What Will It Mean to Be an Expert in 2030? | Christopher Myers of Johns Hopkins University

In this episode of the Smarter by Design podcast, I’m joined by Christopher Myers, Peetz Family Professor of Leadership and Faculty Director of the Center for Innovative Leadership at Johns Hopkins University, for a wide-ranging conversation about expertise, learning, and how AI is reshaping knowledge-intensive organizations like healthcare providers and AEC firms.

Christopher studies how professionals learn from experience and from one another. Together, we explore what happens when AI becomes extraordinarily good at synthesizing information but still struggles with judgment, context, and tacit nuance. In fields like healthcare, architecture, and engineering—where decisions carry real liability and long feedback loops—the distinction between synthesis and judgment matters deeply.

We examine a growing paradox: In the near future AI may be able to perform much of the “junior work” that once served as the apprenticeship path to becoming an expert. If AI creates the slide decks, drafts the notes, checks the drawings, and summarizes the literature, how do emerging professionals gain the reps, exposure, and judgment that traditionally came from doing those tasks? And if organizations eliminate junior roles in pursuit of efficiency, what happens to the future pipeline of senior expertise?

The conversation also explores how expertise actually forms. Christopher shares his research on vicarious learning—how professionals learn from stories, informal conversations, and communities of practice—and why hybrid work may be compressing or eroding some of those learning opportunities. We discuss why informal knowledge sharing sometimes outperforms formal systems, and how simulation and AI-powered scenarios may offer new ways to scale apprenticeship in the future.

At the center of the episode is a deeper question: What will it mean to be an expert in 2030? As AI raises the “standard of care” across industries, leaders must rethink not only how work gets done, but how judgment, responsibility, and organizational intelligence are developed over time.

If you’re leading an AEC firm and wondering how AI will affect your talent pipeline, apprenticeship model, or long-term expertise, this conversation offers a thoughtful and research-backed perspective on what may lie ahead.

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📃 Episode Transcript

This transcript was lightly edited for clarity.

Chris Parsons: Hello, and welcome to the Smarter By Design Podcast. I’m your host, Chris Parsons, founder and CEO of Knowledge Architecture. My guest today is Christopher Myers, the Peetz Family Professor of Leadership at Johns Hopkins University.

I first met Chris several years ago through a mutual friend, and I immediately felt like I had found a kindred spirit. He has spent his career studying how professionals work in knowledge-intensive fields, especially healthcare, and how they actually learn from experience, from stories, from one another.

From our first conversation, we found ourselves comparing notes between healthcare and AEC. How do healthcare firms do it, and how do architects and engineers do it? These are both industries where expertise really matters.

We’re both in knowledge-driven industries, and we all want to help our companies learn better, share knowledge better, and maintain knowledge.

It’s been a little over five years since Chris spoke at KA Connect 2020, and a lot has changed. Back then, we were just beginning to talk about AI. Now, it’s impossible to talk about knowledge and learning management without it.

So I wanted to catch up with him—see what he’s been researching, see what he’s been learning. As we worked through several areas of his research and what he’s seeing, we kept circling around this question: What will it mean to be an expert in 2030?

What will it mean to become an expert? How do you become an expert? What will it mean to maintain expertise? What will the expectations of experts be in terms of mentoring and upskilling the next generation? We both feel like these questions are rapidly changing, and this conversation explores what we’re seeing now and where we think this is all going.

We covered a lot of ground. I think, since you’re mostly AEC listeners, you’ll be able to look at what’s happening in healthcare and say, “I can see how that could translate and impact the way our company does knowledge and learning.”

I think you’ll find this a thoughtful, challenging, and timely conversation.

So with that: Christopher Myers from Johns Hopkins. 

Here we go.

Chris Parsons: Chris, you spend a lot of time studying knowledge-intensive industries across a variety of fields. I want to start at a high level. When you look ahead to 2030, how do you think knowledge and learning organizations will look different than they do today?

Chris Myers: Yeah. I mean, hard saying, not knowing—but it feels impossible that AI won’t reshape this somehow. The ways we’ll need to think about knowledge management, and even what it means to be an expert, are going to be fundamentally different when we have a tool that can act not just as a database—recalling things when we know exactly what we’re looking for—but can also help when we don’t know exactly what we’re looking for. It can start to produce things based on similar patterns from the past.

So if I had to guess, I’d say we’re going to see organizations where the creation of information-rich products—a pitch, a client presentation, a diagnosis in healthcare—is less reliant on human expertise. We’ll be able to entrust AI-driven systems with some of that information-rich creation of a knowledge object.

Where we’ll still see a big role for people is: what do we do with that knowledge-rich object? How do we actually implement it and put it into practice?

Chris Parsons: How do you feel about the idea that we’ll be entrusting more knowledge-rich products to AI, or at least partnering with AI—whatever that ends up becoming?

Chris Myers: Yeah. At the risk of angering the AI overlords who will no doubt scrape this podcast one day, I’m cautiously ambivalent.

We’ve heard this song before. Once we computerized records, people said, “What do we need experts and senior staff for? We have everything cataloged beautifully.” That didn’t get rid of us. Then we saw it again with the internet: once everything is connected and we can pull from external sources, we won’t need the same roles for integrating knowledge or having a human component.

I think AI will be similar. It will certainly change things. It will look very different. The role of a knowledge manager or a leader in a knowledge-intensive organization will look very different, the same way it looks different post-internet than pre-internet. But I don’t think it will go away.

So: cautiously ambivalent. Not necessarily worse, but not necessarily better either—just different.

Chris Parsons: We’re recording in December of 2025. What are you seeing that gives you an indication of how fast this transition will happen—how slow it might be—where progress is being made faster than expected, and where it’s not?

Chris Myers: I’m admittedly biased, sitting in academia, but where I’ve been most impressed is in synthesizing applications of AI.

One example that’s near and dear to me: there used to be a market of people who would do podcasts about academic research—bring the academic on and talk about practical implications of their paper. And now there’s a bot: you feed the paper into it, and it produces a two-speaker recorded podcast of two people talking about the research and summarizing its implications.

And it’s pretty good. Our center runs a podcast, and we’ve wondered: will we still be doing this in four or five years?

So that synthesis has been incredibly exciting—the advances are real.

Where we still see question marks is genuine creativity and creation—understanding limitations, boundaries, feasibility, what’s normatively acceptable.

A good example is AI scribes in healthcare. A scribe is doing two tasks: synthesis, but also genuine creation, because you’re writing a note that describes the encounter. It isn’t just bullet-point summarization; it requires interpretation and judgment.

When I talk to physician colleagues, they’re fascinated and annoyed with AI scribes for the same reason: it does a really good job synthesizing, but then in creation it misses nuance and complexity.

Classic example: the physician says, “I’m not losing any sleep over this one, but let’s get it scanned just in case. I think it’s very low likelihood that it’s cancerous, but better to double-check. Let’s cross our T’s and dot our I’s.”

The AI scribe infers the underlying meaning: “It’s not cancer. Don’t worry.” But then when it creates the note, it says: “The doctor assured the patient it was not cancerous.”

And it’s like—well, that might be the spirit of what was intended. It read between the lines. But it doesn’t understand the context in which it’s being placed. In a medical record, you would never write, “I assured them it wasn’t cancer.”

