What AI can’t replace: the human side of leadership

In this episode of Undiscovered Country , host Peter Mulford is joined by Mickey Connolly, founder of Conversant and co-author of The Communication Catalyst , to explore why better conversations—not better tools—are the key to better business results. Mickey shares practical frameworks for aligning teams, building trust, and creating lasting impact in the age of AI and hybrid work . Tune in to learn how thoughtful communication can reduce waste, drive commitment, and elevate leadership. Listen now!

Most of us want to lead in a way that matters; to lift others up and build something people want to be part of.But too often, we’re socialized (explicitly or not) to lead a certain way: play it safe, stick to what’s proven, and avoid the questions that really need asking.
This podcast is about the people and ideas changing that story. We call them fearless thinkers.
Our guests are boundary-pushers, system challengers, and curious minds who look at today’s challenges and ask, “What if there is a better way?”If that’s the energy you’re looking for, you’ve come to the right place.
Peter: Welcome to The Undiscovered Country, a podcast about the future of work. This is Peter Mulford. Today I’m excited to bring a conversation to you with Mickey Connolly. Mickey Connolly is the founder and chairman of Conversant, a consulting company that focuses on how communication can impact coordinated action and improve organizational culture.
Mickey said a really interesting background. Over 25 years, he has worked with over a hundred thousand managers, educators, and interestingly enough negotiators to improve how they resolve conflict, improve relationships, and get better business results through better conversation. He’s also the co-author of two business books, including The Vitality Imperative and the topic of today’s conversation.
The communication catalyst, a book he wrote in 2015 with co-authored Richard Rech. In that book, he provides an architecture and an approach that you can use to align people on purpose, to propel them into action, and then even review results for continuous improvement. And what we talk about today is how those techniques he first researched and discovered back in 2015 have become even more relevant in the age of accelerated computing and artificial intelligence.
So among the things we discuss, we talk about how he sees AI driven tools, augmenting the quality of human conversations and potentially undermining them. If we don’t pay attention. We talk about some practical measures that leaders can use. To improve their communication efforts in the current environment.
And we talk about some of the skills that leaders might need to unlearn in order to better engineer conversations intentionally in the age of artificial intelligence and hybrid work. It was a fascinating conversation that I enjoyed very much, and I hope you do too. And now I bring you Mickey Connolly.
Peter: I’m here with Mickey Connolly. Mickey, thanks for joining me. It is good to be with you, Peter. So I’ve been a fan for quite some time, of you personally, and I’m a particular fan of two of your books, the Communication Catalyst and The Vitality Imperative.
And, um, I’m eager to speak to you today because I think both of those books are even more relevant today than they were back when you wrote them. I think in, in 2002 and 2015. So I want to get into that, but before we do, maybe we could start with a little bit about your background before we launch into the, the whole tornado of uncertainty, which is the business world today. Um, how would you describe your intellectual history as a business person, uh, and a CEO and a leader, and how did you come to be where you are today?
Mickey: Oh, let’s make this as short a version as we can. Um, okay.
I began, believing I was going to be a lawyer. And did not complete that journey because I was given an opportunity to own a business.
Peter: Okay?
Mickey: And that was a restaurant that I ended up owning 51% of when I was only 24 years old, and ultimately built eight restaurants and formed my first consulting company called Restaurant Resources.
Because I was confusing a bullish economy with my personal brilliance and thought I had much to share, okay? And in that I was really surprised I could go into a restaurant and see things that they didn’t know how to do and tell ’em how to do it, and I’d come back in a few weeks and they wouldn’t have implemented the things they paid me to say.
Okay, so a question. And I don’t mean this glibly changed my life, which was I started asking, who knows, how do you communicate something so people act on it? Interesting. That became such a huge question. It led to me ultimately selling out of the restaurant business, uh, and moving into this area of.
Communication. And what it led me to is two groups of people, the philosophical linguists who really study how does language affect the way humans think and act. Okay? And then the high stakes negotiators. So people who are doing SWAT and hostage, ’cause for them the test for communication is action. You can’t get away with I said it right?
They just didn’t understand. And so studying those two worlds really begin the. Uh, intellectual journey that led to the body of knowledge that runs conversant our company today. So the short version is after I sold out of my original businesses and studied with all these people for a period of years, began working with mainly military and police in the beginning, and ultimately move that into commercial and large nonprofits where we simply work on.
How does human conversation impact how much we can accomplish per unit of time, money, and stress? So that just gave you 35 years. Alright.
Peter: That’s, uh, that’s really interesting. And I, I think where, where did you first meet Richard? Uh, your co-author? Richard Rach. I mean, he, he has a background in law enforcement.
Right. So it sounds like that is, is. Did you seek him out after you realized, you know, high state negotiation? He
Mickey: actually came to, I was early in this part of the, turning my life over to communication, connection, conversation. Uh, he came to a course I was leading when he was the chief of police in Aspen, Colorado.
Peter: Okay.
Mickey: And, uh, he’s got an interesting background because he’s a PhD in psychology. He’s. Also was, uh, obviously the chief of police. Before that he’d been a detective. And before that, a patrolman. Uh, after that, a professor at John Jay School of Criminology in New York, bras Hill Police College in the United Kingdom.
And he, uh, really cared, obviously in that domain of communication for action, how your communications actually make good things happen and stop bad things from happening. So he and I just got. Really close, really quickly, we resonated and we began to work together on things. And then ultimately he left the formal domain of police work and we started the company.
Peter: Terrific. And then, um, that, that’s a, a perfect segue into what I think is, is one of the most compelling, uh. Artifacts that you’ve created at, at Conversant, and that’s the communication catalyst. So I think that this may be a nice segue, uh, for readers who are not familiar with this book. I encourage you to go out and read it immediately.
But, um, in the book, uh, Mickey u and Richard. Are making the point that in any organization, you know, whether it’s the police or the FBI or, or Fortune 500, the greatest untapped lever for creating value in less time is conversation. Right. And, and a lot of, yeah, a lot of people say similar things to that, but you do it in a really unique way.
