#124—Brian Evergreen: Beyond Digital—The Era of Autonomous Transformation

Brian Evergreen, author of Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence, named a Next Big Idea “Must-Read” and for which Brian was shortlisted for the 2023 Thinkers50 Breakthrough Idea Award.  

Building on his experiences working at Accenture, Amazon Web Services, and Microsoft, Brian advises and speaks to organizations around the world, guest lectures at Purdue University and the Kellogg School of Management, sharing the unconventional and innovative methods and frameworks he developed leading and advising Digital Transformation initiatives at many of the world's most valuable companies. There are very few people in the world who has had as much experience of Brian facilitating strategic conversations that lead to big, breakthrough ideas. 

At the core of Brian’s ideas is that in this age of accelerated AI, there is not only room for—but a dire need for human reasoning to remain a core component of business strategy in what he calls “autonomous transformation,” which contrast with and complements what we all call digital transformation.  

In this podcast, he shares: 

• The differences between transformation, reformation and creation — and when each is best employed given the presence of key criteria 
• His "reason-driven Framework," which in contrast to data-driven frameworks, allow the space and opportunity for human experimentation, learning, and innovation 
• The critical difference between digital transformation (moving from analog to digital) and autonomous transformation, which encompasses systems that autonomously handle processes and make decisions, emphasizing that transformation should create value, not just imply digital changes

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Episode Timeline:
00:00—Highlight from today's episode
1:09—Introducing Brian + the topic of today’s episode
2:56—If you really know me, you know that...
3:29— How chess shaped Brian's approach to strategy
04:49— Defining strategy: a decision tree framework
05:56— Challenges of strategy implementation in organizations 
06:58— Reformation vs. transformation, the key differences
09:17— Knowing when to build from scratch
11:24— Reason driven framework for strategic decision making
14:16—  Data vs. reason: Why reason driven decision making is crucial 
24:22— Steps of the reason-driven framework
28:48— Digital vs. autonomous transformation
33:43—Solving for the future
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Additional Resources:
Link to personal website: www.brianevergreen.com
Link to book: Autonomous Transformation: Creating a More Human Future in the Era of Artificial Intelligence
LinkedIn:  www.linkedin.com/in/brianevergreen/

Thank you to our guest. Thank you to our executive producer, Karina Reyes, our editor, Zach Ness, and the rest of the team. If you like what you heard, please follow, download, and subscribe. I'm your host, Kaihan Krippendorff. Thank you for listening.

Follow us at outthinkernetworks.com/podcast

Transcript

Kaihan Krippendorff: Well, Brian, thank you so much for being here with us as we've been trying to get you on the podcast for a while is great that we finally got time with you.
 
 Brian Evergreen: Thank you. I'm glad we're able to make this work, and I'm looking forward to it. Thanks for having me.
 
 Kaihan Krippendorff: So I wanna open up with the same 2 questions, I ask all our guests. The first is just for us to get to know you a little bit more personally, could you complete the sentence for me? If you really know me, you know that.
 
 Brian Evergreen: I would say if you really know me, you know that I grew up an internationally competitive chess player. And if you know me even more deeply, you know that I played against Josh Waitzkin. In my youth. And that's a deep cut for those who know who Joshua skin is or saw searching for Bobby Fisher in the nineties. But if you really know me, that's 1 of the things I make most of my closer friends watch, at least
 
 Kaihan Krippendorff: how has that experience changed you or the way you think or how do they carry forward?
 
 Brian Evergreen: It's been interesting. It's been interesting the way that I think the playing chess because I when we talk about the word strategy, I first heard that word when I was probably 6 or something like that. And when I started playing and learning in tournaments, I was learning from a grand master of chess who was explaining what strategy is, at least from a chess context, and what's the difference between tactics and a strategy, and then every time I played a game, you know, especially a tournament setting, I would be writing down my moves, and then my friends and my team and I would go over after the game, you know, throughout the week when we'd come to chess practice after school. We would go over our games and we would review, you know, this is a strategy I had. This is what I was thinking.
 
