, and that is not a problem. It is the practice..

Chris Lunney

Navigating the Unknown with Whole Intelligence

Chris Lunney opened with a blank slide and a question: “What did you just experience right there?” It was the first of many moments in his session designed to demonstrate that navigating the unknown requires more than analytical thinking. Through the concept of whole intelligence, Chris introduced a framework for integrating all four sources of knowing: the analytical mind, somatic and emotional awareness, subconscious and imaginal insight, and personal and collective understanding. His central provocation was that when we try to plan the perfect path from Point A to Point B, we almost always end up stuck, because we are using a map without a compass.

The heart of the session was a guided meditation centered on a simple but powerful image: opening a refrigerator in the middle of the night to find the exact feeling your heart desires. Participants emerged from the exercise with words, phrases, and clarity that surprised them, and several noted that their answers converged with reflections from earlier in the day. Chris then introduced a framework for converting those insights into trackable experiments using Anne-Laure Le Cunff’s approach from Tiny Experiments: small, timed commitments that generate information rather than demanding proof. The takeaway was both practical and poetic. Start with the compass, then get out the map.

Shannon Hart

Innovation: Stepping off the Edge and Leaving the Agenda Behind

Shannon Hart drew on five years of facilitating innovation sessions at Shell International — working alongside geoscientists, petrophysicists, and data engineers on complex energy challenges — to make the case that real innovation does not come from a perfect agenda. It comes from creating the right conditions and trusting what emerges. She introduced a three-part framework built around base camp, unmapped terrain, and emergence, framing the facilitator’s role as less tour guide and more Indiana Jones: there is a north star, the right skills are in the room, but there is no pre-drawn map.

A three-circles co-creation exercise gave participants a visceral experience of how ideas evolve through collective contribution — how unfinished sparks become richer when passed between people, and how human brains are wired to find pattern and meaning even in fragments. A walk and talk sent participants into the literal unknown for 15 minutes to explore how ambiguity feels in the body. Shannon closed with a focus on emergence: the signs that real innovation is happening (including the moment when no one is sure whose idea it was anymore), and the warning signs of convergence happening too fast. Her final challenge to the room was to protect the quiet sparks, the voices that get lost in cultures that reward whoever speaks loudest and fastest.

Joe Randel

Last Night a DJ Saved My Life: Finding Your Voice as a Facilitator Through Metaphor

Joe Randel came to facilitation through music — as a roadie, musician, radio DJ, and talent buyer — and his session explored how adopting a metaphor as a lens can help facilitators find their voice and navigate the unknown. The DJ metaphor took shape for Joe during Voltage Control’s core certification program, watching a facilitator lead a room of strangers through a dynamic, collaborative experience and realizing: he’s like a DJ. That lens has guided his practice ever since, and his session invited everyone in the room to identify their own.

Through the Wimbledon exercise — placing participants first as fans, then as the opponent’s coach watching the same match — Joe demonstrated how powerfully a lens changes what you notice. The session then moved through two tracks: finding your voice (built from preparation and interpretation, the patterned choices that make any facilitation distinctly yours) and reading the room to sculpt the journey (transitions, sequencing, arc, and the whole session as an act of co-creation). A live simulation challenged participants to decide in 10 seconds whether to cut, blend, or let it end as a strategy session went sideways and time ran out. Every answer was different, every answer was right, and every answer revealed something about the person who gave it. Journey, Joe reminded the room, is not what we prescribe in advance. It is what we sculpt together.

Brian Buck

At the Edges of Belonging: Presence Illuminator, Practicing a Value-Directed Facilitation Identity

Brian Buck opened not with a framework but with an invitation to close your eyes and remember someone who truly saw you. Not your performance, not your output, but you. That meditation set the tone for a session about what it means to shift identity as a facilitator from someone who brings the fire to someone who ignites it in others. Through the concept of value directions — ongoing orientations that give goals their meaning and never fully end — Brian invited participants to ask what kind of facilitator they are becoming, not just what skills they are acquiring.

His three-part fire model gave that question a practical frame: ember (the internal work of arriving regulated and grounded), kindle (holding the container, the stage most experienced facilitators live in most of the time), and illuminate (seeing others so fully that their own fire gets called forward). The session included a paired exercise in two rounds — first in kindle mode, then shifting into illumination using a reference sheet drawn from David Brooks’ book How to Know a Person — and the contrast was immediate. Participants emerged with clarity on challenges they had been carrying for months. Brian closed with the question he hopes every participant carries forward: what is waiting to be illuminated through your facilitator presence?

Robin Neidorf

At the Edge of Knowing: Embodied Practice for Whole-Self Facilitation

Robin Neidorf opened her session with the entire room standing and singing “Are You Sleeping” in three-part harmony — a deliberate choice, because embodied sound regulates the nervous system and puts people quite literally in harmony with one another before a single concept is introduced. Drawing on nearly 30 years of parallel practice in facilitation and yoga, Robin made the case that facilitators routinely leave half their instrument behind. If the facilitator’s body is not fully present in the room, participants’ bodies will not be either.

The session moved through a partnered energy-sensing exercise that surprised more than a few self-described skeptics, a chakra-based framework for identifying which energy center is each person’s natural access point for grounding before sessions, and an extended eye-contact exercise across groups of two, three, four, and five that gave participants a felt sense of what each group size does to the relational field. Two is deeply vulnerable. Three is highly creative. Four is suited for convergence. Five is where people begin to check out. Robin closed with a pipe cleaner exercise where participants built physical models of how they want to feel when facilitating, and a collective om chant that asked everyone to let the body’s vibration do the final work of integration.

Trudy Townsend

Facilitating at the Edge: Building Trauma-Informed Spaces

Trudy Townsend closed the summit by going, in her own words, “straight at” the topic every other presenter had been circling. Trauma, she told the room plainly, is already present in every group you will ever facilitate. You do not need to do anything to put it there. Drawing on the landmark ACEs study — which found that two-thirds of the population has experienced at least one significant childhood adversity, with profound effects on adult health and wellbeing — Trudy grounded the room in the science before moving to practice. Trauma, she was careful to clarify, is not the event. It is the experience of that event living in the body.

Using Dan Siegel’s hand model of the brain, Trudy walked participants through how the amygdala, hippocampus, and hypothalamus work together to protect us, and how the prefrontal cortex — where learning, reasoning, and connection live — goes offline when the protective response fires. She named five types of safety that facilitators can actively tend: physical, psychological, social, moral, and cultural, while making space for a participant who named clearly that some bodies in the room face threats that go well beyond the discomforts most facilitation training addresses. The session closed with a reframe participants carried out of the room: we cannot guarantee a safe space, but we can invite people into a brave one. Trauma-informed facilitation is not a checklist. It is the sum of everything that has been explored across two days at the edge.

The 2026 Facilitation Lab Summit was a reminder that the most important work in facilitation happens at the edges: the edge of what we know, the edge of who we are, and the edge of what becomes possible when a room full of people truly shows up for one another. We are grateful to every speaker, participant, and volunteer who made this summit possible. We look forward to seeing what emerges from these conversations in the year ahead, and we cannot wait to gather again.

You can read full recaps of each session on our blog. And if you’re looking to keep your practice going, join us at our weekly Facilitation Lab meetups—where the learning never stops.

The post Facilitation Lab Summit 2026 Recap appeared first on Voltage Control.

Most organizations are spending real money on AI adoption and getting almost nothing back. Not because the tools are bad. Because the approach is broken. The pattern is predictable at this point. An organization buys licenses, schedules training sessions, maybe runs a webinar series, and waits for transformation to happen. Gartner’s research shows what comes next: within a day, employees have lost 50% of what they learned. After six days, 90% is gone. License counts rise. Active daily usage stays flat. This is not a training problem. It is a design problem. And the organizations figuring that out are doing something fundamentally different from the rest.

Why Training Doesn’t Work (And What the Data Actually Says)

The evidence against one-time AI training is now overwhelming, and it comes from multiple directions. Gartner’s Digital Workplace Summit data shows that 72% of IT leaders say Copilot users struggle to integrate it into their daily routine. When Gartner surveyed what happens after AI training events, they found the same pattern across enterprises: usage spikes briefly, then drops to near zero. The classroom model produces AI literacy at best. It does not produce fluency. Anthropic’s Economic Index, drawn from over a million real conversations, found that experienced AI users get measurably better results than newcomers, and the gap compounds over time. People who have used AI for six months or more have a 10% higher success rate. The difference is not explained by what tasks they do or what tools they use. It is explained by how they interact.

Experienced users iterate, push back, validate, and treat AI as a collaborator. New users delegate and accept. That gap does not close with more training sessions. It closes with practice. The organizations that participated in our first AI Ways of Working executive mastermind confirmed this from the practitioner side. Leaders from enterprises spanning education, healthcare, gaming, and automotive all reported the same thing: training events produce a temporary spike, not a lasting change. The organizations seeing real traction are doing something structurally different. What emerged from that conversation, reinforced by Gartner’s research and confirmed by Anthropic’s behavioral data, is a three-part framework. Not a training program. A design pattern for how organizations build AI fluency that actually sticks.

Step 1: Leadership Modeling

The first step is the one most organizations skip entirely: leaders must visibly use AI themselves. This sounds obvious. It is not happening. In most organizations, the executives who approve AI budgets and mandate adoption are not demonstrating their own use. They talk about AI strategy in all-hands meetings. They do not show their team what it looks like when they use AI to prepare for a board meeting, draft a strategy document, or pressure-test a decision. The gap between what leaders say and what leaders do is the single biggest reason AI adoption stalls. When employees see their manager using AI as a genuine part of their work, not as a demo or a gimmick, it does two things simultaneously. It signals that AI use is safe, removing the fear that experimenting with the tool will be perceived as incompetence or laziness. And it makes the abstract concrete. A leader showing how they used AI to restructure a presentation or challenge their own assumptions about a market entry gives their team a mental model for what “good” looks like. Gartner’s keynote research named this as one of three cultural pillars for AI adoption: leaders use tools and share stories, not mandates.

The distinction matters. A mandate creates compliance. A demonstration creates curiosity. Cynthia Phillips, an industrial psychologist who presented at Gartner’s Digital Workplace Summit, found that 70% of employees are unsure whether they will lose their job by adopting AI technology. They will not voice this fear publicly. They make a silent calculation: “Is this story going to work out well for me?” When leaders model AI use, they are not just showing a workflow. They are answering that silent question. They are showing that AI is part of how this organization works, not a threat to how people work in it. The standard objection is that senior leaders do not have time to become AI power users. That is the wrong frame. Leaders do not need to be the most fluent AI users on their team. They need to be visible ones. A five-minute story in a team meeting about how AI helped them rethink a problem is worth more than a month of training content.

Step 2: Guided Practice

The second step replaces open-ended exploration with small, specific assignments. Most AI training programs make the same mistake: they give people access to tools and tell them to experiment. This sounds empowering. In practice, it produces paralysis. When someone who has never used AI sits down in front of a blank prompt window, the most common response is to try something trivial, get a mediocre result, and conclude the tool is not useful for their real work. Guided practice means giving people five specific things to try, not fifty. It means designing prompts that connect directly to their actual workflows, not generic demonstrations. It means scoping the initial experience so that success is likely and the connection to real work is immediate. Tori Paulman, the Gartner analyst who authored the executive/IC perception gap research, calls this the difference between AI literacy and AI fluency. Literacy means you can use the tool functionally. Fluency means you can operate in context without consciously thinking about the tool.

