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Welcome to Partnering Leadership, a top global leadership podcast for purpose-driven leaders with a growth mindset.
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Seeking to learn from the leadership journey of change makers and business insights from leading global thinkers.
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For additional leadership insights and bonus content, visit us at partneringleadership.com.
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Now here's your host, Mahan Tavakoli.
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Gleb Saperski, welcome to Partnering Leadership.
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I am thrilled to have you in this conversation with me.
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I'm glad to be back, Mahan.
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What a joy it is, Gleb.
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We had a series of conversations, including for Leadership Greater Washington, where I was court chair at the very beginning of the pandemic.
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Had followed your work before then.
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You've co-authored a book with my dear friend Howard Ross.
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But now this time we want to focus on your newest book, The Psychology of AI Adoption at Work from Resistance to Results, which is of great interest to my audience.
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Before we get to that clip, would like to know a little bit more about you, your upbringing, and how it's helped contribute to who you've become.
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So my dad is Ukrainian, my mom is Moldovan, and my dad moved, it was all part of the Eastern Bloc way back when my dad moved from Ukraine to Moldova and he met my mom there.
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And so I was born there.
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So mixed Ukrainian-Moldovan heritage.
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Then I was 10 years old in 1991, when that part of the world was freed from Russian domination.
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And the Soldapia came to the United States.
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So I grew up in New York City.
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In 1999, that was a time of the big tech boom.
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And so the tech boom, lost companies, webband, pens.com, and so on were booming.
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And then just a couple of years later, the companies went bust.
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And so I observed the people in the Wall Street Journal, people who were praised in the Wall Street Journal, the leaders of these companies, were then criticized in the Wall Street Journal, but they weren't state leaders, they were making decisions in the same way.
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And so that helped me realize that we don't really know what we're talking about, the other people on the Wall Street Journal, about making good leadership decisions.
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So decided to study them.
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And so that's where my expertise comes from.
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How to adapt to the future of work and make good decisions.
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And so, as you said, I've been since that time, I've gotten the page in the topic in the study of behavioral science, UNCODEL.
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I taught there.
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I taught as a professor at Ohio State, while also move lighting for companies and doing consulting and training for companies.
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I've written a whole bunch of books, which we've talked about before.
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Never go with your gut.
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That was the one with Howard, then the one we talked about hybrid remote work, and my newest book on the psychology of AI adoption for resistance results, out with Georgetown University Press.
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It's a peer-reviewed book based on extensive research I've done with over 100 consulting projects, many focus groups, and thousands of survey responses from various consulting projects.
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Before we get to psychology of AI adoption, I wonder what you use or how do you try to determine where the future of work is headed in an environment that is changing so extremely fast.
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So I'm not a futurist, to being very clear.
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Now, I did call the AI arrays pretty early on where I was using AI before it was cool.
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So my previous book, ChatGPT for leaders and content creators, came out in January 2043, which was just a couple of months after ChatGPT was released, because I was using earlier versions of AI tools.
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So I learned how to use them.
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And that book was about usage of AI.
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Usage of AI is technology, how to use it, how leaders and content creators should use it.
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So this book is about the psychology of AI adoption, specifically how to get your teams to adopt it effect.
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But to get to your question, the thing that doesn't change with the future of work is humans.
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The future of work changes, being called the technology that changes as people use it and adopt it.
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But humans don't change.
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People's psychology doesn't change.
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So that's how I focus.
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I focus on the things that don't change because that's where my expertise lies and where leaders are getting a lot of mistakes, making a lot of mistakes.
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I'm focusing way too much on technology, and nearly enough on psychology.
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What were you seeing with clients that told you that the problem is not necessarily with the technology, but the psychology?
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So the really interesting thing is that previous technological disruptions, the future of work, the problem is more than technology.
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And people are leaders are pattern matching to previous technological disruptions and adoption and change management, which doesn't work for AI.
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Let me tell you why.
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Let's say you're adopting new technology, let's say you're adopting something like sales enablement like Salesforce, or a new CRM like HubSun, or a new ERP, so Resource.
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You're adopting a HR management system.
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The difficulty is the technology.
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You need to learn how to use.
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You need to learn which buttons to press.
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You need to learn how it works.
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And that technology is something that people have feel a lot of stress to learn about.
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They feel attached to their ways.
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They don't want to learn anything.
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And so there's a lot of tests and stress and learning, which is involved in change management.
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And so what happens is that the change management that leaders are used to has to do with the difficulty of learning.
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So and the hassle beliefs invest your staff members before associated.
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That's what has been in the past, but it's not the reality of AI.
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AI is actually much easier to learn.
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AI tools, you could ask them yourself.
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You could ask Claude, you can ask ChatGPT, you can ask Copilot, you can ask Gemini how to use it for your work.
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You can figure it out.
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I figured out, I figured out, other people have figured it out.
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It's actually not that hard, not nearly as hard as people make it out to be.
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And so these broad AI platforms are excellent.
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They're very flexible.
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You can use them to do nearly anything that you can do with a computer.
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So nearly all kinds of white collar.
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So that's not the block that people are experiencing.
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There are three big blockers that don't have an management, but it means technology.
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And so leaders are just making mistakes right and left, focusing on technology.
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It's about the psychology.
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So here are the three big blockers around.
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First big blocker is fear and anxiety.
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People aren't afraid of looking up information in a CRM.
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They feel it's a lot of hassle to do that.
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It's annoying, but they're not afraid of it.
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People are afraid of training up AI to replace them.
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And so there's a lot of fear.
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And I talk about that in chapter six of my book, again, based on all this research, where I identify a specific psychographic profile of employees that I call the AI alarmists, for whom this fear is the primary emotional blocker.
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First one.
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Two, the second big blocker is identity.
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Now, people aren't frightened of their identity again by looking up new information in the CRM instead of looking up information at various Excel spreadsheets.
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But they're threatened by a tool which can write a great sales.
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They're threatened by a tool that can actually do a great marketing analysis or a financial report, create a great image, write a great article.
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Those things are threats to their professional identity.
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Let's not talk about identity.
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They replace things that people feel cloud, that people feel bring them meaning.
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And so the people who are less afraid for their jobs, but who feel an identity threat, I call them pragmatic resistors.
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So that's the second psychographic profile.
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And those two big psychographic profiles are what cause people to resist individual AI use.
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Now there's also a third blocker.
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This blocker has to do with a psychographic profile I call the reluctant authors.
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Now, these people, they use AI tools individually and extensibly.
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But they feel ashamed.
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So the shame is the biggest shoe.
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They feel ashamed of talking to their team members about how they use.
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They don't want to talk about how they're using these AI tools to draft their emails, to draft their marketing pitch, to actually go and do a financial analysis for them.
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They don't want to talk about it.
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They feel social sleep around.
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Now, show you an example.
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There's an example.
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Last week you did a training for a peer group, visit group.
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So it's invested by a group of leaders, and then they in its work.
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And so I was doing a training, and during the training, one of the executives said that told me a story about the AI document.
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So he's a CR of a selezable company, and they had the VP of sales present the biggest client they had in the past year.
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And all the context, how they got them, and all the goods that they need to present.
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So previous years took the VP of sales about three days to get the presentation.
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Conduct for the operation, do the PowerPoint and so on.
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This year, the VP of Sales gave it to his executive assistant.
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And the executive assistant put together an hour and a half in using the actions.
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And that's their presentation, which felt great.
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The VP of Sales was praised by the other executives to whom he presented it.
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The VP of Sales met with the CEO and told the CEO that, hey, it usually takes me about three days to this time.
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Don't tell any of the other executives because they'll think I'm cheating and I waste.
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And so that's the kind of culture that takes place at many companies.
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And that's at the top level.
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Of course, everyone else does the same thing and follows the same pattern.
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So this is a serious problem where it results in AI tools having individual adoption.
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And people are individually productive, but they don't tell their supervisors about them, they don't tell their team members, and they spend their individual time, additional time gone and phased person.
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And so the company doesn't see the benefit from the AI tools.
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And so for all of these reasons, all of these recent graphic profiles, the AI alarmists, the fear of anxiety, the identity fed with the pragmatic resistors, and the reluctant adopters with the shame.
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That's why you have the MIT study recently showing that something like 95% of AI islands don't show return on investment.
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And so that's a serious issue.
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And so these are the kinds of blockers that leaders are paying attention to because they're too focused on the technology and not nearly nothing.
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You mentioned the fear that exists in some.
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I also wonder whether the narrative that people hear about AI contributes to that fear.
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There isn't a week that goes by where Dario Amade of Anthropic or Sam Altman of OpenAI don't talk about how it is going to make the vast majority of knowledge workers redundant.
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So is there some logic behind that fear?
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Or is it that these people are particularly more fearsome and shouldn't be?
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Of course there's logic behind the fear.
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We recently, just on August 12th of this year, we had an update to the Stanford Canaries and the Coal Mines things.
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It's a peer-reviewed study that look at what's happening with the job market.
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And what they found a year ago in June 2026 or June 2025, they found that AI-exposed occupations, in Bay exposed occupations, young people, 22 to 45 specifically, had 50% less employment ability compared to people in non-AI exposed occupations, young people that's thinking about then that's a year ago, that they updated the study, and now they found that the rate is 90%.
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So it's growing.
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Well, from 13% to 19%.
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But of course, then it's going to keep growing.
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And so right now, young people are the ones whose careers are being disrupted, and we're seeing that differently.
