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

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

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

What is the Design Thinking Process?

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

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

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

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

The 5-Step Design Thinking Process

1. Empathize 

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

Typical Activities

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

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

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

2. Define

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

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

Typical Activities

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

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

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

3. Ideate

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

Typical Activities

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

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

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

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

4. Prototype

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

Typical Activities

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

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

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

5. Test

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

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

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

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

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

Design Thinking in the Age of AI

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

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

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

Putting the 5 steps to work.

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

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

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

FAQs

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

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

• How long does the design thinking process take?

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

• What is the difference between design thinking and agile?

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

• Can design thinking be applied to AI implementation?

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

• What is the most important step in design thinking?

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

The post 5 Steps of the Design Thinking Process: A Step-by-Step Guide appeared first on Voltage Control.

In this episode of the New Friction podcast, host Douglas Ferguson speaks with Peter Bell, founder of Gather.dev and author of the forthcoming O’Reilly book Scaling AI Adoption in Engineering. Bell draws on his work running invite-only peer communities for senior engineering leaders to diagnose why most organizations stall out in AI pilot mode rather than achieving meaningful transformation. The conversation maps three distinct patterns of engineer resistance—skeptics burned by early models, craft-focused developers who resist the shift toward managing agents, and those with principled objections to AI—and offers concrete tactics for reaching each group. Bell and Ferguson explore how AI amplifies existing organizational health: strong DevOps practices compound upward while process debt scales its dysfunction. They examine the mandate trap, measurement via token usage as a diagnostic rather than a performance metric, and the non-negotiable role of psychological safety in any serious adoption effort. The episode closes with Bell’s call for engineering leaders to build hands-on with current models, arguing that firsthand intuition—not secondhand reports from a VP of AI—is what this transition demands.

Show Highlights

[00:00:00] Introduction to New Friction
[00:01:30] Peter Bell on Agentic AI and His O’Reilly Book
[00:04:30] Three Patterns of Engineer Resistance
[00:09:00] Encoding Craft and Taste Into AI Quality
[00:14:30] Delegation Skills and the Agent-Manager Mindset
[00:19:00] Harness Engineers and Developer Experience
[00:26:30] Psychological Safety and Blameless Postmortems
[00:33:00] The Mandate Trap and Measuring Adoption
[00:42:00] Token Economics and Rebuilding the SDLC

Links | Resources

Peter Bell on LinkedIn
Gather.dev
Scaling AI Adoption in Engineering (O’Reilly)
The Future of the Engineering Org (Substack)
Voltage Control

About the Guest

Peter Bell is the founder of Gather.dev, an invite-only peer community for senior engineering leaders navigating what he calls the agentic transition. He is writing Scaling AI Adoption in Engineering: A Leader’s Guide to Driving Alignment, Adoption and Impact for O’Reilly, and publishes The Future of the Engineering Org on Substack. Bell previously served as SVP of Engineering at General Assembly and built Flatiron School’s engineering team to 50 people in 18 months. He hosts the O’Reilly CTO Hour and facilitates CNCF Executive Summits at KubeCon, and is actively building his own multi-agent orchestration systems—keeping him well outside pure pundit territory.

Transcript

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

Peter Bell: Douglas, thank you so much for having me. I’m excited to be here and to kind of have this conversation.

Douglas Ferguson: Absolutely. And we’ve been in conversation a lot over the years and it’s been a lot of fun recently diving back into this new era of hosting conversations around what matters for leaders.

Peter Bell: Absolutely. Well, I’ve really been getting into how agentic AI is transforming the SDLC, the software development lifecycle. I’m writing a book for O’Reilly called Scaling AI Adoption and Engineering, which is like the hard part’s like, “I got 300 humans. How do I get them to work and be different?” And then, I’ve also been doing a lot of builder work myself. I’ve built my own harness and dreaming systems and orchestrators and tools like that. And it’s been really fascinating just to see the impact of this both on a personal level and also at the larger organizations where I’m discussing with the CTOs how this is impacting their teams and their priorities.

Douglas Ferguson: Yeah. I’ve been along on that ride. I’m building some of my own stuff and we’ve been comparing notes. And personally, I’ve found it illuminating to dream about the future a bit more, being able to get hands-on and making some things that are useful for me, and then I can more easily see how this is impacting organizations.

Peter Bell: One of the really interesting things has been fundamentally the model’s changed in mid-November of last year. You got like four, five, you got… I’m forgetting now, the version of Codex. It was at 5.2, 5.3. And what’s really interesting is I’ve found that there is a absolute divide between two types of engineer and leaders. There are the people who have taken a day to go build anything in Claude Code or something similar since then, and there are people who haven’t. The people who haven’t are still like, “Man, I’ve got a VP of AI. They’ll take care of this stuff or my platform team will figure it out.” And the ones who have done are like, “Wait, we need to call an all hands. We are not going to be writing code in a year and we need to ready the organization for our new future.” So I feel like if you’re not doing this, this is one of the few transitions as a leader where you can’t just manage and lead through it because your intuitions are going to be wrong.

Douglas Ferguson: Yeah, that’s an important point. It’s funny, not only have I seen it from leaders, but I’ve also seen leaders really struggling to bring some of their engineers or just any talent along because the talent, the individual has made an impression of what AI is based on earlier versions of the model. You were kind of touching on that a little bit, but I think it’s really important to really sit there for a moment because it’s an important thing to wrestle when you’ve got folks that have made a determination about what a thing is, but it’s evolved tenfold over.

Peter Bell: Well, I feel like there are three broad patterns of pushback and we’re seeing this in everything from juniors to some of the most senior engineers, product leaders across the board. One is, “I tried it last summer and it sucks. I can do faster.” So they just need to try it again. That’s manageable. Another is, “I understand that this works, but this is now not the job I want. I love being the person who goes on Stack Overflow and figures out just where the semicolon should be. I love the crossword puzzle nature of designing an elegant API. I don’t want to be babysitting a bunch of agents.” That’s hard because the job has changed and artisanal software development, I would be surprised if that’s a real profession for most people in the field a small number of years from now. And then, the third is, “AI is bad. It’s going to world apocalypse, destroy the population, destroy the planet, climate impact.” And there are lots of very valid reasons to have an issue with AI. At the same time, it’s here. I don’t expect it to be going away anytime soon. And so the question is as a leader and with responsibility to shareholders, you need to help the people within your team to build the outcomes you need, and not everyone’s going to self-select into that population.

Douglas Ferguson: Yeah. I’d love to dive into those three a little bit more deeply. And maybe let’s start with the I-want-to-be-a-coder category. That’s just my quick little name there for that. But I’ve seen two versions of this. And so I’d be curious if you’ve seen other layers or variants as well, but this notion of people being in love with the craft and being passionate about the craft. And the interesting thing about them is that you can actually use that as a value or as something to lean into to help them explore and experiment with using AI because you can say, “Well, how can we evolve the craft? What does the craft look like when we bring in these new tools? And if the craft is really about quality, what are the principles baked into the craft rather than the practices?” You know?

Peter Bell: Mm-hmm.

Douglas Ferguson: This comes back to the agile days when say they were doing agile because they were doing story points or user stories, but they weren’t really practicing the values or living up to the principles. And so it’s like helping people distill down their craft into principles that then they can apply in the AI world. I think that’s really fascinating.

Peter Bell: I’ve seen a couple of approaches that you’ve absolutely nailed it. One approach is the, “Oh, you don’t believe AI code is any good.” Great. What I want you to do is review all of it and tell us how bad it sucks. And that effectively, firstly, it means you don’t ship crappy AI-generated slop, which is wonderful. And secondly, it means that you get a training corpus that means that the AI stops generating slop in general if you implemented it well and manage your context and deterministic quality gates and adversarial multi-step reviews. I mean, you need to engineer this correctly, but what it allows you to do is it allows that person to say, “AI code sucks,” and for the business to get better by then being precise about how and where it sucks, which then improves the quality of generation. And then, I think the other point, the one that you doubled down on, which I love, is this idea of saying, “We’re effectively moving up another layer.” It’s we can now take infinite pains for these things. You really think that we need to have thoughtfully orthogonally designed names. We need to think about like Eric Evans, domain-driven designs and ubiquitous languages and rich domain boundaries. We can do all that stuff, but rather than doing it, what we’re going to do is teach an agent to care about it and then ensure that we have a review step to make sure that the quality of our architecture is better than we’d probably have time to ship if we’re doing it manually. So yeah, it’s absolutely true that we get to encode taste, which is actually an amazing way to improve the quality, not just of the software you write, but of all the software that is then written by the factory that you’re helping to train.

Douglas Ferguson: Yeah. I mean, there’s kind of no end to the depth that you can go into if you really explore with these new capabilities unlocked from a perspective of craft because you look at test-driven development, that’s something that everyone kind of aspired to, but how many people actually did it? This is a thing that where you can instantaneously have tests if you have well-defined boundaries and well-understood contracts and you can get them for free now. You don’t have to spend the time. And I think it really could put us in a position where we’re being more thoughtful, more mindful about our design processes and things that we might have spent more time designing if we had the time in the past. And also kind of pulling on that thread more, something I’ve seen be really effective for organizations is where if folks are really pushing back and saying like, “I can write better code,” or, “I don’t trust this thing,” have it start doing the code reviews. Ideally in an autonomous fashion, sure, still have human code reviews, but have the humans look at what the AI is discovering as far as the bugs and issues and concerns. And when you’ve got senior engineers where the AI is discovering bugs in their code, it starts chipping on the ego a little bit and they start to realize, “Wow, this thing is actually pointing out things I should have noticed,” and they start to give it a little bit more credit.

Peter Bell: Absolutely. And I think it goes both ways. I think that you get value from the humans reviewing the AI code because it improves as long as you are capturing the context, capturing the transcripts, and feeding them into a well-designed context management system. And then, absolutely, I think we’re going… It’s bizarre, but it feels a little bit like self-driving cars in a couple of ways. In the one way, it’s really hard to get a hundred percent of the way there. Anyone who’s like, “Dude, I just switched on Claude Code and now we’re generating production-ready code,” is either doesn’t know what production-ready code is or hasn’t looked at the code they’re generating. You can absolutely one-shot or few-shot stuff, but it’s a real engineering effort at least today to ensure that it is of the quality and maintainability you’d want. So it’s hard to get all the way. But the other thing is eventually, people are going to think 20 years from now, the idea like, “Wait, Granddad, they used to let humans drive? I mean, how did that work? Didn’t they get drunk and look at their cell phones and kill people?” Isn’t it much safer to use Waymos?” And I feel like we’re going to have exactly the same with writing code by hand, which is, “So wait a minute, in a mythos-level environment where you can get CVEs coming in and they maybe need to be patched in 15 minutes before autonomous agents are basically scanning, seeing what your tool stack is and exploiting a close to zero day, humans can’t patch CVEs in under five minutes, 24 hours a day, but agents can.”

Douglas Ferguson: Yeah. Yeah. That’s pretty soon going to be zero hours.

Peter Bell: Right. I mean, you really need to get to that point where to the extent that you are using third-party open-source tools, which I do still believe bring value, the downside is there’s lots more value in exploiting them. The upside is there are lots more people trying to ensure that they’re not exploitable. So there’s the trade-offs, but to the extent you’re using that, you need to have a dark factory in place by I’m going to say sometime next year for most of your key production code. And if you’re not working on that today, you’re going to be in trouble in Q4 when somebody asks why your competitors are going five times as fast and you’re like, “We’re investing. Give us nine months, we’ll catch up to their velocity.”