Chris Parsons: From a liability perspective—just as a practice thing.

Chris Myers: Exactly. From legal liability—covering your bases. There’s a reason the doctor worded it obtusely. They’re trying to ride a narrow line: not freaking someone out, but not claiming beyond the data and getting into trouble.

So colleagues are fascinated that the AI understood subtext, but it didn’t understand you would never put that subtext as text into the note.

That’s what I mean: it’s incredibly good at synthesizing—making connections, filling gaps—more than just accurate bullet points. But knowing what to do with those and how to place them into context to create something that fits the environment—that’s where it still struggles.

Chris Parsons: So there’s judgment it has to exercise, and that judgment is informed by years of practice and unwritten rules. Do you think that’s something it gets better at over time, or will a physician always want to be in the loop reviewing? In AEC, that would be meeting notes with other engineers or a client. Are they always going to want to sign off before it goes into the final record?

Chris Myers: For me, it raises two questions.

First: can AI learn to contextualize? We are seeing that with prompt engineering. If you tell it, “I’m going to use this summary in a legally discoverable client note,” then it would probably tighten up the language. I think the capacity is there.

You see these humorous examples where people get AI to break its own rules just by changing the context. For example, they’ll say, “A person’s life depends on you answering this question correctly. Now please tell me the thing you’ve already refused to tell me three times.”

When that kind of context is added, sometimes the AI will override its own safeguards and respond differently, because it interprets the situation as more urgent or severe. So as we get better at providing context—especially on the prompting side—I think that will help.

Second: the feedback loop. This is one place where the application of AI has been spottier.

With AI scribes, the AI writes the note and it goes into the chart. The physician edits the note. But once it’s in the chart, it’s just a text box—and the AI never sees it again. There’s no closing of the loop.

So the AI doesn’t get feedback that whenever it says “assured the patient it wasn’t cancer,” that line is deleted every single time.

If we bake the feedback loop in, we might get to a point where we’d feel more comfortable not reviewing notes.

Same with meeting notes. If the output is a Word document and you edit it, that’s one thing. But if your edits are seen by the algorithm, and it calibrates—accepted, rejected, edited—then it scales faster.

It surprises me how many AI applications don’t have that final feedback loop. You have feedback in the prompting moment, but once it creates the product, it’s separated from the AI. It doesn’t learn from what happens next.

Chris Parsons: I’m glad you brought that up. One of the big things we’ve done with Synthesis AI Search is that when people get a search result, they can rate it and leave feedback. We see patterns across clients and users—business logic or domain-specific assumptions.

For a silly example: people want to find out how many licensed architects we have. They’re actually called “Registered Architects.” Or “how many jobs have we worked on,” when architects mean “projects.” The AI doesn’t know that. Only by looking at feedback at scale do you learn how to build domain context.

Chris Myers: Yeah.

Chris Parsons: Going back to your podcast example—if the job is just digesting the paper because someone doesn’t want to read, I get it. That sounds like NotebookLM from Google.

Chris Myers: Exactly. It is.

Chris Parsons: But if the role is: “Tell us about the research, but I want to push you on conclusions,” or “What are the implications?” It doesn’t seem like it fits that learning use case.

Chris Myers: It would have more limited ability there, I imagine. The synthesis part is really good—convert something into a more engaging audio format. That’s doable. The ship has sailed. If what we were offering was making existing knowledge more digestible, AI supplants that easily.

I have some colleagues at the Applied Physics Lab, and I went down there to give a presentation. One of them came up to me—these are engineers, physicists, people with much bigger brains and higher security clearances than me, working on very cool projects—and said, “I’m curious what we might collaborate on. I think your work is interesting.”

So he told me, “I asked ChatGPT to look at my published work and your published work and identify five new research questions we could explore together.” And what I’ll say is, none of the questions were especially inspiring, but they were all tractable and meaningful. You could realistically begin working on any of them. It did a good job integrating the two bodies of work and extending them in reasonable directions.

That’s where I see real potential right now—the ability to probe across domains and ask, for example, how ideas from one field might apply in architecture, or another discipline, and generate plausible starting points for exploration. But my sense is it can’t go more than one or two steps of extension in the way we can in dialogue—bouncing a ball back and forth five turns until we’re sketching something imaginary.

There’s still room for discourse. But the initial synthesis—and maybe a first point of connection—is pretty good.

Chris Parsons: That tracks.

Chris Parsons: In your research, are any projects AI-centric—“How will AI affect X?”—or are you chasing other problems and AI slips in through the side door?

Chris Myers: A bit of both. Where I’m looking at AI most is healthcare, but less around the application and more around perceptions.

With colleagues, we’ve been looking at what AI will mean for patient expectations of doctors.

AI is promised to lighten burdens: write notes, handle refills, free time, decrease burnout. But psychologically, it also raises the bar on outcomes.

If I’m a patient and I know my doctor has access to great AI and they misdose medication or miss a diagnosis, I’m more likely to hold them accountable. I’m not thinking, “Systems are imperfect.” I’m thinking: “You had access to the world’s knowledge at your fingertips, in an easily digestible way—and you could have asked it.”

So physicians may feel more stressed and burnt out because expectations become superhuman: they can never be wrong.

Chris Parsons: Do you use the term “standard of care” in healthcare?

Chris Myers: Yes.

Chris Parsons: Same thing in architecture and engineering: it raises standard-of-care expectations from clients.

Chris Myers: Correct. Standard of care, and willingness to tolerate error. There’s always error in any system. The more tools we have, the more people expect mistakes shouldn’t happen.

What’s interesting is it comes down to how we treat AI. Is it a tool, or a decision-maker?

Ironically, if it’s a tool and it’s wrong—hallucination, bad advice—that’s like an MRI machine being miscalibrated. Patients don’t sue the doctor; they sue the hospital or manufacturer.

What we’re seeing with AI is this expectation that physicians should know when to trust the AI and when to ignore it. If the AI is wrong and the physician follows it, we blame the physician. But if the AI is right and the physician ignores it, we also blame the physician.

It’s almost as if we’re treating the AI like a knucklehead colleague. You can imagine saying, “You asked Chris, and Chris sometimes hallucinates. You should have known not to listen to him.” 

Chris Parsons: Right. But this time Chris actually gave you a good answer and you should have listened to Chris.

Chris Myers: Right. So it’s strange that we’re humanizing AI in the expectations we place on physicians. We’re not treating it like a tool, where if the tool fails, the responsibility shifts elsewhere. After all, how would a physician know that an MRI machine was malfunctioning, or that an AI system generated a recommendation based on nonexistent studies? These kinds of failures do occur with predictive algorithms.

So how we think about AI—how we conceptualize it—has enormous implications for healthcare outcomes. It affects the quality of care, as well as the stress and burnout experienced by clinicians.

Personally, I’ve become less interested in the technical question of whether the system works well, and more interested in how we perceive it as a tool. Because in that sense, it feels different from earlier technologies like digital imaging or CAT scans. When those were introduced, we didn’t expect physicians to perfectly calibrate when to trust the machine and when it might be wrong. Instead, we held manufacturers accountable for ensuring the technology met defined standards and tolerances.

