SPEAKER_00 0:01

Welcome to the Ask for the Skinny Podcast with a guest. I'm proud to present Dr. Gleb Cipurski. Gleb, welcome to the show. Please tell us more about yourself. What is your story?

SPEAKER_01 0:14

Sure, happy to, Peter. So my background is in behavioral science. I help folks make good decisions about adapting to the future of work. That's my expertise. I've been doing this for 27 years now, since 1999. I have a PhD in the history of behavioral science. I got at UNC Chapel Hill, where I worked with historical and contemporary perspectives. I taught there for a bit. Then I taught at Ohio State as a professor and in the precision science collaborative in the history department. And I also at the same time helped companies as a consultant and trainer on making good decisions on the future of work, including in the last six years or so, five years or so, on AI adoption. So how do you figure out how to adopt AI effectively and make good decisions around that? Everything ranging from the psychology of AI adoption at work, and that is the topic of my new book, The Psychology of AI Adoption at Work, from Resistance to Results. So it's a peer-reviewed book. It's out with Georgetown University Press. So I focused on the psychology of how do you adopt AI effectively. So everything from how do you overcome resistance to addressing burnout and well-being issues to effective, successful metrics of AI adoption. So that's what I talk about, and that's my area of expertise. I've published eight books, including this new one. You might have heard of me from Never Go with Your Gut, How Pioneering Leaders Make the Best Decisions and Avoid Business Disasters, and The Blind Spots Between Us, How to Overcome Unconscious Cognitive Bias and Build Better Relationships. Those are my two global bestsellers. I peer regularly in Harvard Business Review, Fortune, Forbes, Psychology of Day, and so on. Scientific American, these sorts of venues.

SPEAKER_00 2:02

Amazing. I have so many questions I would like to address. Our listeners are very much interested in uh burnout and midlife crisis. What I would like to know if that's possible, because maybe I'm just uh going in the right uh in the wrong direction, but uh most of our listeners um complain that uh despite using uh more AI tools, they work well uh more and not less. So um how is how is that possible that for example our productivity can increase, but uh the time that we work is the same or even more uh with uh so many tools uh right now available uh to individuals and also to the companies. Uh what's your point of view? Where did we get it wrong?

SPEAKER_01 2:57

I think it's really about the metrics, it's about what you're measuring. If you're seeing that you're having, let's say, you can easily save two to three hours a day by using AI tools compared to what you would be doing otherwise. Now you can translate that into more leisure time. And let's say uh you're being you're clearly being more efficient. So let's say you're saving three hours a day out of an eight-hour workday. So you're saving three hours, that's something about like 30-ish percent of your time. Now you can choose to have 30% more outcomes, meaning work the same amount of time and do, let's say, 30% more sales, or 30% more client tickets resolved, anything like that, 30% more code written. Or you can choose to have 30% more leisure time and relaxation and so on. And so that's a choice that companies need to make and individuals need to make that where they're going to spend their additional time from the resulting efficiency.

SPEAKER_00 4:10

Interesting. So um I I really like how you uh uh frame it, that that's the the uh the choice that companies will have to make. But uh I will ask you uh maybe more intriguing, even more intriguing question, and and that is um if we have some so many AI tools, is there still more work to be done? Or how is there an infinite uh amount of work that we should do? Or the amount of work is uh it's not uh it's it's infinite and not um let's say the the scale, there's a fixed scale and uh because it all looks like the the more we work, there the more work there is for us. And how come for example, if uh there are hundred uh clients tickets to solve, how come we don't do it in like four hours? But we just solve another ticket and another ticket, and is there is there an uh let's call it end game today? Or uh is there always going to be more work, more tasks? And is this due to uh uh lack of organizational skills of uh let's call them employers? Uh or is that just um a nature of the business that there's uh the work is never done?

SPEAKER_01 5:36

So it depends on the situation. If you are purely and only doing customer ticket support, then of course there's less work to be done. But that is almost never the case for customer support agents. So if they are doing ticket support addressing tickets, they're also doing a lot of other tasks. And the other tasks may not be as easily done using AI tools, or they may be done at a higher quality level using AI tools. Or the same idea is maybe you can solve a customer tickets at your previous level of quality, or you can solve them at a higher level of quality using AI tools. So all of that time can be, in the large majority of cases, spent on doing things in a more productive way. So we're talking about, we're previously talking about efficiency, now we're talking about effectiveness, using an AI tool to do things better. So effectiveness versus efficiency. So, for example, uh, with one, I was doing a training for an insurance company, that's the insurance company of last resort. And you know, it's a private entity, so they're only providing insurance for clients that can't get covered by typical insurance services, by private insurance. So it's a state company, it's the insurer of last resorts. And what they decide to do, what the leadership team decides to do, is they're definitely using AI and it's making them more efficient and effective. So if they were only doing claims at their previous extent, they would have less work available to do. But they decided to focus on improving the quality of their customer service, so decreasing mistakes and improving the speed of their response to clients as part and improving the accuracy of their underwriting and so on, as part of work using the AI tools. So your additional time, if the company decides to not give people more leisure time, your additional time will go into a number of buckets. One is improving quality, improving speed of responses for customer responses, but also in terms of let's say sales. This company can do sales. There are many companies I work with that do sales. And what they do is they just do more sales, so they have more clients. And so they have more clients, they have more issues to resolve because they have more clients. And so the company is having higher revenue. And this is not only me talking, like I'm definitely seeing this among my clients. But there was a recent study coming out from Stanford showing that in the same industries, working companies in the same industries, companies that are more AI forward have nine percent more revenue compared to a companies that are less AI forward. So companies that are worse at AI adoption have lower revenue. But the companies that are more AI forward, they have 9% more revenue and 6% more headcount growth. So it's not like they're laying people off. They are instead seizing market share away from companies that are not as AI forward. So you're having more sales and you're doing more things with less people, that's why you have 9% more revenue and only 6% more headcount growth, but you're still hiring people. And so they're still hiring people, so it's not like AI is causing layoffs in the companies that are AI forward. It might be causing layoffs in the companies that are losing market share that are not AI forward, so it's a different story. And so the companies that are AI forward, they're having more clients, and therefore more growth, and therefore the people who are there are using their time to do things for more clients, and they're even hiring more people because they have more clients to serve.