Um, what was the specific problem you were trying to solve? When you wrote that book and you, you wrote it in a time when there were a lot of people who were, who were out in the world teaching communication skills, but what was the, the problem you were trying to solve or the, the gap you were trying to fill that compelled the two of you to write the book in the first place?
Mickey: Well, a lot of our early clients in large systems were science and technology heavy. Lots of scientists, engineers, empiricists, people who really consider themselves to be rigorous about process and knowledge and, and we started noticing something in those first large systems we were in, which is the challenges they were most frustrated by and had the most difficulty with were not technology challenges.
Okay. They were social challenges, and so we started asking people who were coping with some really famous organizations, some very sophisticated technological, scientific challenges, what helped or hurt them. Handling those at the rate at which they felt like they needed to and over and over again, 80% of the things they brought up were about issues between people, where people were not aligned, where they were not able to turn their conflicts into intelligence, where they spent more time trying to prove each other wrong than combining their intellects to actually make something happen.
And so we just saw that. While all those people had this reverence for design in the domain of science and technology, they did not have an appreciation that human interaction is as subject to rigorous design as any of their technological processes. And that’s why we wrote the book. ’cause we wanted to show there’s a design to the success of human interaction.
There’s a design to the creation and sustainability of relationship. There’s a design to turn our conflicts into intelligence. So that’s why
Peter: That’s interesting. So it almost sounds like you’re saying you discovered that people tend to talk randomly around technical design, but they’re not actually being very specific around.
How they have conversations to make technical designs actually work.
Mickey: Yeah, and it’s interesting today, if we look at the history of our company, I mean, Robin Anselmi, who’s our CEO today, an engineer, Hmm. Uh, Roger Henderson, who’s a global partner, an engineer. He was an aerospace engineer. Uh, so we’ve attracted a lot of people from those domains who love that human action.
Human interaction is also. Subject to design and an appreciation and orchestration of those principles.
Peter: That’s interesting. I, I seem to recall, and I apologize if I get the statistics wrong, but I think there was even a case example in your book where you noticed, uh, that you were able to cut something like an 18 month cycle in half to nine months.
Yeah. Simply by redesigning. Not the process itself, but redesigning how they talked through, through problems. Is that, um, did I get those numbers right before I No, that’s right. Yeah. That
Mickey: was a, a time to market breakthrough, a company that had a standard of about 18 months. And we say that almost anything that you haven’t examined carefully in terms of the quality and design of the human interactions.
If you haven’t done that. There is unnecessary waste in the system of interaction. How we have meetings, how we’re making decisions, how we’re resolving differences, how we’re having performance conversations, how we onboard people into a team. So there’s something in there that there’s a lot of waste in.
So if we just look at it with the right people together, you actually end up seeing where the waste is and you can take a whole lot of it out. Uh, that happened there. There’s another. Very large company that had a, uh, the biggest process running through the whole company that was, had a cycle of taking around 55 days that just getting the right people together and reviewing how they interact and seeing where the waste is and replacing it with well designed ways to move through the process and went from 55 days to 25 days.
Uh, so we’ve seen that happen repetitively.
Peter: That’s, um, that’s around the world. Yeah. Well, and of course I’m sure, uh, anyone listening to this is gonna be intrigued by the idea of immediate, how all of this leads to. Legitimate and credible business results. Let me double click on that for a moment. I think the other thing that’s true of anyone listening to this is we can all generally agree that there are a lot of frameworks out there for structuring meetings, for appropriate meeting design.
I mean, you, you, on one end of the spectrum, you’ve got things like raci on the other end of the spectrum. You’ve got things like, like agile, but there, there’s something really special I think about. What you do, because you’re, you’re not talking about redesigning meetings so much as redesigning how conversations actually occur within meetings, regardless of, you know, what your, your meeting framework is.
So let, let’s see if we can double click on that for a second. I know at the heart of your approach, you’ve got something called the cycle of value, and it’s a, it’s a kind of three part. What I would call a conversational architecture. Could you tell us a little bit about the cycle of value and what the, the three legs of that stool are?
Mickey: Yeah. The three categories of conversation, and there’s more detail in each one, are align, act, adjust, so they’re conversations to turn differences into alignment. There’s conversations that. Create coordinated accountability. Those are the conversations for action. And then there’s conversations for adjustment.
’cause we say that much of leadership occurs in the pause where we stop and reflect and consider. And so that cycle we just see going over and over and over again. And in the first book that just came from us looking at all of the successful large system change projects we’d seen. And those are a minority of the large system change projects.
They’re really successful ones. And what we saw was they tended to operate in that cycle, whether they use those names or not, they had relatively short cycles of alignment, action, and adjustment. Uh, so that’s the, the overall structure of how we design conversations. And they’re, as I said, more detail people are interested, can look at it in the communication catalyst.
Peter: Okay. And so, but what’s the, um, I mean, on the surface of that, it, it sounds fairly intuitive and you know, the way, the way you say it, it, it sounds kind of like, well, of course we should align, act, and adjust. But what, what’s the, what’s the uncommon sense that starts to emerge or that you notice started to emerge when you worked on leadership teams and, and tried to get them to think about redesigning?
Conversations using that kind of architecture.
Mickey: Well, one of the things that’s important is we differentiate between alignment and agreement agreements, actually an intellectual and emotional moment where people resonate with something. And the test force generally nodding in most cultures. Okay. Not at all.
In many. And we say the difference is alignment. Actually commitment, coordinated commitment to how you allocate time, money in people. So conversations that actually get to the coordinated allocation of time, money, and talent, those are much more challenging. The ones where people go, good idea. Love that.
Hey, let’s go have lunch. Uh, and we found that most challenges. In large systems, you could find a place where people got to agreement and confused it with alignment. They didn’t have the challenging conversations to surface differences, to really anticipate how is this gonna go in the real world and resolve those ahead of time.