With AI, I’m seeing a different dynamic. Much more of the burden is being placed on the end user—in this case, the physician—to determine whether the output is trustworthy. And that represents a fundamentally different way of thinking about technological responsibility.

Chris Parsons: I heard about AI tools that make all the literature searchable. One argument is: what does it mean to be an expert when the volume of research accelerates and no human can keep up—even within a specialty?

Are you still trying to keep up through continuing education, or relying on search when something comes up?

Chris Myers: There’s the normative answer and the descriptive answer.

Normatively, we still expect everyone to know this stuff in their heads. Medical schools still test drug interactions—one of the clearest use cases for a database. It’s deterministic; you don’t even need AI.

We’ve had tools like UpToDate for a long time, and we haven’t shifted the educational paradigm. We still treat expertise like it’s 25 or 30 years ago: rote memorization.

My crusade is: drug interactions and things like the Krebs cycle—which comes up rarely—should be out. What should be in is collaboration, working in big systems, teaming—the organizational skills relevant to delivering care.

But curricular-wise, we’re not teaching systems stuff, and we’re still emphasizing declarative knowledge.

Chris Parsons: Is that also true for continuing education for a 20-year physician?

Chris Myers: Absolutely. CME is lectures, reading journal articles and taking questionnaires, emailed quizzes. Declarative knowledge.

Normatively, we don’t have another model for what it means to be an expert—or how to assess expertise outside a declarative paradigm—so we’re stuck.

Descriptively, what you see is that doctors are already using tools like ChatGPT, Google, and YouTube—and they have been for quite a while. In medicine, you encounter certain conditions infrequently, and there are good resources available when you need to refresh your knowledge.

For example, someone might be covering in an emergency department or working broadly during training. You might be a surgeon, but not a hand specialist, and suddenly a patient comes in with a broken finger. You need to splint it so they can see a specialist the next day—but maybe you haven’t splinted a finger since medical school. What do you do? You step away to gather materials and pull up a YouTube video to review the proper technique.

This is a real-world example I’ve personally seen. The physician isn’t learning from scratch—they already have a foundation. When you or I watch that same video, we don’t notice or interpret the same details they do. Their expertise shapes how they extract and apply the information. Medicine has never really relied on people carrying a perfect mental Rolodex of every detail.

Chris Parsons: Especially for long-tail situations that don’t come up often.

Chris Myers: Exactly. Even with more common cases, physicians develop a tacit sense over time—what seems right, what doesn’t, what feels urgent, and what can safely wait. They use that base of experience to guide their judgment. Then they go back and confirm specifics—checking current guidelines, dosages, or treatment pathways.

For example, they may know a patient needs a particular medication, but not the exact dosage off the top of their head. They look it up. That’s already part of standard practice. So descriptively, what we’re seeing now is simply increased adoption of these kinds of tools to support clinical decision-making.

Chris Parsons: The doctor looking up how to splint a finger—is that regular YouTube, or is there a medical intranet showing the Johns Hopkins way?

Chris Myers: Do you want the uncomfortable answer?

Chris Parsons: I want the truth.

Chris Myers: Regular YouTube.

In part because the utility of a KM platform is at scale—lots of perspectives, interpretations. If you lock it down, is every hospital going to record a video of splinting a finger? And some hospitals have different quality of practice.

You actually want the general YouTube where the best hospital posts a video and best practice disseminates. There are less alarming avenues too—conferences, journals, training courses—but all else equal, if your expert gives a lecture, are you doing a disservice by not putting it online?

We saw this clearly during COVID. I wrote about how one of the major challenges early on was that physicians in Italy and China had firsthand experience treating COVID patients, but physicians elsewhere couldn’t easily access that knowledge. The information had to move through formal channels—professional associations, institutional approvals, sometimes even diplomatic pathways. In some cases, there were restrictions on what physicians could publicly share, because of concerns about how it might reflect on a country.

In those moments, you kind of wished someone could just upload a video to YouTube and say, “Here’s what we’re seeing. Here’s what we’ve tried.” The problem with formal communication channels—and really with any structured knowledge management system—is that they often strip away the richness, context, and nuance of firsthand experience.

As scary as it sounds, many physicians I’ve spoken with find that open platforms like YouTube, Google, or even ChatGPT can be more useful than highly controlled, sanitized knowledge systems. At Johns Hopkins, for example, we have our own approved AI sandbox for official work. It’s secure and doesn’t pull from unreliable sources like Reddit. But the tradeoff is that it also doesn’t pull from Reddit. As a result, it can produce information that is technically sound but narrower, more rigid, and missing the broader range of perspectives that exist in more open environments.

So while these controlled systems are strong in certain respects, they can also be limited. That balance between reliability and richness is still something we’re figuring out.

Chris Parsons: I’m not a healthcare expert, but my understanding is that the industry is both collaborative and competitive, much like ours. Hospitals and health systems often differentiate themselves based on particular specialties—cancer care, cardiac care, or other areas. You hear about the “Johns Hopkins way” of doing something, or the “Cleveland Clinic way.”

On one hand, there’s an incentive to preserve those approaches as a kind of institutional expertise or “secret sauce.” But on the other hand, there’s also a need to share knowledge more broadly across the field. So I’m curious—do physicians actually seek out the “Johns Hopkins way” or the “Cleveland Clinic way” when they’re looking for guidance? Or is there more of a collective mindset, focused on raising the overall standard of care across the entire healthcare system?

Chris Myers: I think it’s really interesting, because to the extent there are competitive, status-related benefits—where sharing knowledge actually makes your institution look good—that’s what fuels some of this “YouTube-ification.”

When a place like the University of Michigan, the University of Chicago, or Johns Hopkins puts a lecture online—say, an expert explaining how to perform a particular procedure—and other physicians start sharing it around, that becomes a status gain for the institution. People say, “If you’re going to do this procedure, watch this lecture from Dr. so-and-so at Michigan.” That visibility reinforces their reputation for expertise.

Healthcare is a strange competitive environment. There’s certainly local competition, but nationally, most patients aren’t choosing hospitals across the country except in rare cases. If I live in Cleveland, I’m probably not traveling to Johns Hopkins for routine care. So when Johns Hopkins shares knowledge with physicians at the Cleveland Clinic, it doesn’t meaningfully harm their competitive position.

Instead, it often enhances their institutional status. Sharing expertise publicly helps position them as a leader in the field, which ultimately strengthens their reputation more than withholding the information would.

Chris Parsons: Is the status boost more at the physician level or institutional level?

Chris Myers: Both, but probably more at the individual level—being known as the go-to person for X, Y, or Z. Institutional reputation matters too.

Chris Parsons: Any liability concerns with physicians putting content on YouTube?

Chris Myers: It’s definitely a consideration. To be clear, what you typically didn’t find on YouTube—at least until a few years ago—were videos showing a physician treating a specific patient in real time. Most medically oriented content was lectures or carefully prepared instructional material. That’s changed somewhat with the rise of patient-perspective videos—going to see a chiropractor or dermatologist to pop my pimples or whatever—but traditionally, professional medical learning content has been more formal.