Generic training produces literacy at best. Fluency requires daily applied use in the context of real work. Her recommended approach is what she calls the “Option 3” workflow: an expert builds the prompt or template, a less experienced team member executes with AI, and the expert reviews the output. This preserves learning for the person developing skills while capturing the efficiency of AI. It is slower than having the expert do everything with AI alone. It is the only approach that does not hollow out your talent pipeline in the process. The guided practice step is where most organizations fail because it requires design work. Someone has to identify the five most valuable AI applications for a specific role, build the prompts or templates, and create the conditions for people to try them with low stakes. That is not a training department function. It is a facilitation challenge: designing an experience where people can build capability through practice, not instruction. The practical difference is stark. An organization that sends employees to a 90-minute AI workshop gets a usage spike that decays within a week. An organization that gives a team of five a set of role-specific AI exercises to complete over two weeks, with a shared debrief at the end, gets durable behavior change. The content matters less than the structure.

Step 3: Reflection Loops

The third step is the one that makes the first two compound: structured reflection after practice. This is the piece that separates organizations with scattered AI adoption from organizations where fluency is spreading. After a demonstration, after a guided exercise, after someone tries something new with AI, there is a moment where the learning either sticks or evaporates. That moment is the reflection loop. A reflection loop is simple in concept: after experiencing AI in action, teams are prompted to connect what they just saw to their own work. Not “what did you think of that demo?” but “where in your workflow would this apply?” Not “was that impressive?” but “what would you need to change about how you work to use this?” The mechanism is verbalization. When someone articulates out loud how an AI capability connects to their specific context, they are doing the cognitive work that transforms observation into intention. Without that step, demonstrations stay abstract. People walk away thinking “that was interesting” without building a bridge to their own practice. This is not new learning science. It is how skill development works in every domain. Athletes review film. Musicians rehearse, then debrief with their instructor. Surgeons do morbidity and mortality conferences after complex cases. The pattern is always the same: do the thing, then reflect on the thing, then do it again better. AI fluency follows the same pattern.

What makes reflection loops particularly powerful in the AI context is that they surface the real barriers to adoption. When a team discusses where AI would apply in their work, the conversation inevitably surfaces the actual obstacles: “I do not trust the output enough to send it to a client without heavy editing.” “My manager has not said whether it is okay to use AI for this.” “I tried it once and the result was useless because it did not have access to our internal data.” These are not training problems. They are organizational design problems. And they only become visible when people reflect together on their experience. The enterprises in our executive mastermind who are seeing real traction are running these loops consistently. Not as formal programs. As a practice embedded in how teams already work: five minutes at the end of a team meeting to share what someone tried with AI that week and what they learned. A monthly session where a team reviews their AI experiments and decides what to scale and what to drop. A quarterly retrospective where leadership hears directly from practitioners about what is working and what is not. The cadence matters more than the format. Weekly is better than monthly. Monthly is better than quarterly. Quarterly is better than never. The point is not perfection. The point is creating a recurring structure where AI fluency develops through shared experience rather than individual trial and error.

Why This Framework Works (And Training Programs Don’t)

The reason these three steps work where training fails comes down to a fundamental misunderstanding about what AI fluency actually is. Most organizations treat AI adoption as a knowledge transfer problem: teach people how to write prompts, show them the features, quiz them on best practices. But AI fluency is not knowledge. It is a practice. It is closer to fitness than education. You do not get fluent by attending a lecture. You get fluent by showing up consistently and doing the work. The three steps, modeling, guided practice, and reflection, create the conditions for practice to happen. Modeling removes the fear barrier and provides a mental model. Guided practice gives people a specific, low-risk entry point connected to their real work. Reflection loops turn individual experiments into shared learning that compounds across the team. This is also why the “train the champions” approach that many organizations default to consistently underperforms. Champions without a collaborative model become isolated experts. They develop fluency on their own, but they cannot embed what they are learning back into the team. The team’s processes, meetings, and decision-making structures have not changed. The champion ends up on an island. The three-step framework avoids this trap because every step is inherently collaborative. Leaders model in front of their teams. Guided practice is designed for specific roles within a team context. Reflection loops are group activities. AI fluency spreads through the team, not around it.

The Stakes

The urgency here is not abstract. Anthropic’s data shows that the gap between experienced and new AI users is hardening into something structural. The people who started early are pulling further ahead. Gartner projects that by 2027, 75% of hiring processes will include AI proficiency testing. The workforce is bifurcating between people who can work with AI as a genuine collaborator and people who either cannot use it effectively or have let it do their thinking for them. 59% of the workforce needs fundamentally new skills in the next two to three years. That number does not get solved by scaling up existing training approaches. It requires a different design. The organizations that treat AI adoption as a training problem will keep buying licenses that do not get used, running workshops that do not stick, and watching the gap between their most fluent employees and everyone else widen. The organizations that treat it as a practice problem, one that requires visible leadership, structured entry points, and shared reflection, will be the ones where AI fluency actually takes root and compounds. The tools are ready. The question is whether your organization is designed to help people use them. If you are rethinking how your teams build real AI fluency and want to explore what a practice-based approach looks like, let’s talk.

Frequently Asked Questions

How do you successfully implement AI in an organization?

Successful AI implementation is a practice problem, not a training problem. The organizations that get it right design three things: visible leadership use that signals AI is safe and valuable, guided practice that gives people specific role-relevant prompts to try, and reflection loops that turn individual experiments into shared learning. Training programs alone produce a usage spike that decays within a week. The three-step design produces durable behavior change.

What role do leaders play in AI adoption?

Leaders do not need to be the most fluent AI users in the room. They need to be visible ones. When employees see their manager using AI to prepare for a board meeting or pressure-test a decision, it answers the silent question 70% of employees are quietly asking: “is this story going to work out well for me?” Modeling is the single biggest determinant of whether AI adoption sticks at scale.

Why do most AI initiatives fail to scale?

Most AI initiatives fail because organizations buy licenses and schedule training, then expect adoption to happen on its own. Gartner data shows employees lose 50% of what they learn within a day, 90% within a week. License counts rise while active daily usage stays flat. The failure mode is structural: organizations are treating fluency as a knowledge problem rather than a practice problem.

How can teams build AI fluency together?

AI fluency builds through shared practice and shared reflection, not through individual training. Teams that build fluency together typically run a recurring structure: leaders show their own AI use in team meetings, team members try role-specific prompts in low-stakes contexts, and the group debriefs together on what is working. The cadence matters more than the format. Weekly beats monthly beats quarterly beats never.

What is the best framework for AI transformation?

The framework that works is one that treats AI fluency as a designed practice rather than a delivered curriculum. Three steps consistently separate organizations where AI sticks from those where it does not: leadership modeling (visible, not mandated), guided practice (specific, role-tied, low-stakes), and reflection loops (recurring, team-based, focused on what to keep doing). All three are required. Skipping any one of them produces the standard failure mode.

The post Three Steps to Make AI Actually Stick appeared first on Voltage Control.

Trudy Townsend closed out the 2026 Facilitation Lab Summit with a session that went, in her words, “straight at” the topic every other presenter had been circling. After two days of deep work on presence, belonging, voice, and the edges of facilitation practice, Trudy brought the science: why trauma is present in every room a facilitator enters, what it looks like when it surfaces, and what it actually takes to create the conditions where people can show up whole.

Trauma Is Already in the Room

Trudy opened by noting that trauma does not need to be introduced into a facilitated space. It is already there. To make that concrete, she walked the room through the ACEs study, one of the most consequential pieces of public health research of the past 30 years. Conducted in the 1990s by Dr. Robert Anda of the CDC and Dr. Vincent Felitti of Kaiser Permanente, the study asked 17,500 patients to answer 10 questions about adversity experienced before age 18, covering abuse, neglect, household dysfunction, domestic violence, and other forms of harm. The findings were striking: two-thirds of the population had experienced at least one significant childhood adversity. With an ACEs score of four or more, risk for major diseases was substantially elevated. With a score of six or more, life expectancy was shortened by 20 years.

Trudy was clear that this was just the childhood data. Adults keep experiencing things. And the reason it matters so much for facilitators is the same mechanism that made her opening meditation work: the brain pathways that bring comfort and safety are the same pathways activated by traumatic experience. A scent, a facial expression, a tone of voice, a pattern of behavior in a group can all trigger a response that has nothing to do with what is happening in the room right now, and everything to do with what has happened before.

Her definition of trauma was also worth holding: “Trauma is not the event. It is the experience of that event living in your body. It is what happens inside of us as a result of what happens to us.”

Reading the Nervous System

To explain why this matters in practice, Trudy walked the room through Dan Siegel’s hand model of the brain. The thumb tucked against the palm represents the limbic system, anchored by the amygdala, the hippocampus, and the hypothalamus. The amygdala scans constantly for threat and safety. The hippocampus cross-references what it senses against a quick-reference library of past experience, looking for what is familiar. When something registers as significant, the hypothalamus triggers the body’s protective response, flooding the system with hormones. The fingers folded over the thumb represent the prefrontal cortex, where learning, reasoning, and creativity live. When the protective response fires, the prefrontal cortex essentially shuts down. Facilitators have a phrase for this: someone has flipped their lid.

What makes this especially relevant for facilitation is that people with significant trauma histories often walk around in a state of chronic hypervigilance, already partway toward that threshold. They do not signal this. But they do show signs, and part of becoming a more trauma-informed facilitator is developing the ability to notice them. The room surfaced a wide range, including clenched jaws, elevated breathing, redness in the face, crossed arms and averted eyes, sudden withdrawal or silence, nervous laughter, anxious over-participation, thought spirals, and humor used as deflection. One participant offered a crucial caveat: we often do not know what a behavior means. Someone crying might be angry, not distressed. The practice is not diagnosis. It is noticing.

One participant named something the room needed to hear directly: that when we talk about physical safety, we are sometimes talking about different things. For some people in the room, physical safety was genuinely at stake in ways that go beyond room layout or uncomfortable chairs. The conversation did not flinch from that. And Trudy wove it into her central point: trauma-informed practice requires holding the full range of what people carry when they walk through the door.

Safety Is the Antidote

The fundamental antidote to trauma, Trudy argued, is not comfort. It is safety. When the nervous system begins to register safety, the protective response quiets. The jaw loosens. The breath changes. The prefrontal cortex comes back online. Curiosity returns. And with curiosity comes the possibility of learning, of relationship, of productive disagreement.