SPEAKER_00 9:28

Amazing. But is that more sustainable? Are people working with AI, let's say, the with companies that adopt AI faster, are they experiencing less burnout? Uh, is their quality of life improving, or is there just uh their efficiency improving?

SPEAKER_01 9:49

At this stage, I it's their efficiency that's improving. I don't really see quality of life improvement that much. And here's why people are learning how to use new tools, but they're still working as many hours as before. And so that creates a bigger cognitive strain. When you're learning how to use a new thing and you are still working the same amount of hours as before, then of course it's going to be additionally burdensome. And so this is definitely an issue that can lead and does lead in some cases to more burnout, more mental strain. And so I encourage companies to give people more time off and not expect them to work as many hours. So when I work with clients, I tell them that, hey, uh, people need some additional time off, and they don't need you should not be expecting them to work as many hours because they're learning a new tool that makes them a lot more efficient, but you know, it means that they are have more cognitive strain on them, and that can lead to higher levels of burden.

SPEAKER_00 10:56

Okay. Is there anything we could recommend to our to our listeners as uh someone that uh his company uh forces them to their employer forces them to use AI? Uh what would be a good hint or advice for them? How how to go about it, how to feel it.

SPEAKER_01 11:21

I would advise them to learn how to use AI faster and more in-depth than their colleagues, and then use AI to be more efficient and more productive than their colleagues. So the expectation, they fulfill the expectations from the supervisor, the manager faster, and then use the additional time for self-care during the workday. And so they, if they do that, they will have, they will spend less time working, they will spend more time on self-care, and they will still gain a major advantage in the workplace because they're using AI more effectively than their colleagues. Even if something happens to them and they have a falling out of their with their boss and they need to find a new company, that company will be more likely to hire them because they are more productive and efficient than people who aren't using AI as effectively. So that will be my recommendation.

SPEAKER_00 12:16

What what's your take on the certification? I know that Google has some certificates for AI usage. Um, there are more and more certifications for AI usage. How should employees go about uh certification? How should they prove that they are able or capable of using AI efficiently?

SPEAKER_01 12:36

Yeah, I don't think the Google and the Microsoft certifications are very good because the Google and Microsoft products are behind the leaders, which is uh Claudanthropic, which is the top company available, then ChatGPT OpenAI. So you're not really demonstrating high quality by demonstrating Google or co-pilot capacity, which is where the certifications are. What I would instead recommend people do is build an AI portfolio. So build a portfolio of tools that they developed for their company and for their own workflows that they can demonstrate to employers. And so that is going to be the real demo of your skills, the AI portfolio of tools.

SPEAKER_00 13:23

Amazing. But um what we have talked about is very much applicable to everyone doing programming, everyone doing uh tasks in the office, everyone doing customer support support and similar. But uh how about um other avenues? Uh so uh other professions. How should how should the plumber uh use more AI? Because we know there are so many tools in construction that use AI. How should they prove that they are um AI proficient?

SPEAKER_01 14:05

Well, it depends on the plumber's job. So let's say if you are a solo plumber and you're a solopreneur, then you have a lot of back office and administrative tasks that can be very much done using AI tools. So that's where I would focus on that. If you're just going out to customers and doing sales, AI can be helpful in preparing your sales packets and analyzing customer situations. So using transcription where you talk into your tool and describe what the problem is and ask AI for advice. They're definitely use cases, but they're not nearly as extensive in their work as there are for white-collar professions.

SPEAKER_00 14:46

Um Gleb, before we wrap up, uh, I would like to know a bit more about you, your book, and where can our listeners get in touch with you? Where can they get more info uh like the ones you just uh shared? Where is it possible to read more and get your book and maybe even get in touch with you uh to solve their company's problem?

SPEAKER_01 15:13

Well, if they want to get in touch with me, I'm very available on LinkedIn. Just tell me you heard me on this podcast because I don't uh accept LinkedIn requests that are random. They get too much willing to spam. You can get a copy of my book, The Psychology of AI Adoption Work from Resistance to Results, it's peer-reviewed, George and University Press, um, Amazon, Barnes Noble, wherever you get your books, your local bookstore should have it. It's traditionally published, so widely available. And if you want to get a free sample of the book, you should go to disasteravoidance experts.com, my website, then click or type in forward slash AI book in the URL, and you will get be able to get a free sample of the book itself. Now, if you already bought the book, you can put your receipt number there, disasteravoidance experts.com forward slash AI book, and you will get a free assessment on adopting AI in your company, in your workplace, and uh manual on the seven critical mistakes that leaders make in AI adoption. So again, that's disasteravoidance experts.com forward slash AI book.

SPEAKER_00 16:22

Thank you, Gleb. Thank you for all these insights. And uh thank you for uh giving us all the details where we can get in touch with you. Thank you very much, Peter. It was a pleasure.