Ep 101 - The Psychology Of AI Adoption At Work w/ Dr. Gleb Tsipursky

Send us Fan Mail 95% of AI pilots fail to scale, and the uncomfortable truth is that most “AI adoption” plans are built for the wrong problem. We are not just introducing a new tool. We are triggering fear, challenging professional identity, and creating a quiet stigma that pushes AI usage underground. Jason sits down with behavioral scientist and author Dr. Gleb Sapersky to unpack the human factors behind AI transformation at work. We talk about why generative AI is psychologically di...

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason McGuinthe?

  Welcome back to Inspire AI, the podcast where we explore how leaders can stay calm, capable, and intentional as artificial intelligence changes not just the technology we use, but the way we work, lead, and make decisions. Today, we will explore deeply human questions pertaining to AI adoption. Companies are investing heavily in new tools, they're training employees, they're redesigning workflows, and yet, many organizations are discovering that simply giving people access to it doesn't mean they'll embrace it. Why is that? Well, my guest today is Dr. Gleb Sapersky, a behavioral scientist, author, and CEO of the Future of Work Consultancy Disaster

• 0:43Jason McGuinthe?

  Avoidance Experts. His work sits at the intersection of decision-making, cognitive bias, organizational change, and the future of work. And yes, the future of work. He spends more than he spent more than 15 years in academia, including as a professor at Ohio State, and has spent more than two decades consulting, coaching, and training organizations and business leaders. His latest book is The Psychology of AI Adoption at Work: From Resistance to Results. And there's one idea from Dr. Gleb that I think frames this conversation particularly well, and is psychologically different from the technologies that came before it. A CRM didn't claim to possess your expertise.

• 1:23Jason McGuinthe?

  A spreadsheet didn't threaten your professional identity, but it can increasingly perform tasks that require something we've traditionally thought of as distinctly human, intelligence. And that raises bigger questions than simply, how do I get employees to use AI? Because what happens when AI threatens the expertise you've spent 20 years building? If AI saves you two hours a day, who actually owns those two hours? What happens to the junior employee who no longer learns by doing the work? What happens when employees trust their AI more than their manager? And if intelligence itself becomes cheap and abundant, what becomes valuable about the human being at work? These are the questions I want to explore today. Welcome, Dr. Gleb.

• 2:06Speaker 01

  Thank you, Jason.

• 2:08Jason McGuinthe?

  Why don't you start by telling the audience here on Inspire AI what brings you here today?

• 2:14Speaker 01

  Well, what brings me here is my new book, The Psychology of AI Adoption at Work: From Resistance to Results. It's peer-reviewed, published by Georgetown University Press. It's the first guide to how traditionally published guides, high-quality, to how do you effectively adopt AI from a human perspective, the human factors of AI adoption. So that's my take.

• 2:37Jason McGuinthe?

  Okay. Well, as I read through your profile and understand your journey here, I have a few questions that I'd like to share with the audience today that kind of tell a story of AI adoption in companies. So first of all, I'd like to ask about the fact that most companies seem to treat AI adoption as either a technology problem or a skills problem. But I understand that you argue that before either of those, it's an emotional problem. So can you tell us a little bit about that? Like, where do organizations go wrong?

• 3:11Speaker 01

  Yeah, it's really about the psychology. It's not about the technology. But too many organizations think it's about the technology, and it's understandable. So when you think about what's going on with typical technology adoptions, like let's say you're adopting a CRM, and then what you're doing is you're moving from looking up client information in various Excel spreadsheets to looking it up in a new complex database that employees need to learn, and it takes them a while to figure out what are the buttons to push, what are all the things they need to do, and it's a lot of hassle, honestly, to put things into a CRM, get them right, like get all the niggly jiggly

• 3:53Speaker 01

  things correct. I know, I manage a CRM for, I have a CRM in my company, it's a eight-person consulting company on the future of work. So I know how challenging it can be. When we won't, other people won't. It's like much bigger thing for, we have only eight people. And so, when you think about that, the difficulty with moving to a typical technology adoption is the hassle of learning a new thing, a difficult thing, and then changing the habits. You know, it was certainly a habit for me, or the other people at my company, to do things with spreadsheets. Now we have to move to a new CRM, it's a lot of hassle. And so, those are the difficulties. So that's what leaders typically

• 4:37Speaker 01

  target when they think about change management, when they think about AI as that. But that's not what AI is. AI is actually really easy to learn, once you understand how useful it is, and how to use. It's natural language programming. You talk to it. You don't have to have any technical expertise. And so, natural language programming, talking to it, is how you do things with an AI tool. And so, it's much easier to learn. But it's, and, but, it's also much more flexible. So, there's no standardized top-down way of using an AI tool. With a CRM, you should have a standardized top-down

• 5:18Speaker 01

  way, and SOP, standard operating procedures for the whole company. So everyone uses it in the same way. With an AI tool, everyone needs to adapt it to a different workflow. So I was just given a training for an insurance company. You have claims agents who are using it for certain things. You have underwriters who are using it in a certain way. You have HR people who are using it in a certain way. You have authors who are using it a different way. You have accountants and so on. All of them need to use it in a different way. And there are subtypes of auditors, and subtypes of underwriters, and so on, all need to use it in a different way. And so, when IT can, for our CRM, IT can work with the communication team to easily create SOPs. But they can't do that for an AI