Trudy named five types of safety she pays attention to as a facilitator: physical safety (freedom from bodily threat), psychological safety (freedom to voice thoughts and opinions), social safety (belonging with the people in the room), moral safety (freedom to do the right thing), and cultural safety (freedom to express one’s true identity and beliefs). Participants worked through table conversations about what builds each type of safety in their practice, and what erodes it.What emerged was practically rich. Physical safety includes room layout, scheduled breaks, sensory considerations, and accessibility. Psychological safety is shaped by how a facilitator responds to contributions, including the ones that do not receive enthusiastic affirmation. One participant named that saying “I love that” in response to some contributions and not others is itself a signal that people will notice and feel. Grounding the voice, slowing the pace, and naming what is happening in the room all contribute to co-regulation. Social and moral safety connect to how clearly shared values and agreements are established, and whether the group is invited to create them rather than simply receive them. And several participants pushed for replacing “safe space” with “brave space,” recognizing that a facilitator cannot guarantee safety but can invite people to commit to bravery together.

Trudy closed with the thread that had run through the entire session: trauma-informed facilitation is not a checklist. It is the sum of everything the summit had been exploring over two days. Showing up regulated. Noticing before interpreting. Preparing well. Asking people what they need before the session begins. Attending to power and agency, because trauma almost always involves a loss of both. And trusting that when people feel genuinely safe, something opens in the room that no technique alone can create.

Watch the full video below:

Transcript of Trudy’s Session:

Trudy Townsend:
Y’all, what an incredible couple of days. Wow. Thank you for staying for this. I really appreciate it. I know several of you have flights to catch. I have a flight to catch too, so I get it. If you need to get up and leave, I totally understand. I’m not going to be crushed or anything, but thanks for being here to the very end. I just want to acknowledge that we have been holding a lot over the last couple of days. Our brains are full. Our hearts are full. We’ve been doing a whole lot of touchy-feely stuff.

We’ve been sort of pushing ourselves to those edges and that’s a lot. And I just want to acknowledge that. Thank you. They keep telling me it has to be higher and that’s hard for me. Yeah. Thank you all for being such an incredible audience. From every presenter has told me what an incredible audience you are to work with. So thank you for that. And thank you for the hard work you’ve been doing at Getting to Your Edges. As I start, I know that Dan shared some sort of guiding principles. Does it have to be turned on?

No, I don’t know. Dan shared some guiding principles and I want to share some ground rules too. If my slide starts working, that’d be great. But if not, that’s okay. As we get started, I just wanted to share these couple of ground rules. Every other presenter has really danced around exactly what I’m going to talk about today. And I’ve loved it. Thank you all for setting me up so well. But what I’m going to do is go straight at this topic. And some of you have much more expertise in this topic than I do.

For some of you, this topic is really, really difficult. And for some of you, you can’t wait for me to get started. But I really want you to take what fits, what seems to be great. If I say something that doesn’t jive with you, just let it go. Just leave it. Y’all have been such an incredible audience to just go with the flow, and I so appreciate that. I really do. I really want to ask you to do some deep reflection today. And as you do that deep reflection, I also want you to maintain your own safety. So you’re invited to participate with me. I would love it if you do. And if you can’t, that’s okay too. I get it.

As we broach this topic that I’m probably scaring you all about, please try to care for one another, but also care for yourself. If you need to get up, whatever you need to do is okay. And it’s always okay to just pass. You don’t have to share, but you guys are great at it, so that’s amazing. It’s the end of the day, and you guys have been amazing, but I really want to start with yet another mindful moment. Can we do it? Are we able to do it one more time? One more time, one more mindful moment. It’s really going to help set up my talk, so I appreciate that.

I also just want to acknowledge that for some of us, these mindful moments are so Zen and amazing and incredible. And for some of us, an incredibly quiet mind, closing our eyes, sitting next to someone who is very still is scary. And I get that. So if you’re one of those people where it’s kind of scary to have a quiet mind, I’m going to tell you a little trick. If you just gaze down at your feet and let your body be still for a minute, everybody will think you’re doing it.

So if you really don’t want to do it, that’s cool. No problem. So I’d like to start with this mindful moment, and if you will, just get comfy in your chair. Just get comfy. It really helps if you sort of settle into the chair, maybe push yourself to the back of the chair, let the chair support you. Sometimes it helps if your feet are flat on the ground. And often when we’re mindful, we just start by noticing our breath. You don’t have to change your breath. Just notice the inhale and the exhale. Your body already knows how to breathe, so you don’t have to fix it.

There’s no right or wrong way to do it. Just notice your breath. Now, I’m going to invite you to bring to mind a scent, a smell. Doesn’t have to be a strong one, not an intense one. Just maybe something that feels pleasant, comforting, familiar. Some of us lost our sense of scent or smell during COVID. So if that’s not a great doorway for you, you’re welcome to choose a sound, an image, a place. You get to choose. You don’t have to name it. You don’t have to picture it perfectly. Just let something come to you.

As you hold that scent or sound or image, notice what, if anything, comes with it. Maybe it’s a place, a season, a person, or a feeling, whatever it is. If nothing has come, that’s okay. No need to force it. As this moment kind of concludes, I would just welcome you to come gently back to the room. Maybe start to feel the chair underneath you, supporting you, feel your feet on the ground. And when you’re ready, come back to me. Open your eyes.

Oh, thanks everybody. That calmed my nervous system. I appreciate it. Is there anybody who would like to share what came to them in that moment? You don’t have to tell the whole story, but is anybody willing to share maybe the scent or feeling that you had?

Speaker 2:
I thought of wet earth, the smell of wet earth and vine ripened tomatoes, which my grandmother used to grow in our backyard and I used to eat them off the egg, whole tomatoes. And it just always brings me back to a place of getting safe and loved and grounded.

Trudy Townsend:
And also, there’s nothing better than a garden ripe tomato.

Speaker 2:
Oh yeah. Yeah.

Trudy Townsend:
I love that. I love that. Anyone else?

Speaker 3:
I thought about Cincinnati chili. For folks who don’t know is like clothes and chocolates and all spice. And thought about watching the Super Bowl with family and sort of comfort.

Trudy Townsend:
Oh, I love that. Give me some Fritos and chili anytime. Yeah.

Speaker 4:
So I thought of honeysuckle. It could be the beautiful weather, starting to make summer plans, but honeysuckle goes from my childhood even to now and all the places we love to visit. It reminds me of my close friends I grew up with that I’m still in touch with. It reminds me of moments with my child. I just love it.

Trudy Townsend:
Oh, I love that. We have honeysuckle on our ranch and when you’re riding a four-wheeler and you drive right by that, ugh, that’s a great smell. I love that.

Speaker 5:
So I actually didn’t think of a smell. I am a texture person.

Trudy Townsend:
I love that.

Speaker 5:
So I thought of the … There’s this blanket called the Lola blanket and it is the softest thing on the planet. Think bunny ears velvet. It’s worth every penny. It’s not cheap. I’ve discovered it at a friend’s house. She got it for Christmas. And as soon as she let me touch it, I like dove right in. I head first into this blanket. I’m not even joking. And then my friends were laughing and I was like, “Oh my God, what is this blanket?” It’s the best heaven feeling that I can think of. So I don’t get any points for this, but lola blankets if you’re interested.

Trudy Townsend:
I love that. Love it. Earlier yesterday, it was yesterday, I heard somebody, I think from this region of the room, say, “Wow, our brains are really incredible,” and they are. Just a moment of bringing a scent or a texture or a picture to mind and boom, we’re somewhere else in a different time, in a different place, just completely somewhere else in our mind. It’s really, truly incredible. I wish that I was a Dan. I wish that I was a Brian or a Chris and could just really practice mindfulness all the time because it’s incredible. My mind’s frenetic and so it’s hard for me to do, but our minds are brilliant.

The thing about our brains though is these same pathways that bring us comfort, that bring us familiarity, they’re the same neuro pathways that also work for traumatic experiences. And that’s why trauma is so pervasive. It’s in every single group that you walk into. It’s in the room already. Didn’t have to do anything to put it there, even though sometimes I try my best. And it shows up. No matter what we’re doing, it shows up. It shows up not always in words, but it always shows up in nervous systems. And it shows up in how the group engages.

How many of you have heard of the ACEs study? Raise your hands. This group is so informed. I’m loving it. Love, love, love it. This is really funny. I am probably one of the least nerdiest people I know, but I’m going to spend a few minutes talking about data and some science. There’s a couple of you who don’t know about ACEs, and I love it when I’m the first one that gets to tell you about ACEs. When I’m talking about trauma, I think it’s so important to mention this really groundbreaking study.

So the ACEs study was done in the 1990s, and Dr. Robert Anda from the CDC and Dr. Vincent Felitti, who worked for Kaiser Permanente partnered up to do this study. And they had 17,500 participants who were patients of Kaiser Permanente answer a 10 question survey. These 10 questions were about adversity that the patients had experienced in childhood before the age of 18. The questions were about whether the person

had suffered abuse or neglect, whether they were made to feel loved or special as a child, if their basic needs were met.
There were questions about household chaos and dysfunction, divorce, domestic violence, living with somebody who has a mental health condition or maybe who was incarcerated. So that’s what the questions kind of were. And the way that the ACEs study worked is if that happened to you in childhood, whether it happened once or repeatedly, you marked a one. So you could have an ACEs score from 1 to 10.

And what they found was astonishing. They found that two-thirds … so if you look next to you, the people on either side of you, two-thirds of the population had experienced at least one significant childhood adversity in their lives. Then I think what’s really incredible about this study is it was Kaiser Permanente. So they took that data and they compared it with adult health outcomes. And what they found feels a little devastating to me, but what they found was that there was a dose response between adversity you had experienced in childhood and poor health outcomes as an adult.

With an ACEs score of four or more, your risk of heart disease, cancer, emphysema, many of the top diseases we see in the world were highly elevated. And with a score of six or more, your life expectancy was cut by 20 years. Now, this is incredibly depressing and I understand that. Also, you might think it was the 90s. I’m not going to tell y’all what I was doing in the 90s because I like to think the Botox is working. I live in that sort of world, but if you’re worried about that, these questions have been added to all kinds of public health health risk assessment surveys. They’ve been added to birth of state population health surveys, and the results have been crazy consistent over time.

What this study tells me, and the whole point I want you to take home, is that this matters a lot. Childhood adversity, they’ve also found that chronic stress, living in chronic stress and toxic stress, impact your brain in the exact same way that trauma impacts your brain. It leaves a lasting imprint. I also want to mention, holy smokes, this is just what happened to you in childhood. We’re a bunch of grown up people. Stuff keeps happening. I got hit in the eye with a pickleball like two weeks ago. I went to play pickleball again and I was like, “It was hard.”

Stuff happens and it impacts us and that’s important. And when I think about this and I think about how incredible the brain is that we just think about a scent, we just think about it, bring it to mind and all of a sudden we’re in a different place. What that tells me is every time somebody sees a facial expression, look in my eye, I think about my son when I take a deep breath in front of him. They’re getting a message, some kind of message. And I don’t know what that message is, right? But they’re getting some kind of message. And that’s why this is so important to us as we walk into rooms.

So let’s talk about this word trauma just a little bit more. I always like to level set on what it is. When I first came to understand and learn about trauma, and by the way, I’m not a mental health professional. I’m just a regular person who learned a lot about this. But when I first came to understand it, we were talking about trauma as an event. What we’ve come to understand now is that trauma is not the event. Yes, something happened to you, but trauma is the experience of that event living in your body.