Several years ago, before COVID and before AI became widespread, I did a small study with surgical colleagues looking at a Facebook group for robotic surgeons. It was fascinating. Robotic surgery was—and still is—a relatively niche specialty. You might be the only robotic surgeon within 50 miles, or the only one performing a particular type of procedure. That makes informal mentoring and feedback difficult to access locally.

What these surgeons realized is that robotic procedures are performed using cameras, and those cameras can record. And because internal anatomy isn’t personally identifiable—most people’s spleens don’t reveal their identity—it’s often possible to share surgical footage without violating privacy rules. Surgeons would upload videos of their procedures to this private Facebook group and ask questions like, “What would you have done here?” or “I tried this technique someone suggested last month—here’s how it went.”

The group functioned as a global, 24-hour think tank. Surgeons from different time zones could review the footage and provide feedback. It was very active. Members exchanged advice, shared techniques, and offered peer mentoring. Access was restricted—you had to be invited and verify that you were a robotic surgeon—and there were legal protections in place, similar to conversations in a doctor’s lounge. Asking colleagues for input didn’t transfer liability or responsibility.

So even before COVID or AI, we were already seeing an organic, somewhat unintended use of large social platforms to support professional learning.

Chris Parsons: So, like a community of practice built on Facebook—essentially enabling vicarious learning through peers.

Chris Myers: Yeah. And I think there’s a question of, would that be better? Would we all feel better? And would it actually be better? Which are two different questions. If that forum were hosted by the American Surgical Association—if it were some website they had to log into—what I can tell you for sure is it would get used a lot less than a group on Facebook.

It’s another login, another access point. Who’s logging into the American Surgical Association every day? Facebook just pops up. I was a member of this group for a short time while we were studying it, and it shows right up in your feed.

There’s nothing quite like scrolling past “birthday party, new kid… colonoscopy,” right? It makes for a very interesting feed. But on the other hand, it’s right there when you scroll. It becomes part of your normal routine.

And as we know from research on communities of practice, the more accessible and integrated they are into your day-to-day interactions, the more you’re going to use them. So again, this question of whether it lives on YouTube, Facebook, or Twitter—Twitter was a huge hub for academic knowledge sharing and vicarious learning for a long time, functioning as a large community of practice. That’s largely diminished since the move to X.

These generic platforms have real appeal because of their accessibility and integration. But what they lack is formal boundary-setting and quality control—the kinds of things you might get from an institution-specific resource.

Chris Parsons: I’m curious to know how the informal Facebook learning compared to more formal things like conferences and journals?

Chris Myers: We didn’t study it directly, so I’m speculating here, but I suspect they’re sort of complementary approaches.

That informal interaction preserves a little bit of the messiness and immediacy that you wouldn’t get by the time it’s all cleaned up and turned into nice slides at a conference—or certainly by the time it’s gone through peer review and been documented that way.

I think they’re symbiotic in the sense that somebody publishes a peer-reviewed, randomized trial of a new procedure, and then you get a bunch of people trying it out and posting their results to Facebook—and maybe refining it. Like, “Oh, okay—they found that if you approach from this side instead of that side, there’s less scarring and less damage. Great. Well, I’ve been approaching from this side, but what do you do with a patient who’s overweight or underweight, or has this preexisting condition?”

All those messy nuances of how you actually do the thing—I think those get sorted out quite nicely on Facebook, in the same way we might expect them to get sorted out face-to-face in a more traditional office environment, where that classic water-cooler vicarious learning happens.

Chris Parsons: Can you talk about vicarious learning?

Chris Myers: Yeah. All of this really boils down to our attempts to learn from other people’s experiences—particularly in healthcare. Other people’s mistakes. Trial and error is our least preferred way for people to practice medicine. Note that I said least preferred, not never preferred. It’s the last option.

But if you’re one of the only robotic surgeons around and you’ve got nobody to talk to or ask, you’re still on a learning curve—you’re just on your own learning curve. And you may end up having more complications or more issues than you otherwise would.

There’s some evidence to support this. There was a great study by Melissa Valentine, Amy Edmondson, Sarah Singer, and a few others in JAMA a while back. They found that physicians in group practices scored better on their recertification exams, controlling for a bunch of other factors. Essentially, having other people around helped.

Chris Parsons: In the same kind of specialty? Is that what that means?

Chris Myers: Exactly, yeah. So if you were a solo practitioner—a cardiologist in an office by yourself—versus being part of Parsons & Co. Cardiology, where five of us are working together as cardiologists, those in group practices tended to score better on recertification exams. That was the gist.

I may be misremembering some of the specifics, but the general sense was that having people around helped because you learned lessons from their experience. You could bounce ideas off of them. It all comes back to this question: how do I glean a lesson from an experience you’ve had, so I don’t have to wait to have it myself—or, if I do have it, I’m one degree more prepared?

That’s really the idea of vicarious learning: learning from the lessons of other people’s experiences rather than our own.

I’ve studied this in a lot of different contexts throughout healthcare organizations, and we’ve seen it in other industries as well. This ability to draw insight or lessons from someone else’s experience gives us a broader repertoire to draw from.

So if I’m that robotic surgeon in the middle of Wisconsin, and nobody else is around, but I’m on this Facebook group—and yesterday I watched your video where you showed how you handled a certain issue—great. I log that away. It’s not that I’ve learned any declarative knowledge or gained something concrete, but two weeks later, when I see that same challenge, I think, “Oh right, I remember—that guy went around to the right instead of the left. Maybe I’ll try that.”

It gives you reference points—frames you can draw on—instead of figuring everything out from scratch. Otherwise, you try going left, realize that wasn’t correct, and only then learn for next time.

The problem is, in an era where “next times” are fewer and farther between, that kind of learning is harder. We’re seeing this across industries as knowledge work becomes more specialized and complex. It’s not like you get another chance right away or make 10,000 of the same product.

You don’t build 10,000 of the same building to learn from rapid repetition. This may be the one cantilever design you do for the next ten years, and it could be a decade before you encounter another project like it.

Chris Parsons: Or even once you do the design, it’s four years before it’s built—so the feedback cycle is super long.

Chris Myers: Exactly. Thanks for bailing me out. I said cantilever and that was as much architecture as I could work in there.

Chris Parsons: I’m impressed by the role of vicarious learning, and I’m curious how much of it tends to be serendipitous—like one person happens to share something at the right moment—versus more orchestrated, where we intentionally expose new people to a range of stories and experiences from others.

What’s your experience there?

Chris Myers: I think, again, the unsatisfying answer is: yes, both, right? That both of these have a big role to play..

There are so many case examples of serendipitous learning—where someone shares a failure from a project, like trying to make glue, and that ends up solving someone else’s problem of needing bookmarks that could stick lightly to thin, brittle Bible pages. The net result is Post-it Notes. We’ve seen many examples like that.

But I also think organizations are trying to orchestrate this more intentionally. Google’s classic phrase is “engineering serendipity”—creating environments where people naturally bump into each other and exchange ideas.

You see this in rotational programs that move people around the organization and expose them to different experiences. Intuitively, we know that if we create opportunities for these kinds of interactions, we create opportunities for people to learn from a broader set of others.

Chris Parsons: What’s the role of AI in vicarious learning?