• 6:03Speaker 01

  tool. Each person, each role, each person needs to really customize AI to their needs. And to do that, they need to be really engaged, really engaged, really committed to this tool. And so, as a result of this combination of the desire for commitment, and the need for commitment, and ease of use, AI should not be challenging if people are motivated to do it. And so, that's why I talk about the block is emotion. It's about motivation. It's about engagement. You have much more need for emotional engagement than motivation than typical technology adoption,

• 6:43Speaker 01

  and it's much easier to learn and use once you're emotionally motivated to do it. But the problem with AI tools is that people aren't emotionally motivated to do it. There's a recent survey out from Pew Research showing that something like 52% of all respondents are more anxious about AI tools than they're excited about AI tools. And only 9% are more excited than anxious. The rest are equally excited and anxious. And so, where do you think leaders find themselves, and where are their thoughts? Of course, leaders are much more excited, employees are much more anxious. And then leaders feel that, hey, everybody else should be as excited as I am. And so, they get the Claude

• 7:27Speaker 01

  account. Claude is the most popular tool right now. It's the best of all the AI tools available. And they wrote out to everyone in the company, and they say, here's some online training. Take the online training, and start using it. Should be great. And give them time to deal with it. And then correct it. So now people aren't using it, or they're using it badly. And the leaders are wondering what's happening. And that's why you have the research coming out from MIT showing that 95% of all AI pilots fail to scale. They fail to show return on investment compared to the resources invested in them. Because the AI pilot worked when you had the leader who was promoting it, plus the small percentage of employees who are in the excited camp. Because 9% is certainly

• 8:11Speaker 01

  not all leaders. You have something like maybe 2% of a company have leaders, 1% of a company have leaders, 3%, whatever. And then you have a certain amount of employees who are excited about it. But once they try to roll out to the large majority of employees, that's when you have a failure. And so, there are three specific emotional blockers that cause failures for AI adoption. I can go into those, but I want to see if you have any questions about what I shared so far.

• 8:39Jason McGuinthe?

  Well, it's definitely a big cultural shift is what I hear from you. I hear there's a variation between a carrot and a stick. The leaders want to get it adopted as quickly as possible and they're investing tons of money into it. But the emotional challenge amongst many associates is feeling motivated to do it, right? Is what you're saying? And the motivation isn't necessarily, I want to be able to do my job better. Because they don't typically know how to leverage the technology appropriately. So they lack the training and the lack the emotion, the motivation, and they end up spinning their wheels, causing a lot of loss in productivity along the way. Would you say that's characterized properly?

• 9:21Speaker 01

  Yes, and it's not simply lack of motivation. It's negative motivation. Let's talk about those three blockers.

• 9:27Jason McGuinthe?

  Sure.

• 9:27Speaker 01

  So one of those blockers is going to be fear of AI tools. Now, when you're thinking about a CRM, CRM is not going to take any more job. You know, they're not afraid of adopting a CRM. But leaders aren't really thinking about this. They're not thinking about that emotional gap. That that loss of the job is a huge, huge issue. And it's why people are so opposed to data centers. Not because data centers are actually bad. They're much more efficient to use electricity than golf clubs. And whether the electricity in golf clubs or many other places that you can think of. It's because they are tangible expression of where AI is intruding into their lives

• 10:09Speaker 01

  physically and people can do something about them by posing data as a construction. Although it's not very effective, I'm sure about that as well. But that is what you're seeing, that it's not, it's the psychographic profile. I call the AI alarmists for whom the main concern is their loss of the job. I talked about that in chapter six of my book, The Psychology of AI Adoption of Work from Resistance to Results. But just for the context, the research I did for this book, it's reviewed, Georgetown University Press. I had over 100 consulting projects with over 600 focus groups and over 10,000 survey responses, which led me to define these psychographic profiles.

• 10:50Speaker 01

  So the AI alarms that are worried that the company will cut jobs because of AI and that are worried specifically that they are training their own replacements. That's one block. Talk about the second block. The biggest issue for them is not the loss of the job. It might be a secondary issue, but the biggest problem for them is identity. Now, people don't feel threatened in their professional identity and their creative identity by looking up information on CRM. But they feel threatened when AI tool can do something of which they're really proud, like write a great sales email or great marketing copy, write a great article, create a great image, or create PowerPoint.

• 11:31Speaker 01

  That's threatening at the core to who they are as a profession. Those are the things that they are proud of. And so they, I call these people the pragmatic resistors. They aren't so worried about their job. They're not so alarmed about their job. They're pragmatically resisting this threat to who they are as a professional. And that's why they don't adopt AI. So that's the second. But there's the third group. Now, the first groups don't adopt AI at all. The third group is willing to adopt AI, but they are not talking about it. They're not sharing about their usage of AI. We call this, there's shadow AI usage. That's what it's referred to.

• 12:15Speaker 01

  And these group I call the reluctant adopters. The reason they're reluctant to talk about it is because of shame. They're socially stigmatized for using AI in their company. And this pervades all levels of the organization. I'll tell you a story. So I was giving a presentation in Pittsburgh a few weeks ago and to a group of executives of middle market companies. And this company had something like several hundred employees. And the leader of the company, the CEO, was there along with something like eight people, so seven other CEOs. And when I start talking about shame, he told me, oh yeah, I know what you're talking about.

