Latest / Fit Happens: Why CEOs and Leaders Succeed or Fail / Your Schedule Is Your Job — How AI Affects Job Fit

Transcript

Your Schedule Is Your Job — How AI Affects Job Fit · Fit Happens: Why CEOs and Leaders Succeed or Fail

• Jason Baumgarten [0:00:00] I'm Jason Baumgarten and you're listening to Fit Happens, the podcast where top leaders, investors and board directors share the stories, surprises and hard earned lessons behind finding the right fit. Let's get into it. A few weeks ago, a senior executive told me something I've not really been able to forget. He said, I'm not actually afraid AI will take my job. I'm afraid it will leave me with only the parts of my job I don't really like. Think about that for a second, because the thing most leaders are bracing for — some level of human replacement, the robot walking in and taking a seat at the table from the dystopian novel — may not be the real risk at all. The real risk from technology can often be subtler and a bit stranger. And by the end of the episode, I'm going to hand you a simple exercise that tells you whether the job you have is turning into the job you never agreed to take. Stay with me, because most people miss the shift even while it's happening to them. I'm Jason Baumgarten and you're listening to Fit Happens, the podcast where top leaders, investors, board directors and experts share their stories, surprises and hard earned lessons behind finding the right fit. Let's get into it. The last time we were together without a guest, I made you do some uncomfortable math. You get roughly 4,000 weeks and most of the middle third of them go to work. And I ended with a warning that something is happening right now. Not in five years, not in some distant future, but right now that is changing. The very fit question for almost every leader today is that episode. That something is the composition of work. Not just where we work, not just how often we're in the office. Not whether we use Zoom or Teams, or whether someone's decided to rename a weekly meeting something more cheerful. I mean the actual substance of it. The substance of what we do. That bundle of tasks, decisions, conversations, judgments, rituals, habits, relationships and little invisible acts that make up a job. The things you do on a random Thursday at 10am. Because here's the catch: the job title can stay exactly the same, while the job underneath it changes quite a bit. You can still be the CFO, but the job may no longer be about reporting controls and capital structure. It may be about the investor narrative and enterprise risk. You can still be the Chief Marketing Officer, but the job shifts from being about the brand and agencies and campaigns to customer data and AI-enabled personalization, or whether the company really understands where demand is going. You can still be the CEO. And even that job may not be the one you accepted. Business cycles are absolutely getting faster. Boards and customers and stakeholders want more visibility. Organizations want more humanity, and everyone wants more technology fluency. And despite all these changes, we still want the quarterly numbers no matter what. So the question is no longer simply, am I in the right role? The question is, is the role still the role I said yes to? Is it still a role I want to say yes to? And if not, what can I do about it? And maybe most importantly of all, am I still fit for what this role, this profession, this area I've chosen is becoming? Now, there's a story from the Industrial Revolution I think is often told quite badly, and it's become almost a part of our vocabulary. You've probably heard of the term the Luddite. Well, we hear about the Luddites as people who hated technology. It's become shorthand for those of us that don't want to set up our Wi-Fi or refuse to update our phone or use them at all. But that's not what the Luddites were reacting to. In early 19th century England, skilled textile workers were watching machines change the economics and, more profoundly, the dignity of what they were doing — their craft. The threat wasn't just that a machine could do something faster. The threat was that the work, the pride, the identity embedded in it, was being broken apart and repriced. The craft, that holistic view, was becoming a set of tasks. And once a craft becomes a set of tasks, someone else gets to decide which parts still require you. And that's where the meaning starts to fall away, where you feel a little lost and where some anxiety can set in. Because for executives, professionals, investors, board members, the next shift is not the robot walking into the room, at least not in the near future. It's really the unbundling of roles we've understood to be fixed — or relatively fixed — over a short period of time. And this won't happen all at once. Some parts of your job will get automated, other parts will get accelerated, some parts will get commoditized, available on automated marketplaces, and other parts become much more valuable. And some parts just, frankly, disappear. The challenge is we don't always know which parts are going to head in which directions. So one day we wake up and our calendar is full, our title's still intact, our compensation is still pretty good, but we just feel a little differently about our job. The work feels different, the fit feels different, and we can't quite name it. It's like having a group of friends for years. And you're looking around at a dinner party one night and you're realizing you're not having great conversations anymore and you feel kind of like a stranger. The realization is the moment you feel the shift, but it's not when that shift probably happens. One of the reasons this is so hard at work is that we talk about work in nouns. We talk about roles. CFO, founder, investor, operator, board director, marketer, customer service leader, computer engineer. But work actually happens in verbs. We persuade, decide, coach, allocate, listen, synthesize, write things down, coordinate, build trust, interpret signals. It's much more of the real stuff of the job. And we often don't think about the tasks inside our jobs. And even more precisely, even if we think about