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Now, a recent letter, not from people like Dario Alabunde or Sam Oldman who have an interest in this topic, but from over 200 economists or objective observers, 36 Nobel laureates, they said that in the next several years there will be massive AI-caused job displays.
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People have reasons.
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And of course, we have all these headlines talking about tech companies that are laying people off.
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Definitely people have reasons to be afraid.
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Especially right now, young people have reasons, but everyone has reasons to be afraid.
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So in order to address these reasons, leaders can take concrete steps.
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And so here is what I see works for AI alarmists.
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We can talk about the other two groups later.
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But for AI alarmists, what works is two things.
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So first is to say that, hey, we're not going to fire anyone because we're going to get more productivity due to AI.
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Instead, what we're going to do is focus on growth.
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So not cost-cutting, but growth.
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And we have recent peer-reviewed research came out just a couple of months ago showing that companies that are adopting AI effectively compared to other companies in the same industry are growing 10% quicker.
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10% quicker head count-wise.
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They're taking market share away basically from companies that are effectively adopting AI.
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Now those companies are also, of course, growing top line and bottom line revenue faster than they're growing their head.
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So they need less people to do the same amount of work, and that's showing up in the results.
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But they're not firing.
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And so it's happening.
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Where are people being fired?
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Where are people being laid off?
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They're being laid off, but companies aren't adopting beta effectively and that are losing market share.
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So it's not the companies that are adopting effectively that they're losing people.
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People are being fired.
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It's the companies that aren't adopting better effectively that are being well.
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So that's one.
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You can make commitments to grow.
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And so one thing that I strongly encourage leaders of companies I work with to make commitments to is that focusing on growth rather than cost-creating.
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And let's commitment that the large majority of companies can make, not everyone, but that's certainly something that I strongly encourage.
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Two is something that all companies can commit to, which is that if you find employees, if you use AI effectively, if you learn how to use AI effectively, and if you build tools that make you more effective, your job will be safe because you will be much more productive and effective.
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And so that's something that all companies can and should make.
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So you want to flip the script for these AI belong lists.
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Instead of thinking that AI adoption means that you will lose your job, you need to have them understand that non-adopting AI at the company level and at the individual level will mean jobs.
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So talking about the company-wide level.
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I think now everyone that listens to this podcast and anyone in the working environment knows that they're most likely not going to be spending their career in one organization.
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Therefore, having marketable skills becomes critical, and this provides an opportunity for people to develop more of those marketable skills.
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So that addresses some of the fear that people have in the organization.
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Now, you also mentioned the second one is identity, and it is a challenge.
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I know you also mentioned a case study of a law firm as well.
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I had conversations with law firms in the DC region where some attorneys are somewhat hesitant, not because they don't trust the AI, not because they don't think the output is good.
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Because in the back of their mind, they say, wait a minute, now in a couple of minutes, I'm getting done what would have taken me and my junior associates hours or days.
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What is the value I bring to it?
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The second part of it is the way they have monetized the value that they bring to clients, which has been on an hourly basis in many law firms measuring every six minutes and billing it to a specific client.
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So how can you tackle that identity when so much of the work that is being done both contributes to the sense of value the person believes they're bringing in, on the other end, to the way they are compensated based on how they bill clients?
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So when I work with law firms, one of the conversations changing the billing to clients.
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So I've talked about that specific one before I'm getting the back identity.
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And what I strongly advise them to do is change to value-based billing.
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So to the extent that they can make packages and change to a package-based billing system so that they're going to be aligned with client priorities.
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And so they can still fulfill their work and they can do their function and they can use AI effectively, and the client will be charged overall less than they would have been if they're on working on an hourly basis on without AI.
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So that's in terms of billing.
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We could talk about that more in Probably Professional Services.
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Now, going more deeply to the identity credit issue, what I see as really important is not focusing on using AI for the things that people feel attached to and what gives them meaning in their job.
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So instead of doing what might be the most effective thing from the company perspective, like asking having a survey of, oh, what are your most high-valued tasks that we can automate?
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Instead, you need to have a survey.
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So when I start working with a law firm or any other company, I do a survey on, hey, what are the tasks you eat the most?
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Why do you have to look up a whole bunch of information from a variety of systems to put together to do the stuff that you want to do?
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What do you find really annoying about your daily?
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And so then using those tasks, I prepare a library of prompts and agents that people could then be trained on.
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And I train people on those prompts and those agents which are focused specifically on the tasks that they find the most annoying, the most frustrating, where they gather information from all these systems to do what they really want.
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Now, this is not in the short term the most productive thing for the company.
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But that's where you have to have the psychological judo move on going slow to go fast.
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Because then people will actually adopt these.
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They want to not be doing things that are frustrating and annoying.
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They want an AI tool to do it for.
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And the AI tools are incredibly adaptive.
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You could adapt them to do the things that people love to do or the things that people hate to do.
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And if you start with the things that people hate to do, people will then, let's say, in their psychology, they will then adopt these tools and they will start using these tools.
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Now, eventually, just once they learn how to use these tools and how victim they are, of course they'll be using them for other things, including things that they're excited about.
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But in the short term, you want to go slow to go fast.
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You want to have those tools especially and specifically aligned with things that people need to do.
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And that's how you address the identity threat.
00:21:21.279 --> 00:21:24.480
That's how you address the spragmatic resistors and get them on bolt.
00:21:25.039 --> 00:21:51.200
I love your approach, Gleb, because most specifically, you are centering it on how to meet the needs of the people within the organization who are a critical stakeholder in this process, rather than just jumping to the outcomes or the tools that can produce those outcomes, which a lot of organizations do.
00:21:51.440 --> 00:21:53.279
I am very outcome focused.
00:21:53.440 --> 00:21:54.319
I recommend that.
00:21:54.559 --> 00:21:59.759
However, that's also why then people get in the way of.
00:22:00.160 --> 00:22:02.960
Of the organization actually achieving that outcome.
00:22:03.279 --> 00:22:07.200
You also mentioned the reluctant adopters.
00:22:07.440 --> 00:22:13.599
There's a significant use of shadow AI in organizations.
00:22:14.240 --> 00:22:16.880
Sometimes it bubbles up to the surface.
00:22:17.119 --> 00:22:35.359
I was speaking to a CEO about a month and a half back, and he said there is almost not a single week that goes by when someone presents a brilliant strategic set of PowerPoints to me where when I ask them a question, they have no idea what's behind it.
00:22:35.519 --> 00:22:35.839
Yeah.
00:22:36.160 --> 00:22:40.000
Because the AI tools provide the opportunity to create those.
00:22:40.319 --> 00:22:51.359
So how have you seen that approached by organizations where people are more willing to embrace it and share the best practices with each other?
00:22:51.839 --> 00:22:55.119
So the first step is leadership modeling.
00:22:55.359 --> 00:23:15.440
So instead of the CEO agreeing with that executive, what I talked about in the group is that the CEO should have told the executive that, hey, what you need to do is march back out to the other executives and tell them that you used AI to create and you saved yourself so much.
00:23:15.599 --> 00:23:18.400
And of course, the executive knows all the strategy behind it.
00:23:18.559 --> 00:23:22.400
So it's not like that situation where you present the strategy and don't know it.
00:23:22.559 --> 00:23:23.839
He knows it in and out.
00:23:24.000 --> 00:23:27.279
He was the one who did the problem.
00:23:27.839 --> 00:23:31.359
He was the one who got the client and so on, managed the process.
00:23:31.680 --> 00:23:46.640
But it's much better to use his time to go out and get this year's next biggest client rather than spend three days on creating this PowerPoint presentation to present to these other folks, the other executives.
00:23:46.880 --> 00:23:55.759
So leadership modeling, and that needs to be a conversation that the VP of Sales has with the other executives and with all members of his team and be proud.
00:23:56.240 --> 00:23:58.240
So focusing on leadership tasks.
00:23:58.319 --> 00:24:02.559
Like the thing that leaders, for example, hate to do is performance evaluation.
00:24:02.720 --> 00:24:38.160
So instead of having the leader just think about what the last model was of somebody's performance and evaluate them based on that, which is realistically what happens in the annual performance evaluation, a much more effective task is having the AI look through the past year of deliverables by someone and then create a port based on those deliverables, which then the executive combines with everything that's not in the email and other systems that the AI has access to, and then creates the performance evaluation.
00:24:38.400 --> 00:24:41.119
So that is much more effective.
00:24:41.279 --> 00:25:00.559
And so that's another example of where the leader should talk to his team members or her team members about how they use the AI tool to do the performance evaluation and how it's great for everyone that they did, because now they can have a more objective and less biased performance evaluation that's weighted toward the last month of performance, right?
00:25:00.799 --> 00:25:02.240
And so that's great.
00:25:02.319 --> 00:25:03.599
And the leader needs to map.
00:25:03.759 --> 00:25:05.519
So that's the first leadership model.
00:25:06.240 --> 00:25:07.039
That's one.
00:25:07.359 --> 00:25:09.440
Two, I talked about the trade.
00:25:09.759 --> 00:25:15.359
So you want to do the training based on the things that people hate most.
00:25:15.599 --> 00:25:24.960
And that provides a basis for everyone agreeing to change the social norm to do at least those things using AI tools.
00:25:25.200 --> 00:25:35.599
And so you already get that change of base, the baseline of at least these activities will do using AI tools.