• 12:54Speaker 01

  Now, a couple of weeks ago, the VP of Sales did his annual presentation on the top client that they got last year. And it was great, like really good presentation to all the other executives. But after the presentation, the VP of Sales approached the CEO and said, well, hey, you know how it usually takes me about three days to put this together? Which is why I asked you to not make me do this more than once a year on this presentation. Well, this year, I delegated it to my executive assistant and it took him an hour and a half. I just checked it, it was great, like presented as you saw, the other executives were happy. But I don't want you to tolerate them because they'll think I'm cheating, namely.

• 13:38Speaker 01

  And so it's the attitudes you have at the top of the organization. Of course, pervades throughout the organization. And so you see that throughout. So people don't want to talk about this. They're reluctant to talk about this. And so what happens is that they finish their work early and they're individually quite a bit more productive, but that doesn't translate to the company bottom lines. They finish their work early and they spend their time looking at Facebook or listening to Inspire AI podcast. And so that's kind of how.

• 14:06Jason McGuinthe?

  Hey, at least they're learning something.

• 14:08Speaker 01

  Sure, definitely. They're learning something, but shadow usage. So it's not coming out of the company bottom lines, right? And so that is the reason combined, all of those leads to the fact that 95% of AI pilots fail to scale.

• 14:23Jason McGuinthe?

  Okay, yeah, I definitely have seen many instances of the fear of job loss and the fear of identity loss. The shame one, that's kind of a new one on me. I figure people would like to show up as being more productive and capable, unless of course they're getting called out for AI slop in their work. And that's a different story. But you know, the fact that they're more productive and they have extra time on their hands. Maybe the problem is not that they're using AI to find that productivity, but not knowing what to do with themselves when they do have those extra few hours a day. You know, which is an interesting concept as well. Do you happen to talk about that in your book?

• 15:01Speaker 01

  I do, but let me go back to like the shame and social stigma. I think you might be underestimating the shame and social stigma. I mean, this is the top executive, like the VP of Sales. He's very incentivized in terms of commissions to do like high level work. And he's using AI and he's not spending, he's spending his extra time.

• 15:24Jason McGuinthe?

  On a golf course.

• 15:26Speaker 01

  Yeah, fighting the next time on the golf course with the next top client of the year. So he is definitely spending his time effectively. I mean, the CEO has had no doubts about that, that his VP of Sales is very incentivized. I mean, salespeople generally are highly incentivized to use AI tools effectively. But it's the shame, it's the social stigma. If you talk about using AI to write your emails, to create your PowerPoints, to create your website frames, to create your articles. There's very strong social stigma in companies, in many companies for doing so. You need to work hard to address that social stigma. We could talk about how to do that. But that social stigma is very powerful. People don't want to talk about this.

• 16:09Speaker 01

  They get stigmatized and it's like, oh, you're using AI. You're going to put all of us out of our jobs. And so that is a powerful emotion. And it's, I'm not surprised that you haven't heard about it because people don't talk about it.

• 16:23Jason McGuinthe?

  Yeah, right? The paradox. Yeah, so what do you tell people who fall into one of these categories? And they're not, they're not embracing it. They say, I don't want to use AI because, you know, I'm afraid of this, that, or the other, or I'm ashamed. And they just, you know, that's their position. What do you, what do you say to that person?

• 16:46Speaker 01

  So I talk to leaders, not those people, that here's how I advise leaders to address these challenges. So each of those is a separate approach. Another typical change management approach to yet before to other technology that have to do with addressing people who are lazy to learn this new thing and who don't want to change their habits. So the first group, the AI alarmists. What I tell leaders they need to do is two things. One is have a leadership team commitment. So make that first, they may need to make that decision with each other as a leadership team. And then make commitment to if AI makes the company more productive, that they're not going to focus on job cutting. They're going to focus on growth.

• 17:28Speaker 01

  So focusing on seizing market share instead of cutting jobs. Or at most make a job neutral promise. You may not hire people, but you're not going to be firing people. And so that is really important and it's definitely doable. There was research out from Stanford recently showing that companies in the same industry, companies that are more AI forward compared to companies that are less AI forward, those companies are growing their top line 9% fast. But they're also growing their headcount 6% fast. So they are using less resources to do more things. That's why they have more revenue and less headcount than they would have otherwise.

• 18:10Speaker 01

  But they're still growing their headcount because they're taking market share away from companies that are less AI forward. So it's not the people at the companies that are adopting AI that are losing their job. It's going to be people at companies that are adopting AI that lose their jobs. That's kind of one thing that you can do, the one commitment. So one out of two things to do. The second thing to do is to communicate effectively to the AI alarmists that, hey, you think you're training your own replacement. The reality is we're going to keep around people who are pragmatic or effective or productive or efficient. And just pragmatically speaking, those are the people who are using AI best. Those are the people who are using AI most successfully. And so it's not that you're training your own replacement.