So as you know, one of the things that we say starts the whole thing. Mm-hmm. Is do you have a genuine intersection of interest? Because if we don’t have. Common care, you will not end up coordinating the allocation of resources. So the first thing is, what’s the area of common care? And so we work on, no matter how many differences are there, what is it that requires you to talk to each other?
What’s that place we call the intersection? And as you and I have talked about many times before, there’s a simple construct about how you get to it, which is you start. By looking at all the different interests in the conversation, the people you’re gonna have in the room or in the overall set of meetings.
And you’ve gotta get at what are the purposes, concerns, and circumstances of each of those interests. Purposes as things they’re committed to things, they’re for concerns, things they wish weren’t a problem, things they’re against, and circumstances are just the situations they’re actually in. The factual.
Conditions in which they’re operating. And that purposes, concern and circumstances has been such an elegant way for us to have people come to understand each other in terms of whatever the reason is they got in the room. And once you do that, then you begin to look for where the overlaps, where the intersections between what you’re for, what you’re against, and what you’re in, and what I am.
So alignment starts by first finding an intersection of purpose that people can go, yeah, you’re right. That one matters to me. And there are conversations we have. Structures, designs about how do you get to that and the faster you do, the more, as soon as people have an intersection of purpose, they almost can’t help it.
They start to invent, they begin to brainstorm and think, well maybe this, or how about that? Or we can all remember a time we. Met somebody and immediately found some common ground where we couldn’t help it. We started inventing with one another. Then you have to sort through all that, okay, what’s plausible, what’s realistic?
And we call those invest conversations. So in the aligned conversation, there’s a design to intersect, to invent and to invest. And if you don’t get through the invest conversations you have not have the coordinated, committed allocation of time, money, and people. So. I, I don’t wanna spend the whole time talking about what’s in the book.
I’d like us to have a live conversation here. Mm. But that’s what occurs to me, given your last question.
Peter: Yeah. So there, there was, there was a lot in there, but let me see if I can un unpack that. So the, you know, the idea here is. Whether you realize it or not, you are engaged in aligned conversations, act conversations and adjust conversations, or if you’re effective, you, you will be right.
But at the very beginning of this, there can often be a, a disconnect, one that you might not even be aware of that sits in the space between alignment and agreement. And if I heard you right, the first thing that you want to be sure of is. Is there an intersection? Do you know? Are we all working together on something that we all care about?
And the way to get your head around that is to consider purposes, concerns, and circumstances. In other words, do I understand what, what the other people in the room are for that they’re in favor of? Concerns are, do I have some sense of what they are worried about or might be against? And then more broadly, you’re talking about.
Do I understand the context? What are they, what are they in? What kind of situation is going on with them that might be impinging on their purposes and concerns? Uh, it is an elegant framework. How do you in, in a world where meetings seem to be getting shorter and shorter and thanks to hybrid technology, we seem to be having more and more of them, one after the other.
Uh, how do you operationalize purposes, concerns, and circumstances or, um, if you like said differently, what are the, um, the watch outs, you know, the, what are the things you need to be aware of that could spin you in the wrong direction and, and going down instead of a cycle of value, say a cycle of, of waste.
Mickey: Well, as you know, one of my favorite questions for any leader is, what is it time for now?
Peter: Okay,
Mickey: so timing is a major issue. If we’re having these short meetings that are intended to produce big results and often don’t, uh, the first thing is what conversation is it timed for now? And if we look at the cycle of value, there’s actually.
Three line conversations. There’s three act conversations and two adjust conversations. And we have a diagnostic. We have people take anything that they don’t think is moving the way they want it to, that they can do a diagnostic. And you see which conversation it’s time for. Now, people ask me all over the world, well how?
What does our pattern look like? Like the conversations we’re good at or bad at relative to other people. In most systems, there are some. Differentiated things. However, the most common thing we’ve seen is people make a well-intended leap to action before they have a foundation of alignment, and it ends up causing a huge amount of wasted time and effort, unnecessary conflicts, misunderstandings, and rework.
So. The issue of what conversation is it time for now, even if you don’t ever look in our book and you really stop and rigorously bother yourself with that question, okay, I’m going into this meeting. These are the people who will be there, given those people and what’s going on around us. What conversations is it time for now?
So even in the higher look at it, is this an alignment challenge? Is this a coordinated action challenge? Is this a learn and adjust challenge? Even just at that level, it starts to change the quality of meetings. People often don’t have the right people in the room or the right information to have the conversation.
They fantasize that they’re gonna have and then they end up having extra meetings ’cause they didn’t actually get to a satisfying conclusion.
Peter: Interesting. You know that. Um, that’s I think a nice on ramp. I think we need now to. The communication catalyst in the year 2025. As you know, this, uh, podcast is the undiscovered country.
It’s about the future. So building off of what you just said there, um, what, what I, I mean, high velocity conversations of, of the type you’re describing there, they’re going to look different across cultures, which you talk about, but they’re also gonna look different across medium. Right. So whether it’s, um, if you’ve got a global audience all zooming in or using teams or whether you’ve, you’ve got a hybrid audience or you even have maybe a hybrid audience that has artificial intelligence taking, taking notes, I think it can be tempting for most leaders to feel like, okay, my company is invested in.
Zoom and teams. My company has invested in artificial intelligence as a note taker. Therefore, the way I’m gonna get ROI or the expectation for me to drive ROI is going to be largely a function of efficiency and speed. Right? Yeah. And, and what I would worry about there is that might create. A financial incentive to move faster, or as you just pointed out, to skip over getting alignment and jumping right to some kind of action.
So what have you noticed, uh, Mickey, in the work you’ve been doing lately to say since 2023, um, have emerged as some of the biggest pitfalls when you’re trying to get purpose right in an environment when people are trying to do things faster?
Mickey: Well, as you have generously said, I’ve started to think that some of the things that we were researching and writing about 25 years ago Hmm. Apply in different ways right now. So I don’t know if you remember, but the subtitle to the Communication Catalyst is Fast, but not Stupid.