It’s what happens inside of us as a result of what happens to us. And there’s all kind of different ways you can … Pathways to get to trauma. These are just a few of the types of trauma that we experience across our lives. Shock trauma, that’s like the car accident, the crazy weather events we have these days, that big fire ranch I talked about, ranch, fire, whatever it is. Those are shock traumas, things that happen and sort of out of the blue and shift your worldview or your experience. Relational trauma is the one that most people think of because hurt people hurt people.

Relational trauma is that trauma that happens when you think you’re in a safe relationship and it’s not so safe. So it’s abuse, neglect, the lack of psychological safety. That’s relational trauma. Seriously, chronic stress, toxic stress. Some of us just live in it all day long, every day. It’s bad for you. Vicarious trauma, that’s one that’s a little bit hard to understand, but a lot of caregivers get vicarious trauma. It’s often called secondary trauma as well.

So this is something that didn’t happen to you. It happened to somebody else, but you’ve heard about it maybe so often that now it has shifted your worldview, shifted the way that you think about things. So that’s vicarious trauma. And then there’s historical trauma. There’s this science called epigenetics, which I can’t even talk about it because I’m not that smart. But epigenetics shows us that trauma can live in our bodies for up to two generations.

So what happens to me if I don’t deal with it, happens to my kids and my grandkids. I don’t have any yet. Remember the Botox. So all of these are different doorways. These are not all the doorways. Lots and lots of stuff can happen to us across our life, but we come at it from all different experiences. All of them shape how our nervous system learns to protect us. And that protection is the key word because this trauma, it’s not weakness in the system. It’s literally an adaptation of our system. It is what is keeping us alive.

So we have talked and talked about this. Many of my colleagues have mentioned nervous systems. You as facilitators know this intuitively, but I wanted to really share exactly how it works. Some of you know this better than I do, but I’m going to try to run through it with you all using this hand model of the brain. So a really smart guy named Dan Siegel. Anybody heard of the hand model of the brain? Oh, lots of you. Yeah. So when I get it wrong, help me out. So Dan Siegel came up with this hand model of the brain, and I like it because I think it’s just such a good and easy way to remember this. So if you put your hand up in the air, you can do it or not do it, whatever you want to do. We’re looking sort of at a model of the brain, right?

So this right here is your spinal cord coming up into the base of your skull here. I’m sorry about this. The base of your skull, that’s your brain stem right there. And if you take your thumb and just tuck it in like that, your thumb represents a very small but very powerful region of your brain. This is our limbic system and the limbic system is made up of the amygdala. You all want to say amygdala with me because it’s just such a fun word to say. Amygdala. Yeah. Yeah. The amygdala, the hippocampus, and the hypothalamus. Those are the little organs within the brain.

So the amygdala is our central hub for emotions. It’s crucial at forming and consolidating memories and associating them with feelings or emotions. That amygdala, it’s busy. It’s constantly scanning the environment all the time, looking for cues of safety, looking for threat. That’s what the amygdala is doing. And when the amygdala senses something, anything, it immediately, really quickly, in nanoseconds, sends it to the hippocampus. The hippocampus converts it into short-term memories. They convert short-term memories into long-term ones. And I like to think of it as our quick reference library. So I always think of, I think of this little guy up in my brain just running through the files really, really, really quick, like AI, just running, running really quickly through it. And our brain’s like what’s familiar.

So whatever is going on, that facial expression, a smell, whatever it is, the hippocampus is like, “Woo, I know that. I know what that is. I know how we should feel when I see that.” So that’s the hippocampus. And when the amygdala and the hippocampus sense something really important that’s vital, it sends that message right to the hypothalamus. And the hypothalamus is connected to our nervous system. And the hypothalamus is like, “Oh, I got to do something. I got to protect.”

And all of a sudden, hormones start coursing through my body and releasing all of those things so that I can protect myself. Now it takes really quickly to do that, but it’s a real long time before I’m able to kind of cover that up, shut that down, regulate myself with my prefrontal cortex. So this is your prefrontal cortex. Your prefrontal cortex is really important. It’s where you learn. It’s where you think. It’s where you create reason, right? But when this whole thing is going on, the prefrontal cortex literally shuts off.

And that’s when we say people have flipped their lids, right? So if you flip your lid before your prefrontal cortex can kind of come back online, that looks all kind of different ways and groups. So unfortunately for us as facilitators, people don’t kind of walk around with their hands showing us, right? And people who have experienced lots and lots and lots of trauma, or even maybe just trauma in groups, they walk around in a state of hyper-vigilance and that kind of looks like this, right? They’re right on the edge. They’re ready to flip their lid at any moment because they’re already halfway there.

So when you think about that and you think about two-thirds of the population have experienced trauma in childhood, and the rest of us sure got it as adults, this is happening everywhere we go, and nobody’s walking around showing us where they’re at, unfortunately, although we have started to teach children to do it. But you know what? There are signs. There are signs that it’s happening when we’re facilitating. There are physical signs, right? So think about that system, what’s happening in that system when those cortisol and other hormones get released, right? I get hot. My husband calls it hot mad.
So I get really hot. I sweat when I eat cinnamon bears, so it really happens quickly for me. People’s face get a little red. Sometimes their breathing elevates, but there’s lots of physical signs. There’s emotional and psychological signs. People do all kinds of different things, right? Get quiet, get loud, shut down, all kinds of different emotional signs that we see in rooms, and there’s behavioral signs. People fidget, people get up and down, all kinds of things, right?

We’ve seen this in rooms over and over again. You’ve seen this in rooms. And if we can get really good at seeing this in rooms, we can get really good at calming nervous systems. So what I’d love for you to do is just on your own … I’m going to give you two or three minutes, not a very long time, on your own, I’d love for you to just jot down a couple of the signs and symptoms that you’ve noticed when you’re in groups. Some of you have been facilitating forever and you have all kinds of stories in your head about this. For others of you, it might Might be meetings that you’ve been in or one-on-one conversations or family groups. But what I’d love for you to do is jot down one or two symptoms, three or four symptoms, however many you can think of, in each of these categories.

What were some of the physical signs you’ve noticed? What were some emotional or psychological signs that you’ve noticed or behavioral signs? What we’re looking to get at is what do we notice in rooms when nervous systems are dysregulated? How will we know that’s happening? So I’m going to give you a couple minutes. After that, I would love for you to be able to discuss this at your tables. Remember, when you’re writing stuff down and reflecting to yourself, be as open and honest with yourself in those reflections as you can. You only have to share what’s safe.

Okay. Hopefully you have come up with a couple of signs or symptoms. I’d love for you to share those at your table. Just talk together about the kinds of signs and symptoms you’ve seen. All right. Who has a physical sign or symptom that you want to share? By the way, huge shout out to Katie and Mark. Katie and Mark, thank you so much. They have made this possible. Thank you. Who has a physical sign or symptom they want to share? Come on, you all have not been shy. Right behind you, Katie.

Speaker 6:
I had clenched teeth or narrowed eyes.

Trudy Townsend:
Yeah. Yeah. Oh my goodness. That just is me. I just clenched my teeth like this so much and so many people do. Also in our neck, right? That response is muscle tension and it happens so quickly. Thank you for that one. That’s such a good one. Yeah, back here.

Speaker 7:
I’ve heard kind of a bundle of the emotional responses. It’s all being related to protecting the midline. It’s like a very animalistic. So it’s the averting the eyes, crossing the arms, leaning back, and it’s kind of protecting your face and your center body. It’s like-

Trudy Townsend:
Very Right.

Speaker 7:
… the same way you react to any kind of threat, like it’s a physical threat, even if it’s not.

Trudy Townsend:
1,000% because it’s about survival. Such good insight. Thank you for that.

Speaker 8:
I struggle with this a little bit because I’m unsure if I actually know what any signs or symptoms actually mean, unless I actually explore and ask them what it means.

Trudy Townsend:
Right.

Speaker 8:
Because someone could be crying. I think you have an example of a person, a story was shared with me where a person in facilitation was crying and people were really trying to console and help. And then there was a moment where she was like, “Stop. I’m not upset. I don’t need anyone to hold me. I’m actually angry and I’m processing.” So there was an automatic assumption that people knew what the person was experiencing based on a particular behavior or emotion. So yeah, that’s kind of where I am.

Trudy Townsend:
I really love you bringing that up because you’re right. We don’t know. We’ve all been experiencing these two days, every single one of us. And my experience is super different than your experience, right? Some of us are like loving this. Some of us are like, “I got to go home now.” Right? This is about, we don’t necessarily have to know exactly what to do. All we have to do is notice somebody’s experiencing something, right? And as facilitators, that’s what we’re doing. We are fine-tuning our noticing skills, right? That’s what we’re doing. As we interact with groups, we’re noticing. How about emotional? Over here. Wow, you’re ready.

Speaker 9:
Yeah. For me, I tend to overthink a lot of things. I feel like my brain is overloaded with too many thoughts and I would also go into thought spirals, overthink a lot of things.

Trudy Townsend:
Yeah, absolutely. We all have that over thinker in our groups, don’t we? And sometimes overthinking is so hard because your brain’s going so fast and then somebody asks you a question and you’re like, it can look like dissociation. It can look blank. We see those blank faces. And back to your point over there, that’s not a bad thing. You’re diving into whatever we’re doing, right? You’re thinking it through, you’re overthinking it. It’s not bad. There’s nothing I need to do, but I might notice, right? As a facilitator, I might notice that and maybe think, “I should slow down or not.” Yeah. Really, thank you for that. Anybody else? An emotional sign or symptom? Psychological sign or symptom? I know that can be hard. Yeah.

Speaker 10:
Just based on what you shared right now, the thing that came to mind was hyper-vigilance. You mentioned it earlier. Yeah. I think hyper-vigilance can be a sign of maybe some of us that had to be caregivers very early on in our lives. And so we enter teams and we feel like we need to emotionally support everybody and in a large capacity and so a sign of that could be always kind of being hypervigilant. Is this person being included? Is this other person being listened to? And that can be a sign of that.

Trudy Townsend:
I so appreciate that you brought that up. I failed to mention earlier, it’s all about that fight, flight, freeze. What you’re talking about is fawn, right? That need to take care all the time, that need to people please, that need to overdo that, right? That is also a nervous system response. So thank you for bringing that up. Appreciate that. It gets really heavy, doesn’t it? Back here in the very back. Mark, thanks.

Speaker 11:
I think it’s interesting because it’s really about content as well, right? So being a facilitator requires us to pay attention to individuals as they’re coming in the room because everyone and all these physical, emotional responses are so different in every single person. And so when I see this in X person, it might not mean anything. But if you’re noticing something that’s out of the ordinary, then it becomes the sign that’s showing you.

And that’s the, “Hey, let’s pause. I noticed this.” And it’s giving yourself enough grace to be that observer from the beginning because we don’t know and no one’s going to tell you, “Hey, nice to meet you. I have some trauma and this is what I’m going to give you. So please pay attention to this for the next 20 minutes.” So that’s the part of how do we continue to scan the room, sculpt the room, to look for, again, not something that may be obvious to us, but it’s out of the ordinary for maybe that person.

Trudy Townsend:
I really appreciate that. And what you said made me think of another really important point, you and you. Guess what? It’s not just the participant because when you notice something, you’re having a response too, right? And you’re thinking some kind of way that could be totally not the thing, right? But we still need to notice because it is out of the ordinary for the group or for that person, right? So thank you for bringing that up. Appreciate you. Ooh.