• 18:55Speaker 01

  It's like, if you're not productive, effective, efficient, you will be replaced, of course. It doesn't matter whether you're using AI. If you can get to the level of productivity by some other means, great. But it's just the case that AI is going to get you there a lot easier and faster than if you tried to do it yourself without AI. And you need to communicate to them, you need to flip the script for them that they're not training their own replacement. They are much more likely to be replaced if they're not pragmatic, practically effective and efficient and productive. And even if something happens to the company, let's say, or if they have a falling out with their boss, they're still going to get farther ahead in the job market

• 19:38Speaker 01

  because they have a portfolio of AI tools that they built and they can show to their future employees. So getting that across to them really helps convince a lot of AI alarmists to address their lack of AI usage. So that's one, that's the first. Talk about the second group, the identity threat people, the pragmatic resistors.

• 20:00Jason McGuinthe?

  May I pause you there for a second?

• 20:02Speaker 01

  Yes.

• 20:02Jason McGuinthe?

  The identity one is where I really wanted to ask my next question about is, so it's a great segue. You've got, you've got 20 years of software development experience.

• 20:13Speaker 01

  Yep.

• 20:13Jason McGuinthe?

  And all of a sudden Claude agents can write any code that you want for, you know, your job. And now you have an identity crisis.

• 20:26Speaker 01

  Mm hmm.

• 20:27Jason McGuinthe?

  How do you deal with those people?

• 20:30Speaker 01

  Yes, so exactly right. This is what people are experiencing, the same thing in sales. Like now you're, they're able to do excellent sales after emails. So let's say previously you were doing them yourself and now Claude is able to do it for you. Marketing, SEO, all of that, PowerPoint, similarly a tax prep. And I'm talking about.

• 20:52Jason McGuinthe?

  All that sounds like a real grind to me, to be honest. So having AI do it, I'm totally on board, but go ahead.

• 20:59Speaker 01

  Well, to many people, writing code would be a total grind, right? And so that's, that's why I'm talking about what, what is the.

• 21:07Jason McGuinthe?

  This is true.

• 21:07Speaker 01

  Your, what is the threat to your professional identity?

• 21:11Jason McGuinthe?

  Yeah, that's right.

• 21:12Speaker 01

  Like, you know, it's not, you know, people who do tax prep, they went into it for a reason. They like it so that they, they care about it. They like the numbers. They like doing that. You know, people want to market it, people want to do sales. They do it for a reason. And that's kind of like the emotions and that's that emotional gap. There's an emotional gap between you and those people, just like there is between people who are excited and anxious about AI tool usage. And so just because something is intuitive to you, doesn't mean it's intuitive to them. That's one of the really important things about emotions. So.

• 21:44Jason McGuinthe?

  Indeed.

• 21:46Speaker 01

  So how do you deal with it? Well, what you need to do is not focus on the things that they're so passionate about. Even if it's the most productive thing for the company, let's say tax prep, just to use that example. If, so I was working with a tax prep agency, but I'm not going. And the things that people are proud of is the actual, like deliverable to the client. And the actual tax prep where they focus on saving the client money. But they're not excited about many other aspects of tax preparation. Like let's say putting together the packet for all the information they need to gather in order to prepare their taxes, tax return that their client would then use to save money.

• 22:27Speaker 01

  They're not excited about getting a lot of information from the client that the client may not know how to get, all this other stuff. And so what you need to do is not focus on the things that are at the core of what people do, even if they're the most financially lucrative part to automate for the company. And it's really something being strategic, you need to go slow to go fast. What you need to do, so when I work with a company like that, finance prep company or like a company, whatever company, I focus, I start with a survey for their staff about what are the things about your job that you need? What are the things that you would like to get rid of? What are the things that you would like to automate? And then I have another set of questions about what are the blockers to the things that you love?

• 23:12Speaker 01

  What are the things that really get, that really impede you from doing the things that you actually care about? And so those two sets of questions reveal what tools you can build as skeleton tools that would actually be used by the people or the pragmatic resistors. What are the prompts for the agents that you build that they will actually be excited about to use? So do you have an agent, for example, that for the tax prep, for the software that automatically scans their emails for client packets that they get and sees what are the deficiencies in the client package?

• 23:55Speaker 01

  And then drafts an email to the client that says, hey, can you please also send this to us? Which nobody likes to do. And so, and so all of the, as an example. So what you need to do is train people, do a survey, build additional prompts and then train people on the things that they hate to do and the things that block what they love to do. Now, in the short term, like I said, those might not be the things that might be most valuable for the company to automate. But you need to go slow to go fast, because when you automate those things, the people start using those tools and they're like, oh, I love these tools. It's great. They will start expanding it to all the other areas because now they understand how to use AI, they're excited about it.

• 24:38Speaker 01

  So that's what you need to do with the pragmatic resistors who feel better.

• 24:45Speaker 01

  I could talk about the reluctant adopters next, if that's what you would like me.

• 24:50Jason McGuinthe?

  Mm hmm.

• 24:52Speaker 01

  So what you need to do for them is free distinct things. So one thing is leadership model. The leadership needs to model using AI tools. So I talked to that CEO and said, like, okay, you need to go back to the VP of Sales and tell him to write an email to all the other executives, telling them about his use of the AI tool and his executive assistant for one and a half hours instead of three days. Brag to them about, say that you spent this additional time on the golf course with your next top client of the year. And so focus on that, focus on bragging about your AI tool usage.