Douglas Ferguson: Yeah, your story about the self-driving cars and it makes me think about how we used to write programs on punch cards. And what was that transition like? If you were really into how to provide instructions to a computer via punch cards, your future wasn’t very bright. In retrospect, it seems kind of absurd to think, “Oh, I’m going to hang onto this punch card thing because that’s the way it’s done. And that’s my identity as someone who uses these things.” But I think at the time, there might have been folks that surely felt like, “This is not really programming.”

Peter Bell: Well, and it’s because technologies don’t start off perfectly. And it’s just when we moved from assembler to higher level languages, there were absolutely professional software developments who were like, “Huh, these automated compilers are fine, but then they’re going to work well on a 16-kilobyte memory system on an embedded system.” Or, “I want to double-check that they’re using the registers effectively so that they’re adding and storing in the appropriate register because I think I can do better than that.” And for a period of time, they were right. Today, I’d be pretty hard-pressed to think of anybody who’s literally hand coding hexadecimal to add two registers together to get business outcomes.

Douglas Ferguson: Yeah, and with the compiler optimizations, you’d be hard-pressed to find someone who was better at it.

Peter Bell: And that’s the point. It went from it sucked for almost everyone to. It was good enough for some people to it, was good enough for most people, but there were range cases where it didn’t work to there was no good reason for a human to be doing that. And I think that we’re going to see that in terms of encoding in C or Python or Rust or whatever it is you use to program. The only difference is I think the timeframe is probably going to be compressed given how quick all of these kind of sigmoid curves are kind of stacking on top of each other because so many people are working so hard to improve everything from the underlying silicon all the way up to the harnesses and the context that we wrap the models with.

Douglas Ferguson: Yeah, and also, it’s a reinforcing loop. So the advances in AI create advances in the other areas, which creates more advancement. Advancement advances advancement.

Peter Bell: And the crazy part is like we could stop now. I mean, if we were just to say like, “Okay, 4.8 is good enough, 5.5’s good enough,” there’s so much overhang just-

Douglas Ferguson: Oh yeah.

Peter Bell: … with the current state-of-the-art models, we could became busy for the next decade. And I hear that they’re not stopping. So my case is it’s going to get even better.

Douglas Ferguson: That’s my stance as well. Now, the other piece, because I said there were at least two things that came to mind when I thought about subdividing this I-want-to-be-a-coder perspective. And this one is about pushback on the need to be a manager in this world of working with agents. And so some folks opt to go down the management track, others opt to go down the principal engineer track or something similar, depending on what the terminology is at your organization. And this agentic workforce is pushing everyone into this kind of management model. You can’t just do it yourself. You have to be able to delegate, you have to be able to review work from others. And I think you and I were not that long ago talking about how it somewhat feels like having an army of interns that you’re managing, right?

Peter Bell: It’s really interesting because I think that you’re absolutely right, and the perfect first-level analogy for this is hiring. And I think it’s why a bunch of honestly old ex-technical people like me are having the best time in our lives because like, “Wait a minute, now we can actually ship exactly what we want and bring our understanding of systems design and engineering rigor and good practices, but also shape, merge that with the fact we spent 30 years asking humans to engage and build things for us.” And we’re going to get… It’s not quite the same as managing humans. Actually, you’re not going to need a sick day because your [inaudible 00:15:21] died. That doesn’t happen to Opus. But on the other hand, a lot of the delegation patterns and a lot of the patterns about, “How can I use generalized language patterns?”, the models are good enough now that I’ve got my harness to the point where I spend at least 20% of my time saying, “What would be three ways that you could economically and token-efficiently improve the thing you’ve just shipped?” And I will review the answers, but more often than not, I’ll be like, “Yeah, go with number two.” And so I’m not even proposing what they should do. I’m simply doing what I would do with a very smart… And by this time they start to feel like a junior to senior engineer, not an intern, which is, “Tell me what would make this better for the definition of better that I give you and what would be a token-efficient way of shipping that this week?”

Douglas Ferguson: You know, I think to that point, acknowledging the fact that these delegation skills, these managed tracking, prioritization, all of these skills are going to be super critical in the future and making sure that we spend time upskilling and investing and ensuring that our individual contributors are ready to start taking on those duties. And also, that’s something to even look for when we’re hiring new folks. Do they have those skills? Do they have an innate ability to do some of these things or they kind of wired that way to begin with?

Peter Bell: I think you’re right. This is fundamentally going to change not only the interview process, which I think was already broken in terms of managing the flood of inbound resumes and the validation of competence and fit process. I think that’s going to change, but it’s also going to change fundamentally what we’re looking for. And I think we’re moving towards this world where in R&D, you’re broadly going to have platform and feature as you do now, but the way it’s going to look is you’re going to have what’s effectively harness engineers who are primarily thinking about, “How can I capture more with… You know what? How can I create a step where effectively an instance of something that looks very much like Kent Beck meets Martin Fowler looks through our code and says whether it’s good or bad? How can I extract those insights, capture them in a context-efficient way and help agents to generate a rubric for it and then manage the pipeline for managing that?” So that’s going to be people who are effectively either on the platform team or are embedded in streamlined or feature teams. And then, I think the feature engineers are going to look much more like MTS, like we’re already seeing this member or technical staff where we don’t have front end, backend product engineering design so much as people who are deeply understanding the customer problems and trying to frame experiments and features designed to deliver value to those customers using whatever combination of product and coding front end and backend is required.

Douglas Ferguson: Yeah. The piece you were talking about there around the harness builders or maintainers made me think a bit about DevOps because there was a certain group of organizations that treated DevOps more as a developer experience, especially if you’re thinking about developer experience of internal developers. And then, there were some folks that thought of it more like site reliability or the cloud version of sysadmins, but the organizations that were thinking more around how are we making it more streamlined and more enjoyable to do work as a developer, as an engineer, I think you think about that role, that definition of DevOps and it very much is this kind of universe of how are we building the harness? How does it make for a great developer experience and provide all the tools and functionality we need to excel and create basically agentic teammates?

Peter Bell: Exactly. And I think we’re going to see that there’s two components to this. And the companies that already have some kind of DevOps platform org will be in the best place. And as you called out, if they happen to have already renamed a subset of that DevEx, or developer experience, they’re killing it because they’re thinking about the right things, which is, “How can we help stream align teams, feature teams who are shipping the stuff our customers want? How can we make them stars? How can we make them succeed better? And I think that what you’re going to see is that the platform team’s going to own most of the harness design and management. But I could also imagine at least in an intermediate period for the next couple years, 18 to 36 months and maybe ongoing, you’re probably also going to have embedded harness engineers or DevEx engineers within each team because what you’re going to see is, well, turns out that a good harness, a good set of steps in a pipeline for throwaway React code for an internal admin dashboard is probably different from the level of quality and the type and definition of quality you have for the stuff you use to build your customers every day.

Douglas Ferguson: Mm-hmm.

Peter Bell: And so you’re going to find that different teams working on different projects will require different subsets of… You’re still going to have the same basics, orchestrator, context management, but the details of the rubrics and the steps and the validations are going to be very different across your org depending upon what people are building and how much it matters.

Douglas Ferguson: Yeah. Security and uptime guarantees are totally different when you’re looking at internal tools versus external as well.

Peter Bell: Yeah. Or even similar, so I was speaking with Rob Zuber, the CTO at CircleCI, and he’s like, “Look, it would suck if our admin dashboard went down for an hour. We don’t want to do that. But if we can’t run continuous integration runs for an hour, we’re going to be getting phone calls. They’re two different things.” And so you have to look at the blast radius of the changes you’re making.

Douglas Ferguson: Yeah, that’s an interesting point around even as we’re experimenting with AI and what ways we might leverage it because there’s some obvious use cases around, “Oh, we can have it review pull requests, or we can have it sit here and help generate code,” but there’s tons of nascent opportunities we haven’t pinned down or identified and that’s going to require a lot of experimentation. But there’s a lot of folks in organizations that are afraid to experiment because they don’t know what the consequences are. They haven’t been given the latitude. It hasn’t been spelled out. And I think being very clear where the no-fly zones are and where there’s rife opportunity for experimentation gives people a bit more confidence when they do fly an experiment so they can avoid those no-fly zones but lean in certain areas.

Peter Bell: A lot of this is the context. A couple of this separate things I’d say. The first thing I’d say is don’t expect a hundred percent adoption. That’s not a realistic goal. I was speaking with Angie Jones, who did this transformation off the last year at Block, Jack’s company, before she moved onto the Agentic AI Foundation, and she was like, “We looked for 3 to 5% of people. That was our number, 3 to 5% of the engineers. And they were spending evenings and weekends, they’d installed Gas Town on their personal computers. They were like doing all the things, and we unblocked them and we elevated them.” Although the one interesting thing she also said is she said, “You know what? We also made sure that we picked them from all of our core teams across the company so that rather than just saying, ‘Huh,’ it worked for that admin dashboard or a little bit of app modernization, but it wouldn’t work for hard engineering problems, they weren’t given that chance.” The good news with that is it meant that they created an org-wide transformation very, very quickly. But to give you an idea of the level of executive support that required, that basically meant Angie had somebody from legal and security in her team seconded to her. And it was kind of along the lines of if they couldn’t either approve or reliably say, “No, no, we can’t do that because model hosted in China, probably not a good idea from a security perspective,” if they didn’t have a clear red line they could show or couldn’t approve a tool in a small number of days, they would just pull the lever. And it’s like, “Okay, we’re going to stop everything. Do we need to get Jack in the room?” And because of that, they had such strong executive support they could get things done. I’ve been talking with other companies where they still, “The CISO’s just approved Copilot recently for 10% of the engineers,” and I’m like, “They’re going to get exactly the outcomes you’d expect from that level of support.”

Douglas Ferguson: Yeah. You know, the varying levels of support is an important issue you just pointed out. There’s also even lack of understanding around what governance is. And when you’ve got different folks in the org thinking different things and expecting different things and there’s a lack of alignment there, and then there’s not great governance being provided, you kind of get this perfect storm of like everyone being kind frozen and afraid to do anything because they don’t quite understand. So not only does it take good, solid governance, but great communication as well to make sure that people understand what that means and how to apply it.

Peter Bell: Yeah. And it’s really interesting because I’m all in. I’m all the way AI-pilled. You don’t have to be. I mean, even the book, I’m talking about this idea of pick a lane. It’s like we have a technology adoption lifecycle and there’s going to be no different for this than anything else. And there are valid reasons to be an innovator, an early adopter, early or late majority, maybe a laggard. For example, let’s say you’re in the business of ski resorts, literally you run a bunch of ski resorts. Your biggest business risk isn’t AI. It’s climate change. That’s what you need to deal with. It’s whether or not you’re going to get enough snow. And honestly, if you’re six months or a year late to the party and you’re like, “We’re just going to wait till Microsoft folds it all into 365,” you probably could have made a little more money and shipped a few more features earlier, but who cares? If, however, you are Shopify or like Wix, like a website builder, you probably need to be an innovator or early adopter. Otherwise, you’re probably not going to be here in five years. And so it’s important firstly to pick a lane that is consistent with the business risk and opportunity you have, and then, secondly, to make all of your communications consistent. Otherwise, you get into this… The worst anti-pattern is where the CEO is on NBC telling everyone how AI-pilled you are whilst the CISO is still saying nothing but Copilot.

Douglas Ferguson: Yeah. Yeah, and that kind of gets into this issue we hear time and time again across clients and folks at these executive dinners we’ve been hosting is this issue around trust. And it cuts both ways because you’ve got folks that don’t, and we talked earlier about people that don’t trust the AI. And then, also, you’ve got trust in the organization, and that’s partially because of the phenomenon you’re talking about where CEO’s going on C-SPAN or whatever saying some things, and then might not be in agreement with the CISO, whoever else, but also things are changing so rapidly. The company’s message is going to morph a bit because, “Hey, there’s new things we understand now.” And I think that a lot of individuals are really frustrated by that. And it’s not necessarily a company’s fault, but if we don’t pay attention to that and shape that narrative and make sure that we’re consistent in it and make sure that people understand why it might be evolving, maybe acknowledge, “Yes, we understand we said this last month, but now we know this and so we have to take that into account.” I think it goes a long way to be transparent around the thought process, not just like, “What does everyone need to know?”