Speaker 11:
Got it.

Trudy Townsend:
Over here and then to you, Joe, and then we’ll move on.

Speaker 12:
I think one of the things we need to keep in mind too is sometimes what will show up will depend on the culture.

Trudy Townsend:
I love that. I heard you talking about that back there.

Speaker 12:
So in North America, we’re often a guilt and consequence culture, Western, right? But a lot of my friends who are from Asia are much more of a shame, honor culture. And then other people I work with are a power, fear culture. And so how it shows up will depend on the culture that you’re dealing with and working with.

Trudy Townsend:
So important to talk about. We’re going to talk more about that later. Thank you for that. Joe, and then we’ll try to move on.

Joe:
Yeah. I was just going to mention one that has prompted a lot of reflection for me is humor and how nervous laughter or sort of attempts to redirect conversations always to somewhere humorous triggers for me a series of exchanges and that’s sort of how you respond to them, that it’s something that generally is a positive thing. And so you may give back to it, but in the spirit of what others have said, you don’t know whether it’s sincere humor, whether it’s reflection of something deeper. And so humor is one that to me particularly is complicated to navigate.

Trudy Townsend:
I so appreciate that. I was talking to Brian earlier. He was talking about how much he uses dad jokes, right? And a lot of people, we use all different kind of protection mechanisms. I have another girlfriend that whenever you go to any party with her, she is the show. And I love it because then I don’t have to be the show, but that is a fear response. That’s okay. I love it. I eat it up. That’s great. And I know she needs to be that person. And that’s great.

So thank you for that. It’s true. It shows up in all kinds of different ways. What do you think is the one thing in all these scenarios, whether we know what’s happening or not, what is the one thing that people need when we’re activated, when our nervous system gets activated? What do we need? Anybody got a guess?

Speaker 14:
Awareness.

Trudy Townsend:
Awareness? We as facilitators sure need awareness, right? What do they need when their nervous system is activated?

Speaker 14:
[inaudible 00:41:26].

Trudy Townsend:
Right. What she said is co-regulation.

Speaker 14:
Like a co-regulation.

Trudy Townsend:
Girl, you are working it. Thank you. Yeah. Co-regulation.

Speaker 14:
Attunement of some kind from another human in some way, maybe.

Trudy Townsend:
Yeah. I think what people are looking for is safety because our nervous systems are trying to protect us, right? They’re really trying to get to a place of safety. Listen, this isn’t bad. This is good. It keeps us alive, right? Our nervous system is so important to our survival. It was built for the dark ages where we are running from tigers. So the nervous system is good, but what it is seeking is safety. So safety is a fundamental antidote to trauma. It’s not being more comfortable. It’s not that one person in every room who complains about the temperature that I can’t do anything about.

It’s safety because when the nervous system calms down, lets the prefrontal cortex kick in, it starts to register safety. We naturally shift out of protection mode. Our breath changes. Our jaw loosens. Muscles start to soften. Our attention widens when we feel safe. We’re able to return to curiosity. We’re able to use our words. So safety is what makes learning possible. It’s what makes relationship possible. It’s what makes engagement happen. And it’s what makes conflict workable. For safe, we can engage. We can disagree. If we’re not safe, it’s hard to do that.

Here’s the deal though. There’s not just one type of safety. If you Google safety, guess what? You’re going to get a lot of hard hats. Okay. So there’s not just one type of safety. There’s lots of different types of safety. These are the ones I pay the most attention to when I am facilitating. So physical safety. Are we safe in our body, safe from physical threat? Psychological safety. Are we safe with our thoughts? Do we feel safe enough to voice our thoughts and opinions? Social safety. Do I feel safe with the people at my table? With the people in this room? I feel really safe with this group. You guys are amazing.
And then I feel like I can’t say this one enough in the groups that we’re facilitating. Moral safety. Do I feel safe enough to do the right thing? Cultural safety. Thank you so much for bringing that up. Do I feel safe enough to express my true self, my true beliefs? All right, y’all. Let’s make this real. You guys are professionals. So what I would love for you to do right now, first of all, I need a volunteer at each table. Can you raise your hand if you’re willing to volunteer? One volunteer for each table. Looking at each table? Awesome. Thank you so much.

Your job is not hard. What I’m going to ask you to do is I’m just going to ask you to start the conversation and I’m also going to ask you to make sure that you’re clicking through each type of safety. Okay? So keep the conversation going. We’re not going to have a long conversation. We’re going to have about a seven-minute conversation. But what I want you to do at your tables is I want you to talk through how you, as a facilitator, what are the strategies you use to build physical safety?
What do you do in your planning processes throughout the day? How do you build physical safety for your groups? How do you build psychological safety for your groups? What do you do as a facilitator? How do you make psychological safety happen? Social safety, moral safety, cultural safety. Move through that list and just engage in conversation with one another, sharing the strategies that you use to build safety in the rooms you’re in for each of these types of safety. Okay? Seven minutes, table facilitators. Awesome.

Okay. Are y’all back? Back? Those were good conversations. Really good conversations. This is a talented room. So I’m going to take a couple from each category. There’s a lot of categories. So physical safety. What are some things that you all do as facilitators? What are the choices you make? Conditions that build safety. Got it.

Speaker 10:
So physical safety was one that we initially started talking about … not really sure how to word it, but we dove deeper into it and we thought about intentionally scheduling breaks, making sure that we could, when we’re meeting with people, we asked them about their energy levels, giving them a heads-up that a break was coming up, even going more deeply into also the smells of the shared space, understanding that some people might be more sensitive to smell and that might affect their experience in the moment, making sure that there’s enough chairs so everybody is comfortable. So exploring the idea of comfort and safety together.

Trudy Townsend:
I love that you brought in the senses. That’s beautiful. Thank you so much. Let’s take one more condition that builds safety.

Speaker 4:
Adding to those. We also talked about location and room layout and things like that, that truly physical to the room that might fit that conversation you’re trying to have.

Trudy Townsend:
I love that. I think also where people are at in the room is important, right? Maybe making sure you’re spreading out the power dynamics a little bit across the room. Really good. What are some conditions that erode safety in groups? You don’t have to tell me big stories, but maybe something that you know that really erodes physical safety.

Speaker 11:
It only takes one.

Speaker 6:
I was just going to say, we had uncomfortable chairs at one point and there was different … Just people who were very uncomfortable in certain chairs. And we also were one time at a school that the elevator was not working. And so people who had mobility struggles to get to the second floor of a building that was like right from the very start of the day, put people off in their physical safety.

Trudy Townsend:
Absolutely. What about activities that require physical movement? Yeah. One here, one over there.

Speaker 14:
I was going to say that … Oh, sorry. I was going to say that everybody must stand and stay standing the whole time. I’ve been in workshops where people have done that.

Trudy Townsend:
Yeah. Yeah.

Speaker 14:
You don’t know what people’s issues are.

Trudy Townsend:
Right. Right.

Speaker 15:
I just, in terms of the elephant in the room, wanted to hold some space for the fact that truly there are some bodies that are not safe. There are some of our colleagues here who are more likely to get rounded up than others. There are some of us who have a historical trauma and some of us who don’t, and it feels like we’re not really talking about safety. And if you’re someone like me and I have to look at my students and say, “Here’s what will happen if someone comes to round you up.” When I have to look at trans faculty and say, “Okay, if they will not let you use your passport, you do not have to go to this thing.”

The physical safety that we’re metaphorically talking about, I just want to say to the people who might not actually be physically safe, I would love for us to talk about actual unsafety, people who are being oppressed physically, women, gender violence. When we talk about bodies being traumatized, I think we’re really talking about survivors of sexual violence, right? So I just want to ground for a second for people who might be sitting like I am going like, “I don’t know if we’re all talking about the same safety.”

An uncomfortable chair is not physical unsafety. That’s a lovely way to talk about safety, but I just want to be sure that if you’re sitting there going like, “Yeah, I just don’t want to get rounded up. I’m hearing you.” And I would love for people to think about those things too. Sorry.

Trudy Townsend:
No, I really appreciate that. Thank you for bringing that into the space so much. There is such a rainbow of things because experiences live in our body, fear lives in our body. So thanks for bringing that up. There is a wide, wide list of things that are our responsibility as a facilitator, no matter what we’re facilitating. We’re facilitating programs, we’re facilitating sessions. So thank you for that. Let’s talk about psychological safety because that matters too, right? Are we safe enough to be here? Yeah. What are some things that we do as facilitators that build safety?

Renita:
I think one thing that we talked about and I was identifying myself of how I maybe inadvertently losing safety is how I’m responding to people’s comments and questions. I’m a very much like, if someone says something, “I love that. I love that.” Then I’m thinking about for the comment that I don’t say I love that.

Trudy Townsend:
Yeah.

Renita:
How is that person now like, “Well, wait, was my comment not valid?” And I just didn’t want to keep overusing. So now I’m trying to find other neutral language of acknowledgement that also doesn’t sound like a robot of like, “Thank you for sharing, thank you for sharing, thank you for sharing.” So making sure there’s like a balance of how I am responding to the group because that evokes all types of trauma and validation and work, all the things that are there.
And then even to the point of making sure my voice is grounded in moments of activation too, because I know that in my past I’ve gotten activated by voices. I’m like, “Oh, you sound like my mom yelling at me.” And so when things get activated, I drop my register down and I slow my tone so that it can co-regulate the group too. So all those little subtle dynamics are alive.

Trudy Townsend:
Using those mirror neurons to help co-regulate with others. Thank you so much too for raising how we interact with groups is really vital to safety. So thank you for that, Renita. Yeah, over here.

Speaker 17:
We talked about the importance of kind of like naming the gaps and when we fail to embody our values and what we say we believe, just to level set and also like create room for that to be acknowledged and maybe ideate on what could that look like if we’re not embodying it, how could it be?

Trudy Townsend:
Yeah. Yeah. That’s beautiful. So much of what we’ve talked about over the last two days really are about how do we show up? How do we demonstrate those values? How do we generate the conditions in the room? Yeah.

Speaker 18:
This is something that I definitely bring into the facilitation practice, but I’ve learned outside of the facilitation practice, which is when you’re in a company of rowdy people, say people who might have drunk too much as well. I remember being at a barbecue once in the mountains in this place in central Italy, and it was like a bike spot and I was brought in by a group of bikers. They were super nice, but they definitely drank too much. They had too much to drink.

And so there was the feeling of the edge was kind of ready to flip. And I had this conversation with one of them who had drunk a little bit less than the others, and he said, “Well, the trick here is just to treat them like gentlemen.” And I was like, “Okay, let’s try it.” And the response was perfect. I mean, they were on edge, but they were like, “Oh yeah, I’m a gentleman. Yeah.” I will stay gentlemenly throughout. And so that’s something I also bring into my facilitation practice by giving people a chance to be generators of safety, psychological safety.

Trudy Townsend:
I love that. You bring up something that I think is vital to safety and that’s respect. People need to be respected in order to feel safe. So thank you for that. Yeah.

Speaker 8:
One here.