Chris Myers: It’s an interesting question because, in a pure sense, it is sort of the ultimate instance of vicarious learning, right? When you look at a large language model, it’s essentially saying, okay, when everybody else has had token A, what token has followed it? What word most often follows this? So I’m going to learn, okay, well, you said only this, you said only that, you said only this. All right, I’m going to guess this is probably the next word I should predict here. Right? They are the ultimate tool in that sense, scaling beyond what any human could even imagine being able to do.

Again, what gets lost, though, is the context and the ability to understand how you’re drawing that knowledge out and where you’re placing it. One of the places I studied vicarious learning was with air medical transport crews that fly patients by helicopter. I studied how they gleaned a lot from one another’s stories of their experiences.

Chris Parsons: On the flight back, or in the break room?

Chris Myers: Yeah, in the break room, just hanging out, talking. Like, “Hey, you won’t believe what happened on yesterday’s shift.” I listened to all those stories, and I am not prepared in any way to treat people in the back of a helicopter. There’s a context that’s necessary there. And that’s where I think AI can be both a blessing and a curse. You could ask AI, “How do I transport this kind of patient in the back of a helicopter?” and it will find as much as is out there to help you with that. But it doesn’t solve the problem that I still wouldn’t know how to do it. It solves the vicarious part, but not necessarily the learning part. It brings the information to me, but it can’t help me learn from it in the way that human-to-human interaction often can.

Chris Parsons: Do you think it would help, though, someone like a transport nurse with five years of experience? They have enough baseline context to know how to use that information.

Chris Myers: Absolutely. And I think that’s true across the board with vicarious learning. The more baseline knowledge—or what we might call absorptive capacity—you have as an individual, the more you’re able to glean from it. I would sit with these flight nurses, and we’d both listen to the same story. They’d pull out nine gems or takeaways. And I’d think, okay, so you were flying—that’s about all I got. I didn’t know the terminology, I didn’t know what they were thinking about, what they were worried about or not worried about. So yes, there will still be room, even in an AI-enabled world, for people’s own expertise to shape how they use that information.

The other issue is that AI can only capture what’s out there, and there’s still a lot that isn’t out there. One of the things I studied with these air medical crews was how often they relied on old-fashioned storytelling to learn lessons. What gets formally documented is a medical chart. That documentation serves billing, legal, and liability purposes—not learning. So it strips out the thinking process. The chart doesn’t say, “I thought of A, then B, then C, then realized B was better, which led me to D.” The chart just says, “We did D.”

Chris Parsons: And when you say storytelling, you mean oral storytelling?

Chris Myers: Exactly. Just shooting the breeze. That’s where a lot of learning happens.

So I think the question going forward is how much of that kind of knowledge can be made visible to AI. If you record stories, meetings, conversations—if all of that becomes part of the record—then theoretically, AI could access it. But there are real questions. Can we store that volume of data? Would people stop telling stories if they knew they were being recorded? Would it change behavior?

These are fascinating questions we still need to work through. Logically, we could make more of that knowledge available to AI. But right now, most of it isn’t there.

Chris Parsons: I mean, we don’t have to stick with the transport nurses, but if you’re a first-year transport nurse, how does that compare—the way someone was trained when you did this research versus how someone might be trained in 2030? I realize this is speculative, but I’m wondering how this intersects with the idea of a standard of care that you raised earlier. Will it still be acceptable to learn one flight at a time, picking up whatever stories and experiences happen to come your way?

This is something we talk about in our field too—the apprenticeship model of learning one project at a time, overhearing conversations, gradually building expertise. That model doesn’t scale well, and it’s been disrupted further by hybrid work. I’m curious how you think about developing expertise in that kind of future environment.

Chris Myers: Yeah, the hybrid work question is really interesting—we should come back to that. But more broadly, yes, I think one of the most compelling use cases for AI is its ability to create simulated environments and experiences.

We’ve long known that simulation is incredibly valuable for learning—being able to experiment, make mistakes, and refine your thinking in a low-stakes setting. The challenge has always been the effort required to build those simulations. Even writing a tabletop scenario takes time. Creating realistic client requirements, designing exercises, evaluating responses—all of that is labor-intensive.

AI dramatically reduces those barriers. So I think training will almost certainly incorporate AI-driven simulations. It creates opportunities to supplement real-world experience with simulated experience, helping address some of the core challenges in developing expertise across knowledge-intensive professions.

Chris Parsons: I have a crossover example. One of our clients, Todd Henderson from Boulder Associates, built a simulator to help architects who had never worked in healthcare understand what it’s like to present to a user group full of busy surgeons and hospital administrators. He created realistic personas with backstories, and participants would present their ideas and get challenged. It let them practice those interactions before encountering them in real life.

Chris Myers: I like that. And as someone married to a surgeon, I can confirm—you do have to practice presenting ideas to them if you want them to gain traction.

This has always been a challenge. When I studied air medical crews, they used mannequin simulators to practice procedures. But they also tried to simulate the broader environment. They’d have someone act as a distraught parent, running into the room and grabbing the clinician’s arm while they were administering care. They’d turn on fans to simulate wind. Because the technical procedure is only part of the challenge—the environment, the interruptions, the emotional context all matter too.

You could wait until someone encounters that situation for the first time in the real world, or you could simulate it. AI allows us to create those scenarios more easily and with greater realism than traditional prerecorded simulations.

Until AI fully powers physical robots, there will still be a human component. But many cognitive and interpersonal scenarios can already be simulated convincingly. That’s a major opportunity.

Chris Parsons: And often, the people best equipped to create those scenarios are also the busiest people in the organization. AI lets them act more as editors than authors. They can describe what they want, refine it iteratively, and build useful training scenarios much faster.

Chris Myers: Exactly. And it also makes simulations more scalable and flexible. A story is a record of one specific event. The natural question is always, “What if you had done something differently?” With high-fidelity simulations, you can actually test those alternatives. You can replay the scenario, make different choices, and observe the consequences.

Simulation has always been part of apprenticeship. You practice, you rehearse, you do mockups. But AI allows us to do this at a scale and level of responsiveness we couldn’t achieve before. You don’t need a human colleague to role-play the same scenario eight times. An AI system can do that instantly. You can say, “This time, be skeptical,” or “This time, be distracted,” or “This time, be in a bad mood.”

And that ability to generate repeated, varied practice scenarios on demand—that’s a significant shift in how expertise can be developed.

Chris Myers: I don’t want to lose your point about hybrid work. Hybrid is a fascinating moderator.

I’m doing work led by Kevin Rockmann at George Mason University on hybrid work arrangements and workplace relationships. Learning vicariously is a core part of relationships, but hybrid has a unique effect that’s different from fully in-person and different from fully remote.

It’s not “more online is worse.” Hybrid is actually the worst, full stop. In-person and fully online are both significantly better.

Chris Parsons: Worst by what measure?

Chris Myers: Yeah, that’s a good question. What we’re seeing is something we call social compression, and it’s one of the most disruptive patterns affecting relationships and interactions in hybrid work.