Peter Bell: I think that data messaging is always hard in large organization and change management, and this is a huge change management issue. Plus, the fear is, “Wait a second, am I just encoding my tastes so this thing can replace me? Are you going to need the same number of engineers and product managers?” There’s very valid reasons to be scared. And at the end of the day, the transition is happening and the thing that’s most… What’s really interesting to me is, and we saw this in the DORA report like last year, the rich get richer in every dimension. And what I mean by this, if you’ve already got good DevOps practices, it turns out if you’re doing agentic coding but Sally still has to FTP the files to the server, you’re only going to get so much acceleration. There are continuous integration, continuous delivery, test coverage, feature flagging so you can decouple, deploy from release and run experiments in production, whether you’re using Datadog or Honeycomb, like the telemetry that you’ve got the observability data so you know what’s going on in production. All of those are more critical than ever. And the reason I thought of this is the other thing that’s more critical than ever, blameless postmortems, an environment where everyone… If you can’t create psychological safety, nobody’s going to tell you how their job works because otherwise you might replace them with a machine. And nobody’s going to tell you that they’re scared about being replaced by a machine and they’re just going to tell you that Copilot doesn’t work very well, and that’s not going to work for anyone.

Douglas Ferguson: Yeah, I love that point. And I want to come back to your comment, the rich get richer. And I just want to be really succinct there because you can be rich with process or you can be poor with process, and those rich with process will get richer. It will amplify that wealth of process that you have. But if you’re poor and you haven’t invested in process, you’ve got some dysfunction, it’s going to amplify that dysfunction. So not only does Sally FTPing the file over prevent you from really leveraging the agentic workforce to help out in those areas, it’s probably indicative of some other process debt that you have that’s maybe going to get scaled in its own right. And what about the edges of those moves? None of that can be integrated. And so I think that’s the thing we’ve been encouraging people to think about, and that’s what we really mean by new friction is Sally moving the FTP file is going to present itself as serious friction in our ability to become the next-level organization.

Peter Bell: And I feel that the other thing also is it’s investing in your team, not only in terms of don’t expect 60, 80% of your team to jump straight on board. You find the 3% to 5%, the coalition of the willing, the people who are going to spend their evenings and weekends doing this, not because you tell them to, not even because you want them to, but just because what would be more fun? And there’s a certain point in your life as a builder where playing with this stuff is just fun, and that’s great. But then, you need to then build the tools and the trainings and the systems to help at least the 60% in the middle to make that move across, and you’ve got to give people time to win. If you’re like, “Hey, we need you to ship everything, which is still critical, oh, but also take 15 hours a week to go learn this new thing,” that’s not going to work. One way or another, it’s going to break with anybody who has kids or family or commitment or parents to deal with. And so you really you have to give people the time to adopt. And the good news is you actually don’t usually lose velocity, but you need to give them the permission-

Douglas Ferguson: That’s right.

Peter Bell: … to lose velocity for a quarter so that you can speed up in the next quarter.

Douglas Ferguson: Yeah. It comes back to that psychological safety piece you mentioned earlier. You have to make it safe to experiment in a number of ways. It can’t be a side-of-desk project. They have to have reserved and protected time. And then, also, they have to be treated with, I would say, respect and encouragement when things go wrong. To your point, if you miss a deadline because you’re experimenting with AI, well, we need to step back and look and say, “Did we actually learn stuff? Does that mean we’re going to beat the next deadline by 50%? Okay, well, it all comes out in the wash.” But if instead we just have an immediate reaction, that’s bad, we need to be punitive here, then we’re really going to miss the boat. People are going to stop experimenting.

Peter Bell: And I feel like I remember Etsy back in the day, and it’s different, but I think it’s comparable. They used to have this commit-on-day-one policy and they still do, and I think it’s much broader now, but this was maybe 10, 15 years ago. And most people would be like, “Wait, you’re letting somebody who knows nothing about your systems like commit some kind of, even if it’s just a nominal fix to a button, on day one? What if they break things?” And the feedback, the answer from Etsy was, “If your system is so fragile and brittle that somebody with good intentions can break it on their first day at work, you should be building your systems, not putting more gates in place.” And I think that’s the way to think about all of the agentic engineering as well, which is we need to build both the culture of psychological safety and support, but also these deterministic and adversarial gates, these tools to make sure that if somebody does make a mistake, you catch it early and quickly. And it’s unlikely, A, to go to production, and B, to waste two weeks of their time trying to figure out why these prompts don’t work.

Douglas Ferguson: You know, I joined a startup years ago and my first day on the job as CTO, the junior engineer who had just gotten promoted before I came online, they had promoted him from… he had just wrapped up his degree at UT, and so they converted him from intern to a full-time engineer. And it was within my first week and he managed to delete the production database. And, luckily, there were processes in place, we got it recovered, et cetera, et cetera. And I was posting, I can’t remember, it might have been Hacker News or it was somewhere that I posted just like, “Oh my gosh, first week on the job, da, da, da, da, da.” And then, of course, someone commented like, “Don’t let them near production systems anymore.” And my comment was, “I have more confidence in that individual on the production systems now than I do some of the other folks.” Because that experience, watching them go through it and them doing what they could to… A, the fact that they reported it, they didn’t try to hide it, the fact that they were just terrified and they’re going to be walking around on pins on needles anytime they’re on a production machine. And I think that’s the lesson we should learn. It’s like, “Not how do we punish someone, but how do we actually learn from our mistakes?”

Peter Bell: Absolutely. I guess last anecdote for that, so I remember one of the… I think it was the first CTO of the United States, there was a guy who helped to turn around HealthCare.gov, I believe it was, back in the time, I’m thinking Obama days maybe. And what was interesting is he gave this talk at a group I was involved with and he said he couldn’t. So imagine you brought in, this thing is months behind schedule, it’s not working, it’s a piece of junk, and you need to fix it using the same people with no different budget. You can’t change out the team. How do you turn it around? And the first thing he did, he actually kind of seeded it where one of the people stood up and said, “I lost some data from production.” And everyone’s like, “Contractors like Washington, D.C., I mean, this is like, ‘Okay, you’re never going to get another federal contract again.'” And he said, “Great, let’s take a moment. Let’s have a round of applause for that person for being honest and sharing. Great. What did we learn and how can we build processes so that doesn’t happen again?” And that was, he said, the turning point where they could start to build the psychological safety so people were focused on sharing the problems they had so they could fix them rather than hiding them and hoping to run out the clock.

Douglas Ferguson: Yeah. So important, especially in this era of AI where we’re moving so quickly and adopting new things, and creating those environments is so critical.

Peter Bell: Absolutely.

Douglas Ferguson: So I want to switch gears a little bit. You kind of touched on this a bit when you mentioned that in reality the adoption’s about 3% to 5%, and yet we see a lot of organizations with these top-down mandates, and we actually refer to it as the mandate trap. It’s one of the things we’re noticing right now, and we’re trying to coach any of our clients away from any of those behaviors, but I’m curious what you’ve noticed. And specifically when you think about this adoption rate of 3.5%, maybe how to get it up, how do we measure success in this AI world, I guess, is kind of what I’m getting at because that’s how we get past the mandates is being able to measure the process, I think.

Peter Bell: Absolutely. So firstly, I should clarify, I think you’re going to see 3% to 5% of super adopters, and then you’re going to see… I mean, the number, Steve Yegge got into a lot of trouble on Google… on Twitter or by X by saying, “You know, 20% of people are killing it, 60% of people will come along, and 20% will never touch it. And the same’s true at Google and anywhere else.” And first-level round numbers, he’s about right. There’s a few people who are killing it, a bunch of people who are willing to follow along, and a small tail who just have no interest in going. So first thing to do is drop this, not like fire, but don’t focus on the last 20%.

Douglas Ferguson: No.

Peter Bell: What you do is you elevate and you unblock that first 3, 5, 15%, whatever it is. You make sure that they get the tokens they need, the support they need, the resources they need, and you elevate them. Then, you help ask them, “Great, now you’re doing this. How can we do this as an org? Join a council, do lunch and learns. Can we build a small DevEx or platform team that has shared skills and shared resources? How can we get more observability and capabilities within our platform?” So a lot of this is about unblocking and supporting. Another part then is creating a path for the middle, the kind of quiet middle who just want to go home in the evenings, but unopposed to AI, you just need to tell them how to do it. And then, the other piece of this is in addition to that, you need to support these groups in figuring out what problems they have. So you were talking about management and metrics. The success metrics are, honestly, business ones, and you can take proxy metrics as long as you don’t performance-manage them. You learn something by token usage. If somebody’s not blown through a $20 a month plan, they’re probably not using it enough. But if somebody spent 8,000 bucks last month and has spent 6,000 this month and their output increased, they’ve probably improved the efficiency of the levels of the models they’re using. So it’s not that token maxing is good, but it is okay to know how many tokens people are using as a diagnostic to put them into populations which you can then support with adoption in different ways. The true success metrics are pretty straightforward, all right. It’s revenues, it’s customer retention, it’s all the numbers you care about. The challenge becomes that it’s hard to map those to a particular feature deliverable or a particular agent. So I think the main thing to do is the AI token usage and stuff like that, that is a diagnostic to help you to cluster people around common failure patterns of adoption so that you can give them the training and support to learn how to get through, “Oh, that’s the kind of thing we see when somebody’s still on an IDE.” We should teach them how to use skills with agents. That’s the thing we see when somebody’s waiting for one agent 15 minutes at a time, we should show them how to use multiple agents and so on towards moving them towards using a dark factory. And then, the other piece is classic DORA, DX Core 4 space metrics, I think still have a place. They’re not the answer, but things like cycle time, meantime between failures, PR rates, all of those can be gamed, but if there’s no reason to game them, they can be useful diagnostics to help you to see how you appear to be doing.

Douglas Ferguson: Yeah. And it’s really interesting, too, when you think about cohort analysis, you could look at that in a number of ways. You could look at any of our standard metrics like cycle time, a few others you mentioned as it relates to folks that are heavily using AI, barely using it to not using it at all, because then there’s an interesting story to be told there. It’s like, “Well, what kind of outputs are we seeing from these individuals and different teams as well?” The other thing around cohorts that’s fascinating to me is what are we noticing as far as the friction that we might be seeing from each of those cohorts? And because you mentioned looking at, “Well, what signals are indicative of someone still being in the IDE or whatever some of these types of behavior shifts that we’re looking for?” And I think, likewise, if we diagnose where are the sticking points in the organization and what are those indicative of? It’s like, “Hey, if we’re going to be investing in this 20% that’s really leaning in and we’re trying to remove obstacles, well, let’s actually make note of the obstacles they’re running into. And then, how do we codify that into repeatable patterns or better ways of supporting them?”