00:25:35.759 --> 00:25:44.400
And so now your people could talk about it because we have training, and everyone would agree that yes, that would be good to use the AI tools to do those things that we can eat.
00:25:44.720 --> 00:25:46.880
Let's do and three.
00:25:47.119 --> 00:25:55.440
You need to set up an asynchronous communication channel where everyone shares about new users they're using for AI.
00:25:55.680 --> 00:26:01.279
A Teams channel, a Slack channel, other venues, show card where you share about it.
00:26:01.440 --> 00:26:06.559
Now it's great if you could have a synchronous as well, meaning like a weekly meeting where you talk about it.
00:26:06.720 --> 00:26:10.079
But I recognize that's not something that all companies can do.
00:26:10.240 --> 00:26:13.359
So synchronous if possible, but at least asynchronous.
00:26:13.519 --> 00:26:15.039
So something everyone can do.
00:26:15.279 --> 00:26:26.160
Where the leader needs to actively praise and reward people who come up with innovations that save them time or make get them to be more effective in their work.
00:26:26.400 --> 00:26:28.079
So needs active praise.
00:26:28.400 --> 00:26:36.799
And ideally, it's a kind of reward bonus or something like that, if this is the tool that other people can use for their own.
00:26:37.759 --> 00:26:40.240
So this creates culture.
00:26:40.400 --> 00:26:48.880
You're shifting up culture to making it more acceptable to use AI for a wide variety of cases that weren't covered in the initial trick.
00:26:49.200 --> 00:27:05.039
So that's the pre-combination things that help address the reluctant adopters and help address sense of shame, leadership modeling, and getting over that shame and stigma there, the believership, the believer feels, and sharing it with others.
00:27:05.279 --> 00:27:12.960
Then the training, and finally asynchronous, deal with synchronous, but at least asynchronous communication, whether you're warned and praise from the leader.
00:27:13.440 --> 00:27:17.599
That's a great way of setting the right culture within the organization.
00:27:17.920 --> 00:27:28.400
Now, one of the things you also mention that I have seen is that middle managers are critical to this process.
00:27:28.640 --> 00:27:41.440
How have you seen middle managers play the most effective role in maintaining this AI experimentation and use as part of the culture of the organization?
00:27:42.160 --> 00:27:49.839
All three of these cases, middle managers need to be the ones who transmit and model these issues to all of their employees.
00:27:50.079 --> 00:27:58.079
So the executive might the course for the AI artists telling it's about communication, right?
00:27:58.319 --> 00:28:01.839
Telling them that, hey, will I be fired at anyone because we're using AI?
00:28:02.240 --> 00:28:11.680
We'll be focusing on growth, and anyone who uses AI effectively, they will be the ones who will be promoted and they'll be the ones who will be successful.
00:28:12.000 --> 00:28:16.880
But the team members, the staffers, need to really hear it from their supervisors.
00:28:17.039 --> 00:28:20.400
So they hear that safe messaging needs to be consistent.
00:28:20.559 --> 00:28:22.480
And so that's going to be really important.
00:28:22.880 --> 00:28:24.960
Then, two, the trainings.
00:28:25.200 --> 00:28:33.359
The training is going to come first, the middle managers need to be trained on using AI tools, and they need to train their teams and using AI tools.
00:28:33.440 --> 00:28:37.200
And so that's going to address the clegmatic resistors.
00:28:37.519 --> 00:28:42.720
And finally, for the shaman stigma, that modeling needs to come, of course, from middle managers.
00:28:42.799 --> 00:28:46.400
So executives will be modeling for everyone.
00:28:46.640 --> 00:28:52.000
But the middle managers will be real role models for their staffers.
00:28:52.160 --> 00:29:02.960
And they will be the ones who, in that Slack channel or Teams channel, they'll be praising, rewarding their staffers for AI use cases.
00:29:03.119 --> 00:29:14.000
So the middle managers need to be doing all the things of AI adoption, at least separately from their own AI skills that they need to develop, which we talk about separately.
00:29:14.160 --> 00:29:17.359
But you're asking about those three problematic those three profiles.
00:29:17.440 --> 00:29:23.440
So that's what they need to do regarding those three profiles to ensure effective AI adoption for a company.
00:29:23.920 --> 00:29:25.599
I appreciate that, Gleb.
00:29:25.680 --> 00:29:33.839
And you share a lot more thoughts with respect to how to get AI adoption within the organization.
00:29:34.079 --> 00:29:48.559
Now, is it possible for AI to make individuals in the organization more productive while weakening their ability to determine whether the output is any good or not?
00:29:49.039 --> 00:29:51.039
I think there are a couple of things that are going on.
00:29:51.279 --> 00:30:09.839
So one thing, this was published in a recent Harvard Business Review article for research by a company called Betherop, found that something like two hours a month is wasted by colleagues fixing problematic output from their colleagues.
00:30:10.079 --> 00:30:12.799
And that's what they call AI work slaw.
00:30:12.960 --> 00:30:16.480
So two hours per month and the$296 on average per company.
00:30:16.559 --> 00:30:18.319
So it obviously adds up.
00:30:18.559 --> 00:30:20.160
Now, what happens?
00:30:20.319 --> 00:30:22.079
Why is there AI work slaw?
00:30:22.240 --> 00:30:23.039
What's the problem?
00:30:23.200 --> 00:30:24.000
What's the cause?
00:30:24.240 --> 00:30:26.720
One is malicious compliance.
00:30:27.039 --> 00:30:31.519
So you have people who are the photic resistors and the AI alarmists.
00:30:31.759 --> 00:30:33.759
They don't want to use AI.
00:30:34.079 --> 00:30:39.839
And if they're really forced to use AI, they use it in a malicious compliance fashion.
00:30:40.079 --> 00:30:44.480
Meaning they just press the AI button and then they get the output.
00:30:44.559 --> 00:30:49.599
They don't check it, they don't evaluate it, they just send it downward, and they're like, okay, I did what you told me to do.
00:30:49.680 --> 00:30:51.200
I use the AI tool.
00:30:51.440 --> 00:30:54.400
And then you get what you pay for with these people.
00:30:54.960 --> 00:30:59.119
And so that's the results, this AI works like.
00:30:59.440 --> 00:31:02.000
So that results in a lot of problems.
00:31:02.240 --> 00:31:14.079
And that malicious compliance is a major reason why the 95% figure they took out, where 95% of AI pilots don't scale, they don't show it to our animals.
00:31:14.240 --> 00:31:14.960
So that's one thing.
00:31:15.279 --> 00:31:22.319
Now, the second problem is that the young people who are coming up through drinks, they're not taking footprint.
00:31:22.640 --> 00:31:28.160
They're teaching these young people is not focusing on the right things.
00:31:28.400 --> 00:31:35.200
Because the future of the workplace is not doing the initial generation of content.
00:31:36.079 --> 00:31:37.519
It's called generative AI.
00:31:37.599 --> 00:31:45.440
It's able to generate reports, it's able to generate writing, it's able to generate financial analysis, images, whatever you want it to do.
00:31:45.680 --> 00:31:52.559
The skill of the future is going to be evaluating the production of AI tools.
00:31:52.720 --> 00:31:54.160
And they're not being taught that.
00:31:54.240 --> 00:31:56.240
They're just being taught the initial generation.
00:31:56.559 --> 00:32:01.839
And that's very problematic because that's not the future of these skill sets.
00:32:02.000 --> 00:32:09.920
So that has to do with how young people are being taught and trained, even within companies, but especially in the educational system.
00:32:10.160 --> 00:32:21.839
So this is a big problem in the educational system and even within apprenticeships, because within apprenticeships, you know, they're still being taught in the old ways, they're not really being taught in how to use AI tools.
00:32:21.920 --> 00:32:27.440
So right now, what you're seeing is that AI tools are creating content fast and fast.
00:32:27.839 --> 00:32:33.599
Young people don't know how to evaluate the outcome of AI tools very effectively.
00:32:33.759 --> 00:32:42.400
And because previously they would have been evaluating the content of the tool during the process of creation as they're creating the content.
00:32:42.799 --> 00:32:51.680
But they don't have nearly as much experience in evaluating the outcomes you and I do, because we've seen outcomes that are good, outcomes that are bad.
00:32:51.839 --> 00:32:53.759
They've seen many fewer outcomes.
00:32:54.000 --> 00:32:59.599
And so they're not taught the skills of evaluating the outcomes, which is what they really should be taught to do.
00:32:59.839 --> 00:33:01.759
And so that is the second type of problem.
00:33:02.559 --> 00:33:11.680
Is that something that is trained that you can go in a new form of college or training for the company and gain?
00:33:11.920 --> 00:33:17.039
Or is it something that comes based on repeated exposure and experience?
00:33:17.359 --> 00:33:18.720
Of course, it could be trained.
00:33:18.960 --> 00:33:21.359
Right now, they're being trained on creating.
00:33:21.440 --> 00:33:25.759
But um, I think that that is not something that is going to be useful in the future.
00:33:26.000 --> 00:33:35.920
Instead, what they need to be trained on is here's the ideal final output, here's the context, here's how you edit it, here's how you use the AI tool to create it and edit it.
00:33:36.079 --> 00:33:37.359
Of course, it can be trained.
00:33:37.599 --> 00:33:42.960
And certainly something that people can be trained on, but they're not currently being trained on.
00:33:43.039 --> 00:33:47.839
The educational system is very much behind, much further than companies, on AI adoption.