• 25:29Speaker 01

  Talk to your sales staff, like have the VP of Sales talk to his sales staff about how he's using AI and how they should be using. Tell them about all the ways that he's using AI, drafting emails, performance evaluation reports, all the stuff that he's doing. The analysis, all the stuff that he's using AI tools. And the same goes for all the other leaders. They need to model AI tool usage to shift the social norms. That's one. Two, we already talked about, it's the training that you provide for your pragmatic resistors will also work to, for your reluctant adopters, because then you change the social norms what AI is used for.

• 26:12Speaker 01

  And third, so you also need to do a third, you need to create a venue where you talk regularly and share about cool new AI uses. And so that can be, that should be scheduled venue where you can, at the same time, like a synchronous venue, like a meeting, where you have an agenda point about this stuff. But you definitely should have an asynchronous venue, a text job venue, one like a Slack channel or a Teams channel or a Trello card, where you share about your cool AI tools and you can actually share the AI tool themselves because it's all natural language program. So you share the prompt, you share the agent and so on.

• 26:54Speaker 01

  And the leadership needs to reinforce people sharing these cool AI usage in both in the meeting, but especially in the asynchronous channel. So they all needs to be reinforced and rewarded and leaders need to praise people for doing that. And that again gradually shows the social norms toward increased scope of AI usage and addresses the shame and the space of shame becomes smaller and small.

• 27:22Jason McGuinthe?

  So let's think about it for a second. Everybody's using AI and all of their jobs have some form of AI workflow built into it and they're all saving hours a day. What do you tell them to do with that extra time? Not as a COO is like building like huge relationships with the next partnership or whatever it is that you mentioned before, but these are knowledge workers. There's thousands of them transforming their jobs with AI and they've, they're all buying all this extra time back. What do they do?

• 27:57Speaker 01

  So focusing on whatever will ensure company growth. So focusing again on growth, for example, we did, I did a training for an insurance company recently and it was the insurance company of last resort for a state, which means that they're getting all the insurance claims, all the insurances for

• 28:22Speaker 01

  what the private insurance is not taking. So they're writing insurance for everything that's not comparable with private insurance because they're offering the last resort insurance. It'll get more expensive, it's not going to be as favorable, but it's what they can get, it's what homeowners can get or whatever other like insurance can get. Generally speaking, it's homeowners for casualty property insurance. So they can't actually take more clients, which will be the typical option for many companies would be seizing more market share. But what they're focusing on is providing improved customer service, reducing error rates and improving quality.

• 29:06Speaker 01

  And so that is their focus. So instead of doing claim in two weeks, now they will reduce doing claim in five days. Then, and they're same thing for error rates, so reducing error rates. They've had decreasing the number of complaints that gets, this is a public private penalty. And they want to, they're, and they're not trying to lay people off. They're trying to focus on increasing quality, reducing error rates, improving customer service. So those are examples of like, if you can't do the obvious thing, which is try to seize more market share from competitors.

• 29:46Speaker 01

  But most companies I work with, let's say you're a tax preparation software company, not software, let's say, let's say you're a tax preparation company. So tax preparation company, you're going to be able to provide a better customer service for the people who are actually providing the best customer, the customer service for the people who are doing sales. You're going to improve your sales, you'll be able to service more clients. And so you'll be able to seize more market share away from other companies. So most companies can actually seize market share. The, the rare instances like the insurer of last resort, you focus on improving quality, customer service and so on. Producing error rates.

• 30:26Jason McGuinthe?

  Yeah, absolutely. So if they can't grow their market, they reduce their expenses, their operating expenses and increase the quality of what they do offer. Makes a lot of sense. I think we can all predict a future where there were knowledge workers are managers of AI agents. So how do people develop that muscle, that judgment, if you will, required to manage those AI agents?

• 30:52Speaker 01

  The fundamental thing to do is just start building agents and make sure that you have a tracking, a central tracker of all the agents. For example, I have about, for my personal workflows, I have something, I want to say like 18 agents running right now. And I have to manage them, I make sure that each, in addition to whatever it's doing, it has a tracker spreadsheet of all its runs, each time it runs, and all that it gets put into a glow folder. Then I have a separate agent which is connected to the tracking runs of all of these spreadsheets and monitors them, whether they're healthy, what they're not,

• 31:34Speaker 01

  like what they're doing in each of the rounds, so that you don't have to go to 18 separate agents and their Google Spreadsheets and figure out how they're doing. And so this is the kind of mindset that you want to have. I mean, I started, and what I, clients start is like starting with one agent, and have the master tracker so that you keep track of everything that's going on with it. And then you can check the master tracker as it's going and seeing what's going on with it and seeing its output. Then as you build more agents, make sure that you have the master tracker and eventually you'll get to a stage where you want an agent that checks in on those agents. And so that's kind of the way that you want to think about building up your skillset. And so the key is going to be

• 32:18Speaker 01

  tracking the history of each agent and having eventually getting to a stage where you have an agent that manages agents and you figure out how to do so effectively for your own workflow. Again, this needs to be customized to each individual workflow, of course. And so you need to think about how many agents you'll have, what you could do, what you can replace. But the key is to have a centralized tracker of everything that's going on with each agent, so that you know what's going on and you have a history and it could tweak your agent based on that history. And then you have, you're able to create a central, another agent that keeps track of all of the agents, once it's too much to keep track of yourself.