Peter Bell: Absolutely. And I think we’re seeing that what’s nice is I think that 5 to 15, 20%, what they do is they’re actually as the obstacles they usually run into are self-induced by the company. And I have lots of friends who are CISOs, but like security, compliance, governance, audit, risk, it’s those groups that are designed to keep things the same so we don’t break it all, which is a noble mission, but we need to understand that there’s risk to not changing and support those teams in being enablers and not blockers. And then, once you’ve got that in place, then what they can do is they can… The truth is what they’re doing is hard and it probably is requiring evenings and weekends, but what they can do then is synthesize the good practices, create standard skills libraries, create a standard factory harness and standard adversarial reviews, improve the quality of the observability and the DevOps and CI and CD pipelines, all the things that are going to make it easier for other people on the teams to then kind of join along. And the other thing, it feels to me like I remember when you mentioned TDD earlier, test-driven development, you had to get… Most of us got test infected. We’d read the books, we kind of saw the stuff, it didn’t really make sense. And then, you paired with somebody from like a Pivotal Labs or a Thoughtworks back in the day and you’re like, “Oh, that.” And after two or three hours of pairing, it made perfect sense. And just as you had to get test infected, I think there’s huge value in getting AI infected where a coworker just sits down with you, pairs on a couple of features, and shows you how they’re leveraging skills, how they’re jumping between agents and how they’re building these kind of pipelines so that they can start to trust the quality of code that’s being shipped.

Douglas Ferguson: Yeah, I mean, sure we see the CISO friction all the time like, “Oh, we really want Claud Code, but security has only approved Copilot,” or whatever. And so that certainly is an obstacles we should as leaders be trying to remove if we’ve got folks on the team eager to push things forward in ways that are responsible and secure, then we should pave the way there. But I think the latter half, the stuff you got into, I think is a little less obvious. It’s totally clear if they’re trying to use a tool that’s not available, but what about these models and patterns? Because you can’t go just grab a book off the shelf. You can’t go read about Spotify’s model of how to do this. And so are we creating opportunities to sit with peers and see how they’re each using skills and really look at what is some of that minor friction that they’re running into that’s not maybe apparent or where they’re scratching their head a little bit? A great example, buddy of mine mentioned that someone on his team was… He’s a VP of engineering, and someone on his team had one-shot this piece of code that was like, I don’t… It was like 250,000 lines of code or something insane. And admittedly, the engineer came in and said, “This seems to be working,” but I’m like, “I can’t even fathom how to read this much code. I’m stuck.” And so then, that became a conversation around, “Well, this is some new friction. Look at this thing. It’s brilliant. It seems to work. When we poke it does the things we want it to, but we need to understand this better.” And the thing they came to was, “What if we then use the AI to decompose this into more meaningful, smaller chunks that are easier to read? We’re still get this out the door way faster than we ever would have previously, but let’s induce some slowness here because we want process and we want care and quality. And I think that’s a great example of the types of friction we should be listening out for and helping our teams work through because that’s what’s going to create the models of the future.

Peter Bell: Exactly that. So there’s a guy called Sam Schillace. I first came across him when he was SVP engineering at Box. Now, he’s a deputy CTO at Microsoft. He has helped them to build this tool called Amplify, which it’s like the best harness nobody seems to know anything about. They don’t promote it very much. But what’s interesting is he’s got this Sunday letters from Sam on Substack, and he’s got a couple things that he’s built into his pipeline. One was Cranky Old Engineer, which is basically the salty old engineer like, “That’ll never work under load. You’ve got to ensure that there’s a fallback and you back off your retries against the API or whatever it is.” But now he’s got COS, Cranky Old Sam, which is based on Cranky Old Simplicity as well, which is basically saying, “Hmm,” and it will literally go back to the agent that’s generated a code that’s passing the functional test that’s meeting the performance requirements saying, “Would there be a simpler way to do this?” And proposing unifications and simplifications and simpler ways of solving the same problem. And it turns out that a lot of this is just reprompting and loops. And if you’re willing to burn the tokens, most of the problems the agents solve, you can get other agents to tell them how to fix.

Douglas Ferguson: Yeah, it’s amazing. It’s so fascinating. I mean, and to your point, willing to burn the tokens, I think that’s going to be a conversation that evolve even more so over the next six to 12 months. As we’re seeing the cost of tokens rise, more competition against models, the IPOs are certainly going to influence us because now the market’s going to be a driver and have a voice in the cost of these tokens. So it’s going to be fascinating to look and see how we start to optimize around token consumption and when, where, and why to use them.

Peter Bell: And I just want to throw one thing in there because what happens is every so often people who want AI not to succeed, and I get it, I understand why, will be like, “Oh, token costs are going to become crazy, so we’re just going to hire humans to do it.” If there was one piece of generalized advice I could give is don’t bet against the models. I don’t think that’s a good long-term bet to take. And there’s no question that sanity is going to prevail. Tokenomics is a real thing now just as FinOps is for cloud. We’ve started by, let’s say, we’re just going to run all this Kubernetes stuff in the cloud and it’s going to be perfect. And then, the CFO comes calling like, “Why did our operating cost go up by $12 million last year?” “Oh, we forgot to switch off the… We had this test run and we forgot to switch it off for six months.” That was like a million and a half. And so then you started to bring sanity and improve operational and then the spot versus reserved instances and all the rest. We’re going to do the same here, but here it is you should never use a model to do something that code can do perfectly well. Long running, don’t use supervisor model, use deterministic pipelines. If you’re extracting text from a PDF, have a Python script extract it and just put the text into the model. Don’t burn the tokens on a 4.8. And it’s all of that. I just ran a bunch of evals. I had a bunch of stuff running on Opus that I’ve now downgraded to Sonnet and then to Haiku with evals and test sets and they just auto-tuned the prompts until it would work. So all of this is just a simple engineering problem. We know how to engineer the costs out of stuff. So, yes, token costs are real, and no, don’t believe that somehow magically you’re going to stop this, it won’t.

Douglas Ferguson: So what you’re making me think of is that Chaos Monkey might be coming back but in the age of AI.

Peter Bell: I think it’s going to be so many of the things we’ve seen before coming back just at another level, and it’s going to be really interesting to see how they all play out.

Douglas Ferguson: For sure. Well, as we come to our end here, I want to give you an opportunity to leave our listeners with a final thought.

Peter Bell: Absolutely. I’ll give a two-for-one. The first thing is do it yourself. If you’re a CTO, you shouldn’t be writing production code and blocking that for three months as you’re going to performance review season. But if you’re not spending time building with these models, you won’t get the right intuitions, and that’s the only way to keep up. And second, have a sense as to where we’re going. This isn’t about Copilot, this isn’t about IDE. This isn’t honestly about the kind of interfaces we’re seeing now. We are building systems that will write the software, and our job is to identify the experiments and the verifications and build the toolings to make that work. And understand that until you fundamentally rebuild the SDLC for agents, you are not going to get the kinds of ROI that you’d expect out of these systems.

Douglas Ferguson: Important words. Great to be chatting with you today, Peter, and looking forward to talking again soon.

Peter Bell: Douglas, thank you so much for the invite. So much fun.

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

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What Two Real Cases Reveal About AI Governance

Joe Mariano said something at the Gartner Digital Workplace Summit and it should be on a poster in every AI governance committee in the country. “AI is a tool. It is not a legal shield.” Two recent cases prove him right. In both, an organization deployed an AI system, the system did exactly what it was designed to do, the organization got sued, and the organization lost. Not because the model misbehaved. Because nobody on the human side was watching. These are not abstract risks. They are the first two real precedents we have for AI liability in the enterprise, and they both turned on the same thing: a monitoring and data gap that the legal system treated as the company’s responsibility, not the AI’s. If you are a leader thinking about AI governance, you are no longer thinking about it in theory. You are thinking about it inside the ruling that other companies have already lost.

The Air Canada Chatbot Case

In November 2022, Jake Moffatt visited Air Canada’s website to book a last-minute flight to attend his grandmother’s funeral. He asked the airline’s AI chatbot whether bereavement fares were available. The chatbot told him yes, that he could book the flight at full fare and apply for a bereavement refund within ninety days of travel. He booked. He flew. He filed for the refund. Air Canada denied the claim. The actual policy required bereavement-fare requests before travel, not after. The chatbot had hallucinated a refund window that did not exist. Moffatt took the airline to the British Columbia Civil Resolution Tribunal. Air Canada’s argument was the part that should make every AI governance lead pay attention. The airline argued that the chatbot was, in their words, “a separate legal entity that is responsible for its own actions.” The tribunal rejected that argument flatly. In Moffatt v. Air Canada, 2024 BCCRT 149, the tribunal ruled that Air Canada was responsible for all information on its website, regardless of whether it came from a static page or a chatbot, and ordered the airline to pay damages. The decision is short, the legal reasoning is clean, and the precedent is simple: if your AI tells a customer something false, your company said it. The chatbot does not have its own lawyer. It does not have its own bank account. It does not have legal standing. It is a tool you deployed, and the output is your output. Air Canada’s failure was not that the chatbot hallucinated. Hallucination is a known property of generative systems, and any organization deploying one in a customer-facing context should plan for it. The failure was that nobody checked. There was no monitoring layer, no review pipeline, no human in the loop verifying that high-stakes policy claims matched the airline’s actual policy. The model behaved as models behave. The organization behaved as if the model would not.

The iTutorGroup EEOC Settlement

In August 2023, the U.S. Equal Employment Opportunity Commission settled a case against iTutorGroup, a tutoring company that had used an AI-driven recruitment system to screen applicants for tutor positions. The system was configured to automatically reject women aged 55 and over and men aged 60 and over. More than two hundred qualified applicants were filtered out before any human ever saw their applications. The EEOC argued, and iTutorGroup agreed in a consent decree, that the company had violated the Age Discrimination in Employment Act. iTutorGroup paid $365,000 in damages and committed to anti-discrimination training and oversight changes. It is widely cited as the first EEOC enforcement action targeting algorithmic discrimination, and it set the regulatory tone for what was to come. The interesting thing about this case is that the AI did not malfunction. It did exactly what its rules told it to do. Somewhere in the configuration, somebody had set age thresholds. The AI applied them. Hundreds of times. What was missing was the question of whether anybody should have set those thresholds in the first place. There was no review of the screening logic against employment law. There was no monitoring of who was being filtered out and why. The data quality, the rule design, the oversight layer, all of it sat inside an AI deployment that nobody thought needed governance because the AI itself was working. That is the iTutorGroup pattern, and it is more dangerous than the Air Canada pattern because it does not look like an AI failure. It looks like an AI success.

The Pattern: Monitoring Gaps, Not Bad Models

Joe Mariano walked through both of these cases at Gartner DWS 2026, and the framing he landed on is worth repeating: the failures here were not in the technology layer. They were in the layer above the technology, where humans decide what the AI is allowed to do, what data it sees, and who is watching when it is doing it. The Air Canada chatbot worked as a generative chatbot works. It produced a plausible answer to a question it did not have grounded knowledge to answer. The failure was that the airline deployed it on a high-stakes policy page without a verification pipeline. The iTutorGroup recruiter worked as a rules-based filter works. It applied the configuration it was given. The failure was that the configuration had been set by humans without legal review, and there was no monitoring on the output to flag the discriminatory pattern. Both failures, in other words, traced to the same place: the human-decision system around the AI was not designed. The technology was deployed faster than the governance scaffolding around it could catch up, and the legal exposure that resulted was real. This is the part most AI governance conversations skip. They focus on the technology, on which model, on which vendor, on which compliance certifications, when the actual exposure lives in the workflow. Who reviews high-stakes outputs before they go to customers. Who audits the rules the AI is using. Who is watching for patterns that look fine inside the model but look discriminatory in aggregate. The Gartner data backs this up. There are over 1,000 proposed AI rules and regulations worldwide right now, and not one of them has the same definition of AI. The regulatory landscape is going to get harder, not easier. Companies that are still treating AI governance as a policy document, rather than as an active facilitation problem inside their organization, are going to keep producing the next Air Canada and the next iTutorGroup.