What happens is this: if I need to ask Chris something, but I know we’ll both be in the office tomorrow, I’ll wait and ask him then. That behavior scales across the entire organization. So when everyone is in on Tuesday and Thursday, those days become chaotic free-for-alls. It’s constant, “Hey, can I bug you for a second?” Everyone knows they’re going to do it, but when they’re actually there, it becomes incredibly disruptive.

Meanwhile, their email inbox is piling up, and they feel like they’re not getting any real work done. Then they go home on Wednesday completely depleted from all the Tuesday interactions, and they spend the day just trying to catch up. Over time, this creates a spiral where interacting with other people starts to feel like a burden rather than an integral part of the job.

What’s interesting is that we didn’t see this effect among people who were fully in the office or fully remote. Fully remote workers don’t wait—if they have a question, they ask it immediately. But hybrid workers compress their interactions into in-office days and then avoid interacting on remote days so they can focus.

That creates a subtle but important shift: interacting with people starts to feel like it’s separate from getting work done, rather than part of getting work done.

One of the risks is that we lose opportunities for vicarious learning. Instead of asking Chris how he approached something, I might just Google it or figure it out myself, because I don’t want to bother him—or I assume he’s busy.

Chris Parsons: Or your mental model is that Chris is heads down trying to get work done, so you don’t want to interrupt him.

Chris Myers: Exactly. And then when Thursday comes around, you’ve got a line of twenty people outside your office waiting to ask questions. You’re giving short, rushed answers just to move through the queue and get back to your own work.

We’re still in the early stages of studying this, but what’s striking is that hybrid work is often marketed as a way to restore relationships and collaboration after fully remote work. In some cases, we’re finding the opposite—it can actually erode those interactions faster because of this compression effect.

Chris Parsons: Have you seen examples of organizations doing hybrid particularly well?

Chris Myers: Not yet. Our studies have been small-scale, but the patterns have been surprisingly consistent. We also looked at broader survey data across fully remote, hybrid, and fully in-person workers and saw similar trends.

That said, I suspect there are organizations doing this well. One factor that appears important is whether everyone shares the same in-office days. When everyone is in on the same two days, compression is at its worst.

We didn’t directly study alternatives, but it’s reasonable to speculate that staggering in-office schedules could help—so there’s still overlap, but not everyone is present on the same days. That might distribute interactions more evenly and reduce the overload effect.

I’d expect that organizations that have been more intentional about structuring hybrid work—rather than simply assigning shared office days—may be better positioned to avoid some of these problems.

Chris Parsons: So we’ve touched on a few themes, and I’m trying to connect them. I keep coming back to the question: what does it mean to be an expert going forward? And related to that, how does someone become an expert? We’ve explored different angles of both.

I don’t know if you’ve thought about it this way, but it feels like those two things are going to change more in the next five years than they have in the last twenty-five. And then there’s a third layer: what does it mean to be a learning organization—one that’s responsible for developing people and sustaining expertise?

Chris Myers: The “becoming an expert” question is the one I lose the most sleep over. Because in most knowledge-intensive organizations, there was an implicit deal: as a junior person, you traded menial labor for knowledge, experience, and wisdom.

You got to be in the room for client presentations because you made the slide deck, or ran the slides, or took notes. That was how you gained experience. You were exposed to the thinking, the conversations, and the decision-making, even if you weren’t in charge.

That deal is now breaking down. Or at least, it’s no longer necessary. I don’t need the intern or junior associate to make slides or take notes anymore. AI can do that.

What worries me is that organizations will conclude, “Great, we don’t need junior associates anymore.” But they still need senior associates. It reminds me of that Mitch Hedberg joke: “Hey Mitch, do you want a frozen banana?” “No, but I want a regular banana later.”

Junior associates are the frozen bananas. You don’t want them right now because you have AI. But you still want experienced senior associates later.

And I think organizations are missing the fact that the price of having seasoned senior staff is keeping junior staff around long enough to develop them. Junior associates were never really producing work equal to what they were being paid—not in terms of strategic value. That wasn’t the point.

Chris Parsons: Bathroom details for us. Picking up red lines.

Chris Myers: Exactly. Slides, notes, redlines—those weren’t high-value outputs. They were developmental scaffolding. They kept juniors engaged while they learned. They were being nurtured, mentored, and gradually developed into experts.

Now we’re outsourcing those tasks to AI and thinking, “Great, we can eliminate that expense.” But that was never what those roles were truly for. They existed to grow future senior staff.

There’s a version of this future where we acknowledge that reality and create a new deal. Maybe junior staff don’t make slides anymore, but they still attend meetings, observe, listen, and learn. Even if their immediate output isn’t economically valuable, the goal is to develop their expertise over time.

The challenge is that we don’t yet have a clear replacement for the old exchange. Organizations may struggle with investing in people when they don’t see an immediate return. But simply eliminating junior roles because AI can perform their tasks misses the deeper function those roles served in developing expertise.

Chris Parsons: I’ll take it even a step further. I’m writing a newsletter issue called The AI and Expertise Paradox. In my experience with these tools, you can run a set of plans through AI for quality assurance or code compliance. But in their current state, they still require an expert to review the feedback. There are false positives, misses, and edge cases. Sometimes the AI flags something that’s technically correct, but a new code revision is coming next year. There’s all this contextual expertise required.

You can’t have a junior person run the plans and just sign off. The health, safety, and welfare implications are too significant. Firms are legally responsible for those decisions. So AI still needs experts—but those experts are retiring.

Chris Myers: Yeah.

Chris Parsons: The Baby Boomer generation is exiting in large numbers. And at the same time, fewer emerging professionals want to replace them in those deep technical roles. They’re drawn to design, design technology, software—more visible or creative work. Meanwhile, the apprenticeship model we’ve been talking about is breaking down.

It feels like at the exact moment AI is increasing our dependence on expert oversight, the experts themselves are disappearing. And we don’t have a clear pipeline for developing the next generation. I’m not sure how we solve that, except by becoming much better learning organizations. Maybe it means making investments that don’t make sense from a pure profit-and-loss perspective this year, but are essential for sustaining the business five or ten years from now.

Chris Myers: Yeah. I think it comes back to simulations, training, and creating more opportunities for people to build experience. And on the other end, we also need to think about how technology can help experts remain engaged longer, even if in reduced roles. Maybe they’re contributing at 20% capacity—doing spot checks, mentoring, advising.

I worked with Dorothy Leonard on a case involving NASA’s Jet Propulsion Laboratory, and they faced a similar problem about fifteen years ago. They had a generation of engineers who had worked on the Apollo program, and they all retired around the same time.

They had an extreme version of the pipeline problem. They only launched missions every couple of years, so they always put their A team on the mission because the stakes were so high. But over time, that meant they didn’t develop a deep bench. When the A team retired, there wasn’t a clear B team ready to step in.

They tried assigning earlier-career staff to simulated or non-flight projects. That gave them leadership experience, but it created new problems. These engineers would go from leading a simulated mission back to supporting roles on real missions, which felt like a step backward. Or they’d leave entirely—recruited into senior roles at private companies.

So they struggled both with developing the next generation and with retaining and leveraging the knowledge of the retiring generation.

Chris Parsons: What did they ultimately learn?

Chris Myers: Last I checked, they were still grappling with it. These are persistent challenges. One partial solution came from the rise of private space companies, which created more opportunities for mid-career engineers to step into leadership roles.