Peter: Yep. Yeah, yeah, yeah, yeah.
Specifically it’s um, fast, but not stupid ways to track value, uh, right. For customers, investors, and employees. Right. Or some, again, my apologies, some version of that, but the
Mickey: spirit fast, but not stupid to me is starting to feel even more relevant today. Can you say more about that? Well, for instance. Many people are loving that in artificial intelligence.
I can throw a bunch of information in it. It can give it back to me in a summarized way faster than I could have done that myself. Mm. Okay. Great. That was fast. However, anything I’ve seen so far where people are doing that. It all depends on the quality of the guidance you give it. What’s the question you ask?
What’s the context you create? What’s the purpose? What’s inbounds, what’s out of bounds? Then you can get something really good back. So fast does not mean smart. It depends on whether or not we are thoughtfully connected. Um, I think in this.
Wild-eyed, rapid and unpredictable emergence of artificial intelligence in our lives. Mm-hmm. And I really do mean unpredictable. It’s gonna evolve so much for us to talk about, well, where’s it gonna be in five years or 10 years, or 50 years? Well, that’s the zone of speculation. And then we’ll see. But there’s some things that I think are relevant now.
One is the difference between. Memory and relational knowing. Okay. To me, so far, what AI is, is the most sophisticated system of recognition and recall in the history of humankind. Okay. But it’s really based on the collection of what is known.
Relational knowing, and you and I have this, every time we’ve been together, there’s something that one or both of us discover we weren’t thinking before because we stimulate each other. Uh,
Peter: you in
Mickey: particular and because
Peter: I’m following the direction of my robot overlords Mickey, so
Mickey: Yeah, that’s right.
Because relationally we discover, we imagine we get in front of the moment and. If people think everything you throw in AI is gonna make things faster, that’s not the same as making things better. Hmm. So we don’t want to confuse what AI can do, which so far I think is the most extraordinary demonstration of recognition and recall in the history of memory management.
With what is particularly human, you know, we’ve said for years and other people have said the same thing, that our organizations are often run inconsistent with the nature of being human. As Cal Delaney is one of our global partners, says AI is a different way of knowing. It’s not replacing all the ways of knowing, which I think is really good.
Lemme
Peter: lemme, so the
Mickey: spirit of discovery in these meetings. Where we go in there and don’t just trot out what we already know and get everybody to buy into it and do it, but we actually have conversations where that kind of real commitment and emotional relationship to something arises in the conversation.
You can’t get that predestined by AI in my experience.
Peter: Lemme see if I can, if I can double click on on something I think you’re saying in there. So if I were to. To broadly summarize this and you know, Mickey, if I put words in your mouth that don’t belong there, just, just spit ’em out and re recast them.
But it, it sounds like you’re drawing a distinction between AI for automation and AI for augmentation. I mean, roughly speaking where I think you, you know, you get the general idea automation is where you’re just using AI to do things you don’t wanna do anymore. Uh, and presumably because that, that’ll free up your time for doing something, something else.
And then there’s AI for augmentation, whether it’s around, you know, you, you talk about knowledge retrieval or dissemination or exploration. So let’s, um, let’s, let’s wave our, let’s, let’s point our light for a moment on the augmentation piece for a moment. So, given your, your very clear view, that conversation is a kind of.
Economic lever, right? And that, uh, conversational skills matter for getting better results faster. And then we look at, one of the things that ai, one of the augmentation capabilities that AI has is the, IS tools like sentiment analysis and conversational analytics, right? If you were to extend your brain out into the future.
How might, in your view, those augmentation capabilities shape our abilities to listen at scale and more and communicate more effectively at scale, in your view, which are very human activities, right? But ones that could be augmented by, uh, ai.
Mickey: So we are already seeing this move to have. Different kind of artificial intelligence systems, uh, represent the emotional nature of human interchange. I’m saying that very carefully represent. Mm. So the sentiment analysis, what’s the mood there? Uh, all of that. And how do you have the mood in the AI voice?
Actually have people relax and open up and feel like they’re in a safe place. And to me, that is the most sophisticated form of manipulation that I’ve experienced in my lifetime.
Peter: Say more about that. You mean, um, the sentiment analysis produced by AI is a kind of manipulation, right? Because.
Mickey: We have to be really careful with it because, you know, there’s already stories about people falling in love with their AI coach and we have movies about this already, and uh, okay.
I think that’s avoiding the nature of being human. You know, I had a cartoon from many years ago that showed these two older people sitting on a porch. And talking about the difference between fantasy and reality and. One of ’em said, well, I’m definitely in favor of fantasy. It’s so much easier to cope with.
Okay. Funny. So the, the Fantasia of relationship is that we could have an system that somehow takes away the risk of actually trusting one another, being able to work with each other, being able to turn our differences into intelligence rather than just conflict.
I have not yet seen, and I’m seeing some of the more sophisticated advanced coaching models and experiencing those. I haven’t seen anything that actually creates the authentic moment of human mutual discovery that cannot be pre-designed. There’s all sorts of things you can do. One of our design principles is any great process makes the right thing easy and the wrong thing hard.
I think there’s a lot of things that AI can do that make some things easier and eliminate some things that would’ve been unnecessary mistakes. I don’t think it can replace
what our earlier was talking about, the relational, knowing the moment of. Serendipity and discovery it. I think it’s why so many organizations right now are really struggling with the question about, are we gonna make people come back to the office? Because there is a concern about, wait a minute, what about those serendipitous moments?
What about where you and I talk to each other? ’cause we happened to be there the same day. What about the occasions, whether they’re virtual or in person where we’re. Discovering something, not just reciting something. So I think you could put it in the context of imagination, of discovery, of the trust that’s so deep that it has to say things we otherwise never would’ve said and discover things we never would’ve discovered.
So far, I have not been able to see or experience in the artificial intelligence models that we’re playing with. That genuine kind of surrender to the conversation.