Why AI Does Not Absorb Accountability

There is a comforting fiction that some leaders are still telling themselves about AI deployment, which is that the model carries some of the liability. It does not. Across multiple jurisdictions, in multiple legal frameworks, the rulings are converging on the same answer: the deploying organization is accountable for the output, full stop. This makes sense the moment you say it out loud. The AI did not sign a contract with the customer. The AI did not file a lawsuit. The AI did not get sued. Your company did all three of those things. The model is a tool the company chose to deploy, and the output of that tool is the company’s output, the same way that an internal email written by a junior employee is the company’s email. What this means in practice is that the conversation about AI governance has to move from “is the model trustworthy” to “is our deployment of the model accountable.” Those are different questions. A trustworthy model deployed without governance is still a liability. An imperfect model deployed with rigorous governance is, in many cases, fine. The trustworthy-but-ungoverned configuration is what produced both cases above. The Air Canada chatbot was, by industry standards, a perfectly normal AI product. The iTutorGroup recruiter was, by configuration standards, perfectly capable of being used legally. Neither model was the problem. The deployment around it was.

Four Governance Patterns That Actually Work

If the technology is not the gap, what closes the gap? Mariano’s session offered four patterns, and they map cleanly to what we see when we walk into client governance work. Brain-first deployment. Before AI is brought into a workflow, the team uses human judgment to define the goal, the boundary, and the success criteria. The AI is then brought in to assist a human-defined process, not to replace the human-definition step. Air Canada skipped this. The chatbot was deployed on a policy page without anyone defining what counted as an acceptable policy answer. Human in the loop for quality control. Some volume of AI output gets reviewed by a human before it goes to a customer or a decision. The exact percentage depends on the stakes, but the principle is non-negotiable: zero human review on high-stakes outputs is a deployment, not a governance posture. iTutorGroup ran a recruitment AI with apparently no auditing of the rejection pattern. That is the failure case. Data quality management. AI systems accessing wrong, stale, or biased data will produce wrong, stale, or biased outputs with full confidence. Both Air Canada and iTutorGroup had data quality problems at the root. The chatbot was answering questions about a policy it had not been grounded in. The recruiter was applying rules that had not been audited against current law. Neither case was a model problem. Both were data problems wearing model clothing. Continuous skill and process maintenance. Governance is not a one-time training. It is an ongoing practice, with periodic reviews, audits, and skill refreshes for the people running the system. The model evolves. The regulations evolve. The use cases evolve. A governance framework that was designed twelve months ago and has not been touched since is, by definition, stale. These four patterns are not novel. They are the basic discipline of any high-stakes deployment, applied to AI. What is new is that the legal system is now treating them as the standard of care, and organizations that ignore them are losing the cases.

The Real Move: Treat Governance as Facilitation

Here is the move most organizations miss. AI governance is not a document. It is a set of ongoing agreements between security, legal, business, and operations about what the AI can do, who is watching, and what happens when something goes wrong. Those agreements have to be negotiated. They cannot be written by one team and handed to the rest. This is where the work gets uncomfortable, because it requires the same cross-functional conversation that most organizations are structurally bad at. Legal does not want to talk to Engineering. Security does not want to talk to Marketing. The business unit that wants to deploy the chatbot does not want to slow down for a review. And so the governance conversation never happens, and the deployment goes out, and somebody loses a tribunal. The organizations getting this right are the ones that treat AI governance as a facilitated, recurring practice, not a sign-off process. They have a standing forum, with the right people, that meets often enough to keep up with what is being deployed. They produce decisions, not policy documents. They review the deployments that have shipped. They ask, every time, what would happen if this output ended up in front of a regulator or a tribunal. That is the New Friction. AI eliminated the old friction, which was execution time. The new friction is the human-decision layer that has to keep up with what AI now lets you ship. Organizations that do not invest in that layer ship faster, get sued more, and lose the cases. Organizations that do invest in it ship slightly slower, ship better, and stay out of the tribunal. If you are building or refreshing your AI governance posture right now, the question is not which model you trust. The question is which decisions your organization can keep up with, and which conversations you are willing to have to keep up with them. That is the work. That is the entire work. If your organization is in the middle of that conversation, or trying to start one, that is where Voltage Control comes in. Read our New Friction primer for the full framework, or reach out if you want to talk about where your governance posture is stuck.

Frequently Asked Questions

Can companies be held liable for AI mistakes?

Yes. Both the Air Canada and iTutorGroup cases establish that the deploying organization is responsible for AI output, regardless of whether the output came from a human or an AI system. Air Canada explicitly argued that the chatbot was a separate legal entity. The tribunal rejected the argument. Across jurisdictions, the legal direction is consistent: the company that deploys the AI owns the consequences.

What happened in the Air Canada chatbot case?

A customer asked Air Canada’s chatbot about bereavement fares. The chatbot hallucinated a refund policy that did not exist. The customer relied on it, booked the flight, and was denied the refund. He took the airline to the British Columbia Civil Resolution Tribunal, which ruled in Moffatt v. Air Canada, 2024 BCCRT 149, that the airline was responsible for the chatbot’s output. Air Canada paid damages.

How do organizations govern AI systems effectively?

Effective AI governance is a recurring facilitation practice, not a static policy document. The organizations doing this well bring legal, security, business, and operations into a standing forum that meets often enough to keep up with deployments, audits real outputs, and produces decisions. The four operating patterns are brain-first deployment, human in the loop for quality control, data quality management, and continuous skill maintenance.

What is AI accountability in the workplace?

AI accountability is the principle that the organization deploying the AI is responsible for what the AI produces. That responsibility cannot be delegated to the model, the vendor, or the AI system itself. It lives with the humans who decided to deploy the system, configured it, fed it data, and chose how much oversight to give it.

Who is responsible when AI makes wrong decisions?

The deploying organization. In every major AI liability case to date, including Air Canada and iTutorGroup, the courts and tribunals have held the company responsible for the AI’s output. The model is treated as a tool, and tool failures attach to the operator, not to the tool.

The post AI Is Not a Legal Shield appeared first on Voltage Control.

In this episode of the New Friction podcast, host Douglas Ferguson speaks with Jeff Grabill, Dean of the College of Arts and Sciences at the University at Buffalo, recorded in the immediate aftermath of the IHE US AI Summit 2026, which both men attended. Grabill recounts what emerged from that two-day working convening: the foundation of the Buffalo Statement, a collective public agenda for AI in higher education, and reflects on why the room’s patience, grounded confidence, and willingness to question prior assumptions exceeded his expectations. The conversation explores why universities, often criticized for moving slowly, may possess exactly the right instincts for AI transformation: designing conversations intentionally, engineering productive friction, and moving fast and slow at the same time. Ferguson and Grabill dig into how AI has relocated rather than eliminated friction, particularly in learning environments, where effortless output now threatens the productive struggle that actually builds expertise and ideas. They close on a librarian’s insight from the summit — “I don’t care if AI created it, I care if it’s true” — and Grabill’s call for businesses and universities to actively seek one another out as partners in working through this moment.

Show Highlights

[00:01:26] The Summit and a Missing Public Agenda
[00:05:17] Vulnerability as the Summit’s Secret Ingredient
[00:09:40] Disciplinary Identity and the AI Conversation
[00:18:02] Why Higher Ed Needs a Design Practice
[00:20:07] Moving Fast and Slow at the Same Time
[00:25:44] Unpacking the New Friction
[00:29:46] Engineering Productive Friction in Education
[00:51:34] Truth Over Authorship

Links | Resources

Jeff Grabill on LinkedIn

About the Guest

Dr. Jeffrey T. Grabill serves as Dean of the College of Arts and Sciences at the University at Buffalo, the university’s largest academic unit, a role he assumed in August 2025. Before UB, he spent four years as Deputy Vice-Chancellor for Student Education at the University of Leeds, and nearly two decades at Michigan State University — ultimately as Associate Provost for Teaching, Learning, and Technology, where he co-founded the Hub for Innovation in Learning and Technology. He developed and led a research center on written communication and technology and co-founded Drawbridge, an educational technology company that spun out of his research group. Grabill is the co-author of Design for Change in Higher Education, published by Johns Hopkins University Press, and his work focuses on how rhetoric supports citizenship, learning, and institutional change.

Transcript

Douglas Ferguson:
Welcome to New Friction. I’m Douglas Ferguson. AI just made execution almost free. So why are organizations still stuck? Because the friction didn’t disappear. It moved and it multiplied. It’s no longer in building. It’s in deciding what to build, how to align, and how to move forward when the path isn’t clear. That friction, the human side of change, is what this series is about. Each episode, I sit down with leaders who are living it, navigating the real challenges of AI transformation, not the tools, the people. The task that took two weeks now takes two minutes. The work isn’t the bottleneck anymore. The conversation before the work is. That’s the work this show is about.
Today I’m with Jeff Grabill at the University of Buffalo, where he’s the dean of the College of Arts and Sciences. He’s also the author of Design for Change in Higher Education from the Johns Hopkins University Press. Welcome to the show, Jeff.

Jeff Grabill:
Douglas, thank you. It’s always a pleasure to talk with you. I learn a lot when we do, so thanks for the invitation.

Douglas Ferguson:
Oh yeah, it’s amazing. And the feeling is mutual. So let’s talk about the last two days. We just spent two days with 300 people drafting a public agenda for AI and higher ed. And if we strip away the panels and reflect on what was the single biggest thing that’s actually shifting right now, I’m curious what’s emerging for you, especially in the afterglow.

Jeff Grabill:
A couple of things. Yeah, so the conversation was, I thought, fantastic and exceeded my expectation. So let me back up a step and walk through your question. So the purpose of the meeting, we’d noticed, and I wasn’t the only one who’d noticed, but those of us at the University of Buffalo had noticed, not just that there was no public agenda for AI. There was an Ezra Klein podcast about that. I think lots of people have noticed it.
But also that there was no public universities in particular, but universities more broadly weren’t stepping into that space. And we’ve all been floundering a little bit. And as soon as we started socializing with people that the fact that there’s no public agenda for AI is a problem, and absent leadership in Washington, frankly, who’s going to step into that space and start to do more than wring our hands about it, but start to articulate how we might collectively and collaboratively proceed.
So the notion of having a meeting, a working meeting, not a conference, in which we put the lack of a public agenda on the table and put the question of what is the role of the university in relationship to that public agenda on the table, I was really pleased at how many people rallied to that. This is a great idea. So we had nearly twice as many people at that meeting than we thought. We tried to curate that meeting as best we could with expertise. It was a working meeting. We are going to produce the Buffalo Statement or some such thing which starts to sweep together the meeting.
So what surprised me about it? I was pleased, but not necessarily surprised at what I just said, that the reaction was immediate, the reaction was positive, and people have rallied to it. I was surprised in the meeting at how positive people were and how grounded people were. In other words, there wasn’t a lot of hand wringing and anxiety, and we’ve got to catch up in that room. And there was actually a sense of patience and confidence that universities have been around a very long time, and there’s a reason for that, and that there’s some durable value in what universities have to offer and we ought to lean into that because that will probably be durable.
But at the same time, there is a role for us. Universities are suffering a little bit right now. The public opinion of universities is as low as it’s been in a really long time. And so we also recognize that if we step in as a platform and a convener and a collaborator, we have a little bit of work to do to get people to trust us the way they used to trust us. So surprised at the confidence and the positivity, the patience, those were the things I took away, and I was pleased by those surprises.

Douglas Ferguson:
Absolutely. I was actually very impressed by just how the group showed up and collaborated, and especially in a moment in time where there’s not a lot of collaboration across disagreement. I found people bringing differing points of view, and actually taking the time to consider and listen and think about how those points of view might get integrated. I took it as a testament to your curation of who was in the room, but also still impressive, and it gave me a sense of hope.

Jeff Grabill:
Yeah. No, thank you for picking that up. I picked it up too because that meeting didn’t work if that didn’t happen. So if people came in and sat back on their hands, or looked at their phones for two days and refused to be a little bit vulnerable, it requires some vulnerability to sit at a table with some people you don’t know and dwell in that space. Let’s try to figure some things out together. I think that’s why the meeting worked. I’m really pleased you noticed it. I think you nailed it. That’s why we had a productive two days.