00:33:48.000 --> 00:33:58.720
So I think this is something that leaders need to integrate into their onboarding programs and into their educational programs into their apprenticeships within a company.
00:33:58.880 --> 00:34:04.000
Right now, the onboarding and apprenticeship programs, they're not well set up either.
00:34:04.160 --> 00:34:10.000
So even companies are not really integrating AI into their apprenticeship and onboarding.
00:34:10.079 --> 00:34:11.760
And this needs to be something companies.
00:34:12.320 --> 00:34:19.280
Do you also argue, Gleb, that there is no final state of AI maturity?
00:34:19.519 --> 00:34:30.639
So if there is no stable destination, in your view, what organizational and leadership capabilities should we be trying to build?
00:34:31.199 --> 00:34:41.039
Well, it's prosaic to say we need to build continuous learning, but we definitely need to focus on continuous learning much more with AI tools than with previous technology.
00:34:41.199 --> 00:35:01.679
Because the future, it's not here right now, but the future in the next several years, here is something I can very definitively forecast because this is something that I'm very much seeing with companies that are at the forefront of AI adoption, is every individual staff member who will be at the company will not be working on individual tasks.
00:35:01.840 --> 00:35:06.559
Instead, they'll be managing agents who are working with individual tasks.
00:35:06.800 --> 00:35:10.880
So every individual will be a supervisor of AI agents.
00:35:11.039 --> 00:35:19.679
And they will be building AI agents customized to their specific workflows, which replace the individual tasks that they're doing right now.
00:35:19.920 --> 00:35:24.960
And so that is the skill of the future that people really need to learn.
00:35:25.280 --> 00:35:38.320
Managers, by contrast, will be managing both a set of agents that they're using themselves, like they talked about, performance evaluate agent, and they will be managing people who are managing agents.
00:35:38.480 --> 00:35:48.559
And so they need to be able to ensure that those agents are doing handoffs from one employee to another effectively, that they're coordinating with each other effectively.
00:35:48.719 --> 00:35:52.000
That's a different skill that again managers currently have.
00:35:52.239 --> 00:35:56.480
And so managers, middle managers, need to learn those skills as well.
00:35:56.639 --> 00:36:02.559
And so there's a set of skills that employees need to learn, a set of skills that all managers need to learn.
00:36:02.800 --> 00:36:09.119
And as agents get better and better, people need to learn how to improve their current agents.
00:36:09.360 --> 00:36:23.039
Managers also need to learn how to improve their current agents and how to make sure their employees are learning about the kind of agents, the key, the capabilities and about increasing agents so that they're becoming more and more productive over time.
00:36:23.280 --> 00:36:26.559
So that's going to be the future in the next few years.
00:36:26.800 --> 00:36:32.719
And this is something that people need to focus on if they want to be successful in our increasingly AI disrupted world.
00:36:33.199 --> 00:36:34.559
It is incredible, Gleb.
00:36:34.719 --> 00:36:53.199
And we could spend hours talking about how managing an AI agent and how you ask for outcomes and set boundaries for an AI agent would be dramatically different than managing an individual that has a certain level of judgment.
00:36:53.440 --> 00:36:55.599
There's a lot that goes into that.
00:36:55.760 --> 00:37:10.559
So it's almost like a new set of management capabilities and skills that both individuals that have AI agents working for them, or the managers that have AI agents as well need to learn.
00:37:10.800 --> 00:37:18.400
Now, one other thing I wanted to touch on, Gleb, is there's so much with respect to AI that you can be doing.
00:37:18.559 --> 00:37:24.079
And one of the main things that I hear from people is like, I'm overwhelmed.
00:37:24.320 --> 00:37:27.199
I can understand how it can be overwhelming.
00:37:27.519 --> 00:37:41.679
So, in your view, how should leaders decide where they should lead, where they should experiment, where they should follow quickly, where they should wait, and how should they determine that?
00:37:42.400 --> 00:37:49.840
There's no reason I brought up the performance evaluation as an example, because that's a task that when they talk to leaders, it's the one that they hate the most.
00:37:50.079 --> 00:37:55.280
It's the one that they feel the most annoyed by, it's the one that they feel they want to get off their plate as much as us.
00:37:55.599 --> 00:37:58.159
So best of it's supposed to just focusing on it.
00:37:58.320 --> 00:38:02.159
Again, it has to do with psychology, it has to do with human motivation.
00:38:02.320 --> 00:38:08.079
You'll be most motivated yourself to focus on the work on the tasks that you hate the most.
00:38:08.239 --> 00:38:10.000
You will want to get that off your plate.
00:38:10.079 --> 00:38:12.079
Your employees will want to get that off your place.
00:38:12.400 --> 00:38:13.920
And so that's what you should focus on.
00:38:14.159 --> 00:38:22.239
Look at the tasks that you hate the most that you want to get off your plate, because AI can do pretty much anything that you can do with the computer.
00:38:22.480 --> 00:38:30.400
And so you want to use AI for the task, the biggest hassle and the biggest trouble in your day.
00:38:30.559 --> 00:38:32.559
And so focus on those tasks.
00:38:32.719 --> 00:38:36.639
Focus on getting rid of those tasks, focus on getting them off your plate.
00:38:36.880 --> 00:38:45.519
And once you know how to use AI tools effectively, you'll be able to make your own judgment about the other tasks, the ones that bring the highest value.
00:38:45.760 --> 00:38:52.159
But you want to start and focus early onward on those tasks that you hate, that your people have.
00:38:52.559 --> 00:38:54.960
And that way you'll go slow to go fast.
00:38:55.119 --> 00:38:56.719
Because remember, it's not about the technology.
00:38:56.960 --> 00:38:58.079
Technology is great.
00:38:58.239 --> 00:38:59.599
It's about the technology.
00:38:59.920 --> 00:39:02.159
That's a brilliant way of putting it, Gleb.
00:39:02.239 --> 00:39:11.840
And it connects to a Gallup survey I saw today, which at first can sound counterintuitive until we think about the point that you made.
00:39:12.239 --> 00:39:33.360
Where, as compared to organizations that haven't been as AI forward, the AI forward organizations seem to have both people's satisfaction had gone up and their dissatisfaction had gone up on average.
00:39:33.599 --> 00:39:36.639
And the reason is this was average across different companies.
00:39:36.800 --> 00:39:55.280
I would submit if we dig into the data, we would see that the ones that have done what you've talked about, which is approached it as a people-centric focus, their people are happier as a result of AI implementation.
00:39:55.519 --> 00:40:04.800
The ones that have purely focused on productivity for the benefit of the organization, the dissatisfaction has gone up.
00:40:05.039 --> 00:40:14.639
So I love the perspective you bring to it, which is a great way to bring people along and help them want to embrace this change.
00:40:14.880 --> 00:40:20.639
So, Gleb, for the audience to find your book and follow your work, where would you send them to?
00:40:21.119 --> 00:40:24.719
If you want to find me, the easiest way is LinkedIn.
00:40:24.800 --> 00:40:26.400
So I'm very available there.
00:40:26.559 --> 00:40:36.320
Just make sure to tell me when you reach out to me that you heard me on the partner leadership podcast because I get way too much spam and I'm not getting accepted otherwise.
00:40:36.480 --> 00:40:44.559
Now, for my book and my brother work, go to my website, designsteravoidance experts.com forward slash AI book.
00:40:44.800 --> 00:40:47.280
You'll find the for the book itself.
00:40:47.440 --> 00:40:52.400
You can get the Barnes Noble at Amazon, books, five bookstores everywhere, as people say.
00:40:52.559 --> 00:40:58.719
So it's out now, George Town University Press, Theater View, like I said, visually published, high quality.
00:40:59.039 --> 00:41:05.920
Or if you want to get a free introduction and a chapter of the book, you can go to the website that I mentioned before.
00:41:06.159 --> 00:41:10.320
Disasteravoidanceexperts.com forward slash AI book.
00:41:10.480 --> 00:41:13.360
So get book sample with a directional chapter.
00:41:13.599 --> 00:41:17.440
Disasteravoidance experts.com forward slash AI book.
00:41:17.599 --> 00:41:34.320
And if you already got the copy of the book pre-ordered or ordered, just put the receipt number from any bookstore into the website, and you'll get a free assist on adopting AI in your company and a manual on the civic mistakes that leaders make in AI adoptions.
00:41:34.559 --> 00:41:40.800
Again, that's going to be at disasteravoidance experts.com forward slash AI book.
00:41:41.599 --> 00:41:50.559
I appreciate the conversation with you, Gleb, and your book, The Psychology of AI Adoption at Work from Resistance to Results.
00:41:50.719 --> 00:41:52.639
Thank you so much for joining me, Dr.
00:41:52.719 --> 00:41:54.320
Gleb Zapersky.
00:41:54.960 --> 00:41:57.280
Thank you again, Van Han, for having me on the camp.
00:41:57.360 --> 00:41:59.039
It's been a pleasure to have another conversation.
00:42:00.800 --> 00:42:05.039
You have been listening to partnering leadership with your host, Mahan Tavakoli.
00:42:05.679 --> 00:42:21.280
If you enjoyed this episode, please leave a rating and review of the podcast on your favorite podcasting app and forward the conversation to a friend or colleague so you can help more people discover their purpose, grow professionally with meaning, and have a greater impact.
00:42:21.519 --> 00:42:27.679
For additional leadership insights and bonus content, visit us at partneringleadership.com.