the tasks, we don't think about how much time each one of them really eats up. Do we spend most of our time writing emails? Do we spend most of our time making decisions? Do we spend most of our time analyzing data? What is the bulk of the time — again, in the 4,000 weeks mentality? And this is where some of the academic research becomes quite useful. The best researchers don't ask, will AI replace this job as it is? That's too broad. What they're doing is they're looking at the tasks inside the job, what is exposed. Economists at MIT have written for years about why automation hasn't just erased human labor. Because technology substitutes tasks and complements others. It could remove some areas of drudgery and raise output and increase the value of human judgment in some areas, or common sense. That's a hopeful version. The other one is that the part of your job you gave energy to, that gave you energy, gets automated. And what remains might be some politics and conflict and human mess that, frankly, you're not real excited about. And if the part of your job that you're just adequate at gets automated, while what remains is where you're distinctive, you might actually become more valuable than you were before that technology took hold. That's why the future of work is not just a labor market question. It is, at its core, a fit question — at least when it comes down to the humans involved, as opposed to the labor economists looking at the numbers in aggregate. The same technology change that can make one person's work more meaningful can make another person's work more miserable — same exact job, but two different people. Now, there's a fair amount of doom and gloom out there, and it's a little scary. But I believe two things are true. First, if you're really distinctive at anything, you'll be able to get paid for it. Perhaps in the most classic example of that, there are still about 3,000 people in the horse-drawn carriage industry today, scattered around the world making wagon wheels and buggy seats and all of the components that go into horse-drawn carriages. Now that might be down from the half million or so there were in the mid-1800s, but if you're extraordinary at that craft of buggy making and you love the work and it's your fit, you feel in the flow — the job still exists. You just need to be one of the best, not of the average. So let's talk about generative AI for a moment. Most of the early conversation has been either pretty utopian or pretty apocalyptic. It will free us or replace us. It will write your emails, do your strategy analysis, write a poem for a loved one, and at the same time do your board deck and maybe slip in a kid's college essay. But I probably wouldn't recommend a number of these. The more interesting question to me is what does it do to that lived, day-to-day experience of a role? Now, there were some fascinating studies done on customer support agents using an AI assistant, and the big headline was that productivity went up. What was interesting is that the gains in productivity weren't distributed evenly. A lot of the less experienced, lower-skilled customer service agents benefited far more. The tool captured the patterns of the best performers and made them available to anyone else. Now, this is what call center agents have been doing for decades. The ways in which one agent works gets codified, turned into training and tools and spread to everybody else. That's not new. What's new is the ability to capture those best practices and automate them has become a lot easier. So what that means is that AI doesn't just automate the work. It actually takes these tacit knowledge bases that we've all developed over years, decades, and the things that used to live in the heads of your best people — and it makes those things available to other people in your organization, or possibly in your whole industry. Now, if you run an organization, that might sound wonderful. Faster onboarding, more consistency, less dependence on a few heroes to swoop in and save the day. But if you're one of those high performers, it becomes a harder question. If one of the things that made you really valuable and loved in your role was that pattern recognition, the institutional memory, the tacit knowledge — what happens when the company can bottle you up and drop it into somebody's workflow? Now, interestingly, it doesn't mean that senior people will be less important. In many cases, it may make the very best people more important, because what they're really good at isn't just having some tacit knowledge, but creating it, adapting it, and changing it as the environment changes around them. But it does change what "best" means. The best leader isn't somebody who hoards answers; it's the ones who ask better questions, build trust, detect nonsense a little bit earlier, and know when the AI gets something confidently wrong. And this won't be a minor skill. There's an old line often attributed to Hemingway about bankruptcy happening gradually and then suddenly. I think a lot of career drift works out of fit in the same way. Gradually the role changes, the work shifts, the meetings become different, the organization values different things, and suddenly you're in a job you technically still have, but you don't recognize — and you're not getting a whole lot of satisfaction. I watched this a few years ago. A senior executive had been extraordinary in a prior era of her company — an era they were still in. She had built teams, scaled systems, knew all the customers, knew the rhythm of the business. She had credibility, she had built it over decades. But what she didn't necessarily see was that the competitive environment had changed pretty drastically. The business had become a lot more data-driven, more digital, faster moving. And the questions started to change. The other executives around her weren't smarter; they frankly were just more willing to learn. Maybe not even willing, but they just had to, because they weren't her. They kept trying new things and were a little less proud of how it had always been done. Where she would reach for the expert she knew existed in Building C, they would try to figure out