Douglas Ferguson:
Just to be real specific for the listeners, one great example of that is you had folks that were super optimistic about things and they were looking at the data center impact as a job creating event. And then you had folks that were looking at data center impact on the environment and were very concerned about it. But there’s room for both of those points of view, and it wasn’t argumentative. It was very curious. And people were creating space for the opposite points of view. And I think we need more spaces like that today, and especially in this AI conversation.

Jeff Grabill:
I completely agree with you. And some of the academics in the room that were most skeptical and oppositional are my colleagues in the College of Arts and Sciences here. And I was thrilled that they were there, but I rotated tables, I sat on a table for a while with some of them. I was even happier the way they showed up. They’re worried. This is their science. They understand things, they have expertise. But they did create space in the conversation, and I think in their own minds, for possibilities that are a little bit different than their defaults.
There was an economist in the room who kept talking about this moment with regard to the labor market, for example, another example of a really productive conversation is that we have to question all of our priors about how innovation cycles and disruption cycles work because this one doesn’t seem to be tracking with our priors. And so I thought that was a really, it’s a very economist way to speak, but I thought that that was something that happened across the room, and that everybody sort of checked their priors a little bit and opened the possibility that maybe those priors were wrong.

Douglas Ferguson:
There was a moment in one of the panels where, and I’m blinking on the individual’s name, but he offered a call to action to all the universities to tap their historians and maybe present to all the faculty and staff around these historic moments of what it was like to live through the printing press revolution, or any of these other technological revolutions that how can we draw from those moments and maybe imagine ourselves, and how it’s similar, how it’s different. I thought that was really awesome to think about, hey, we have these resources on campus. Let’s maybe share them with our peers, not just the students and the researchers.

Jeff Grabill:
No, I think it’s a key insight. I’ve also, to build on that, have a business school dean in my past whose favorite people on campus were historians. Because he not only read history, but he talked to them because the coffee conversations that he had with historians were better at grounding his sense of where the financial markets were going to go in the future. Because the things that seem sort of disruptive and new to us, you talk to a good historian, they’ll say, “Well, maybe we’ve been here before.” And they unpack it for you. And then in a way, we have been here before and in a way we haven’t. And I think that kind of wisdom, history’s useful in that regard. So not the first time I’ve heard it, but I thought I remember that. And that was a really useful contribution too. The historians in the room loved it.

Douglas Ferguson:
Yeah. It’s reminded me of Jeff from Worcester’s comment about how it’s not about using the AI tools, but it’s about how experts in a discipline will leverage it for the best use of that in that discipline. And so not only how are we leaning into our historians, but our economists, our librarians. They need to develop their use cases that are very idiosyncratic and how they bring their expertise into these new tools.

Jeff Grabill:
Yeah. He and I had a real meeting of the minds about that because he’s a new dean of Arts and Sciences. He’s four days in. I’m 10 months in as a new dean of Arts and Sciences. The University of Buffalo’s been an AI forward university for a long time. We intend to stay there. And I have a lot of skeptical, grumpy, worried academics across my college. And the only ask I’ve made of them with regard to AI is, I need you to engage. You can’t sit it out. So if you sit it out, I’m going to be frustrated with you. So please don’t. Engage. You can be grumpy, you can be skeptical, you can be yourself. Be yourself as an individual, be authentic, be yourself as a disciplinary creature, which is what Jeffrey was talking about. Be that person and we’re going to be fine.
But if you check out and disengage, that’s not who we’re supposed to be as leading public research universities. We don’t get the option to disengage. That’s not the job. And then to their credit, they roll their eyes sometimes at me, and the dean is opining again, but they’ve engaged. And there were people in that room the last two days who might not have been there had we not collectively worked on this in the college here at the University of Buffalo.

Douglas Ferguson:
Yeah. I’m thinking about that conversation more deeply now. And one of the things that really stuck with me listening to Jeffrey talk about that was this idea that it’s okay to be a cynic, but you need to be an informed cynic. If you just dislike it because you dislike it, well, that’s not helpful to developing thought and points of view.

Jeff Grabill:
Yeah. And I think that’s a really important challenge. I mean, I used to say this when I was mentoring graduate students, and it’s a real privilege to be a university professor. Yes, everybody who gets there works really hard, is also super lucky. Lots of people help you get there. You catch some breaks. But it’s a privilege and it’s a responsibility.
And one of the conversations I used to have with the graduate students that I mentored is really challenging them to own the responsibility. It means something to be a university professor at a leading research university, and there’s expectations for us. To say what I said earlier, we don’t get the option to sit it out. That’s not the job. The job is to push the edges, push the frontier, think the thoughts that are not supposed to be thought or that are challenging. This is why we have academic freedom.
Our job is to be on the edge and to support people in doing that work. We’re supposed to do hard things. And those are hard conversations to have with people. And they’re hard for me to own because sometimes I’m just tired. But that’s the work. And to get back to the meeting, that’s what I wanted to do with the meeting is give us a space to signal to each other that we’re not alone as a group of intellectuals, intellectuals in private industry and intellectuals in the university who know that this is the work that we have to do.

Douglas Ferguson:
And I want to come back to this idea of no public agenda. If I remember correctly, Armada pushed back and said there’s already one, and the danger is reacting our way out of it, I think was her position. And so basically not doing anything or abandoning it because they’re scared of looking slow.

Jeff Grabill:
Yeah. I thought that was a key moment. So this is one of the things where I changed my mind. Because I mentioned earlier, we’re going to write this Buffalo Statement, and hopefully it’s good and people pick it up and work with it and engage with it. I’ve written a draft of it before the meeting because I didn’t want to go in the meeting cold. And we’re going to write it together. The meeting was a writing workshop in many respects.
She changed my mind about that. And her point was there is no public agenda for AI in the United States. There’s no policy agenda, there’s no blueprints that a government would provide, for example, or a set of institutions might provide. What her argument was, universities have a public agenda for AI, and we don’t need another public agenda. We just need to be ourselves and do our thing.
And I’m not entirely sure I completely agree with her, but in another way, I think she’s really right. And I suspect that this Buffalo Statement’s going to reflect that in the sense we shouldn’t wait around for the federal government, for example, to fix itself. God knows how long that’s going to take. In the meantime, we need to proceed, and here’s what it might look like for us to proceed with public universities in particular leading.

Douglas Ferguson:
Yeah, that was something Eric and I were talking about a bit yesterday. He was referencing some research that’s in a book that I need to read, and I need to reach back out to him about this. But this idea that a big challenge in higher ed is this kind of layers and layers of purpose, and how those different purposes will conflict with each other often, or maybe not conflict, but it’s which one do you focus on? And this came up a bit at dinner, even not only multiple purposes, but how people define and personally define a purpose. What does student outcomes mean? So I’m curious how that… It seems like that was an idea that was kind of orbiting some of this stuff, and maybe even wrapped up in Armada’s kind of thinking there.

Jeff Grabill:
I think it was, and it did come up at dinner. So let me back up a step. So for a period of time, I was on a reasonably large writing committee for Michigan State’s strategic plan. I’m not sure whether it’s the one they still have, but it’s the one they wrote just before around the pandemic. Anyway, we would talk with external stakeholders about purpose. What is the purpose of Michigan State?
And external stakeholders were surprised at any purpose that wasn’t education or that didn’t foreground education. So most of the public doesn’t really see the research purpose, for example, of a research university because they didn’t necessarily experience it. But if you talk to academics at a research university, it is the primary purpose. They are primarily there to do that kind of intellectual work. They do the education work too, but they often see it as a zero sum game. The more time I spend on education, the less time I get to spend on my research.
And that’s a very real tension inside universities, and that’s just with those two purposes. And when you start to layer on all the ways in which universities have become social service institutions. We have students who are homeless. We are their home. We run giant food service operations. We have performing arts venues. We have community engagement programs. We provide a set of services in every community that we’re in. If we’re unfortunate enough to be in a big athletics conference, we run professional sports franchises on the side.
That’s the accumulation of purposes that tends to weigh down universities. One of the things that’s been really interesting about the pressure that the Trump administration has put on universities is it has caused universities to go back to basics, and say, look, we do research and we educate, and we’re going to try to focus our energies on those two things. And that’s not a bad return to focus and purpose.

Douglas Ferguson:
Yeah, that’s fascinating. You also mentioned this idea of blueprint, and maybe there isn’t a blueprint. We’ve been noticing a lot that industry folks are struggling with the fact that there’s no model to follow. And that is, I would say, at high levels of the organization, and all the way down to the individual level. We just haven’t developed these ways of working, these patterns. Folks have done agile for years, for example. But now AI is shifting things, and so they can’t reach out for the Spotify model or these examples. We can just run that play and do it again. And so I think everyone’s in this moment of redefining what it means to work, what it means to show up and exist in these systems.

Jeff Grabill:
Well, and this is why I’m hoping that, and this gets into your area of expertise, you’ve talked with Eric about this, I keep waiting for higher education, my business, to discover design and to discover facilitation and relationship to design because we just haven’t. This is a moment of real uncertainty. The patterns don’t work, our analogs don’t work particularly well. And so all of my instincts are that we design our way through them.
And it’s kind of what we tried to do in some… So we designed an interaction for two days to try to produce an outcome and a set of relationships and a set of conversations. I really do think that wise organizations and wise institutions are going to lean into those design practice. And if they don’t have a design practice, partner with people like you who can help them develop a design practice.
I keep waiting for higher education to… We did it at Leeds at scale. They’re still doing it at the University of Leeds at scale, but it’s really hard to develop design capacity inside a university organization and get people into that mindset that this is the way in which we’re going to come up with some provisional answers to who we think we are and where we think we need to go. Because as was said at the meeting the last two days, the only thing we can’t do is everything that we’ve done in the past just as we’ve done it in the past.

Douglas Ferguson:
Yeah. And you hit the nail on the head with the word mindset. And I would argue that it’s not just difficult in universities, it’s difficult anywhere where the mindset doesn’t exist or hasn’t taken root because then you’re talking about real behavioral change at a deep worldview perspective. We have to shift how people think about the world, or think about work, and we have to shift, and it’s happening fundamentally. They’re relearning, they’re reshaping things, and that takes time and commitment.

Jeff Grabill:
Yeah. And it gets back to something that was also true or said in the meeting. And I used to say this at the University of Leeds all the time, and it drove people nuts until they experienced it over a couple of years, that you have to move fast and slow at the same time. And it’s possible to move fast and slow at the same time. And some of that means sprinting to get some activation energy, but nobody can sprint all the time. And so there’s a fast moment. And then getting it right is something that we can do slowly if we’ve started and we have some rhythm and some pace in the work that we do.
So I’m a big believer in designing ways of working inside universities that allow us to move fast and slow with really intentional different modalities. Let the virtues of university slowness work its way out. But within those long, loopy cycles, let’s use some fast moments to have some activation energy, some pace, some rhythm to get us through the moments where we get stuck because in an institution that tends to move slowly, when we get stuck, that quickly becomes sort of catastrophic inertia. We just never move again.
And so developing some ways of working which are different for higher education, not different for some organizations, that allow us to leverage the virtues of slowness, but be able to move at a different speed when we need to. For us at Leeds, that was the key. And I’m really curious to see whether we use that as a way of thinking our way through this AI moment because we’re making it up as we go along, and that’s not the worst thing in the world if we’re intentional about it.