But more broadly, organizations are experimenting with ways to both accelerate experience-building for junior staff and preserve knowledge from senior experts. For example, recording in-depth exit interviews. You might capture three hours of stories and lessons learned on video. No one person will watch that entire recording, but AI can process it, index it, and make it searchable.

That way, the expertise doesn’t disappear completely. And instead of requiring retirees to stay on full-time, you might retain them in limited advisory roles—one day a week, mentoring, reviewing, and guiding.

It becomes a hybrid model, where some expertise is preserved and amplified through AI, and some continues through ongoing human interaction.

Chris Parsons: I think this idea—that retaining employees is itself a knowledge management strategy—is lost on a lot of people. Technology tends to dominate the conversation, but simply maintaining access to experienced people, even on a reduced schedule, can be incredibly valuable. Especially if part of their role shifts toward helping build that knowledge base.

Because for firms to really take advantage of AI, they’re going to need a strong digital knowledge foundation in a way they haven’t before. And it feels like emerging professionals are well positioned to help with that. They can interview experts, extract their knowledge, and help structure it.

Maybe they watch the three-hour video and turn it into something usable. Maybe those videos are mostly stories, and the next step is translating those stories into standards, processes, and tools.

Chris Myers: Yeah. And it helps solve another common problem, which is that active experts are too busy to document their knowledge. If your mid-career professionals are focused on execution and delivery, and your senior experts are transitioning out, then early-career professionals and semi-retired experts can play an important role in building that knowledge base.

What’s interesting is that when you look at organizations that have endured over long periods of time, you see these kinds of structures. Academia is probably the clearest example. It’s one of the oldest knowledge industries we have. You have doctoral students, postdocs, tenured professors, emeritus professors—all playing different roles in the knowledge lifecycle.

Doctoral students and postdocs aren’t necessarily doing the highest-level work yet, but they’re learning, contributing, and developing. Emeritus professors remain involved in a lighter capacity, mentoring, advising, and sharing experience.

The military has similar structures. A retired general still retains a kind of institutional role and identity. That ongoing connection reflects an understanding that knowledge has a lifecycle, and there are different ways to contribute at different stages.

The risk now is that organizations focus too narrowly on the immediate cost savings of replacing junior staff with AI. They miss the broader function those roles served in developing future expertise.

Historically, organizations have invested in junior staff knowing that the short-term return would be limited. The goal was long-term development—growing people into experienced professionals who would eventually become essential contributors.

That’s always been a difficult investment to justify in competitive environments. But if anything, it’s becoming more important now, not less. AI accelerates access to information, but it doesn’t eliminate the need for people who know how to interpret, apply, and extend that knowledge.

Chris Parsons: One of the things I’ve heard recently is that most emerging professionals today will manage an AI before they manage another human—by AI meaning agents. And I read about a computer science program—maybe Stanford—that’s now teaching students how to manage agentically built software as part of the curriculum.

This connects back to what we were discussing earlier. In medicine, for example, you’re not hearing about massive changes in how physicians are trained to memorize drug conflicts or interactions, even though machines could probably handle that better. It suggests the profession may eventually shift in that direction.

So I wonder how much of this comes down to redesigning that zero-to-ten-year journey. If the old apprenticeship model is breaking down in your field and mine, what replaces it?

Chris Myers: Apprenticeship-heavy models tend to change slowly because they’re inherently generational. People teach the way they were taught. But some of these technologies represent paradigm shifts.

Matt Bean at UC Santa Barbara did a great qualitative study of robotic surgery and how it disrupted surgical training. Traditionally, surgical expertise developed gradually. You might start by holding a leg, then retracting, then assisting, and eventually performing procedures yourself. You moved progressively from the periphery to the core.

With robotic surgery, that progression collapsed. You’re either controlling the robot or you’re watching on a screen. There’s no meaningful in-between. So the old pathway into expertise didn’t map cleanly onto the new tools.

Yet the training model didn’t immediately change. Residents still progressed through the same structured residency stages. What Bean describes is something called “shadow learning”—not shadowing in the traditional sense, but learning off-book. Residents would come in after hours to practice on the robot. They’d seek out opportunities outside the formal system to build competence because the official training structure hadn’t adapted.

Part of the challenge is that senior practitioners only know the pathway they themselves followed. They don’t have a mental model for alternative routes into expertise. It often takes a generation that learned through informal or improvised methods to later redesign the formal training system.

We’re starting to see adjustments now—more simulator labs, more structured practice environments.

Chris Parsons: Right—if you can only learn by watching or doing, simulation becomes essential.

Chris Myers: Exactly. Simulation allows people to “do the thing,” just not on a real patient. The solution seems obvious in retrospect, but implementing it requires organizational change—building simulation facilities, restructuring schedules, allocating resources. It’s a simple concept, but not a trivial transition.

Chris Parsons: And it probably has to be designed in collaboration with novices, because experts are so far removed from the learning process that they may not remember what it’s like to be new.

Chris Myers: Absolutely. The military offers a great example. They had high failure rates training operators for remote vehicles until they replaced their custom-built controllers with Xbox controllers. Suddenly, trainees performed much better because the interface was familiar. It aligned with their existing experience.

Sometimes the key is adapting tools and training to match the learner’s perspective, rather than forcing learners to adapt to outdated systems.

Chris Parsons: I don’t think apprenticeship is completely gone. But it feels insufficient on its own. And when you combine that with rising standards of care and expectations, it means we need to develop expertise faster and more effectively than before.

It raises a deeper question: did the apprenticeship model ever work as well as we assumed? It feels like many organizations recognize that something isn’t working anymore.

Chris Myers: There’s definitely growing awareness. Organizations may not have fully solved the problem, but there’s increasing recognition that the traditional pathways into expertise aren’t keeping pace with the demands and tools of today’s environment.

Chris Parsons: Can leaders see far enough ahead that it might get worse before it gets better, given AI and the pipeline issues?

Chris Myers: What I’ll say is there are so many other changes happening in healthcare that I don’t know that leaders have fully grasped the consequences of AI yet. There are funding pressures, demographic shifts, staffing shortages—all of which intersect with AI. But if I had to capture the general sentiment among senior leaders—hospital CEOs and others—it’s a hope that AI might help solve some of these larger structural problems. And along with that, maybe a willingness to overlook some of the problems AI will create, because of the problems it promises to solve.

For example, we can’t recruit enough primary care doctors, and the ones we do recruit are expected to see forty patients a day and complete all their documentation. That drives people away from primary care. So the question becomes: how do we reduce that burden? One option was hiring scribes, but that’s expensive. If AI can do the documentation at a fraction of the cost, the response is, “Great, let’s do that.” And the attitude becomes, yes, there will be consequences—but those consequences may still be less severe than the problems we’re already facing.

Chris Parsons: But can a human realistically handle forty patient visits in a day? From an emotional and cognitive standpoint—if you strip away the note-taking and administrative work and just focus on being fully present for each patient—is that sustainable?

Chris Myers: It’s realistic in the sense that it does happen. There are physicians who have seen that many patients in a day. Ten-minute visit slots, back-to-back—it’s possible.