whether the way the expert was doing something was even the right way to do it. And what was interesting is that slowly this executive's great advantage became a huge trap. She kept answering questions with the authority of somebody who had been there for decades, and it was keeping her from actually adapting. And the reality is she was extremely talented and she had contributed to this company's success and people respected her. But it turns out what was needed in the job had changed and she wasn't changing with it. And it was kind of unfair in some ways. She was excellent, but increasingly mismatched. What she needed to do was stop and actually learn new things, not rely on all of that deep institutional tacit knowledge she had. And this is one of the reasons why leaders need to stop asking only what job do I want, but what is the job I want going to turn into? And which parts of it actually matter? Not what title do I seek, but what's the day-to-day work I want to do? And is that stable or changing? Now, to zoom out for a second, this is where boards get into trouble with succession — and I'd argue most of us get into trouble with succession within our roles. We define the next leader by the last job. We write the spec around the current role, the current pain points, the current model. We then say we need somebody who's done this before. And some of the time we're right. But the question is not who's done it before, but who's the right fit for what this job is becoming. And that sounds really small, but one of these ways of doing it selects predominantly for evidence and experience, and the other selects a little bit more for motivation and trajectory. Good succession, good hiring, good career decisions require both. We do need proof somebody can operate at the level required, or at least a pretty good set of evidence that we believe. But we also need to have some creativity and imagination to know whether their strengths and motivation are aimed at the future of the role or at where it was. And this is why I've always been skeptical of these generic competency models. Somebody strategic and collaborative and transformational. Fine, but who's against any of this? The words don't tell you what a leader will actually be doing — often under what conditions, in what mandate, with what people, and at what speed. A leader could be fantastically strategic in a stable environment and reckless in an ambiguous one. It's not the presence of these attractive traits; it's the match between your distinctive capacity and the work in the moment that the company or the organization requires. And right now we're in a place where that work is changing really quickly. Now, McKinsey's research on generative AI made a point worth pausing on: that this wave of change is not confined to routine manual work. It's much deeper into knowledge work — the higher-wage, higher-education jobs. And for a long time, a lot of professionals assumed that automation was something that happened to other people, in factories, call centers, back offices. If you were in a conference room using a laptop, the world felt pretty safe. You traffic in judgment and ambiguity. But that's actually exactly where generative AI has increasingly moved into. Now, it may not replace judgment, but it produces pretty plausible things all day long. And it may actually be better at some of them than you are. So one of the things we need to think about is the value of the human leader. It doesn't disappear, but it's going to shift. It's not going to be about creating that first draft of the speech or even the second. It's going to be about understanding what should be done, what is the prioritization — not about collecting and hoarding information, but actually trying to figure out what information is critical that you're not capturing or that you need. It's going to move away from buzzwords and sounding smart to being right more often, and away from activity and busyness and more about discernment. That word discernment is going to matter enormously, because the more content and options and verbiage a system can produce, the more valuable the person is who can say, hey, that's garbage. This is risky, this is naive, this is buzzwords — and can really dive into what's critical. I was at a meeting recently where someone asked a question about how something was going to work, and the person on the other side of the Zoom call unleashed this torrent of buzzwords. And at the end — I jotted them down — I realized he had no idea what he was talking about. He was like the human equivalent of a bad AI agent. But the reality is that's going to happen at a speed in which it takes more skill to absorb and discern. So there's a historical parallel that I've been coming back to, and this has been written about, but you may not have heard about it. When electricity first entered factories, it didn't really transform productivity. A lot of factories swapped steam engines for electric motors, but kept the same layouts. The big gains came later when managers realized electricity let them redesign the factory itself. The machines didn't need to be clustered around a power source, and the architecture of how things moved around the factory could actually change. The technology was the enabler, but the redesign was the amplifier. And that's where a lot of us are with AI right now. We're putting new tools onto our existing workflows. Our writing is getting a little faster, we're coding more, we're doing research more quickly, and it's useful — but it's not as transformative at the individual and group layer. The question that you have to start asking is, how should I redesign my work, my hours, with this new technology? So go back to the 4,000 weeks and say, how am I spending my time and how should I change it now? I'm not saying drop everything and become a prompt engineer or a forward-deployed engineer. Don't outsource all your decisions to a chatbot. But very practically — what do you do today that you just don't need to do because it doesn't require you? What should you do more of, both for the job and for the fit? What do you love that you want to spend more of your time on? And how