Douglas Ferguson:
Yeah. I had already kind of bookmarked the word intention because you mentioned that earlier, so I love that you came back to it. And that points to the fact that this is a design challenge. Design is just being intentional about what we do. It’s taking a step back and looking at it. And I would argue good design also includes it’s human centered and brings everyone into the conversation so that we can be thinking about the broader impacts and how systemic things might be.
And the other thing that came to mind for me when you were talking about the slow versus fast is often people think of them as just opposites. I’m either slow or fast. But thinking about how we intentionally design slow moments versus fast moments, and also taking, I love the martial arts mantra of slow is smooth, smooth is fast. And so sometimes we need to do slow things so that then other things become fast. And I think that’s where, you look at a design sprint, there are moments specifically designed into that protocol where we’re going to slow down with each other so that then the follow on work, we can move very rapidly on because we have high degree of a confidence in it.

Jeff Grabill:
Yeah. And that also accommodates… One thing that’s true about a university is, and it’s true probably of most organizations, but universities have, well, I don’t know, we just have a broad spectrum of cognitive styles and dispositions. And that’s also a strength of how people think together. Let’s stay with the sprint. Some of what happens in the sprint is just too fast for people. They need time to disconnect, they need time to think, they need time to process.
And so there’s the moment where you try to slow down within a sprint, but I also think we need some post-sprint space as well for us to not over-engineer what comes out of that sprint and be intentional as well about the reversibility about where we landed in that sprint. And then give some people some time, but not too much time, to think and to dwell and to walk around with it. There’s a long bit of intellectual history of discovery which includes walking. Newton and many others. The relationship between a long walk and a scientific breakthrough is a long one.

Douglas Ferguson:
Absolutely. Yeah. And the other thing that came to mind when you were talking about taking time to think is the fact that whenever we’re working visually together, as we go off and diverge and think about things, the visual prototypes give us an anchor so that we know that we’re at least in the same vicinity, our vectors are aligned, so that then when we start to diverge, we know we’re diverging from the same place. And there’s a lot of power in that, our ability to move more swiftly later because then we’re not way off track when we try to reintegrate later.

Jeff Grabill:
Yeah, that makes perfect sense. Can I ask you a question?

Douglas Ferguson:
Oh, please do.

Jeff Grabill:
Yeah. So I’m interested. We haven’t had a chance to talk about this notion of the new friction. This is the podcast container. And you talked about the friction moving a little bit. It once was here and it’s moved there. Could you unpack what that means? Because I’m also trying to listen to you in relationship to where that friction might be moving in higher education, and whether it’s the same or different, but I wanted you to unpack that a little bit more so that I could listen to you a little bit. Is that fair?

Douglas Ferguson:
Yeah, that’s totally fair. And in fact, it’s funny, that was the next question I was leading up to, just waiting to see when the conversation naturally got there. So perfect. Yeah. So our argument lately, or we kind of developed a thesis around this idea of the new friction. And on the surface, you might look at how AI is making things so easy to create and build and make things that it’s eliminated that friction or it’s eliminated friction in general. Because that was kind of the main friction people would run into, especially in industry. It was like, oh, we need to make a strategic plan. We need to make a blog post. We need to write some software. The LLM is very powerful at helping us do those things, draft them, refine them, bring new thoughts to the table, et cetera.
And so even though it eliminated that friction, or definitely smoothed out a lot of that friction, it sort of reallocated the friction across the org. It introduced new frictions, or it highlighted old frictions that have always been there. We often talk about friction or dysfunction in an org that was kind of just existing on the sidelines, or we could hide it in the margins, but now we can’t ignore it anymore because literally we can make things so fast that now all this dysfunction around decision making, alignment, actually discernment, coming together and actually disagreeing, like creating space so we can disagree, a lot of organizations fail at that.
What we were remarking around the success of your meeting, a lot of organizations are horrible at creating disagreement in a healthy way, that healthy conflict that’s so important. And so my vantage point and stance is that we should be attending and taking note and inventory of the frictions that matter now. It could be old frictions that we kind of ignored. Which those are the hardest ones to diagnose because we’ve lived with them forever. We kind of accepted them as normal. And so it’s easy to discount them.
But we should be noticing new frictions that are emerging that were never there before. And an example of that is it’s so easy to go generate, let’s say, a strategy doc or a new design brief. And once you create it, it comes out looking finished. It is so polished. It is so done. It is immaculate and beautiful and gorgeous. And it’s using turns of phrase, or just really like, I would say intoxicating, right? And then folks see that and they go, “Oh my gosh, this is like, okay, dusting off my hands, job done.” And they tend to throw it over the fence or put it in the repository. And things are stacking up and stacking up and piling up and piling up. And there’s no time for disagreement. There’s no time for discernment and is it the right thing? Are we headed in the right direction?
And so it comes back to your point around intentional slowness. Even though the speed is, I’m going to use the word intoxicating again, we shouldn’t do speed at all costs. That shouldn’t be the posture that LLMs can just make us fast. Well, how can we direct it towards something that’s of more value? And so it’s somewhat diagnosing the frictions that have always held us back, but are now getting amplified by this AI amplifier, and new frictions that it’s creating. My hypothesis is that it would also be heavily prevalent in universities too because I think it’s maybe a principle of how this technology is going to just impact humans.

Jeff Grabill:
No, that makes perfect sense. Yeah. Because it made me think a little bit about some of the relatively, well now very early research on AI and productivity, so year, 18 months ago, which measured productivity mostly in terms of the frictionless way of making things. It made a certain employee more productive because they were faster. They could make product, but it wasn’t necessarily better product. And I think that that’s where it shows up.
So the best example, and this also came up in the meeting, and I think this is the primary friction point right now in education with regard to AI is what will this do to learning? And to put it on the back of an envelope, there’s no learning without effort. And so effortless productivity, for example, effortless productivity is not going to produce learning. It’s just not going to happen. And so when you have students who can effortly produce a beautiful document, they might have produced a beautiful document, but they haven’t learned anything because it’s frictionless.
So I think one of the things that’s very true about education is that there has to be friction. There has to be failure. Students have to fall down and skin their knees. A good teacher is going to engineer friction. And a good teacher’s also going to pick students up when they fall down, give them a chance to reflect on… I used to write assignments that students couldn’t do. And the students who were doing a bad job with the assignment just sprinted off and started doing it. And the students who did well with the assignment came back to me a day later and asked me questions like, “What? What? I don’t think we can do this.” And I would say, “Thank you.”
And the whole point was to get them to ask the right questions when they’re given an ambiguous ask, as opposed to running forward and trying to do it. And eventually everybody would get really frustrated and everybody would fail and everybody would fall down. And we’d pick them up and they would learn a lot from that. And so that was exceptionally useful.
So that’s the friction now in education is where’s the friction located? Because friction isn’t bad. It’s actually quite productive. And so the friction used to be here in education, and in most educational contexts, the friction is now somewhere. And I think educators are really struggling about where to design friction in the educational environment. The irony of all of this to me, who spent 20 years, 20, almost 30 years thinking about education as well as my research, is that the thing that’s going to pull us through it is the thing that we know how to do really well.
We know how to design really effective high impact learning experiences for students. We know how to do friction well. The problem is that in the worst of our learning environments, and if we admit it to ourselves as educators and universities, we have a lot of frictionless learning environments right now, and they’re being destroyed by AI.
And so this was a theme in the meeting. We don’t have to invent new ways to solve this problem in higher education. We know what to do. We just have to do it in a different place than we used to do it. So there’s both a really interesting, there’s hope there, but it is also, for people like me, there’s real frustration because the answers to the AI friction problem, if you will, are in front of us. We’ve known them for a very long time. We just have to get educators to spend the time and energy to engage in them.
And that’s the problem. Because what’s happening right now in education, on the education side of higher education, is that AI is causing us to spend more time and energy on education than we’re used to spending and that most faculty would like to spend on teaching. And so this will be the tension for higher education is do we get people to spend the time and energy for the next couple of years to sort this out? Because we can and we will. The faster we do that, the better and happier we’re going to be as faculty and the more productive and happier our students are going to be, which is another design problem.
So how do we focus the intention of the organization with intention to get in a room or two and solve the problem and iterate on the solutions over the next couple of years? That’s the right way to do it. The wrong way to do it is to get on social media and moan and wring our hands and try to do the things in the future that we’ve done in the past because AI has eaten homework and we’re going to have to sort that out. Does that make any sense?

Douglas Ferguson:
Absolutely. And you’re making me think about how there’s this conversation around the importance of critical thinking as being a really important skill of the future as it relates to leveraging AI and making sure we’re preparing people just to be good stewards of this technology and preparing them for just living in the future.
And then there’s debate of what is critical thinking? And it’s like, how do you define it? And as you were talking about your professors being gifted and skilled at creating friction, specifically friction that creates the best learning moments. And I started to think about how, in a way, if you could define critical thinking as individuals starting to internalize this idea of the friction that helps me learn and how do I intentionally introduce the friction that helps me learn? And so if you develop as an individual those skills of being able to inject that at any moment.
And so I wonder if there is a… Well, I personally find it fascinating to use LLMs to help me create friction. And the research world of AI, they refer to that as adversarial. You could use an agent to be an adversarial agent to critique and break down the work that was generated by another agent. And there can be layers and layers of adversaries looking at things from different vantage points.
But at the same time, when we’re talking about bringing it into AI Team moments or even Copilot moments, we can ask the LLM to bring in friction rather than to generate things. How is it helping us slow down and think and inject some perspectives or just some skepticism around what we’re trying to accomplish? But the thing I don’t know, and probably would require more research to prove out, is if the LLM is doing that, does individuals witnessing that and receiving those questions and that pushback help them internalize that behavior more, or do they just become reliant on that?

Jeff Grabill:
Well, I mean, there’s so much in there. So one of the areas in which I’ve worked for a long time, and we’ve spun some technology out of the research center that my colleagues and I had at Michigan State. So we had a company for roughly 15 years. We have a software service that scaffolds a feedback intensive pedagogy. And it’s really simple. And one of the principles though is that human beings learn as much, in some cases maybe perhaps more, from giving feedback than from receiving it. But they do learn from receiving feedback. But nobody learns anything unless they, on the receiving side, get some instruction in how to process feedback.
Because this gets to the growth mindset whole there is an emotion and a cognition component of receiving feedback. And so helping students learn how to receive feedback is an instructional need and an instructional task for professors in any discipline. Critiques in art and in creative writing programs can be brutal. And if you don’t help students emotionally and cognitively learn how to receive that feedback and put it to productive use, it could be the best feedback in the world, but it’s not going to make a difference.
Conversely, you have to teach students how to give feedback. And when you do that, you teach students to read in particularly intentional ways. And to put whatever they’re looking at, whether it’s a schematic or a poem or a report or a piece of art, to put that performance, that object, in relationship to some criteria. And when they do that thoughtfully with some intention, they learn something about what they’re also trying to do because they’re trying to do the same thing.
And then when they have to formulate that into feedback that is also useful, it’s criterion referenced, it has some connection to what their colleague is trying to make or do or perform, and to then to craft it in such a way that that other human being can accept it from them, that’s real work. That’s friction. And those two learning modes can happen at the same time in a human intensive feedback moment. Now, these machines can give us feedback too, and that’s useful, but we still have to help human beings understand how to receive that agent provided feedback, how to engage with that agent provided feedback. There’s some metacognitive work in there.
So I think a really rich learning environment in the relatively new future is going to be a mix of agent interactions and human interactions and human to human interactions. All these ratios we can play out. I think that can work. I think I’m getting a little bit tired of the human in the loop metaphor, but we’ll use it, but not asking humans to do that work and just relying on agents to do that work, I think, again, is one of those instances in which that’s frictionless and we probably don’t want that.
To add another layer, and then I’ll stop talking, you’re good at designing agents. I’m not. But I want to get good at designing agents. And we’re going to have to teach our students, as we move from chat to agents, one of the places in which some friction is going to be located is the design of agents themselves, and the kind of intentionality and thoughtfulness that we put into that and how we learn from that.