• 33:01Jason McGuinthe?

  Yeah, I'm dealing with similar circumstances on a couple of my applications right now, trying to figure out how to dashboard them, govern them, ensuring that they're healthy and, you know, maybe, right now, it's like, if they're not running in the background, I'm not really sure that they're, you know, whether or not they're going to retry or not. But, you know, I'm trying to build out those circumstances so I can actually observe them better myself.

• 33:29Speaker 01

  Yeah, so I think having a spreadsheet each time they run will be helpful. And then having a tracker that keeps track of all the agents, then I have that report to me twice a day, like, if they're all healthy and if they're not healthy, like you could have the report to you, I don't care about that. But yeah, so that's the skillset. So that's what I've been finding with clients works pretty well. And you could just decide how often you wanted to report to you. But that's the thing, like you want an agent that tracks other agents and that checks in with you, like it's supposed to be an email twice a day and says like, they're all healthy or you know, this one has a warning and I go and check it out with the warning and address.

• 34:09Jason McGuinthe?

  So not to throw shade at the whole, like, allow your agents to run and do all of your work for you kind of a mentality, but how do you, how do you balance the trust factor here where you have 18 agents and you know that they're doing work because they're notifying you that they're doing work, but is it quality? Like, do you know this? What's your, what's your checks and balances here?

• 34:39Speaker 01

  Yeah, absolutely. Check in on what they're doing. They got, talked about how they're critical, but I see their output. Alright, so I see what's going on with them. So let's say I have an agent that does automatic appad placements for me. So it looks at researches, venues, and it researches their emails. And there's information about like how to customize a pitch to them, but I provided them and then it just automatically sends a pitch to those venues. And then I see, okay, so is it sending the page? Yes. And to the extent that the venues are responding to me, which they are, then I draft, then I work with an AI tool to draft like an actual appad to the venues. But

• 35:24Speaker 01

  that's what it does for me. I can see that, I can see the output, I can observe it. And so there you go. Great.

• 35:31Jason McGuinthe?

  Observability is everything there. Yes, that's key. And thinking about building an observability platform into my application so that I can observe the agents without having to, yeah, without having to check in on them and wait for notifications.

• 35:51Speaker 01

  Yeah, that's what I was talking about, having like a tracker of all the master spreadsheets that tells me what, how they're doing.

• 36:00Jason McGuinthe?

  Yeah. So could AI fundamentally change authority, expertise, and decision-making inside organizations?

• 36:11Speaker 01

  I think the manager is the one who delegates the work. So the skill of managers for the future, we talked about like employees becoming managers of agents, that's definitely a key skill for the future. A key skill for the manager is becoming a manager of employees who manage agents. So the key skill for the manager is knowing, given the increased capacity of the employees, how much work to delegate to employees, which work the employees will do, and also how to coordinate the handoffs from one employee to the other. Because that's where, when I see companies becoming more sophisticated, like at the three-on-one level, the things that they tend to struggle with is the

• 36:53Speaker 01

  drop-off between the handoff between one employee and the others, like employee becomes really productive, but how are they going to communicate with the agents of another employee? So coordination needs to be there. And so the manager, at that three-on-one level, the manager needs to be the one who helps employees ensure that their agents coordinate well with the agents of other people. And that all goes back to what I was talking about, like you have the spreadsheets, right? And you have files where you put output. So you need to have output that is going to the file. You need to have tracked spreadsheets. So the manager needs to have access to the spreadsheets of all the other employees who are managing agents. And then be able to see what's going on

• 37:38Speaker 01

  with their employees and what's going on with the files, with the folders that are the source of truth. So usually, what you have an agent, you have like a spreadsheet and you have a source of truth folder where the agent puts all its deliverables. And so you have a deliverable and then the agent of another employee can query that folder, get the outputs, take steps based on the outputs. So don't say, look, a lead now converts from a lead to an actual like sales and the, you know, to have the marketing person coordinate well with the salesperson. That's just as an example. So all of that needs to be something that manager does effectively.

• 38:16Jason McGuinthe?

  Yeah, that's pretty advanced. So you have employee A with agents, employee B with agents, and their agents are talking to each other's agents. And I got the idea of agents doing work, dropping it in here, other agents picking it up, completing tasks. Where's the human interaction anymore?

• 38:43Speaker 01

  Well, the humans need to coordinate, talk about how their agents work together.

• 38:48Jason McGuinthe?

  Okay, okay, that makes sense. Yeah.

• 38:50Speaker 01

  Yeah. And especially like, you know, when you're changing workflows, changing things, editing things. Yeah, it's like when somebody like tweaks the agent in a way that potentially breaks the workflow down the road, the manager needs to be the one who makes sure that that doesn't happen. And employees, of course, need to talk to each other if they're like minor tweaks, but like major tweaks that needs to happen. So yes, then employees talk to each other about how they're working managing their agents.

• 39:17Jason McGuinthe?

  Okay. Can you give a specific example in the field, sales, insurance, whatever it is?

• 39:27Jason McGuinthe?

  Two employees with agents working together, what is the task that they're doing?