00:00:01.760 --> 00:00:08.480
Welcome to Partnering Leadership, a top global leadership podcast for purpose-driven leaders with a growth mindset.
00:00:08.720 --> 00:00:15.039
Seeking to learn from the leadership journey of change makers and business insights from leading global thinkers.
00:00:15.279 --> 00:00:21.359
For additional leadership insights and bonus content, visit us at partneringleadership.com.
00:00:21.679 --> 00:00:24.239
Now here's your host, Mahan Tavakoli.
00:00:29.280 --> 00:00:32.079
Gleb Saperski, welcome to Partnering Leadership.
00:00:32.159 --> 00:00:34.399
I am thrilled to have you in this conversation with me.
00:00:34.640 --> 00:00:35.920
I'm glad to be back, Mahan.
00:00:36.399 --> 00:00:38.479
What a joy it is, Gleb.
00:00:38.880 --> 00:00:50.000
We had a series of conversations, including for Leadership Greater Washington, where I was court chair at the very beginning of the pandemic.
00:00:50.240 --> 00:00:52.159
Had followed your work before then.
00:00:52.240 --> 00:00:56.399
You've co-authored a book with my dear friend Howard Ross.
00:00:56.799 --> 00:01:08.480
But now this time we want to focus on your newest book, The Psychology of AI Adoption at Work from Resistance to Results, which is of great interest to my audience.
00:01:08.640 --> 00:01:16.640
Before we get to that clip, would like to know a little bit more about you, your upbringing, and how it's helped contribute to who you've become.
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So my dad is Ukrainian, my mom is Moldovan, and my dad moved, it was all part of the Eastern Bloc way back when my dad moved from Ukraine to Moldova and he met my mom there.
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And so I was born there.
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So mixed Ukrainian-Moldovan heritage.
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Then I was 10 years old in 1991, when that part of the world was freed from Russian domination.
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And the Soldapia came to the United States.
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So I grew up in New York City.
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In 1999, that was a time of the big tech boom.
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And so the tech boom, lost companies, webband, pens.com, and so on were booming.
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And then just a couple of years later, the companies went bust.
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And so I observed the people in the Wall Street Journal, people who were praised in the Wall Street Journal, the leaders of these companies, were then criticized in the Wall Street Journal, but they weren't state leaders, they were making decisions in the same way.
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And so that helped me realize that we don't really know what we're talking about, the other people on the Wall Street Journal, about making good leadership decisions.
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So decided to study them.
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And so that's where my expertise comes from.
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How to adapt to the future of work and make good decisions.
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And so, as you said, I've been since that time, I've gotten the page in the topic in the study of behavioral science, UNCODEL.
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I taught there.
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I taught as a professor at Ohio State, while also move lighting for companies and doing consulting and training for companies.
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I've written a whole bunch of books, which we've talked about before.
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Never go with your gut.
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That was the one with Howard, then the one we talked about hybrid remote work, and my newest book on the psychology of AI adoption for resistance results, out with Georgetown University Press.
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It's a peer-reviewed book based on extensive research I've done with over 100 consulting projects, many focus groups, and thousands of survey responses from various consulting projects.
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Before we get to psychology of AI adoption, I wonder what you use or how do you try to determine where the future of work is headed in an environment that is changing so extremely fast.
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So I'm not a futurist, to being very clear.
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Now, I did call the AI arrays pretty early on where I was using AI before it was cool.
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So my previous book, ChatGPT for leaders and content creators, came out in January 2043, which was just a couple of months after ChatGPT was released, because I was using earlier versions of AI tools.
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So I learned how to use them.
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And that book was about usage of AI.
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Usage of AI is technology, how to use it, how leaders and content creators should use it.
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So this book is about the psychology of AI adoption, specifically how to get your teams to adopt it effect.
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But to get to your question, the thing that doesn't change with the future of work is humans.
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The future of work changes, being called the technology that changes as people use it and adopt it.
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But humans don't change.
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People's psychology doesn't change.
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So that's how I focus.
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I focus on the things that don't change because that's where my expertise lies and where leaders are getting a lot of mistakes, making a lot of mistakes.
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I'm focusing way too much on technology, and nearly enough on psychology.
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What were you seeing with clients that told you that the problem is not necessarily with the technology, but the psychology?
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So the really interesting thing is that previous technological disruptions, the future of work, the problem is more than technology.
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And people are leaders are pattern matching to previous technological disruptions and adoption and change management, which doesn't work for AI.
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Let me tell you why.
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Let's say you're adopting new technology, let's say you're adopting something like sales enablement like Salesforce, or a new CRM like HubSun, or a new ERP, so Resource.
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You're adopting a HR management system.
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The difficulty is the technology.
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You need to learn how to use.
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You need to learn which buttons to press.
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You need to learn how it works.
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And that technology is something that people have feel a lot of stress to learn about.
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They feel attached to their ways.
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They don't want to learn anything.
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And so there's a lot of tests and stress and learning, which is involved in change management.
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And so what happens is that the change management that leaders are used to has to do with the difficulty of learning.
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So and the hassle beliefs invest your staff members before associated.
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That's what has been in the past, but it's not the reality of AI.
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AI is actually much easier to learn.
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AI tools, you could ask them yourself.
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You could ask Claude, you can ask ChatGPT, you can ask Copilot, you can ask Gemini how to use it for your work.
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You can figure it out.
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I figured out, I figured out, other people have figured it out.
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It's actually not that hard, not nearly as hard as people make it out to be.
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And so these broad AI platforms are excellent.
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They're very flexible.
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You can use them to do nearly anything that you can do with a computer.
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So nearly all kinds of white collar.
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So that's not the block that people are experiencing.
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There are three big blockers that don't have an management, but it means technology.
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And so leaders are just making mistakes right and left, focusing on technology.
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It's about the psychology.
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So here are the three big blockers around.
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First big blocker is fear and anxiety.
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People aren't afraid of looking up information in a CRM.
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They feel it's a lot of hassle to do that.
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It's annoying, but they're not afraid of it.
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People are afraid of training up AI to replace them.
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And so there's a lot of fear.
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And I talk about that in chapter six of my book, again, based on all this research, where I identify a specific psychographic profile of employees that I call the AI alarmists, for whom this fear is the primary emotional blocker.
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First one.
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Two, the second big blocker is identity.
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Now, people aren't frightened of their identity again by looking up new information in the CRM instead of looking up information at various Excel spreadsheets.
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But they're threatened by a tool which can write a great sales.
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They're threatened by a tool that can actually do a great marketing analysis or a financial report, create a great image, write a great article.
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Those things are threats to their professional identity.
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Let's not talk about identity.
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They replace things that people feel cloud, that people feel bring them meaning.
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And so the people who are less afraid for their jobs, but who feel an identity threat, I call them pragmatic resistors.
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So that's the second psychographic profile.
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And those two big psychographic profiles are what cause people to resist individual AI use.
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Now there's also a third blocker.
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This blocker has to do with a psychographic profile I call the reluctant authors.
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Now, these people, they use AI tools individually and extensibly.
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But they feel ashamed.
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So the shame is the biggest shoe.
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They feel ashamed of talking to their team members about how they use.
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They don't want to talk about how they're using these AI tools to draft their emails, to draft their marketing pitch, to actually go and do a financial analysis for them.
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They don't want to talk about it.
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They feel social sleep around.
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Now, show you an example.
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There's an example.
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Last week you did a training for a peer group, visit group.
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So it's invested by a group of leaders, and then they in its work.
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And so I was doing a training, and during the training, one of the executives said that told me a story about the AI document.
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So he's a CR of a selezable company, and they had the VP of sales present the biggest client they had in the past year.
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And all the context, how they got them, and all the goods that they need to present.
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So previous years took the VP of sales about three days to get the presentation.
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Conduct for the operation, do the PowerPoint and so on.
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This year, the VP of Sales gave it to his executive assistant.
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And the executive assistant put together an hour and a half in using the actions.
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And that's their presentation, which felt great.
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The VP of Sales was praised by the other executives to whom he presented it.
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The VP of Sales met with the CEO and told the CEO that, hey, it usually takes me about three days to this time.
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Don't tell any of the other executives because they'll think I'm cheating and I waste.
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And so that's the kind of culture that takes place at many companies.
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And that's at the top level.
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Of course, everyone else does the same thing and follows the same pattern.
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So this is a serious problem where it results in AI tools having individual adoption.
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And people are individually productive, but they don't tell their supervisors about them, they don't tell their team members, and they spend their individual time, additional time gone and phased person.
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And so the company doesn't see the benefit from the AI tools.
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And so for all of these reasons, all of these recent graphic profiles, the AI alarmists, the fear of anxiety, the identity fed with the pragmatic resistors, and the reluctant adopters with the shame.
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That's why you have the MIT study recently showing that something like 95% of AI islands don't show return on investment.
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And so that's a serious issue.
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And so these are the kinds of blockers that leaders are paying attention to because they're too focused on the technology and not nearly nothing.
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You mentioned the fear that exists in some.
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I also wonder whether the narrative that people hear about AI contributes to that fear.
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There isn't a week that goes by where Dario Amade of Anthropic or Sam Altman of OpenAI don't talk about how it is going to make the vast majority of knowledge workers redundant.
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So is there some logic behind that fear?
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Or is it that these people are particularly more fearsome and shouldn't be?
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Of course there's logic behind the fear.
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We recently, just on August 12th of this year, we had an update to the Stanford Canaries and the Coal Mines things.