Peter: So if I play back what I think I’m, I’m hearing there, it sounds like you’re describing the geometry of a, of a space. You know, we could call it a use case space where it seems like there are problems to be solved that require great conversation, but that are not what, that AI is simply not suited for.
Even though, um, people may be, be, be using it. And moreover you’re saying watch out. If you try to use AI in this particular space, you can get into trouble as evidenced by, um, you know, I think you talked about people falling in love with their, their AI bots or being manipulated. And in fact, um, at the time of this, this recording, I don’t know if you read this, this is the 17th of July.
There was a report that went out this morning that said something like 72% of teenagers in the US now prefer. Uh, having conversations with their AI bots than with other humans. Um, you know, I, I’ve, I’ve gotta go and fact check how they did that research. But you’re, you’re, you seem to be suggesting, look, when it comes to improving your conversation skills, um, there is a space in which you might be tempted to use ai, but that perhaps for the moment you shouldn’t, because it lacks something, it lacks the kind of human connection.
Uh, that’s required to get this, to do this really well. Lemme turn that around. I, it’s, yeah,
Mickey: I think it’s inevitable that we’re gonna keep exploring ai, so I don’t mean, there may not be many places where I would say don’t use ai. I’d say you want to maintain a reverence for the difference between replicating human behavior and being human.
Peter: Yeah, we like to say at, at BTS we say it’s simulating human behavior. And that’s actually, um, uh, I, that’s a practical as well as a technically accurate right description about what most, most of these models do. So how would you, that’s, that’s interesting. So to make that practical and, uh, to, to land this point.
What does that actually look like for you? In other words, if you were to, again, one of the things I love about your book is, um, you, you’re very good at supplementing theory with practical tools and even checklists, for example. Um, you know, from framing questions for an aligned session to even debriefing protocols and a, and an adjust session.
So if you were to write a book today about how you would recommend people sit comfortably in the space between. Uh, actual human interaction and simulated interaction. What, what would you put in there? What’s the, the quick checklist of things you’d recommend? They do?
Mickey: I think it goes back to all the places, whether it was Six Sigma or Deming, or when we first started seeing the difference between efficiency and effectiveness.
You know, where efficiency was the. Most lean use of time and money to get something done and effectiveness was actually fulfilling. The reason that we are in action, and I think what I’d really watch is, are we actually fulfilling what we intend and I’m using fulfilling carefully rather than just get something done.
Mm-hmm. Often we lose touch with the reason for action. You know, it’s why Simon Sinek’s work was so emotionally appealing to so many people. When he talked about often the question we’re worst at is why we’re doing something. Uh, that’s why Einstein said all means are, but blood instruments if they lack a living spirit.
Peter: Okay?
Mickey: So I think the living spirit, the reason that we care the. Purpose behind action that lives in the domain of effectiveness, not efficiency. And we often just get caught up in getting things done quickly without staying conscious of are we fulfilling the reason we’re even doing So? I think if I were to.
Who knows, I may soon, but if I were to go back to writing today mm-hmm. It would, I would re-look at those two distinctions in the age of artificial intelligence, about what’s the difference between efficiency and effectiveness. ’cause I think human beings have an emotional, intellectual, and physical monitor on whether or not they’re being fulfilled or satisfied or successful in what they’re doing.
That I don’t see arising from artificial intelligence. Certainly at this stage of it, you know, the, the question of fulfillment and when that’s missing work is less successful. You know, I’ve told you before that Ann Marie Allen, who’s a now retired global partner from Conversant, and before she ever came with us, was the.
Chief Knowledge Officer at Hewlett Packard, somebody I really, really respect, a scientist engineer. And she says, if you want to understand great performance, follow the joy. Hmm. And for an engineer to say that, that’s really interesting to me. Now what’s, where’s the joy? That’s where people are experiencing making a meaningful contribution.
They’re proud to make. And basking the impact in a way where they can tell I just left things better than I found them or we did. Uh, and I think that’s deeply human, that experience of having left things better than we found them. It’s why in the opening to the vitality imperative, I ask the reader, have you ever mowed a lawn and stopped in the middle to admire the short grass next to the long grass?
Because I think that’s something a lot of us have done. ’cause we just like being able to tell, we just made a difference. So what I think we want to keep in mind is what’s the role of artificial intelligence? That way of knowing in us fulfilling the things that are deeply satisfying. ’cause when we are in that domain of deep satisfaction, we produce.
Bigger, better results with less time, money and stress. So that’s where I would go. I would be doing the modernizing of the distinction between efficiency and effectiveness and showing that AI is mainly in the efficiency domain and our relational. Knowing that comes from human interaction is really the source of effectiveness, of fulfillment, of joyful accomplishment.
Peter: So, wow. There, there was, there, there was a lot in there. And of course, you, you jumped ahead to, um, the question, I always like the last question I always like to ask of an interview e, which is, if you were writing a new book on the future of work, uh, what would you focus on? So you just gave us that answer.
So I think instead I’ll, um, I’ll pivot in a, a different direction and simply say this. Uh, you’ve, you’ve given us a lot to think about, about the sort of things that leaders ought to learn or experiment or embrace in the days ahead. Uh, given everything we’ve talked about and where, in your sense of where the world is going, what are the things you think that leaders are going to need to unlearn around conversations in the next decade, and even more specifically, things that might have served them right up until now?
Um, but that they ought to think about perhaps letting go, uh, in the days ahead, which is, of course, always harder to do than simply picking up something new or letting go of something that’s obviously bad.
Mickey: I think the area of unlearning and new learning is gonna be in the domain of position power.
Peter: Okay.
Say more about that.
Mickey: I think in this drive for. Efficiency and getting things done. Whether you’re a parent with position power over your kids or you’re a CEO, running a complex organization, we tend to use our authority to move things, especially when we feel like we’re pressed for time. And I think we’re getting more and more distributed work, less and less in-person time.
