Motley Fool Money - Irresistible Change: How to Spot Real Growth

Starting point is 00:00:00 When you hear a lot of bluster from the CEO and there's mandates involved, those are two leading indicators that the rank and file probably are not going to be given a chance to become bought in. They probably don't have the agency that is required for them to truly adopt change and make it stick. That was IBM former General Manager of Design, Phil Gilbert. author of the new book, Irresistible Change, a blueprint for earning buy-in and breakout success. I'm Motleyful producer, Mac Greer. Motleyful contributor Rich Lumello recently talked with Gilbert about change and about how investors can tell the difference between the real deal and CEO Blester.

Starting point is 00:00:54 Welcome to Motley Fool Conversations. I am Motley Fool contributor Rich Lumello. Today I'm joined by entrepreneur, executive, and author Phil Gilbert, his book, Irritley Fool, irresistible change explores what it takes to make change something organizations actually want to adopt. For investors, understanding which companies can truly reinvent themselves, may be one of the most important edges in long-term portfolio construction. Today, we'll explore how to identify real transformation, how to separate change from theater, and how culture, customer experience, and incentives ultimately drive shareholder value. Phil, welcome to the Motley Fool. Great. Thank you, Rich. I'm glad we have a chance to, you know, kind of sit down and talk.

Starting point is 00:01:33 I guess it's always helpful just to get like a little bit of background. Got a unique experience, kind of an entrepreneurial, but then, you know, kind of big business background. Now you're an author. Tell us a little bit about your background. Yeah, sure. I was really a serial entrepreneur and kind of pictured myself as a startup guy. My third company was a company called Lombardi Software. We were in this pretty mundane middleware space called business process management, headquartered in Austin, Texas. And in 2010, IBM bought us. And to be honest. It was a great exit. Shareholders happy. Customers were happy with a company like that to support them. And I kind of figured I would work with the team for a year or so to get them through the

Starting point is 00:02:13 integration, but always figured startup number four was out there. A speed bump got put in that path. And that's what turned into probably the most fascinating 10 or 12 years of my entire career. And that's what the book is about. The change that I led at IBM of how about 400,000 people across 180 countries did their work every day. Yeah, absolutely fascinating. And I guess we can kind of jump right in, taking a look at the book itself, you argue that most change initiatives struggle or fail. As investors, how can we spot ones that are likely to fail early, if that makes sense? Or probably going to fail early? From your experience, you know, taking a look at something where it's like, this doesn't have legs. Yeah. You know, typically what I look for is when you hear a lot of bluster from the CEO and there's mandates involved, those are two leading indicators that the rank and file probably are not going to be given a chance to become bought in. They probably don't have the agency that is required for them to truly adopt change and make it stick. And so I tend to look for organizations that are quietly changing and where the CEO is not out over his or her skis in. And so I tend to look for organizations that are quietly changing and where the CEO is not out over his or her skis in. building it up and certainly not building it up on earnings calls before there's real evidence

Starting point is 00:03:29 of it succeeding. Yeah. Actually, it's funny because literally as I was kind of asking that question, my follow-up was going to be, is there specific language in earnings calls that signal, you know, kind of more of a compliance mindset versus the opposite, you know, kind of wanting to do this? Yeah. Thou shalt. Right. And we saw a lot of it, and I think a lot of it was corrected. I think several of the best CEOs course corrected, but, you know, the return to office mandates immediately following the pandemic were a pretty classic transformation play. It's where your culture had adopted a new set of behaviors that you didn't feel was the long-term behavior that your company needed to compete. And so you had all these return to office mandates and these things

Starting point is 00:04:13 were talked about quite a bit. You just saw and you felt all of the resistance from the employees against the way it was being done. For my clients, I always said, if you've got to mandate somebody to be back in your office, you're probably doing something wrong. And so why don't you start with developing the environmental culture that you want and then introduce your employees to it?

Starting point is 00:04:33 And if it's truly better, they will come. Now, that's by and large what's happened. But in those early days of return to office, what you saw in story after story after story of compliance theater, where people would give friends their badges to swipe, or they would swipe in and immediately swipe out, whatever it might be. So that was a kind of just a real case study, a very short-term case study that's very visceral that everybody can, I think, relate to.