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It's a peer-reviewed study that look at what's happening with the job market.
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And what they found a year ago in June 2026 or June 2025, they found that AI-exposed occupations, in Bay exposed occupations, young people, 22 to 45 specifically, had 50% less employment ability compared to people in non-AI exposed occupations, young people that's thinking about then that's a year ago, that they updated the study, and now they found that the rate is 90%.
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So it's growing.
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Well, from 13% to 19%.
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But of course, then it's going to keep growing.
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And so right now, young people are the ones whose careers are being disrupted, and we're seeing that differently.
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Now, a recent letter, not from people like Dario Alabunde or Sam Oldman who have an interest in this topic, but from over 200 economists or objective observers, 36 Nobel laureates, they said that in the next several years there will be massive AI-caused job displays.
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People have reasons.
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And of course, we have all these headlines talking about tech companies that are laying people off.
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Definitely people have reasons to be afraid.
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Especially right now, young people have reasons, but everyone has reasons to be afraid.
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So in order to address these reasons, leaders can take concrete steps.
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And so here is what I see works for AI alarmists.
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We can talk about the other two groups later.
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But for AI alarmists, what works is two things.
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So first is to say that, hey, we're not going to fire anyone because we're going to get more productivity due to AI.
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Instead, what we're going to do is focus on growth.
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So not cost-cutting, but growth.
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And we have recent peer-reviewed research came out just a couple of months ago showing that companies that are adopting AI effectively compared to other companies in the same industry are growing 10% quicker.
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10% quicker head count-wise.
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They're taking market share away basically from companies that are effectively adopting AI.
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Now those companies are also, of course, growing top line and bottom line revenue faster than they're growing their head.
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So they need less people to do the same amount of work, and that's showing up in the results.
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But they're not firing.
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And so it's happening.
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Where are people being fired?
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Where are people being laid off?
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They're being laid off, but companies aren't adopting beta effectively and that are losing market share.
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So it's not the companies that are adopting effectively that they're losing people.
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People are being fired.
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It's the companies that aren't adopting better effectively that are being well.
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So that's one.
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You can make commitments to grow.
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And so one thing that I strongly encourage leaders of companies I work with to make commitments to is that focusing on growth rather than cost-creating.
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And let's commitment that the large majority of companies can make, not everyone, but that's certainly something that I strongly encourage.
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Two is something that all companies can commit to, which is that if you find employees, if you use AI effectively, if you learn how to use AI effectively, and if you build tools that make you more effective, your job will be safe because you will be much more productive and effective.
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And so that's something that all companies can and should make.
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So you want to flip the script for these AI belong lists.
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Instead of thinking that AI adoption means that you will lose your job, you need to have them understand that non-adopting AI at the company level and at the individual level will mean jobs.
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So talking about the company-wide level.
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I think now everyone that listens to this podcast and anyone in the working environment knows that they're most likely not going to be spending their career in one organization.
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Therefore, having marketable skills becomes critical, and this provides an opportunity for people to develop more of those marketable skills.
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So that addresses some of the fear that people have in the organization.
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Now, you also mentioned the second one is identity, and it is a challenge.
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I know you also mentioned a case study of a law firm as well.
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I had conversations with law firms in the DC region where some attorneys are somewhat hesitant, not because they don't trust the AI, not because they don't think the output is good.
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Because in the back of their mind, they say, wait a minute, now in a couple of minutes, I'm getting done what would have taken me and my junior associates hours or days.
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What is the value I bring to it?
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The second part of it is the way they have monetized the value that they bring to clients, which has been on an hourly basis in many law firms measuring every six minutes and billing it to a specific client.
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So how can you tackle that identity when so much of the work that is being done both contributes to the sense of value the person believes they're bringing in, on the other end, to the way they are compensated based on how they bill clients?
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So when I work with law firms, one of the conversations changing the billing to clients.
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So I've talked about that specific one before I'm getting the back identity.
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And what I strongly advise them to do is change to value-based billing.
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So to the extent that they can make packages and change to a package-based billing system so that they're going to be aligned with client priorities.
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And so they can still fulfill their work and they can do their function and they can use AI effectively, and the client will be charged overall less than they would have been if they're on working on an hourly basis on without AI.
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So that's in terms of billing.
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We could talk about that more in Probably Professional Services.
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Now, going more deeply to the identity credit issue, what I see as really important is not focusing on using AI for the things that people feel attached to and what gives them meaning in their job.
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So instead of doing what might be the most effective thing from the company perspective, like asking having a survey of, oh, what are your most high-valued tasks that we can automate?
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Instead, you need to have a survey.
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So when I start working with a law firm or any other company, I do a survey on, hey, what are the tasks you eat the most?
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Why do you have to look up a whole bunch of information from a variety of systems to put together to do the stuff that you want to do?
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What do you find really annoying about your daily?
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And so then using those tasks, I prepare a library of prompts and agents that people could then be trained on.
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And I train people on those prompts and those agents which are focused specifically on the tasks that they find the most annoying, the most frustrating, where they gather information from all these systems to do what they really want.
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Now, this is not in the short term the most productive thing for the company.
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But that's where you have to have the psychological judo move on going slow to go fast.
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Because then people will actually adopt these.
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They want to not be doing things that are frustrating and annoying.
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They want an AI tool to do it for.
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And the AI tools are incredibly adaptive.
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You could adapt them to do the things that people love to do or the things that people hate to do.
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And if you start with the things that people hate to do, people will then, let's say, in their psychology, they will then adopt these tools and they will start using these tools.
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Now, eventually, just once they learn how to use these tools and how victim they are, of course they'll be using them for other things, including things that they're excited about.
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But in the short term, you want to go slow to go fast.
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You want to have those tools especially and specifically aligned with things that people need to do.
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And that's how you address the identity threat.
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That's how you address the spragmatic resistors and get them on bolt.
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I love your approach, Gleb, because most specifically, you are centering it on how to meet the needs of the people within the organization who are a critical stakeholder in this process, rather than just jumping to the outcomes or the tools that can produce those outcomes, which a lot of organizations do.
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I am very outcome focused.
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I recommend that.
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However, that's also why then people get in the way of.
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Of the organization actually achieving that outcome.
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You also mentioned the reluctant adopters.
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There's a significant use of shadow AI in organizations.
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Sometimes it bubbles up to the surface.
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I was speaking to a CEO about a month and a half back, and he said there is almost not a single week that goes by when someone presents a brilliant strategic set of PowerPoints to me where when I ask them a question, they have no idea what's behind it.
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Yeah.
00:22:36.160 --> 00:22:40.000
Because the AI tools provide the opportunity to create those.
00:22:40.319 --> 00:22:51.359
So how have you seen that approached by organizations where people are more willing to embrace it and share the best practices with each other?
00:22:51.839 --> 00:22:55.119
So the first step is leadership modeling.
00:22:55.359 --> 00:23:15.440
So instead of the CEO agreeing with that executive, what I talked about in the group is that the CEO should have told the executive that, hey, what you need to do is march back out to the other executives and tell them that you used AI to create and you saved yourself so much.
00:23:15.599 --> 00:23:18.400
And of course, the executive knows all the strategy behind it.
00:23:18.559 --> 00:23:22.400
So it's not like that situation where you present the strategy and don't know it.
00:23:22.559 --> 00:23:23.839
He knows it in and out.
00:23:24.000 --> 00:23:27.279
He was the one who did the problem.
00:23:27.839 --> 00:23:31.359
He was the one who got the client and so on, managed the process.
00:23:31.680 --> 00:23:46.640
But it's much better to use his time to go out and get this year's next biggest client rather than spend three days on creating this PowerPoint presentation to present to these other folks, the other executives.
00:23:46.880 --> 00:23:55.759
So leadership modeling, and that needs to be a conversation that the VP of Sales has with the other executives and with all members of his team and be proud.
00:23:56.240 --> 00:23:58.240
So focusing on leadership tasks.
00:23:58.319 --> 00:24:02.559
Like the thing that leaders, for example, hate to do is performance evaluation.
00:24:02.720 --> 00:24:38.160
So instead of having the leader just think about what the last model was of somebody's performance and evaluate them based on that, which is realistically what happens in the annual performance evaluation, a much more effective task is having the AI look through the past year of deliverables by someone and then create a port based on those deliverables, which then the executive combines with everything that's not in the email and other systems that the AI has access to, and then creates the performance evaluation.
00:24:38.400 --> 00:24:41.119
So that is much more effective.
00:24:41.279 --> 00:25:00.559
And so that's another example of where the leader should talk to his team members or her team members about how they use the AI tool to do the performance evaluation and how it's great for everyone that they did, because now they can have a more objective and less biased performance evaluation that's weighted toward the last month of performance, right?
00:25:00.799 --> 00:25:02.240
And so that's great.
00:25:02.319 --> 00:25:03.599
And the leader needs to map.
00:25:03.759 --> 00:25:05.519
So that's the first leadership model.
00:25:06.240 --> 00:25:07.039
That's one.
00:25:07.359 --> 00:25:09.440
Two, I talked about the trade.
00:25:09.759 --> 00:25:15.359
So you want to do the training based on the things that people hate most.
00:25:15.599 --> 00:25:24.960
And that provides a basis for everyone agreeing to change the social norm to do at least those things using AI tools.
00:25:25.200 --> 00:25:35.599
And so you already get that change of base, the baseline of at least these activities will do using AI tools.