Speaker 19:
As facilitators, we all say these catchphrases like, “Let’s create a psychologically safe space or this is a judgment free zone.” Well, we can’t guarantee a psychologically safe space. We can’t guarantee a judgment free zone either. So one thing you said, one thing my group talked about was you had an example is that you say, “Let’s create a brave space.” And would you explain to us what you meant by that?

Speaker 20:
Yeah, this is not my work. If you do a search for a brave space, there’s an awesome quote that goes over it in more detail, but the gist is basically, to that point, we can’t guarantee that something that said is going to make someone not feel safe, but we can all, as part of our agreements, is agree to be brave and to be brave to have these tough conversations.

Trudy Townsend:
I love that. That’s a great reframe. Hi, Dan.

Dan:
Hey. Yeah, I think too, I [inaudible 00:57:20] arrival, just splashed everything everywhere. Yeah, I think part of this we’ve talked a lot about in the session and what we do in that moment, but I also think there’s that piece prior to the session too, how we’re building that awareness together. I find too, we place the burden on the person who’s experiencing the traumatic event and the challenges.

Trudy Townsend:
Right. Point them out.

Dan:
Yeah. Say I’m not. And how do we share that as a collective burden amongst the group so that we can name it too? I think that’s another important piece that I’ve seen work really well.

Trudy Townsend:
Yeah. Yeah. Renita.

Renita:
Sorry. I’m not sorry. I have things to say. But I want to build on what Dan said, because it just brought to my attention something that’s worked in the past, and I’m going to do a Monday as I’m crafting this session, is for people to stop and think about, write down what you need to be safe. I think we are proactively trying to create these things, but people individually haven’t thought like, “Well, I feel safe when, and these are the conditions that I need,” versus, “Well, I don’t feel safe. Well, why?” “Well, I don’t know why. Well, let’s actually take some time to see what safety looks like for you or for what trust looks like.” So that way they can also have a way of taking care of themselves and that can be built into the collective agreements that Dan was also just was talking about too, so letting people have time to think through it.

Trudy Townsend:
I love that. I was running out of time, so I skipped over this. Can I see my slides again really quick? Flynn, like the lights. Oh, good. Renita has taught me so much about interviewing clients, talking with clients, the pre-work with clients. I skipped over this exercise, which was probably a wrong facilitator move, but I mean, maybe we could ask these questions, right? Maybe we could actually ask people what they need.

What I really want to tell you is that trauma-informed care isn’t a list of things, right? It’s not a checklist you can do. It is all the things we’ve talked about over the last two days. It’s being present. It’s understanding the signs and symptoms. It’s preparing well. It’s showing up regulated, whatever that looks like for you. It’s noticing all day long, pre, mid, Post. That’s what trauma informed care is. And all of us are already doing some form of this.

We’re already doing it. But if we can train ourselves to get better at spotting the cues, if we can train ourselves to get better in the interview phase, to get better in the prep phase, to pay more attention to the world around us, what is happening for the people I’m about to encounter? What feels safe for them? What doesn’t feel safe for them? What is their culture? What is their beliefs about safety? If we can get really good at that part, think about the incredible engagement. If I’m safe to engage in a conflict with someone who has more power than me, just think about how we can change the dynamic, change what happens in the world. But in order to do that, we have to access our prefrontal cortex.

We can’t walk around with our lids flipped all the time. It also really matters for us as facilitators to figure out how to regulate in that moment. So it’s noticing ourselves. I love all of this mindfulness stuff that we did. I love the sensing stuff, the embodiment stuff. Not all of us are really good at that. I wish I was better at it. But if we can work on fine tuning those systems within ourselves, creating habits for ourselves, we can get even better at this. Thank you for putting up with my sort of nerdiness. I didn’t get to everything, which is just me. I never get to everything.

Thank you for doing this with me. Thanks for stretching to this kind of hard edge at the very end of a very long two days. I appreciate you more than you know. I am kind of at time, right? 4:30? Yeah. Well, you told me 4:30, buddy. So anyways, oh, I had this whole bit about power. You guys understand that … I just want to say this one more thing. Power matters because when people are traumatized, usually it involves a loss of power, a loss of agency, a loss of control.

So when you’re thinking about those power dynamics, that’s important. It’s really important. And so it’s not only about safety, it’s also about agency. So much about agency. So you guys are already doing this. You know what you’re doing. Let’s just get better at it and better at it. Okay. So I’m going to get to here.

And what I would love for you to do is think about a strategy that you heard at your table. Think about one thing that you’re going to get better at. Put that up on the board. Thank you so much to the whole desk in the back that’s making all this happen. Amazing. Thank you to this incredibly talented graphic recorder. Thank you to the volunteers. We’re going to create brave spaces. We’re going to be more vulnerable. We’re going to think about acknowledging safety. I’m going to regulate myself. I love this, y’all. Thank you so much. Thanks for letting me be here.

The post Facilitating at the Edge: Building Trauma-Informed Spaces appeared first on Voltage Control.

“AI just made execution almost free. So why are most organizations still stuck? Because the hard part was never building the thing. It’s getting two hundred people to agree on what to build, how to govern it, and who’s responsible when it breaks. That’s the new friction.” — Douglas Ferguson

Something has been bothering me for the past year. Every organization I work with is adopting AI. The tools are getting better, the pilots are expanding, the budgets are growing. But most of them are stuck. Not because the technology doesn’t work. Because the people problems got harder.

When execution becomes almost free, every other friction in the organization gets amplified. Decision-making friction. Governance friction. Trust friction. The friction of getting two hundred people to move in the same direction when the ground keeps shifting under them

That’s what I’ve been calling the new friction. And it’s what our new podcast is about. New Friction is a series of conversations with leaders who are living this right now. Not thought leaders theorizing from the sidelines. Practitioners who hit the wall and built something on the other side of it. Each episode, I sit down with someone navigating the real organizational challenges of AI transformation. We talk about what broke, what they tried, and what actually worked.
What we’ll be exploring

• The bottleneck that moved from building to deciding
• The 4x perception gap between leaders and the workforce on whether AI is delivering value
• The shift from individual “AI wizards” to multiplayer organizations
• The slow erosion of how the next generation of professionals learns judgment

These aren’t hypothetical problems. They’re the conversations I’ve been having with leaders at some of the most ambitious organizations in the world at our executive dinners, in our consulting work, and in late-night strategy sessions.
I’ve been capturing them. And now I’m turning them into something you can listen to.

Episode 1 drops May 26th. You’ll be able to find it wherever you listen to podcasts, and on our YouTube channel. Subscribe now so you don’t miss it.

Transcript

Speaker 1 (00:05):
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, it’s 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. Hey folks, I’m super excited to launch this series and the truth is I’ve been thinking about this for a long time. I started out as a CTO, which many of you may know, and AI has been on my radar for years.

(01:13):
Back in 2018, my friend Steven was launching Kung Fu AI, and I came on as an advisor. In fact, we launched our companies right around the same time, and we were there together. He went deep tech, and I went deep human because I could see this coming. The technology was going to get extraordinary. The hard part was going to be us. By 2019, we had Cam Hauser demoing an AI facilitation tool built on GPT2 at one of our facilitation lab events. That’s how early this conversation started for us. So when AI broke into the mainstream a couple of years later, I wasn’t totally surprised. I didn’t exactly see it coming in the way it came, but I was ready. And for the last two years, I’ve been in the rooms where AI transformation is actually happening. Boardrooms, workshops, executive dinners with leaders across the country, and the same thing keeps happening.

(02:08):
The tools work, the pilots succeed, and then organizations grind because nobody can answer the human questions underneath. Who decides? Who’s accountable? How do we know it’s working? What do we do with the people whose jobs just changed? That’s the conversation nobody’s having out loud. Everyone is having it in private. So we’re going to have that conversation here in public. Honestly. In this series, you’re going to hear from heads of product and VPs of engineering who deployed AI and watched their teams get faster and somehow worse at the same time. Transformation leaders who had to rebuild trust after a rollout went sideways. Researchers who can tell us what’s actually true versus what’s hype, and builders who figured out something that worked and are willing to say what it cost them. No frameworks for sale, no predictions about AGI, no vendor pitches dressed up as wisdom. Just the conversations I wish I’d had access to five years ago.

(03:08):
The first series drops soon. If the friction I just described sounds like the friction now emerging in your work, you’re exactly who I made this for. Subscribe wherever you’re listening, bring a colleague. And if you’re a leader who’s living this and you’d want to come to the show and talk through what you’re seeing, reach out. I’d love to hear from you. You can find me at voltagecontrol.com or on LinkedIn. Let’s go figure this out together.

(03:35):
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 Introducing New Friction appeared first on Voltage Control.

“Collaborative AI” is the buzziest term of 2026. Vendors use it. Analysts use it. LinkedIn thought leaders use it. Most of the time it means almost nothing, because the term has been stretched to cover three completely different things at once. A diagram of multiple agents handing tasks to each other gets called collaborative AI. A single user prompting ChatGPT for help gets called collaborative AI. A team using a shared model in a meeting gets called collaborative AI. Three different things, one term. And the thing that actually matters, the thing that is genuinely new about how teams are starting to work with AI, gets buried under the other two. This piece is a working definition. Not the marketing one. The one that lines up with what we actually see when we walk into rooms where teams are doing this well, and what is missing from the rooms where they are not.

The shallow definition (and why it does not help you)

The most common use of “collaborative AI” right now describes a multi-agent architecture. One AI agent generates a draft, hands it to a second agent for review, hands the result to a third for formatting. The agents are collaborating with each other. The diagram is impressive. The phrase has obvious appeal. This is a useful technical pattern. It is not collaboration in any sense that matters for how people work. There are no humans in the loop. The collaboration is between models. Calling this “collaborative AI” is like calling a pipeline “collaborative software.” The work flows through stages, but no one is collaborating. The shallow definition gets worse when it is applied to a single person using a chatbot. Someone types a prompt, the model returns text, the person edits it, sends another prompt. This is not collaboration. It is iterative tool use. Useful, fast, and individual. The output reflects one person’s thinking improved by a model. No one else’s perspective is in the room. If you are looking for what actually changes when AI shows up in a team’s workflow, neither of those definitions will help you.

The working definition

Here is the one that holds up in practice. Collaborative AI is the practice of bringing AI into the room with a team, where it influences collective thinking and output in real time, with shared visibility into how the model is contributing. Three pieces matter, and all three have to be present. In the room with a team. Not one person alone with a chat window. A group, working together, with an AI participating in the work. This could be a workshop, a strategy session, a stand-up, a planning call. The AI is on the screen, not in someone’s pocket. Shapes the team’s collective output in real time. The model is generating, summarizing, surfacing patterns, drafting alternatives.

Whatever the team is producing is being changed by the AI as the team works. Not after the meeting, in someone’s editor. During. Shared visibility into how the model is contributing . This is the part that gets skipped, and it is the part that determines whether the AI helps the team or quietly hurts them. Everyone in the room knows the model is contributing, knows what it has produced, can see what is generated AI versus team thinking, and has the chance to push back. The AI is a participant, not a hidden assistant. When all three are present, you get something that does not happen with individual AI use or multi-agent pipelines. You get a team that can think faster together, with a shared artifact that captures what the model contributed and what the people contributed, and a record of where they pushed back. That is collaborative AI. Everything else is either delegation (one person and a model) or automation (models talking to models).