Douglas Ferguson:
Yeah. And I think that’ll get easier the more abstraction layers that get added there. And when people build harnesses that allow people to conceptualize these things more easily, or that are aligned with models that they have already existing in their worldview. Because if you think about in the world of agents, it’s sort of like managing a small team. And so once the harnesses are starting to take on some of those perspectives, or maybe others adjacent perspectives that make it easier to understand and easier to approach, but if I had to guess today, taking the hiring metaphor, and this came up at dinner too, it’s like, how do you hire agents? So it comes down to like what’s the job description? And then how do you think about what’s required of them to do great work, and then how do you measure that great work and how do you delegate tasks in an efficient way? If those problems are well-defined and solved within the harness, I think it becomes a lot easier for people to understand how to employ an agent.

Jeff Grabill:
Yeah. How important for you is it to understand what’s going on with that agent or to understand something about the construction of a harness for you to understand what the agent’s really doing and not doing? Because sometimes it’s what the agent’s not doing that’s as interesting as what an agent does. Does that make sense as a question?

Douglas Ferguson:
Yeah. I think as someone who’s just eternally curious, it does fascinate me how the LLMs come to their conclusions and whatnot. But when I put on my business hat, and I’m CEO of Voltage Control Douglas, some of that doesn’t matter. It’s like, did I get the result that I needed? Did I learn something critical? Did it make me realize something new, a gap that I was missing?
I have a chief of staff agent, and one of the things the chief of staff agent does is it looks over everything that happened in the prior week. So all of our meetings, everything in the calendar. We do our strategic meetings in a Miro board. So that Miro board is consumed as well as all the Slack messages that we’re sending back and forth. And it builds up a list of all the important themes, as well as any potential gaps we might be missing, to make sure that when I go into the strategic meeting with the team, I’ve got a list of things that I need to be concerned with.
And the interesting thing about that is I don’t just use that as the agenda, but it often surfaces things that it’s fallen off my radar. Much like a great chief of staff would say, “Hey Douglas, don’t forget about X, Y, and Z client.” And then it’s like, “Yeah, we need to make sure we resurface that.” And so that’s, as far as how it’s functioning, and I’ve even run into some issues with that. And I had another LLM actually fix the issues. So rather than me going in and updating the prompts, I had my developer agent go in and correct that routine that the chief of staff is running.
And so I care less about the prose that’s in that prompt and I care more about the outcomes that we’re driving for the organization when I’m purely in the CEO hat. But of course, my computer scientist, like internally curious Douglas, often looks under the hood because I’m like, “Hey, how’s this working?” I want to tinker with it for sure.

Jeff Grabill:
Yeah. So this tension sort of came up in the meeting here at Buffalo for the last two days about the alignment of incentives in an organization like yours and the alignment of incentives in an organization like mine. And if it works, and this is not a criticism, but if it works, you’re really happy with it because it helps you with the bottom line incentives. And then for me, we might care less about whether it works and more about peering under the hood and making sure that we’re curious.
And I think that’s a really, this another thing that I learned yesterday listening to my colleagues talk about why it’s so important to build these things, and that we would build them differently for different purposes and different organizations. And peering under the hood is a really good idea. But also the fact that we can peer under the hood is a really important, that has to be true, that there has to be some traceability and visibility in terms of data and operations. And we have to be able to see under the hood.
Anyway, I’m rambling a little bit, but it’s interesting. I’m glad you’re curious. But some of our students are not curious and they need to be. And so another place in which we need to engineer some friction is we need to say, “No, I need you to peer under the hood and tell me what’s inside and tell me what you understand about it.”

Douglas Ferguson:
Yeah. It’s sort of like coming back to the age old show me your work. If you can’t justify it, you need to understand some of the underpinnings here. And I think the thing where it comes up the most for me is if I’m using it to help me draft things, then it surfaces up some new thought that I haven’t seen before, especially on content side of things, then I’m really digging in and going, okay, where did that come from? Where’d you come up with this? Because I want to, A, make sure it didn’t hallucinate, but also I want to understand the sources. And I think that’s not a technical under the hood. That’s more of a under the hood of the where’s this coming from and where’s the real knowledge?

Jeff Grabill:
And it’s a really important displacement. So at the end of the day, I’m a writing teacher. And one of the ways in which effort in activity is going to be displaced in how we teach people to write, which I hope we still do, is, for example, writing teachers used to spend a fair amount of time with certain writers in their classroom at the sentence level, just helping them understand why these sentences are not grammatical English or don’t work particularly well. And that’s valuable work. You don’t have to do that with all writers, but we would always have to do that with some writers. We shouldn’t have to do that anymore.
And writing teachers are noticing this in universities that they’re getting writing now, this came up also in the last two days in our meeting, they’re getting writing that at the sentence level is lovely. There’s nothing for them to do. And in fact, just as there’s no reason for a student to turn anything in for the last 15 years with a spelling error, there’s probably no reason for a student to ever turn anything in from this point forward with a syntax problem. But this writing can also be completely devoid of ideas and wrong.
So that’s where the effort is. And that’s an okay place to have effort. And that’s how you develop expertise. And it’s also a reason why knowing something about something is really important. I was talking to somebody at the meeting about the advice I gave to my kids when they went away to college. And the primary bit of advice was study something you love, period, full stop. That’s the most important thing to do. I don’t really care about anything else, but you have to study about something you love.
The secondary bit of advice was it’s a good idea to know something about something. So my preference was that they chose something that had some intellectual depth and history to it. So none of them were philosophy majors, but I would’ve been thrilled. I got an anthropology major, and politics, philosophy, and economics majors and thrilled. I mean, they know something about something. And that’s a durable good. And that was a big thing that came out of our meaning as well.

Douglas Ferguson:
I was thinking about this not that long ago, this idea of like, I wonder where the anthropologists will go in this world of AI, where they’ll take their thinking and research. Because I think there’s a lot to be done there. I’m waiting for it. But we’re kind of running up on time here.
I’ve got two things I wanted to end with. One was you quoted Punya Mishra, the technology’s impact on education has been modest, but its impact on society has been profound. And you argued that education has a mandate, a remit to prepare people for society and understand society and reflect it back. And so I’m curious, just any thoughts you have for our listeners just around that idea, and how AI is definitely going to reshape society.

Jeff Grabill:
Yeah. No, I was reading some… Punya’s at Arizona State. He’s clever, he’s engaging. If people are interested in education and technology, they should find Punya. So I was looking at an article that Punya and a couple of his colleagues have written. And this is true. Those of us who pay attention to technology and education know that Silicon Valley’s been trying to disrupt education for the last 20 years. And they never really do because the impact of technology has been modest on education. The television was supposed to disrupt everything. The computer was supposed to disrupt everything. The internet was supposed to disrupt everything. We’ve absorbed them and made them useful.
What has disrupted education is society. Society moves, and education has to adjust to it. Punya and his colleagues made the argument that the impact of technology on society is more profound than it is on education. And it is. It’s society that we respond to, not precisely the technology that we respond to. That seems right to me.
And I think that for those who are interested in technology and education, it’s something to think about. I mean, I’ve been rolling my eyes for a long time about technology disrupting higher education. And most of the Silicon Valley people who are trying to do this don’t understand education as a business and they don’t understand it as a cognitive and emotional activity, and therefore they always miss the mark. But I think Punya and his colleagues have it about right.

Douglas Ferguson:
Yeah. I think we’re on the precipice of seeing big societal change, especially I mean, next week there’s the big Apple event, and my speculation is that we’ll see an announcement around Gemini and Apple. And if Gemini truly replaces this… I mean, if Siri goes from being a third grader to being a PhD student that you’re talking to, that experience of actually transcribing your voice correctly, and being able to actually do things right there on the phone, people’s access to this stuff is going to just explode astronomically, and that’s going to have a huge societal impact.

Jeff Grabill:
I think so.

Douglas Ferguson:
Overnight, I think.

Jeff Grabill:
Yeah. No, and that’s how it works. Remember the camera on the phone? I thought that was the most ridiculous thing in the world. It wasn’t.

Douglas Ferguson:
Yep. Changed the course. So I want to leave you with one final one. You’re talking about AI doing the writing. And certainly there’s lots of universities. I think this even came up in the meeting. It’s like, you have to use AI, but don’t use it for your coursework or you’ll be subject to a review board. And I love this one. I think one of the panels, or one of the breakout working sessions when they were doing readouts at their tables had shared out this idea of I don’t care if AI created it. I care if it’s true. And it just hit me across… It was like slapped across the phase. I was like, “Oh my gosh, I love that they said that.” And to me, it was just undeniable. You hear it and you go, yep, that is such a good perspective and I hope that more people adopt it.

Jeff Grabill:
Yeah. I was at that table, and the woman who said it is a librarian. And it’s exactly what she said. She said, “At the end of the day, as a librarian, it has to be true. It doesn’t necessarily have to come from a human being.” And that’s going to be a big shift for us because we’re very deeply human people in libraries, but I think that that’s where we need to land. I was struck by it too. And for our table, that sentence, we wrapped in lights and sprinklers and confetti because we thought it was a winner as well. It’s a really interesting insight, isn’t it?

Douglas Ferguson:
Yeah. And it’s so simple. And I think that some of the best insights are profound in their simplicity.

Jeff Grabill:
No, I agree. They cut through it, don’t they?

Douglas Ferguson:
Yeah. Amazing. Well, as we leave our listeners, is there anything that you would like to offer them up as a final thought?

Jeff Grabill:
Yeah. I mean, your listeners, you have some educators in your audience, but mostly not. And so I guess my ask is sort of the ask of the meeting, and that is wherever you dwell, there’s probably a university or two around. If it makes sense for your business, or if it makes sense for how you choose to spend your time as a human being, your universities, trust me, they want your partnership. We’re trying to sort this out. We’re trying to do the right thing with your children and your grandchildren, the students in your community. For you, we’re trying to do the right thing for you.
And so if the universities aren’t smart enough to ask you to help them, if you want an act of generosity on your part, knock on their door. Particularly if you have something to offer in this space that we’ve been talking about. And see if there’s an opportunity for you to partner with the universities in your community so that together we can think our way through where the friction ought to be and how the friction has moved around, because I think it’s a really important question.

Douglas Ferguson:
Yeah, I love that invitation. And if folks aren’t familiar with how universities are structured, is there a role or individual or title that would be the best target for industry to reach out to?

Jeff Grabill:
That’s a really great question. We are completely impenetrable organizations.

Douglas Ferguson:
The fortress.

Jeff Grabill:
We are a fortress. We’re like a Hydra. Who knows? So it’s not obvious. So I might start with a creature called the provost. If you have a relationship with your university where you give some money, you probably have a development officer. You can always ask your development officers, “Hey, can you connect me to this person or that person?” And if you don’t have a dean of Arts and Sciences, find the creature at that university who’s the closest thing to a dean of Arts and Sciences and write them and say, “Hey, I heard this dean of Arts and Sciences saying that I should write my dean of Arts and Sciences if I wanted a conversation.” And hopefully one of those people in the Hydra will respond to you. But the development people might be the best ways because they certainly want to nurture that relationship and they will make the connections for you.

Douglas Ferguson:
Amazing. Well, Jeff, as always, it was a pleasure chatting. I learned a lot, and I think our listeners will really appreciate the time. I would just say, really impressed with all the great work and please keep it up.

Jeff Grabill:
Douglas, it’s always a pleasure to talk with you. You and your organization do really special work. So thanks for the invitation. It was fun.

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

The post Engineering Friction: What Higher Education Knows About AI That Industry Doesn’t appeared first on Voltage Control.

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.

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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.

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