Uh, more and more virtual interaction. The relational issues of whether or not somebody trusts your judgment, believes you’re operating in their best interests, believes it’s safe for them to surrender to your leadership. Those things that position power interferes with, you know that. Well, I’m the CEO and you’re not.
And, and yet people, even with beautiful good intentions under time pressure tend to use their authority to cause something. And that creates compliance, not commitment, which means you need more supervision to actually get the thing to actually happen. And yet, we’re in a time where we’ve tried to make our organizations more lean by getting out layers of supervision and as.
I said, we’re in more virtual environments, we’re seeing each other less often. Each human moment becomes more precious, and if you ruin the possibility of the moment by simply overriding it with your authority, you actually are gonna be creating a wave of waste in the set of relationships that are affected by that.
So I think learning the difference between. My position gives me the chance to learn from and orchestrate the contributions of a lot of people versus my position lets me just tell people what to do and they’re supposed to do it. I think intellectually people know the difference between those two actually getting viscerally, how it shows up day to day, learning to have.
The quality and timing of my connections with people govern our committed action and the amount of supervision it takes to do great things versus use my authority. I think that’s gonna be crucial in this next era of societal and organizational leadership.
Peter: That’s, uh, that’s a great note to end on, and of course, uh, Mickey, that reminds me of, um, uh, an engagement with a, a certain CEO that you and I both know that we worked with.
Uh, I don’t know if you remember when he said this, uh, do you remember this when he said this to us and a group of leaders? He said that, um, bureaucracy and authoritarianism in an organization is what you get when trust is missing.
Mickey: Yes.
Peter: Uh, and it sounds like you, you would probably layer on top of that, um, communication, basic communication skills, and maybe even a little, uh, humility.
So thank you. Uh, absolutely.
Mickey: Absolutely. As
Peter: always, Mickey, thank you for, uh, another, um, interesting conversation and, uh, I look forward to seeing the first chapter of your new book, uh, when you write it. But in the meantime, for everyone else, it’s the Communication Catalyst by Mickey Connolly. And, uh, Richard rhe from, I guess 2015 and even more relevant today.
Thanks a lot and uh, alright. Thanks for the invitation, Colorado.
Mickey: End the conversation.
Peter: Take care. Bye.
Related Content

Technology choices are often made under pressure - pressure to modernize, to respond to shifting client expectations, to demonstrate progress, or to keep pace with rapid advances in AI. In those moments, even experienced leadership teams can fall into familiar traps: over-estimating how differentiated a capability will remain, under-estimating the organizational cost of sustaining it, and committing earlier than the strategy or operating model can realistically support.
After decades of working with leaders through digital and technology-enabled transformations, I’ve seen these dynamics play out again and again. The issue is rarely the quality of the technology itself. It’s the timing of commitment, and how quickly an early decision hardens into something far harder to unwind than anyone intended.
What has changed in today’s AI-accelerated environment is not the nature of these traps, but the margin for error. It has narrowed dramatically.
For small and mid-sized organizations, the consequences are immediate. You don't have specialist teams running parallel experiments or long runways to course correct. A single bad platform decision can absorb scarce capital, distort operating models, and take years to unwind just as the market shifts again.
AI intensified this tension. It is wildly over-hyped as a silver bullet and quietly under-estimated as a structural disruptor. Both positions are dangerous. AI won’t magically fix broken processes or weak strategy, but it will change the economics of how work gets done and where value accrues.
When leaders ask how to approach digital platforms, AI adoption, or operating model design, four questions consistently matter more than the technology itself.
- What specific market problem does this solve, and what is it worth?
- Is this capability genuinely unique, or is it rapidly becoming commoditized?
- What is the true total cost - not just to build, but to run and evolve over time?
- What is the current pace of innovation for this niche?
For many leadership teams, answering these questions leads to the same strategic posture. Move quickly today while preserving options for tomorrow. Not as doctrine, but as a way of staying adaptive without mistaking early commitment for strategic clarity.
Why build versus buy is the wrong starting point
One of the most common traps organizations fall into is treating digital strategy as a series of isolated build-vs-buy decisions. That framing is too narrow, and it usually arrives too late.
A more powerful question is this. How do we preserve optionality as the landscape continues to evolve? Technology decisions often become a proxy for deeper organizational challenges. Following acquisitions or periods of rapid change, pressure frequently surfaces at the front line. Sales teams respond to client feedback. Delivery teams push for speed. Leaders look for visible progress.
In these moments, technology becomes the focal point for action. Not because it is the root problem, but because it is tangible.
The real risk emerges operationally. Poorly sequenced transitions, disruption to the core business, and value that proves smaller or shorter-lived than anticipated. Teams become locked into delivery paths that no longer make commercial sense, while underlying system assumptions remain unchanged.
The issue is rarely technical. It is temporal.
Optimizing for short-term optics, particularly client-facing signals of progress, often comes at the expense of longer-term adaptability. A cleaner interface over an ageing platform may buy temporary parity, but it can also delay the more important work of rethinking what is possible in the near and medium term.
Conservatism often shows up quietly here. Not as risk aversion, but as a preference for extending the familiar rather than exploring what could fundamentally change.
Licensing as a way to buy time and insight
In fast-moving areas such as AI orchestration, many organizations are choosing to license capability rather than build it internally. This is not because licensing is perfect. It rarely is. It introduces constraints and trade-offs. But it was fast. And more importantly, it acknowledged reality.
The pace of change in this space is such that what looks like a good architectural decision today may be actively unhelpful in twelve months. Licensing allowed us to operate right at the edge of what we actually understood at the time - without pretending we knew where the market would land six or twelve months later.
Licensing should not be seen as a lack of ambition. It is often a way of buying time, learning cheaply, and avoiding premature commitment. Building too early doesn’t make you visionary, often it just makes you rigid.
AI is neither a silver bullet nor a feature
Coaching is a useful microcosm of the broader AI debate.
Great AI coaching that is designed with intent and grounded in real coaching methodology can genuinely augment the experience and extend impact. The market is saturated with AI-enabled coaching tools and what is especially disappointing is that many are thin layers of prompts wrapped around a large language model. They are responsive, polite, and superficially impressive - and they largely miss the point.