What collaborative AI looks like when it works

A leadership team gathers to align on a strategic question. The question is on the screen. So is a model. The facilitator runs the team through a structured divergence: each person types a position privately, the model surfaces themes across the responses, the themes go up on the wall. The team sees the patterns the model found and the dissents the model missed. They argue with the model’s framing. They edit the themes. They re-run the synthesis with their corrections. Two hours in, the team has alignment on a position they could not have produced in two hours without the AI. They also have a record of what the model contributed and where they overrode it. The output is theirs. The model accelerated the path to it. Now imagine the same team, same question, without collaborative AI. Three options.

Option A. Each person prepares their position alone, with their own AI assistant. They come to the meeting with polished drafts that look similar because the underlying models trained on similar content. Discussion devolves into refining the most articulate draft instead of surfacing the real disagreement. The model contributed to each person individually. It did not contribute to the team.

Option B. They run the meeting without AI, fill the wall with sticky notes, take photos for the recap, and the synthesis happens later, in someone’s editor, with a model. The synthesis returns from the model and people argue about whether it captured the room. The model is reading, not collaborating.

Option C. They run a multi-agent system that takes meeting transcripts, summarizes them, drafts strategic options. The output looks like collaboration. No one is in the room with the model. The team is consuming AI output, not shaping it. Each option uses AI. Only the first is collaborative AI as the term should be used.

What it requires from teams (and most teams do not have)

The reason collaborative AI works in some rooms and not others has nothing to do with the model. The model is the same. What changes is what the team brings. A facilitator who can hold the room with AI in it. Most facilitation training assumes the facilitator’s job is to manage human dynamics. With AI in the room, the facilitator’s job expands. Who decides when to use the model? When does the model’s output get accepted, and when does it get pushed back on? Who notices when the model is steering the conversation toward a generic framing the team would not have chosen on its own? These are facilitation moves that did not exist three years ago. Teams that have someone who can run them get collaborative AI. Teams that do not, fall back to one of the three options above. Shared norms about transparency.

The team has to agree, before the session, on what AI use looks like in the room. Is everyone using it? Are some people privately using it while others are not? Is the model running publicly on the screen, or quietly assisting one person? When AI use is visible, the team can engage with it. When it is hidden, it distorts the room. A working understanding of what the model is good at and what it is not. Models are excellent at synthesis, summarization, divergent generation, and surfacing patterns across text. They are bad at judgment under uncertainty, weighing competing values, and noticing what is missing from a conversation. Teams that know this use the model where it helps and override it where it does not. Teams that do not, drift toward whatever the model recommends. These three capabilities are not technical. They are practices. And practices are slow to build, because they require facilitated repetition.

The branding problem

Most “collaborative AI” content you will read in 2026 will be one of the two shallow definitions, dressed up in language that makes it sound like the working one. Vendors have an incentive to call any AI feature collaborative because the word is selling well. The diagram is collaborative. The chatbot is collaborative. The agent network is collaborative. None of them require what real collaboration requires, which is more than one person in the same room making decisions together. The risk for buyers is straightforward: you procure something labeled collaborative AI, deploy it across the organization, and discover that it is a productivity tool for individuals. People use it alone, at their desks, between meetings. The team-level capability you were trying to build never materializes, because the tool was never going to build it. The capability is built by humans, not software. The good news is that the actual practice of collaborative AI does not require a particular vendor. The model layer is a commodity. What is scarce is the facilitation layer on top, and that is what teams have to build for themselves.

Where this fits in the broader shift

This is one piece of a larger pattern. The friction that matters in 2026 is no longer execution speed. AI eliminated that friction. The friction that matters is consensus, alignment, and trust at the team and organization level. AI accelerates execution; it does not, on its own, build alignment. In some configurations it makes alignment harder, because individual users move so fast that the team cannot keep up. Collaborative AI is the response to that. It is what happens when teams refuse to let AI become a private productivity boost and instead bring it into the room as a shared participant. The benefit is real: faster alignment, better synthesis, decisions that more people genuinely own. The cost is that someone has to facilitate it, and most organizations have not built that capability yet. That is the work in front of leadership teams right now. Not picking the right collaborative AI vendor. Building the team practices that make any AI collaborative.

What to do this quarter

If you are leading a team and want to start moving toward collaborative AI:

Pick one recurring meeting. Not a high-stakes one. A regular planning or review session where the team is already aligned on the format. This is your test environment.

Put a model on the screen. Shared, visible, running. The output of the model goes up where everyone can see it, edit it, push back on it.

Name AI use explicitly. When the model contributes something, say so. When someone overrides it, say so. The transparency is what makes the next session better.

Run it for four weeks. The first session will feel awkward. The second will be better. By the fourth, the team will start to develop instincts about when to invoke the model, when to override it, and how to use it without losing their own judgment. After four weeks, you will know if you have built the practice. You will also know what you need from a facilitator, from governance, and from team training to scale it.

The teams that build this capability now will compound on it for the next decade. The teams that wait for the right vendor or the right tool will still be looking for the right vendor when the friction has moved somewhere else. That is the difference between collaborative AI as branding and collaborative AI as a capability. The branding will keep shifting. The capability is yours once you build it.

The post Collaborative AI appeared first on Voltage Control.

Most writing about AI change management is about using AI to do change management work. AI-powered surveys, AI-coached stakeholder communications, AI-generated resistance reports. That angle dominates the top results on this search term. McKinsey, Prosci, IBM, ICAgile, Udemy all lean there.

We mean the inverse: managing the human and organizational change that AI adoption requires.

This is not a semantic distinction. The gap between those two framings is exactly the gap between organizations that get AI working and organizations that stall for two years wondering why. When you treat AI change management as a tooling problem, you solve for the wrong variable. The tool is rarely the reason transformation fails.

The case that makes this concrete involves two trucks and a container port.

Two trucks break down in a port. They are thirty meters apart, on the same lane, carrying the same cargo. One port zone recovers from the disruption in seventy minutes. The other takes more than two hours. The zones share everything that matters: the same bridges, the same lane widths, the same weather, the same sixty-second mechanical fault. The only difference is coordination. In the slow-recovery zone, a single algorithm dispatches every vehicle. In the fast-recovery zone, that same algorithm shares infrastructure with a fleet of trucks driven by independent logistics companies, each operating under its own objectives.

That is the finding M. Dalbert Ma, a researcher at London Business School, reported to the BIG.AI@MIT conference this year, after studying approximately one year of operations at one of the world’s largest container terminals. The autonomous zones ran 3.8% more efficiently under normal conditions. A single sixty-second fault cost them a 12.2% delay on the operations that followed. Rain, which forces every vehicle to slow and creates temporal buffer between sequential operations, erased the fragility entirely.

This is what most AI transformation stories leave out. The efficiency gain is real. So is the cost you pay when something disrupts it. Real AI change management is the work of managing that tradeoff before the fault arrives, not after.

The 5 Frictions of AI Transformation

Every AI transformation engagement we have worked runs into the same five blockers. Not technology failures. Human and organizational frictions that the tool vendors do not mention and the training programs do not cover.

We call this the 5 Frictions framework. Each friction is distinct, each stalls transformation in a different way, and each requires a different response.

The Identity Friction. When knowledge workers are asked to share their specialized knowledge with AI systems, a real fear surfaces. The fear is not irrational. Their expertise is the basis of their value. AI that learns from their domain know-how threatens the moat they have spent years building. At one of our executive dinners this spring, the framing that landed cleanest was this: when people are asked to give their knowledge away, they experience it as becoming disposable. Identity work is not soft. It is the most operational blocker on your AI adoption agenda, and leaders who skip it discover the passive resistance later, when workflows are technically live but adoption stays flat.

The Leadership Friction. Leaders who are not personally using AI cannot guide teams that are. If you are not using AI on the order of every hour, you cannot evaluate which of your teams’ experiments have merit, which are theater, and which represent a genuine capability gain. You are coaching a sport you have never played. The practitioners in the room can tell immediately. This framing has now come up at every executive dinner we have run across Boston, Boulder, Houston, and Dallas, without prompting. The corollary the Boulder room added sharpens it: even when the CEO mandates AI adoption, if there is no facilitation and no design behind the rollout, the burden falls on already-overwhelmed individual contributors and the initiative fails.

The Capability Friction. Every AI-first workflow your organization designs makes a structural tradeoff. When execution time collapses, coupling tightens. When coupling tightens, buffer disappears. The same mechanism that produces the efficiency also produces the fragility. And beneath the efficiency numbers, something is accumulating invisibly: the growing gap between your organization’s apparent capacity and its actual adaptive capacity. JoAnna Vanderhoef named this Capability Debt at the BIG.AI@MIT conference in 2026. We will return to it in detail below, because it is the friction with the most solid research base and the most counterintuitive implication.

The Measurement Friction. The metrics organizations reach for first, time saved, tokens consumed, story points closed, either cannot be measured cleanly or create perverse incentives. At three consecutive dinners this year, we asked the room what they were measuring and whether it was working. Not once did a satisfying answer emerge. What surfaced instead were stories about measurement going wrong: algorithms that extrapolated to billions of hours saved, CEOs who set token-consumption targets that had teams running meaningless jobs at night just to hit the number, story-point metrics that broke down the moment AI made commits larger. The measurement problem is real, and the solution is not a better metric. It is a different understanding of what counts as progress during early-stage AI adoption.

The Sequencing Friction. Who does what, when, in what order? Most organizations have not answered this. They have an AI strategy document and a handful of enthusiastic early adopters and no clear answer to the role questions: who is the AI champion, who is the AI lead, what decisions belong to an AI governance council, what does AI ops mean for their context. Without those answers, every initiative stalls at the first ownership dispute. The sequencing friction is often invisible until it surfaces as a conflict, and by then it has already cost the organization months.

These five frictions do not appear on procurement spreadsheets. They are not solvable with a training event. They are the actual work of AI change management.

What the Evidence Shows

The Capability Friction is worth dwelling on, because it has the most solid research base and the most counterintuitive implication for how organizations should design their AI adoption roadmaps.

JoAnna Vanderhoef’s concept, Capability Debt, describes the growing gap between an organization’s apparent efficiency and its adaptive capacity. It accumulates subtly, as absence. Absence of novelty detection. Absence of the junior employee who stumbled into the strange request and learned how to triage it. Absence of the reviewer who noticed the model’s output was technically correct and strategically wrong. Absence of the senior whose judgment was trained on edge cases the automated pipeline now handles without them.

You do not see the debt until you need to do something the system was not built for. By then, the people who would have done it have atrophied the capability, or have never built it at all.

This is the part of AI transformation that is easy to underweight in a board deck. Efficiency is legible. Judgment loss is not. It hides inside the year-over-year improvement metrics and inside the reduced headcount and inside the deliverables that ship faster and look clean until a situation arrives that needs taste, or context, or the ability to know what is not in the data.

A team of researchers at MIT, Yale, and Microsoft, led by Mert Demirer, formalized the mechanism for where the debt accumulates fastest. They call it AI chains. An AI chain is a sequence of production steps in which the automated steps are contiguous. The human at the end verifies only the final output. The economic incentive is to keep adding steps to the chain until the marginal failure probability overwhelms the saved verification cost.