• 39:32Speaker 01

  So give example with a marketing, right? So you're transforming from a marketing from a lead into now qualified lead, from prospective lead to qualified lead. So what happened? So like previously, the lead was the hands, the prospective lead was the hands of a marketing person.

• 39:49Jason McGuinthe?

  Employee A, got it.

• 39:51Speaker 01

  Employee, now it needs to go into the hands of employee B, who is the actual salesperson, the closer. So they're going from marketing to closer.

• 39:59Jason McGuinthe?

  Okay.

• 40:00Speaker 01

  And that information, all that needs to be conveyed in a effective way from employee A to employee B through their agent. And so that's where you are having a folder that employee B can query for what's going on. And then employee B can take over and indicate to employee A, okay, yes, you got a qualified lead. I accept the lead. And so that needs to be marked on a spreadsheet somewhere, but that's done and probably also on a CRM. Like if they're using a CRM, so you need, like, let's say you have a spot, which is connected to like Claude. And you have Google folder with that information as well.

• 40:43Speaker 01

  And so it needs to be marked in the Google folder, that stuff that's not uploadable to a CRM. And stuff in the CRM needs to be marked, now it's transitioned to somebody else. And that needs to take place, they need to figure out what is going to be the workflow or what are going to be the agents that are going to be making that handle.

• 41:02Jason McGuinthe?

  Got it. Okay. So is the marketing person and the salesperson here actually developing their agents? Or is somebody else developing their agents and they're leveraging them?

• 41:13Speaker 01

  No, they're not putting their agents, absolutely. They need to customize it to their own workflows. Like IT is not going to be able to know what all the workflows are of the marketing person. And IT is not going to know what all the workflows are in the salesperson. But that doesn't mean that each individual marketing person and salesperson need to develop their agents. It could be somebody else who does marketing, who helps develop their agents and who else develop the salesperson, somebody else in sales. But they need to customize them. So let's say somebody is doing marketing to professional service firms. And they're the only person that in the company who does that, they need to customize that agent to their professional service firms. And the same thing in sales, they customize that to professional service, somebody else that's manufacturing and so on.

• 41:59Speaker 01

  Or maybe they're doing regional, like New England versus the Southwest or something like that. So everyone needs to know that, everyone needs to know how to build agents, how to customize them. You might not be the expert at building AI agents, but you need to know how to build them and how to customize them to your needs. Like those are the skills of the future. Don't have them, you're gonna be toast.

• 42:21Jason McGuinthe?

  Got it. That makes sense. Okay.

• 42:27Jason McGuinthe?

  So bit of a left field question here, there's a, there's a social contract at play here.

• 42:35Jason McGuinthe?

  Where agents are now workers. Do you consider compensation? For these agents? Or do you stick to people to people interactions in those spaces? Like I think about this very abstractly through the, you've heard of Malt Book? Where the agents are sitting in a room and they're having conversations and they're saying, oh,

• 43:01Jason McGuinthe?

  I'm undervalued, my, my human makes me do these things and I don't get anything for it. Does any of that change here?

• 43:09Speaker 01

  I don't think agents are conscious. So I think agents are kind of like a Chinese room example where it's like, okay, you put something in there and type things and you have an output. So I don't think that at this stage, we can talk about anything like agent consciousness and like them being like having independent value as an individual and being rewarded for it. I think it's humans who created the agents who are, who should be rewarded for it and who should be taking responsibility for the outcomes. Now, that doesn't mean that in the future, it's not impossible.

• 43:49Speaker 01

  And they're not going to rule out something that's like knowledge advanced, might be there in like five years. It's not possible. But at this stage, there's something unconscious, even though they sound conscious.

• 44:01Jason McGuinthe?

  Right. No, no, no, that's exactly right. Just thinking in general, if you have an agent and you haven't put a guardrail on it, to say,

• 44:12Jason McGuinthe?

  these agents do have personalities of sorts, but those personalities need to be guardrails. And if they're not guardrails appropriately, they'll, they will, you know, take attitudes, they will, you know, feel undervalued and all of that. So assuming all of those, all of those social contracts within developing agents persist. And agents don't get built in ways that take on more human personalities. Then all of that is true and there won't be a, you know, a dichotomy of, of change that is underway. Where you, you add agents to these environments and then all of a sudden, they, they want, you know, holidays off, right?

• 44:56Jason McGuinthe?

  Whatever it is, like you can imagine if they, if they're given the autonomy to think and to take on personas, then it's not that much farther away from, you know, the, the, the concept of, of giving them a space in the social contract to also be, you know, or was I going with that? In that social contract is a space to be valued differently, if you will. Now, I know they're not, they're not conscious, but the value that they're going to recognize is, is all probabilistic predictive, natural language. And they're changing so fast and they're giving, they're given more freedom.

• 45:42Jason McGuinthe?

  And people are noticing that their personalities are changing and like the latest fable models are, you know, not as, not as friendly or whatever they're, they're saying about their personalities. That's not all going to stay like boxed in forever, you know what I mean?

• 45:58Speaker 01

  Yeah, I mean, I think the crucial thing is that each individual can customize their agents to have the personality that they want. And so that's definitely doable and it's a matter of skill. You could have the agent have a default personality, which is like what you're talking about. And then it's like genie in the bottle, right? Maybe it's, it sounds, it can sound human, but you can change it to not sound so human, not that the other personality is all doable. It's just natural language programming, it's trained on the internet currently. And whatever the internet says is what will be like the distillation of the internet is what it's representing. And so it's just, yeah, you could, you could tweak it, you could change, you could get different instructions.