can this new technology help you? So remember that executive I talked about earlier? The one who said he wasn't afraid AI would take his job, but only leave him with the parts he didn't like? That was pretty funny and pretty profound. But for many of us, the risk is not replacement — it's that the residue isn't so fun to be around, that so much gets automated that what's left isn't a lot of fun. Now, some people love that stuff, but if it's not you, I think it's really critical to look hard at the work that will remain after the technology does what it can to your job. And what is that remainder? Do you like it? And there's some status hiding in there too. Most of us were rewarded for being the person who knew something, who knew where something was, who had the answer, who could produce the deck faster, remember the precedent, know how it was done before. Write the thing that sounded good. And all of those super valuable abilities may still be there. But in a place where more people can do the things that required years of expertise, you have to think about what your value is — and put that value and the fit of where you want to be spending your time front and center. Now, in my own work in executive search, this changes a little bit how we should evaluate leaders. I don't really want to know that someone's just fluent in AI. That phrase is already too broad to mean anything. I really want to dig into three things. One, whether people can separate whether they're using tools from how they're actually changing their operating models, individually and as a group. Two, can they build trust and help people also take that change into their daily lives? And finally, do they understand what needs to be slowed down, what key decisions need to stay with humans — not automated or sped up. And efficiency is not the same as effectiveness. Bad meetings summarized instantly is still a bad meeting. The question is, can you start using the summaries to say, let's stop having these kinds of meetings? A flawed strategy deck pointed in the wrong direction, even if it's produced faster, is still a terrible idea. If you take a bad culture and put a lot more data and dashboards around it because AI can automate it, it's still a bad, toxic culture. If you're a leader who's using AI to communicate more often, but you still have nothing to say, it's still not helpful. And so AI may speed up a lot of things that are bad. The question is, can you speed up things that are good and use the AI to do more of those things? And this is where fit also becomes really important. We all have a responsibility to ask what kind of work we're creating as the technology changes the terms of the job. Are you using technology and tools to give people time back for higher-value work, or just cranking up the throughput? Are you actually digging into redesign, or just throwing new tools out and asking everyone to use them? The reality is people don't wake up and say, I want to do a terrible job and do things I hate. They want to do things that matter to them. So what do we do with all this? The starting point is to stop treating your job description as the truth. Your calendar is actually closer to the truth. It's those 4,000 weeks back again. And your energy alongside it is closer to an evaluation of whether, for you, the job is giving you what you want. Today, a lot of that might be reactive, but how do you make it more proactive and closer to where things are going? Use your calendar, not value statements or decks. What gets time gets your attention. So here's the exercise I promised you at the top. Take your job as it exists today and start by understanding where your time goes. Then divide it into three columns. Column one: what do you believe will be automated or accelerated — where technology, whether it's AI or frankly something else, will really make that a little bit different? And be honest. Don't protect something just because it's something you feel valuable about. It's important to really put your prediction on paper. In the second column, what do you think is going to be more valuable? Don't just write generic things like, it's human so it'll be more valuable, or it has trust associated with it. In fact, AI can do some of those things even better than humans because it can personalize at a scale that we just can't. But where do things like creativity or ethics or taste matter? And why? Frame the problem. Be really specific. And then column three — what's going to get left over? What's that residue? What's the stuff that doesn't fit neatly into either of the other two categories? Now look at those columns and ask yourself a simple question: do you want that job? Not the job you have today, not the title or the compensation, but that emerging job. Do you want to spend the next 400 weeks doing more of column one, two, or three? Because column one is going to move whether you like it or not. You may not be perfect in your prediction, but frankly, you're probably the most likely to predict it of anyone else. And if you lead an organization, start to do this exercise with your best people. Why? Because if they get stuck in the guck and the residue, what are they going to do? They're going to leave. What are you handing back to them to do? And what's moving away from them? So here's what to carry away from all of this. Column one will move with you or without you. Column two is where your fit should get stronger. And column three is where your fit can frankly die without anyone noticing. Careers drift out of fit gradually and then suddenly. Kind of like the old adage of bankruptcy. So don't wait for the sudden. Find the gradual now. Predict where it's going while you can still do something about it. Anyone who tries to predict the future with AI will certainly be wrong. But what we can do is think about how technology might shift what we're doing and start to plan ahead. Do that contingency planning so you don't find yourself hating everything you're left with. And remember, it's all about where you spend your time. I'm Jason Baumgarten, and this is Fit Happens. Thanks for spending a few moments of your very precious and finite time with me today.