Effective coaching isn’t about constant responsiveness. It’s about clarity. It’s about bringing experience, structure, credibility, and connection to moments where someone is stuck.
At the other extreme, coaches themselves are often deeply traditional. A heavy pen, a leather-bound notebook, and a Royal Copenhagen mug of coffee are far more likely to be sitting on the desk than the latest GPT or Gemini model.
That conservatism is understandable - coaching is built on trust, presence, and human connection - but it’s increasingly misaligned with how scale and impact are actually created.
The real opportunity for AI is not to replace human work with a chat interface. It is to codify what actually works. The decision points, frameworks, insights, and moments that drive behavior change. AI can then be used to augment and extend that value at scale.
A polished interface over generic capability is not enough. If AI does not strengthen the core value of the work, it is theatre, not transformation.
What this means for leaders
Across all of these examples, the same pattern shows up.
The hardest decisions are rarely about capability, they are about timing, alignment, and conviction.
Building from scratch only makes sense when you can clearly articulate:
- What you believe that the market does not
- Why that belief creates defensible value
- Why you’re willing to concentrate risk behind it
Clear vision scales extraordinarily well when it’s tightly held. The success of narrow, focused Silicon Valley start-ups is testament to that.
Larger organizations often carry a broader set of commitments. That complexity increases when depth of expertise is spread across functions, and even more so when sales teams have significant autonomy at the point of sale. Alignment becomes harder not because people are wrong, but because too many partial truths are competing at once.
In these environments, strategic clarity, not headcount or spend, creates advantage.
This is why many leadership teams choose to license early. Not because building is wrong, but because most organizations have not yet earned the right to build.

At BTS, we’re constantly challenging ourselves to innovate at speed. And right now, it feels like we’re standing at the edge of something massive. The energy? Electric. The velocity? Unprecedented. For many of us, the current pace feels a lot like the early days of the pandemic: disorienting, high-stakes, and somehow exhilarating. And honestly—it should feel that way. Our teams have been tinkering with AI, specifically LLMs, for the past 2.5 years and it has really been in the last eight months that I can see the profound impact it is going to have for our clients, for our services and our operating model.
The opportunity isn’t about the technology. The world has it and it’s getting better by the minute. The issue is people and people’s readiness to adopt it and be re-tooled and re-skilled. It’s about leadership. AI is deeply personal, it’s surgical. In fact, that’s its genius. So, getting full scale adoption of AI, re-tooling everyone in the company by workflow, so that they can invent new services, unlock new customer value, unlock new levels of productivity, even use it for a better life, is the current race. The central question I’ve been wrestling with, alongside our clients and our own teams, is this:
What does AI actually mean for leadership and culture?
And the answer is clearer by the day: AI isn’t just a new toolset. It’s a new mindset. It demands that we rethink how we lead, how we learn, and how we build thriving organizations that can compete, adapt, and grow.
The productivity paradox revisited
Let’s start with the elephant in the boardroom. There’s been a lot of buzz around AI and its promises. But many leaders have quietly wondered: Will any of this actually move the needle? A year ago, we were asking the same thing. We had licenses. We had curiosity. We had early experiments. But the results were modest, a 1% productivity gain here or there. But by April, we were seeing:
- 30–80% productivity gains in software engineering
- 9–12% gains in consulting teams
- 5%-20% improvements in client success and operations
Just as importantly, the innovation unlock and creativity across our platforms due to vibe coding along with new simulation layers, is leading to new value streams for our clients. This isn’t theoretical. It’s not hype. It’s real. The difference? Adoption, ownership, and a shift in how we lead in order to energize the AI innovation within our teams. The challenge now isn’t whether AI creates value. It’s how to unlock and scale that value across teams, geographies, and business units—and do it fast.
Two Superpowers of the Agentic AI Era
In working with leaders across industries, I’ve come to believe in two superpowers (there are more as well) that will unlock the potential of this AI era: Jazz Leadership and a Simulation Culture.
1. Jazz Leadership
Forget the orchestra (although personally I am a big fan.) The successful team cultures that are innovating with AI feel more like jazz. In jazz, there’s no conductor. There’s no fixed sheet music. There are core bars and then musicians make up music on the spot based on each other’s creativity, building off of each other’s trials, riffs and mistakes, build something extraordinary together. This is how experimenting with AI today, in the flow of work, feels like.
For each activity across a workflow, how can new AI prompts, agents, and GPTs make it better, codify high performance, drive speed and quality simultaneously? How can we try something totally different and still get the job done? How might we re-invent how we work? That’s how high-performing teams operate in the AI era. The world is moving too fast for command-and-control leadership, a perfect sheet of music with one leader who is interpreting the sheet music and directing. What we need instead is improvisation, trust, shared authorship, courage and a playful spirit because there are just as many fails as breakthroughs.Jazz leadership is about creating the conditions where:
- Ideas can come from anywhere
- People see tinkering and testing as key to survival and AI failures mean your team is at the edge of what’s possible for your services and ways of working
- Leaders say, “I don’t have all the answers, but I’ll go first, with you”
- People feel “I’m behind relative to my peers in the company” and the company sees this as a good sign because the pace of learning with AI means higher chance of success in the new era
At BTS, we recently promoted five new partners who embody this mindset. They weren’t the most traditional leaders. But they were the most generative. They coached others. They experimented and are constantly re-tooling themselves and others. They inspired movement. They are keeping us ahead, keeping our clients ahead and driving our re-invention. Jazz leaders make teams better, not by directing every note—but by setting the stage for breakthroughs. It is similar to the agile movement, similar to how it felt in Covid as companies had to reinvent themselves. It’s entrepreneurial, chaotic and fun.