The jobs that get automated fastest are the ones where AI-suitable work clusters together. Lecture preparation is one such job. Research, drafting, slide generation, and example synthesis are all AI-suitable, and they are sequential. A single verification at the end is sufficient. The chain collapses into one unit of human work.

Tutoring is the opposite. AI-suitable steps are interleaved with diagnostic steps that require real-time human judgment. The chain cannot form.

The second consequence is more important than the first. Jobs that form long AI chains are also the jobs where learning loops get shortest. The junior who used to do the research, draft the slides, and watch the senior edit them loses three apprenticeship cycles per deliverable. What was formerly a sequence of moments where skill formed now happens inside the model.

When your team maps its AI automation roadmap, the blocks to be careful about are the contiguous ones. They are where the efficiency gain is largest. They are also where the Capability Debt compounds the fastest.

What We Actually Saw in the Field

Research describes the mechanism. The practitioners in our dinner rooms describe the texture.

Two patterns showed up without prompting at every table this spring.

The first is the training blip. John Ippolito, at the time VP of Enterprise at Miro, shared a Gartner workplace-event graph at our Boston dinner that became the anchor reference for the rest of the evening. The graph shows a flat line of token consumption over time, with a single one-day spike coinciding with formal AI training, then an immediate return to baseline. Adoption of tools is rising. Real usage is not. Every practitioner at the table confirmed it independently. Rachel Brown from CIBC described what works instead: an every-other-week internal showcase where early adopters demonstrate live, the most junior employees show what they have built, and the room asks questions in a safe space. Not training. Social learning, designed and facilitated deliberately.

The most wasted line item in most AI transformation budgets is the training event that produces a one-day blip and nothing durable. The replacement is cheap and repeatable. But it requires someone willing to design and facilitate it, and that role has no title yet in most organizations.

The second pattern is measurement going wrong, and the stories are consistent enough across cities to treat as a pattern rather than an anomaly. Rachel’s head-of-AI at CIBC built a time-savings algorithm that extrapolated to billions of hours saved company-wide. Obviously wrong, and obvious only after the number became absurd. Jason Fournier, CEO of Imagine Learning, described a split his team lives with cleanly: they can measure curriculum creation precisely (from eighty thousand dollars and eight months down to four hundred dollars and four weeks), but cannot measure knowledge-worker productivity gains with any confidence the numbers mean what they appear to mean. Morgan Brown from Wayfair measured AI coding tools by story points, found the metric broke down, and discovered on further investigation that commits were growing in size even as counts fell. Ben Tao from Rockwell put the early-KPI trap cleanly: codify performance targets too soon, and you suppress the experimental behavior that would have surfaced the valuable patterns. “Do that too early, you’re suppressing the good seed.”

A third pattern, specific to organizations that have worked through the early frictions: the role redesign. At our Boulder dinner, one attendee described a customer service team that celebrates publicly when someone automates a meaningful portion of their work, specifically the phrase “automated 40%” landing in a team all-hands, followed by a deliberate conversation about what that person should do with the freed capacity. Two emergent role shapes are forming in that team: a white-glove tier for the most escalated and complex customer interactions where human judgment is irreplaceable, and an agent-orchestrator tier for the people who supervise and maintain agentic workflows. Those are not job titles they inherited from an org chart. They are shapes the team discovered by working through the frictions rather than around them.

For how to structure measurement that accounts for the phase your organization is actually in, rather than where you wish it were, see our piece on how to measure AI transformation success beyond productivity.

The Design Move Most Organizations Skip

Here is what separates the organizations that stall from the ones that scale.

Renée Gosline, in a MIT study presented at the BIG.AI conference, calls it beneficial friction. Her team ran a controlled experiment. Participants worked on cognitive tasks with AI assistance. In the control condition, the AI made its recommendation and the participant accepted or rejected it. In the treatment condition, before accepting or rejecting, the participant was asked to articulate their own reasoning, or to predict what the AI’s reasoning was. That small intervention, which took thirty seconds, measurably reduced over-reliance on AI and preserved the participant’s critical thinking.

This is the design move most organizations skip. They treat friction as waste. They are correct that some friction is waste. They are wrong that all friction is waste. The friction that forces a human to articulate their own judgment before anchoring on the AI’s output is the friction that carries the capability forward.

At the organizational level, beneficial friction looks like this. Decision rights reviews before an AI pipeline goes into production, where the team has to name who owns the outcome the pipeline is producing. Novelty drills, where a percentage of the work that could be automated is routed to humans anyway, so the capability stays alive. Signal sampling, where humans regularly review a random sample of AI outputs not for QA but for drift. Shadow-session reviews, where someone who has not been in the pipeline’s daily operation comes in and asks whether the pipeline is still doing the right thing.

None of these are productivity moves. All of them are capability moves. The point of beneficial friction is not to make the system slower. The point is to keep the system teachable.

AI Change Management Is a Leadership Problem

The organizations navigating this well understand something the organizations that are stalling do not. The new friction is not a technology problem. It is a leadership problem.

When execution was expensive, leadership’s job was to clear the path: remove the blocker, approve the budget, unstick the review cycle. That job is largely done. The organizations still doing it well at the leadership level are optimizing a bottleneck that is mostly already gone.

The new job is different. When execution is cheap and judgment is scarce, leadership’s job is to carry the organization’s judgment capacity forward. That means designing the decisions that matter, surfacing the dissent that would otherwise stay hidden, ensuring that the people who will need the skill later are getting the practice now. Getting executive buy-in for AI initiatives before the first pilot, not as damage control after, is one of the clearest signals we see between transformation programs that sustain and ones that die in the third quarter.

This is facilitation work. Not facilitation in the narrow sense of running meetings well, although that is part of it. Facilitation in the broader sense of helping groups think together, decide together, and build the shared judgment that a single expert, however capable, cannot hold alone. Why AI amplifies the need for great facilitation is something we return to across multiple pieces in this series, because it is the most consistently underweighted factor in every transformation program we have seen.

The organizations that treat AI change management as a tool rollout are solving for the wrong variable. The tool is the easy part. The hard part is building the organizational muscle that keeps judgment distributed across the people who will need to exercise it when the situation changes. And situations always change.

The port example makes this visceral. The efficiency advantage held until the sixty-second fault. Then the organization that had preserved coordination independence recovered faster, because it had not consumed the slack the recovery required. Your organization is running the same experiment right now. You will not know the outcome until the fault arrives.

What to Do About It

The organizations working through this well share three habits.

They take Capability Debt seriously as an accounting category. Not formally on the balance sheet, but in the same way a good engineering team takes technical debt seriously. They know where it is accumulating. They know what they are choosing to trade for it. They revisit the decision when the debt load feels wrong. How to structure an AI transformation roadmap that actually works is fundamentally a question about which automations to sequence in which order, and that sequence question is not just a technical planning decision. It is a capability preservation decision.

They clarify roles before the conflict forces the issue. The question of whether you need an AI champion versus an AI lead, and what an AI governance council actually owns, is not administrative. It is the answer to who is accountable when the model produces something strategically wrong, and who has the standing to say so before it ships. Most ownership failures in AI transformation are not failures of intent. They are failures of structure that nobody bothered to define in advance.

They treat facilitation as infrastructure, not as a soft skill. The change management framework for AI adoption in the enterprise we work from is built around this premise. It is not a template. It is a diagnostic. Where is the Capability Debt accumulating? Where is the Identity Friction blocking adoption? Where is the Leadership Friction showing up as strategy without the fluency to back it? Running a cross-functional AI alignment workshop before a pilot goes into production costs a day. Running a post-mortem after the pilot fails costs a quarter and the trust of the team that ran it.

The capacity to carry judgment through an organization is the durable advantage. Tools will change. Models will change. The organizational capacity to decide well under uncertainty will not.

What Is at Stake

The organizations that hold the line on beneficial friction will move slower in the short term. They will look less impressive in the quarterly efficiency reports. Their AI transformation stories will be harder to tell in press releases.

They will also move further in the long term, because they will still have the people who can do the work the model cannot yet do, and the judgment that closes the gap when the data does not.

The organizations that optimize everything for speed will discover the fragility on the worst possible day. Not because the AI failed. Because the people who were supposed to catch what the AI missed have atrophied the capability to catch anything.

The new friction is not a problem to be eliminated. It is a signal telling you where your organization’s judgment is concentrating. Work with it, and the organization gets stronger. Optimize it away, and you are running Dalbert Ma’s automated zone, waiting for rain.

Frequently Asked Questions

How is AI change management different from using AI in change management? The dominant interpretation of this phrase, the one that fills the top search results, treats AI as a tool that improves how change management is done: faster surveys, smarter stakeholder analysis, AI-generated communication plans. Our interpretation is the inverse: AI change management is the practice of managing the human and organizational change that AI adoption itself requires. The tool is not the problem. How organizations navigate identity, leadership development, capability preservation, measurement, and role sequencing is the problem.

Why do most AI transformation initiatives fail? Most stall because organizations treat AI as a technology rollout when it is actually a leadership and facilitation problem. The tools work. What breaks is the judgment capacity of the organization, the shared decision-making the model cannot replicate, and the distributed expertise that gets quietly hollowed out when contiguous workflows are automated end-to-end.

What is Capability Debt in AI adoption? Capability Debt, named by JoAnna Vanderhoef in 2026, is the growing gap between an organization’s apparent efficiency and its adaptive capacity. It accumulates when AI absorbs work that used to build human judgment. The debt is invisible in productivity metrics and only shows up when the situation changes and the people who would have handled it have atrophied the skill.

How does beneficial friction improve AI outcomes? Beneficial friction is a small intervention that forces a human to articulate their own reasoning before accepting an AI output. Renée Gosline’s 2026 MIT study showed a thirty-second reasoning step measurably reduced over-reliance on AI and preserved critical thinking. At the organizational level, beneficial friction looks like decision-rights reviews, novelty drills, signal sampling, and shadow-session reviews of automated pipelines.

What role does leadership play in AI transformation? When execution was expensive, leadership cleared the path. Now that execution is cheap and judgment is scarce, leadership’s job is to carry organizational judgment capacity forward: design the decisions that matter, surface dissent, and ensure the people who will need a skill later are getting the practice now. That is facilitation work, not project management.

How do you maintain judgment when automating workflows? Treat AI automation roadmaps as capability preservation decisions, not just efficiency decisions. Be most careful with contiguous AI-suitable steps, since those are where Capability Debt compounds fastest. Build beneficial friction into the workflow as a structural feature rather than a removable safety check. Keep humans in the chain even when the model could handle the step, because the capability is the thing the organization is actually buying.

Ready to work the new friction?

If your organization is navigating these frictions, there are ways to go deeper.

Talk to us about the AI Transformation Program. We will help you map where your organization is accumulating Capability Debt, where the five frictions are showing up, and what to do about it.

Read the full frame. Our pillar page lays out the thesis and the three pillars: New Friction, Multiplayer, and Spark.

Build the capability. Our facilitation certification teaches the skills that matter most when the bottleneck is judgment, not execution.

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