00:25:35.759 --> 00:25:44.400
And so now your people could talk about it because we have training, and everyone would agree that yes, that would be good to use the AI tools to do those things that we can eat.
00:25:44.720 --> 00:25:46.880
Let's do and three.
00:25:47.119 --> 00:25:55.440
You need to set up an asynchronous communication channel where everyone shares about new users they're using for AI.
00:25:55.680 --> 00:26:01.279
A Teams channel, a Slack channel, other venues, show card where you share about it.
00:26:01.440 --> 00:26:06.559
Now it's great if you could have a synchronous as well, meaning like a weekly meeting where you talk about it.
00:26:06.720 --> 00:26:10.079
But I recognize that's not something that all companies can do.
00:26:10.240 --> 00:26:13.359
So synchronous if possible, but at least asynchronous.
00:26:13.519 --> 00:26:15.039
So something everyone can do.
00:26:15.279 --> 00:26:26.160
Where the leader needs to actively praise and reward people who come up with innovations that save them time or make get them to be more effective in their work.
00:26:26.400 --> 00:26:28.079
So needs active praise.
00:26:28.400 --> 00:26:36.799
And ideally, it's a kind of reward bonus or something like that, if this is the tool that other people can use for their own.
00:26:37.759 --> 00:26:40.240
So this creates culture.
00:26:40.400 --> 00:26:48.880
You're shifting up culture to making it more acceptable to use AI for a wide variety of cases that weren't covered in the initial trick.
00:26:49.200 --> 00:27:05.039
So that's the pre-combination things that help address the reluctant adopters and help address sense of shame, leadership modeling, and getting over that shame and stigma there, the believership, the believer feels, and sharing it with others.
00:27:05.279 --> 00:27:12.960
Then the training, and finally asynchronous, deal with synchronous, but at least asynchronous communication, whether you're warned and praise from the leader.
00:27:13.440 --> 00:27:17.599
That's a great way of setting the right culture within the organization.
00:27:17.920 --> 00:27:28.400
Now, one of the things you also mention that I have seen is that middle managers are critical to this process.
00:27:28.640 --> 00:27:41.440
How have you seen middle managers play the most effective role in maintaining this AI experimentation and use as part of the culture of the organization?
00:27:42.160 --> 00:27:49.839
All three of these cases, middle managers need to be the ones who transmit and model these issues to all of their employees.
00:27:50.079 --> 00:27:58.079
So the executive might the course for the AI artists telling it's about communication, right?
00:27:58.319 --> 00:28:01.839
Telling them that, hey, will I be fired at anyone because we're using AI?
00:28:02.240 --> 00:28:11.680
We'll be focusing on growth, and anyone who uses AI effectively, they will be the ones who will be promoted and they'll be the ones who will be successful.
00:28:12.000 --> 00:28:16.880
But the team members, the staffers, need to really hear it from their supervisors.
00:28:17.039 --> 00:28:20.400
So they hear that safe messaging needs to be consistent.
00:28:20.559 --> 00:28:22.480
And so that's going to be really important.
00:28:22.880 --> 00:28:24.960
Then, two, the trainings.
00:28:25.200 --> 00:28:33.359
The training is going to come first, the middle managers need to be trained on using AI tools, and they need to train their teams and using AI tools.
00:28:33.440 --> 00:28:37.200
And so that's going to address the clegmatic resistors.
00:28:37.519 --> 00:28:42.720
And finally, for the shaman stigma, that modeling needs to come, of course, from middle managers.
00:28:42.799 --> 00:28:46.400
So executives will be modeling for everyone.
00:28:46.640 --> 00:28:52.000
But the middle managers will be real role models for their staffers.
00:28:52.160 --> 00:29:02.960
And they will be the ones who, in that Slack channel or Teams channel, they'll be praising, rewarding their staffers for AI use cases.
00:29:03.119 --> 00:29:14.000
So the middle managers need to be doing all the things of AI adoption, at least separately from their own AI skills that they need to develop, which we talk about separately.
00:29:14.160 --> 00:29:17.359
But you're asking about those three problematic those three profiles.
00:29:17.440 --> 00:29:23.440
So that's what they need to do regarding those three profiles to ensure effective AI adoption for a company.
00:29:23.920 --> 00:29:25.599
I appreciate that, Gleb.
00:29:25.680 --> 00:29:33.839
And you share a lot more thoughts with respect to how to get AI adoption within the organization.
00:29:34.079 --> 00:29:48.559
Now, is it possible for AI to make individuals in the organization more productive while weakening their ability to determine whether the output is any good or not?
00:29:49.039 --> 00:29:51.039
I think there are a couple of things that are going on.
00:29:51.279 --> 00:30:09.839
So one thing, this was published in a recent Harvard Business Review article for research by a company called Betherop, found that something like two hours a month is wasted by colleagues fixing problematic output from their colleagues.
00:30:10.079 --> 00:30:12.799
And that's what they call AI work slaw.
00:30:12.960 --> 00:30:16.480
So two hours per month and the$296 on average per company.
00:30:16.559 --> 00:30:18.319
So it obviously adds up.
00:30:18.559 --> 00:30:20.160
Now, what happens?
00:30:20.319 --> 00:30:22.079
Why is there AI work slaw?
00:30:22.240 --> 00:30:23.039
What's the problem?
00:30:23.200 --> 00:30:24.000
What's the cause?
00:30:24.240 --> 00:30:26.720
One is malicious compliance.
00:30:27.039 --> 00:30:31.519
So you have people who are the photic resistors and the AI alarmists.
00:30:31.759 --> 00:30:33.759
They don't want to use AI.
00:30:34.079 --> 00:30:39.839
And if they're really forced to use AI, they use it in a malicious compliance fashion.
00:30:40.079 --> 00:30:44.480
Meaning they just press the AI button and then they get the output.
00:30:44.559 --> 00:30:49.599
They don't check it, they don't evaluate it, they just send it downward, and they're like, okay, I did what you told me to do.
00:30:49.680 --> 00:30:51.200
I use the AI tool.
00:30:51.440 --> 00:30:54.400
And then you get what you pay for with these people.
00:30:54.960 --> 00:30:59.119
And so that's the results, this AI works like.
00:30:59.440 --> 00:31:02.000
So that results in a lot of problems.
00:31:02.240 --> 00:31:14.079
And that malicious compliance is a major reason why the 95% figure they took out, where 95% of AI pilots don't scale, they don't show it to our animals.
00:31:14.240 --> 00:31:14.960
So that's one thing.
00:31:15.279 --> 00:31:22.319
Now, the second problem is that the young people who are coming up through drinks, they're not taking footprint.
00:31:22.640 --> 00:31:28.160
They're teaching these young people is not focusing on the right things.
00:31:28.400 --> 00:31:35.200
Because the future of the workplace is not doing the initial generation of content.
00:31:36.079 --> 00:31:37.519
It's called generative AI.
00:31:37.599 --> 00:31:45.440
It's able to generate reports, it's able to generate writing, it's able to generate financial analysis, images, whatever you want it to do.
00:31:45.680 --> 00:31:52.559
The skill of the future is going to be evaluating the production of AI tools.
00:31:52.720 --> 00:31:54.160
And they're not being taught that.
00:31:54.240 --> 00:31:56.240
They're just being taught the initial generation.
00:31:56.559 --> 00:32:01.839
And that's very problematic because that's not the future of these skill sets.
00:32:02.000 --> 00:32:09.920
So that has to do with how young people are being taught and trained, even within companies, but especially in the educational system.
00:32:10.160 --> 00:32:21.839
So this is a big problem in the educational system and even within apprenticeships, because within apprenticeships, you know, they're still being taught in the old ways, they're not really being taught in how to use AI tools.
00:32:21.920 --> 00:32:27.440
So right now, what you're seeing is that AI tools are creating content fast and fast.
00:32:27.839 --> 00:32:33.599
Young people don't know how to evaluate the outcome of AI tools very effectively.
00:32:33.759 --> 00:32:42.400
And because previously they would have been evaluating the content of the tool during the process of creation as they're creating the content.
00:32:42.799 --> 00:32:51.680
But they don't have nearly as much experience in evaluating the outcomes you and I do, because we've seen outcomes that are good, outcomes that are bad.
00:32:51.839 --> 00:32:53.759
They've seen many fewer outcomes.
00:32:54.000 --> 00:32:59.599
And so they're not taught the skills of evaluating the outcomes, which is what they really should be taught to do.
00:32:59.839 --> 00:33:01.759
And so that is the second type of problem.
00:33:02.559 --> 00:33:11.680
Is that something that is trained that you can go in a new form of college or training for the company and gain?
00:33:11.920 --> 00:33:17.039
Or is it something that comes based on repeated exposure and experience?
00:33:17.359 --> 00:33:18.720
Of course, it could be trained.
00:33:18.960 --> 00:33:21.359
Right now, they're being trained on creating.
00:33:21.440 --> 00:33:25.759
But um, I think that that is not something that is going to be useful in the future.
00:33:26.000 --> 00:33:35.920
Instead, what they need to be trained on is here's the ideal final output, here's the context, here's how you edit it, here's how you use the AI tool to create it and edit it.
00:33:36.079 --> 00:33:37.359
Of course, it can be trained.
00:33:37.599 --> 00:33:42.960
And certainly something that people can be trained on, but they're not currently being trained on.