2. Simulation Culture
The ability to simulate is a super-power in this next agentic, AI era. Simulation has always been part of creating organizational agility, high performance and leadership excellence. But AI and high-performance computing have transformed it into something bigger, faster, and infinitely more powerful. It means that building a simulation culture is within all of our grasp, if we tap its power.Today, companies simulate:
- Strategic alternatives - from market impact all they way to detailed frontline execution
- New business, new markets and operating models
- Major capital deployment e.g. build a digital twin of a factory before breaking ground
- Initiative implementation
- Workflows current and future
- Jobs to assess for talent and critical role readiness
- Customer conversations and sales enablement motions
With a simulation culture, where you regularly engage in scenario planning and expect preparation and practice as a way of working, billions in capital is saved, cross-functional teams are strengthened, high performance gets institutionalized, win rates increase, earnings and cash flow improves.
Where to get started
Below are a few examples of what leading organizations are doing. Consider testing these in your own organization:
- Conversational AI bot platforms used to scale performance expectations and the company’s unique culture.
- Agentic simulations built into tools so people can prepare and practice with 100% perfect context and not a wasted moment.
- Digital twins of the job created so that certifications and hiring decisions are valid.
- Micro-simulations spun up in hours to align 50,000 people to a shift in the market or a new operational practice.
Final Thoughts
- Lead like a jazz musician. Embrace improvisation, courage and shared creativity.
- Build a simulation culture. Because in a world that’s moving this fast, practice isn’t optional—it’s how we win.
This is a brave new world. Not five years from now. Right now.Let’s shape it—together.

When OpenAI launched GPT-5, the reaction was muted. No flashy new tricks or “wow” demo moment. If you stopped there, you might think nothing’s really changed. But the real story is bigger and far more important for leaders. OpenAI didn’t just release an updated model, they triggered a collapse in the cost of top-tier intelligence across the market. That cost shift will accelerate innovation in ways we’re only beginning to imagine, and it’s happening already. It’s important to note that there are two main ways people and companies use GPT-5.
- Through the ChatGPT app, individuals and teams interact with the AI directly, writing prompts, asking questions, or creating content. It’s plug-and-play, no coding required, and now GPT-5 is the default model even for free users (with some usage caps).
- Through the API, companies connect GPT-5 to their own systems or products so it can power customer support tools, automate large-scale analysis, or run AI features inside other apps.
The headline here is that OpenAI cut GPT-5’s API price to $1.25 per million input tokens and $10 per million output tokens numbers that would have seemed impossible not long ago. In simple terms, tokens are chunks of words. A million tokens of input is roughly 750,000 words, which is the equivalent of several full-length books. “Input tokens” are the text you feed into the model, and “output tokens” are the text it generates in response.
The new API pricing makes a big difference for large-scale, embedded use cases. Companies can now process massive amounts of data, run more experiments, and serve more customers for a fraction of the cost. Workloads that once felt budget-breaking are now affordable, opening the door to AI innovation at an entirely new scale. Combine this new cost structure with the decision to make GPT-5 the default in ChatGPT, and you have a dual shift: high-powered AI is dramatically cheaper for heavy users and instantly accessible to hundreds of millions of people, including your competitors. Intelligence that once required careful budgeting and scarce expertise is now abundant and that abundance changes the game entirely.
When intelligence gets cheap, the game changes
Just a couple of years ago, AI was expensive and resource-intensive, so leaders had to be selective about where and how they applied it:
- Licensing and compute costs were high: Running large models at scale through an API could cost thousands of dollars a month, even for modest use cases.
- Access was limited: The best models were behind higher subscription tiers or enterprise contracts.
- Specialized expertise was needed: Integrating AI often required dedicated data scientists or engineers, which added cost and slowed speed to value.
- Budget trade-offs were constant: Leaders had to choose a few high-priority projects for AI investment and delay or reject others.
In other words, leaders had to ration AI usage just like any other scarce, expensive resource. In a low-cost world, the constraint shifts from budget to imagination. The central question stops being “Can AI do this?” and becomes “How can we reimagine the way we work if this is possible everywhere?”
That’s when innovation accelerates. Experiments that once required hard trade-offs can now be run in parallel, testing ten ideas for the cost of one. AI copilots can quietly monitor, reconcile, and draft decisions in real time, expanding your team’s capacity without adding headcount. Entire archives or research libraries can be parsed in minutes. Intelligence can be embedded into the devices your people already carry, putting expertise within reach at any moment.
Two ways leaders commonly get this wrong
For some, the old assumption still holds: AI feels too expensive or too specialized to deploy widely. Their only exposure has been high-cost pilots, niche specialist teams, or consulting projects where each experiment felt like a big-ticket gamble. That may have been true last year it’s not true today.
For others, the issue isn’t what they say, it’s what their strategy reveals. They’ll tell you they know AI is now cheaper and more accessible but they still budget and resource it like a premium feature. It’s reserved for high-priority initiatives or “innovation” workstreams, rather than being built into core workflows and systems.In both cases, the result is the same: they’re underestimating how radically the playing field has changed. Intelligence is now abundant. The gate is no longer money it’s imagination and execution speed.
The organizations that win will be those that treat AI not as an experimental add-on, but as infrastructure integrated deeply enough that the question isn’t whether to use AI, but how to keep evolving it as the cost curve continues to drop.Strategies built without this shift in mind risk missing opportunities in a competitive landscape that’s already moving forward. The advantage now belongs to those who experiment, learn, and adapt faster than the cost curve drops.
We’d love to help you with your AI strategy: Contact us to get started.

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"La inteligencia artificial no fracasa; lo hace el liderazgo que la gestiona".
Un análisis sobre el papel clave de la visión estratégica, la cultura organizativa y la responsabilidad directiva para convertir la IA en una verdadera ventaja competitiva.
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Isaac Cantalejo, vicepresidente de BTS, reflexiona sobre el verdadero impacto de la inteligencia artificial en las empresas y advierte sobre el exceso de expectativas, poniendo el foco en liderazgo, transformación organizativa y decisiones estratégicas.
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Six top CEOs from varying industries discuss the future of leadership in an AI world.
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