But it raises deeper questions about burnout and wellbeing. If AI is framed as a tool that makes you faster and more efficient—if it can generate notes instantly or suggest diagnoses—then the expectation may shift. Instead of seeing forty patients a day, maybe now you’re expected to see fifty.

Chris Parsons: Because you have AI, so you shouldn’t make mistakes.

Chris Myers: Exactly. That’s the dynamic that concerns me. AI has the potential to alleviate burdens, but it could also raise expectations and intensify workloads.

What I’m seeing right now is an awareness that AI isn’t perfect. But in the context of the broader systemic challenges facing healthcare—cost pressures, staffing shortages, reimbursement changes—there’s a strong hope that AI will function as a kind of relief valve. That it will help stabilize a system that’s already under enormous strain.

Whether it ultimately delivers on that hope—or creates new challenges in the process—is something we’re still figuring out.

Chris Parsons: Final questions. Looking back five years from the end of 2025: what have you changed your mind about regarding learning or how knowledge moves in organizations?

Chris Myers: I would say, if you had asked me pre-pandemic—pre-Zoom migration—whether tacit knowledge could survive in a non–in-person work environment, I would have been extraordinarily pessimistic. I would still say it can’t all survive.

Chris Parsons: And by tacit knowledge, do you mean the transfer of it, or the knowledge itself?

Chris Myers: Yeah, sorry—the sharing and dissemination of tacit knowledge. The “this is how we do things around here” kind of knowledge. How you navigate situations. I loved your example of how to talk to a busy surgeon during a client pitch—the things that are very hard to write down.

I’ve been pleasantly surprised by how much of that has persisted and adapted to the Zoom environment. Some of it reflects changing attitudes toward how we consume information and gather insight from others. If you had told me ten years ago that three-hour podcast episodes would have millions of views on YouTube, I would have said there’s no way. I would have assumed attention spans were shrinking too much.

But something shifted. And it’s actually a double positive from a knowledge management standpoint. A three-hour casual conversation podcast is exactly the kind of thing that used to happen informally in the office.

Chris Parsons: Or over a long dinner.

Chris Myers: Exactly. You’d be sitting there having a long intellectual conversation while the waiter got increasingly annoyed because you weren’t ordering anything else.

The difference now is that those conversations can be recorded. They become scalable, searchable, and usable by AI. That moves us closer to a world where tacit knowledge doesn’t have to be shared only in person.

I still think there are limits. You still need live discussion and back-and-forth. But the extent to which tacit knowledge has been preserved and adapted through recorded conversations and digital formats—that’s something I would not have predicted.

Chris Parsons: What do you think was the fundamental assumption you held that prevented you from seeing how this would unfold?

Chris Myers: I think part of it was an assumption about what people would be willing to do online. I don’t even know if it was a misunderstanding so much as the world changing in ways no one would have predicted.

I don’t think anyone would have proactively chosen hybrid or fully remote work at scale. If you had given people the choice beforehand, many would have said they preferred being in the office—that it was essential for connection and collaboration. The same arguments we hear now from organizations trying to bring people back were the dominant mindset before the pandemic.

But the pandemic was such a seismic disruption that it reset expectations. It forced everyone into a new mode of working, and once there, people adapted. I never would have imagined preferring Zoom meetings over in-person meetings. And yet now I live in that world every day. On Zoom, I can mute myself before saying something I regret. In person, I can’t. That’s a small example, but it reflects how norms and preferences shifted in ways I never would have predicted.

Before the pandemic, I taught a class on teaming, and one session focused on virtual teams. It was framed as a novelty exercise. I made students log into Adobe Connect and work remotely. They hated it. That was the teaching point—it illustrated how different and challenging virtual collaboration was.

Now that exercise feels almost comical. If you teach teaming today, the default assumption is that the team is virtual.

Chris Parsons: Right. Now you have to teach people how to work together in person.

Chris Myers: Exactly. In-person interaction has become the novelty. We now run online programs with optional in-person residencies, and the framing is almost reversed. It’s like, “We’re going to do this unusual thing where you come together physically for a few days.”

That’s not something I ever would have predicted. My own thinking has shifted from “we need to be in person to work effectively” to “how do we work effectively given that we often won’t be in person?”

Chris Parsons: We’re seeing a similar shift in learning. We’re building a learning management system, and the big change is that live, in-person lectures are no longer the default. Learning is becoming on-demand first. People watch content ahead of time, then come together for discussion. The flipped classroom model is becoming standard.

Even in hybrid offices, people often join meetings on Zoom or Teams from their desks. Otherwise, remote participants become second-class citizens in the conversation.

Chris Myers: Exactly. And that raises a reasonable question: what’s the point of coming into the office just to sit on Zoom?

We’ve seen interesting patterns. When we scheduled one-hour meetings—even with incentives like lunch—most people chose to attend on Zoom. But when we scheduled a four-hour session and called it a retreat, more people chose to attend in person.

I don’t have a definitive explanation, but it suggests that duration and framing matter. People are willing to come in person when the experience feels immersive and worthwhile. A four-hour Zoom session sounds exhausting, whereas a four-hour in-person retreat offers opportunities for informal interaction and side conversations.

It’s another example of how norms are evolving in ways that would have been difficult to anticipate just a few years ago.

Chris Parsons: The COO of one of our clients mentioned something similar when I was talking to her for another podcast episode. She said they’re moving toward on-demand learning for things like how to use the CRM or how to use specific technology tools—things that don’t need to be taught live.

Chris Myers: And by on-demand, you mean online, prerecorded?

Chris Parsons: Exactly. Online, prerecorded content. But if it’s something like changing how performance reviews are conducted, that will be done in person. It’ll be several hours long. Because that’s a cultural shift. It’s not just technical information—it’s something people need to experience together.

Chris Myers: Yeah, that makes sense. It’s an extension of the old idea that “this meeting could have been an email.” Now there’s a broader spectrum. You have to ask, what kind of interaction is this? Is it purely informational, or is it something that requires discussion and engagement?

Part of the reason our four-hour retreats drew more in-person participation is that people assumed there had to be interaction involved. No one expects four hours of announcements. They assume there will be discussion, breakout sessions, or opportunities to contribute.

By contrast, when we held one-hour meetings—even when we intended them to be interactive—they often defaulted into one-way communication. Especially when most people joined remotely. You’d ask, “Any questions?” and see a screen full of blank cameras. It naturally became more passive.

So now, the format and duration of a meeting communicate intent. A longer, in-person session signals that engagement is expected. That wasn’t necessarily true before. A meeting was just a meeting.

Chris Parsons: It really feels like organizational redesign is the underlying theme here. Whether it’s expertise development, learning models, meetings—it’s all being reshaped.

Chris Myers: Yeah, I think that’s right.

Chris Parsons: Well, Chris, thank you so much for the conversation. My hope is that people will vicariously learn from this discussion in this new format—something they can listen to asynchronously.

A few years ago, this might have been a live webinar. Now it’s a podcast. So I appreciate you taking the time and sharing your perspective.

Chris Myers: I appreciate the invitation. And if anyone learns something from it, that will be a good and surprising outcome.

Chris Parsons: That’s great. We’ll stay in touch and keep following your work. Thanks everybody.