00:33:43.039 --> 00:33:47.839
The educational system is very much behind, much further than companies, on AI adoption.
00:33:48.000 --> 00:33:58.720
So I think this is something that leaders need to integrate into their onboarding programs and into their educational programs into their apprenticeships within a company.
00:33:58.880 --> 00:34:04.000
Right now, the onboarding and apprenticeship programs, they're not well set up either.
00:34:04.160 --> 00:34:10.000
So even companies are not really integrating AI into their apprenticeship and onboarding.
00:34:10.079 --> 00:34:11.760
And this needs to be something companies.
00:34:12.320 --> 00:34:19.280
Do you also argue, Gleb, that there is no final state of AI maturity?
00:34:19.519 --> 00:34:30.639
So if there is no stable destination, in your view, what organizational and leadership capabilities should we be trying to build?
00:34:31.199 --> 00:34:41.039
Well, it's prosaic to say we need to build continuous learning, but we definitely need to focus on continuous learning much more with AI tools than with previous technology.
00:34:41.199 --> 00:35:01.679
Because the future, it's not here right now, but the future in the next several years, here is something I can very definitively forecast because this is something that I'm very much seeing with companies that are at the forefront of AI adoption, is every individual staff member who will be at the company will not be working on individual tasks.
00:35:01.840 --> 00:35:06.559
Instead, they'll be managing agents who are working with individual tasks.
00:35:06.800 --> 00:35:10.880
So every individual will be a supervisor of AI agents.
00:35:11.039 --> 00:35:19.679
And they will be building AI agents customized to their specific workflows, which replace the individual tasks that they're doing right now.
00:35:19.920 --> 00:35:24.960
And so that is the skill of the future that people really need to learn.
00:35:25.280 --> 00:35:38.320
Managers, by contrast, will be managing both a set of agents that they're using themselves, like they talked about, performance evaluate agent, and they will be managing people who are managing agents.
00:35:38.480 --> 00:35:48.559
And so they need to be able to ensure that those agents are doing handoffs from one employee to another effectively, that they're coordinating with each other effectively.
00:35:48.719 --> 00:35:52.000
That's a different skill that again managers currently have.
00:35:52.239 --> 00:35:56.480
And so managers, middle managers, need to learn those skills as well.
00:35:56.639 --> 00:36:02.559
And so there's a set of skills that employees need to learn, a set of skills that all managers need to learn.
00:36:02.800 --> 00:36:09.119
And as agents get better and better, people need to learn how to improve their current agents.
00:36:09.360 --> 00:36:23.039
Managers also need to learn how to improve their current agents and how to make sure their employees are learning about the kind of agents, the key, the capabilities and about increasing agents so that they're becoming more and more productive over time.
00:36:23.280 --> 00:36:26.559
So that's going to be the future in the next few years.
00:36:26.800 --> 00:36:32.719
And this is something that people need to focus on if they want to be successful in our increasingly AI disrupted world.
00:36:33.199 --> 00:36:34.559
It is incredible, Gleb.
00:36:34.719 --> 00:36:53.199
And we could spend hours talking about how managing an AI agent and how you ask for outcomes and set boundaries for an AI agent would be dramatically different than managing an individual that has a certain level of judgment.
00:36:53.440 --> 00:36:55.599
There's a lot that goes into that.
00:36:55.760 --> 00:37:10.559
So it's almost like a new set of management capabilities and skills that both individuals that have AI agents working for them, or the managers that have AI agents as well need to learn.
00:37:10.800 --> 00:37:18.400
Now, one other thing I wanted to touch on, Gleb, is there's so much with respect to AI that you can be doing.
00:37:18.559 --> 00:37:24.079
And one of the main things that I hear from people is like, I'm overwhelmed.
00:37:24.320 --> 00:37:27.199
I can understand how it can be overwhelming.
00:37:27.519 --> 00:37:41.679
So, in your view, how should leaders decide where they should lead, where they should experiment, where they should follow quickly, where they should wait, and how should they determine that?
00:37:42.400 --> 00:37:49.840
There's no reason I brought up the performance evaluation as an example, because that's a task that when they talk to leaders, it's the one that they hate the most.
00:37:50.079 --> 00:37:55.280
It's the one that they feel the most annoyed by, it's the one that they feel they want to get off their plate as much as us.
00:37:55.599 --> 00:37:58.159
So best of it's supposed to just focusing on it.
00:37:58.320 --> 00:38:02.159
Again, it has to do with psychology, it has to do with human motivation.
00:38:02.320 --> 00:38:08.079
You'll be most motivated yourself to focus on the work on the tasks that you hate the most.
00:38:08.239 --> 00:38:10.000
You will want to get that off your plate.
00:38:10.079 --> 00:38:12.079
Your employees will want to get that off your place.
00:38:12.400 --> 00:38:13.920
And so that's what you should focus on.
00:38:14.159 --> 00:38:22.239
Look at the tasks that you hate the most that you want to get off your plate, because AI can do pretty much anything that you can do with the computer.
00:38:22.480 --> 00:38:30.400
And so you want to use AI for the task, the biggest hassle and the biggest trouble in your day.
00:38:30.559 --> 00:38:32.559
And so focus on those tasks.
00:38:32.719 --> 00:38:36.639
Focus on getting rid of those tasks, focus on getting them off your plate.
00:38:36.880 --> 00:38:45.519
And once you know how to use AI tools effectively, you'll be able to make your own judgment about the other tasks, the ones that bring the highest value.
00:38:45.760 --> 00:38:52.159
But you want to start and focus early onward on those tasks that you hate, that your people have.
00:38:52.559 --> 00:38:54.960
And that way you'll go slow to go fast.
00:38:55.119 --> 00:38:56.719
Because remember, it's not about the technology.
00:38:56.960 --> 00:38:58.079
Technology is great.
00:38:58.239 --> 00:38:59.599
It's about the technology.
00:38:59.920 --> 00:39:02.159
That's a brilliant way of putting it, Gleb.
00:39:02.239 --> 00:39:11.840
And it connects to a Gallup survey I saw today, which at first can sound counterintuitive until we think about the point that you made.
00:39:12.239 --> 00:39:33.360
Where, as compared to organizations that haven't been as AI forward, the AI forward organizations seem to have both people's satisfaction had gone up and their dissatisfaction had gone up on average.
00:39:33.599 --> 00:39:36.639
And the reason is this was average across different companies.
00:39:36.800 --> 00:39:55.280
I would submit if we dig into the data, we would see that the ones that have done what you've talked about, which is approached it as a people-centric focus, their people are happier as a result of AI implementation.
00:39:55.519 --> 00:40:04.800
The ones that have purely focused on productivity for the benefit of the organization, the dissatisfaction has gone up.
00:40:05.039 --> 00:40:14.639
So I love the perspective you bring to it, which is a great way to bring people along and help them want to embrace this change.
00:40:14.880 --> 00:40:20.639
So, Gleb, for the audience to find your book and follow your work, where would you send them to?
00:40:21.119 --> 00:40:24.719
If you want to find me, the easiest way is LinkedIn.
00:40:24.800 --> 00:40:26.400
So I'm very available there.
00:40:26.559 --> 00:40:36.320
Just make sure to tell me when you reach out to me that you heard me on the partner leadership podcast because I get way too much spam and I'm not getting accepted otherwise.
00:40:36.480 --> 00:40:44.559
Now, for my book and my brother work, go to my website, designsteravoidance experts.com forward slash AI book.
00:40:44.800 --> 00:40:47.280
You'll find the for the book itself.
00:40:47.440 --> 00:40:52.400
You can get the Barnes Noble at Amazon, books, five bookstores everywhere, as people say.
00:40:52.559 --> 00:40:58.719
So it's out now, George Town University Press, Theater View, like I said, visually published, high quality.
00:40:59.039 --> 00:41:05.920
Or if you want to get a free introduction and a chapter of the book, you can go to the website that I mentioned before.
00:41:06.159 --> 00:41:10.320
Disasteravoidanceexperts.com forward slash AI book.
00:41:10.480 --> 00:41:13.360
So get book sample with a directional chapter.
00:41:13.599 --> 00:41:17.440
Disasteravoidance experts.com forward slash AI book.
00:41:17.599 --> 00:41:34.320
And if you already got the copy of the book pre-ordered or ordered, just put the receipt number from any bookstore into the website, and you'll get a free assist on adopting AI in your company and a manual on the civic mistakes that leaders make in AI adoptions.
00:41:34.559 --> 00:41:40.800
Again, that's going to be at disasteravoidance experts.com forward slash AI book.
00:41:41.599 --> 00:41:50.559
I appreciate the conversation with you, Gleb, and your book, The Psychology of AI Adoption at Work from Resistance to Results.
00:41:50.719 --> 00:41:52.639
Thank you so much for joining me, Dr.
00:41:52.719 --> 00:41:54.320
Gleb Zapersky.
00:41:54.960 --> 00:41:57.280
Thank you again, Van Han, for having me on the camp.
00:41:57.360 --> 00:41:59.039
It's been a pleasure to have another conversation.
00:42:00.800 --> 00:42:05.039
You have been listening to partnering leadership with your host, Mahan Tavakoli.
00:42:05.679 --> 00:42:21.280
If you enjoyed this episode, please leave a rating and review of the podcast on your favorite podcasting app and forward the conversation to a friend or colleague so you can help more people discover their purpose, grow professionally with meaning, and have a greater impact.
00:42:21.519 --> 00:42:27.679
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