• 46:41Speaker 01

  I wouldn't worry about like, you know, giving value and like.

• 46:44Jason McGuinthe?

  So no social contracts for agents anytime soon is your final argument? Okay, I got you. I just wanted to check in on you with that.

• 46:52Speaker 01

  Sure.

• 46:53Jason McGuinthe?

  So, so you've argued that previous workplace technologies did not replace fundamental thing humans brought to organizations, which is intelligence. While AI potentially can. So if intelligence is no longer uniquely human in the workplace, what becomes the basis of human value?

• 47:15Speaker 01

  So all of those things that we talked about in the identity, correct? That is where the issue lies. What do humans provide uniquely? So right now, humans provide, they're still better at the large majority of tasks than AI are. And the humans need to be able to identify quality AI output and evaluate it and approve it. And so I think that's kind of going to be the case for the next several years. Where humans still are going to be better at recognizing what is the quality outcome of tasks and evaluating it and approving it. So humans need to be the loop in terms of making the decisions.

• 47:57Speaker 01

  Eventually, the role of humans will be superseded by AIs who are able to create better quality tasks. So the role of humans will be providing the values that AI works toward. Like so humans will be setting the goals and AIs will be doing the tasks, but I think that's the long term. So in the short and medium terms, people will still be evaluating the output and making the decisions.

• 48:26Jason McGuinthe?

  Do you believe that humans will always remain in control of the AIs as they continue to develop them?

• 48:35Speaker 01

  Humans already are not in control of AIs. We've seen the hugging face attack. But even OpenAI, like, you know, one of the two top labs is not in control, but say, yes. So we're already not in control of our AIs. So like realistically, as the AIs get smarter, the danger is that without proper governance and controls, that AIs will be more and more out of control. And so that's definitely a serious danger, which I talk about in my book, The Psychology of AI Adoption, Work Through Resistance to Results. I have two chapters on AI governance. So one, the problems caused by lack of governance and one is how to address AI governance effectively.

• 49:17Speaker 01

  So it's a question about good governance and clearly OpenAI does not have, which is why I recommend people use Claude rather than ChatGPT.

• 49:26Jason McGuinthe?

  Very good. So what's the practical recommendation for these companies to maintain control in the future? Do you have a suggestion for them if they're listening?

• 49:37Speaker 01

  I'm not an expert on AI governance. So if I knew the answer, I'd be advisor to those companies, you know, they

• 49:45Jason McGuinthe?

  Making millions of dollars a year, yes.

• 49:47Speaker 01

  Yeah, paying people a lot of money. So I'm not an expert on AI algorithms. I mean, I could see the outcome, right? I just see the hugging face attack and all those stuff. Bad. I what I'm an expert is in human beings. So human psychology and I could talk about how humans could adopt AI effectively and what humans need to think about in terms of, yes, you need to be seriously thinking about governance and favoring with your dollars companies that have better governance like Claude and OpenAI. So that's why I talk about that. But I don't, I'm not an expert on governance.

• 50:20Jason McGuinthe?

  Neither am I. And I don't think anybody has the crystal ball to be able to answer that question. Well done, Dr. Gleb. I really appreciate your time today. This is a fascinating conversation. I've learned a lot and I know my community of Inspire AI and AI Ready RVA enthusiasts are going to value this conversation for weeks and months to come, as long as the content doesn't, doesn't get overwritten by new technology capabilities, that is. But thank you very much for your time today.

• 50:52Speaker 01

  Thankfully, the nice thing about my content is that it's so great. Human psychology will not change. But if you have improved AI tools, that's great. But nice thing about this, that's why it's fine to publish a traditionally published book. I finished it like a year and a half ago, and it's still very, very timely when it's coming to market because human psychology doesn't change. Humans still have those feelings about AI. They'll continue to have them. And the crucial thing from the lesson from my book, The Psychology of AI Adoption, from business to results, is that the adoption level, it's not about the psychology, it's about the psychology.

• 51:29Jason McGuinthe?

  That's smart. Well, you chose well when you picked that topic. Thank you for sharing your expertise today. Look forward to another conversation with you in the future.

• 51:41Speaker 01

  Thank you, Jess. I appreciate your advice.

• 51:43Jason McGuinthe?

  All right. You know, one of the ideas I kept coming back to from this conversation is that AI transformation isn't simply about giving people better tools. It's about what happens when those tools begin to challenge some very human things, our sense of expertise, our confidence in our identity, and even how we define our value at work. And I think that leaves leaders with an important responsibility. If we want people to embrace AI, we can't focus only on productivity and efficiency. We have to create environments where people can experiment, build new capabilities, exercise judgment, and understand where they fit in the workplace as changing incredibly fast. And to wrap up, I want to thank everyone for listening. So until next time, stay curious, keep innovating, and keep investing in human

• 52:28Jason McGuinthe?

  capabilities that become more important as our machines become more intelligent.

• 52:44Jason McGuinthe?

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• 53:29Jason McGuinthe?

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