Starting point is 00:04:59 Right. One of the things that you kind of touch on or emphasize is emotional engagement. How does that show up, you know, from your perspective, how does that show up like in measurable metrics? Are there key performance indicators that kind of suggest that it's working? Well, there certainly are. I mean, we had many. A lot of times for investors, they are not necessarily the kinds of metrics that are necessarily publicized, not necessarily because the entity doesn't want to or is hiding them, but they're just typically not the kinds of things that investors or the Wall Street is asking about. But certainly, you know, most organizations, most large organizations these days have employee engagement scores. Most of them are keeping track of how their customers and

Starting point is 00:05:37 stakeholders are evaluating them through even rudimentary things like net promoters score. these are the kinds of metrics that are kind of leading indicators to where the culture truly is. And so those are the things that I would try to dig into and try to uncover as much as possible. And based on your experience, what does a healthy adoption curve look like inside of a company where you know like, okay, this is working? Oh, I had something, I relate the story in the book to the first time that I had just been asked to lead this transformation and I was kind of making a world tour of visiting companies that, had great human-centered cultures and companies that weren't known for that. And also some of the schools that I knew we would be recruiting from to get some of these new skills. And I was out at Stanford and I was meeting with David Kelly, who is the founder of the D-school at Stanford.

Starting point is 00:06:24 And talking him through the fact that we're going to turn IBM into a human-centered organization. Design thinking is going to be at the heart of what we do. And this is how we understand problems. And he was kind of checked out and I don't think necessarily believed it. But at the time, this was kind of an audacious claim. And he said, at one point, he said, Phil, you know, the good news is in order for any culture to adopt something, you only need to get 25% of the people. That's the tipping point. And then he stopped and he goes, wow, that's 100,000 people. And then he kind of engaged in the conversation much differently. But the funny thing is, is we found that to be true. And so we actually adopted a tactic where, you can obviously,

Starting point is 00:07:03 now you can chunk down, in our case, 400,000 people to 100,000. Well, that's helpful. 100,000, easier than 400, but we actually found that it worked even further chunked down by essentially, you can think of it as almost division. So what we found is that in the product group at the time, we had about 20 or 25,000 people actively working on products, coding, designing, product managers. Once we hit about 5 or 6,000, everything changed. And that happens for a couple of reasons. first of all, if you've read anything about how tribes work, it just takes one trusted member of another tribe in order for you to trust that tribe. So once you get kind of 25 or 30 percent of the people, you probably know firsthand at least one, but probably multiple people who are being positively impact by the change. And then the other thing is, in order to get to 5,000, 6,000 people, it took us a year and a half or 18, 20 months, something like that. Well, by that, time, people are also shifting jobs. And so now they're dragging these new skills and these new ideas with them to their new teams. And even if that new team hadn't been formally brought into the

Starting point is 00:08:14 program, the new ideas were starting to percolate. So we actually started using that as a metric pretty actively as we would move from kind of unit to unit or capability to capability. we then sized that whole thing and then we intentionally targeted in a sense the easiest 25% to get. And that became a very useful tactic for us to kind of help us

Starting point is 00:08:41 scale out the change. Once you hit that 25% tipping point, the rest go very, very fast. You've worn many hats. What can public company CEOs learn from entrepreneurs? I think the main thing is this notion of never being settled with the status quo. And the possibilities of value creation are so much more interesting than the possibilities of cost reduction. To me, that's the single

Starting point is 00:09:09 biggest difference between, I'll see, the stereotypical big company C-suite mindset and the entrepreneurial mindset. The entrepreneurial mindset is like, forget cost, I can create value. And And the stereotypical big company mindset is, oh, wow, here's a new way to save money. If you think about what's happening with some of the, and I don't think many of these recent layoffs have actually been because of AI. I think it's primarily pandemic overhiring and the productivity that we got from pandemic era tooling. But if you just explore the logic of laying people off because of AI. What you're essentially saying is, I'm perfectly happy with today's outcomes. I can now get them cheaper. That is not an entrepreneurial mindset. The entrepreneurial mindset would be,

Starting point is 00:10:00 oh my gosh, I've got this human capital. And it may need to be shifted some. I may need some different, some more of this and some less of that. But I have this human investment. Now I have this superpower technology, what value can I now create? What market share can I go grab with that combination? And there are a few companies that have that mindset at the top today, but only a few. And they'll be the winners. Are there examples where transformation actually destroys value or harms value? I hope not. Yeah. I mean, I can't think of any off top of my head, but I'm just curious if in your studies or maybe like overreaching change, you know, kind of goes too far? Not if it's done right.

Starting point is 00:10:43 Okay. No, never. Again, you would only get in that situation if some idiot thing was mandated and not learned from. Now, is there a creative destruction that happens? Absolutely. I mean, you can intentionally blow things up in order to come out with something better at the end. But if you don't come out with something demonstrably better at the end, and demonstrably better, to me, is quite simple. It's better outcomes in the marketplace.

Starting point is 00:11:06 It is more differentiation for your product, higher margins for your product, greater, you know, customer share. Those are the outcomes that transformation should be driving toward. Outcomes that are purely cost-based, to me, are not transformation. That's not transformational. That's just business as usual. Okay. And this is like such an interesting, you know, kind of perspective to have. And this book is something that, you know, especially in this era of change with AI, you know, kind of where it is right now, I think that for our investors, if you're building a portfolio today, are there certain transformation signals that, you know, they should be prioritizing as they try to look through this lens that you're kind of presenting us with? I think, again, kind of going back to one of our first things, digging deep on how these companies, companies are starting to embed AI and how they're thinking about embedding AI into their organizations. If they're talking about individual productivity metrics, they're probably not measuring the right thing. You know, even really forward-thinking CEOs are saying things like, well, 30% of our code is being generated by AI today. Well, who cares? What does that even mean? Why isn't it a million percent of your code? I mean, right? AI can generate whatever you want it to generate. The question is, Are your products any better?

Starting point is 00:12:24 Right. Finding companies who are talking about how AI is differentiating their customer experience in tangible ways with real quantitative feedback against it, again, like NPS or some other metrics, those are the companies that are really thinking about this the right way. So anyway, to put a cap on that, if people are still touting vanity metrics, like percentage of code complete or how many memos were generated or how many marketing emails, went out or blah, blah, blah, I can guarantee you those are not companies that get it. Right.

Starting point is 00:12:59 They're quite happy with their vanity metrics. The companies that are really seriously thinking about how they're evaluating the value of AI in the context of their business outcomes are the companies who are at least trying to really get it right. One of the things that I've been working on lately, and I haven't seen anybody do it, but I've been doing a lot of work and I'm about to write a thing on it is, you know, there's a lot of talk about we're now managing tokens. We're managing AI like headcount. We're managing tokens like headcount. Seems to me that if that's the case, then we ought to be looking at revenue

Starting point is 00:13:33 per token numbers in the same way that we look at revenue per headcount. And then you need to think about, well, how do you normalize the cost of a headcount versus the cost of a token? You know, tokens today cost, I think Anthropics tokens on a blended basis between the input and output tokens are about $30 a piece. What's your revenue per million tokens? Do you normalize that against headcount? So now you monetize headcount and you monetize tokens and what's your revenue per that target. These are the kinds of business metrics that I think when companies start doing this, those are the companies that are really going to be figuring out how to make the most use of AI and the most balanced use of AI on their teams. I was with a client last week and they were

Starting point is 00:14:16 talking about the cost of tokens and how their teams at enterprise scale, their teams are now allocated tokens. And the teams are now running out of tokens because of these individual usage mandates. They're using tokens way too early in the development process for it to be useful, and they're running out of their allocation of tokens. And so they're having to use the human expertise for the harder problems that crop up downstream in the production of a product. And so now they're starting to try to get their heads around. How do we limit usage upfront so that we have tokens available for some of the harder problems at the end. I don't know that anybody has it figured out yet, but these are the kinds of questions. And as you can identify companies that

Starting point is 00:15:00 are talking about it in this depth of terms, I think these are companies to be watched, because they're the ones that are going to figure out how to create a differentiating and sustainable business model on the back of the real costs of these things and the real power of these things. I guess I'll wrap up by asking, what does irresistible change? ultimately teach investors, again, you know, thinking of our audience about long-term value creation. I think to determine organizations where change will actually have a meaningful shift in their fortunes, finding companies that are approaching transformation in a way that is real and not just theater, it is key. You know, somebody who had identified, and there were people that did, who identified IBM in 2017,

Starting point is 00:15:52 they're quite happy today. So it takes more work, it takes rigor, it absolutely takes kind of reading the tea leaves and reading between the lines. But if you really find out, you know, the companies where CEOs are making long-term commitments and not just mandating theater, but long-term commitments to change

Starting point is 00:16:15 and where employee engagement is rising, where people are excited to go to work and stay at work, you know, I think personnel retention rates are probably a pretty interesting indicator of companies who are going through transformation and where people are staying. That's a pretty good indicator. And you have time. This is one area where you have a few quarters, if not a few years, to take a look and see how it's taking hold. Because so much of the investment market is still so reactive that if you find a company that is truly undergoing a transformation and you believe in it and the CEO and the board have the fortitude to see it through,

Starting point is 00:16:59 you will see a benefit. Well, Phil, thank you so much for coming on to Motley Fool Conversations. For the listeners, it's Irresistible Change is the book. Phil Gilbert is the author, especially in this day and age with AI and everything else going on. It's a really interesting read for the investors out there. So thank you again for your time. Thank you, Rich. As always, people on the program may have interest in the stocks they talk about, and the Motley Fool may have formal recommendations for our gifts. So don't buy yourself stocks based solely on what you hear. All personal finance content follows Motley Full editorial standards and is not approved by advertisers. Advertisements are sponsored content and provided

Starting point is 00:17:38 for informational purposes only. To see our full advertising disclosure, please check out our show notes. For the Motley Full Money team, I'm Matt Gris. Thanks for listening, and we will see you tomorrow.

Motley Fool Money - Irresistible Change: How to Spot Real Growth

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