 This is what I played. There's no other real, I think, format, especially for kids to practice creating a strategy, executing it, and getting real time feedback. That I I'm aware of aside from maybe if you maybe in Team sports, there's a version of that. But and then also, you know, when you go over Grand Master Games and study what their strategies were at. So I think that's really shaped the way that I think about strategy.
 
 And so when I first joined Accenture strategy, my whole world view I realized was fundamentally different than the way a lot of other people think about Stripe.
 
 Kaihan Krippendorff: That is beautiful. That leads us already to our next question, which is what's your definition of strategy?
 
 Brian Evergreen: So for those who have played a lot of chess, you'll probably see how my definition of strategy is which I have gone, you know, to the length of creating a specific definition is sort of rooted in in the way that chess might teach 1 to think. So for me, I define strategy as the process of creating and selecting decision trees of choices under conditions of uncertainty and competition with the aim of achieving one's goals. It's a decision tree because, you know, back to that chess example, if you have you know, if I'm saying, okay. If I do this and she does that, then I'm gonna do this, and I'm coming up with all these different decision trees of how things could go. And instead of, like and sometimes we find in our organizations, we lock in on strategy, and then we pivot to execution and don't ever reconsider the strategy.
 
 But you don't have that luxury in chess where every single move, you have very real time feedback of, oh, I was planning to go over here to try to win their queen. But now that they move their if we know where to the other side of the board, you know, I have to I have to reconsider. I have to come up with something, you know, a new a new plant.
 
 Kaihan Krippendorff: Right. Right. I got it. Yeah. The interesting try to try to map the process of trust to say growing a company and is each game a day or is each day in a year?
 
 I guess we kinda think of it as each game being a year, maybe, or a few years. Yeah.
 
 Brian Evergreen: I think it's a few years in the sense that and that's 1 reason I think that strategy is so hard for organizations is that, especially if you have an done strategy elsewhere in your life and the first time you're doing it is in an organizational context, the amount of time it takes to get feedback. Like, you develop a strategy, you start putting resources in motion. 1, almost inevitably, the way that the strategy ends up taking shape and execution is gonna look different than what you came up with in the first place. And then 2, you know, the learning cycle is so long before you actually get feedback about whether or not it worked. And there's so many potential points of failure that You could have outcome bias.
 
 You could blame the execution team. There's so many different ways that that it can be challenging to learn from and really develop the skill of strategy in an organizational context.
 
 Kaihan Krippendorff: Yeah. Makes sense that people are different stages in that journey and different levels of expertise or on the learning curve, and so you're playing them with people with very different whatever levels of appreciation of strategy. I really wanna get into what autonomous transformation is. But rather than ask you to define it, I'd love you to break down first 2 distinctions that I found particularly interesting versus distinction between Reformation and transformation and creation. Could you just explain what you mean by the difference between this 3?
 
 Brian Evergreen: Yeah. So I'll start with transformation, which we, you know, we all know and love. We talk a lot about digital transformation. And so, you know, the true definition of transforming something is to change the nature or structure of it. And so digital transformation is when you're moving from analog to digital paradigms and you're transforming the nature or structure of the value that you're creating from a customer perspective.
 
 Right? So moving from going in person to blockbuster or Hollywood video and running a DVD or a DHS to now I'm streaming virtually. That's the nature of receiving entertainment has completely been transformed. Right? But there's so many organizational initiatives that we have where we're moving from analog to digital or from paper to digital.
 
 But the nature of the process is exactly the same. And so that doesn't meet the definition for transformation. So when I was researching and writing autonomous transformation, I thought, well, if I'm gonna really define autonomous transformation, I have to somehow account for all these initiatives that we have that are not really transformative. So what word or what definition would really meet that. So it was interesting because I usually, you start with a word and look up the definition.
 
 But this time, I have the definition. I need to go backwards and find what word really fits something that where it's improved. Going from analog to digital improves your process. And make you can make it be 12 times more efficient. But if it's the same fundamental process, it's not transformed.
 
 And so the word that that technically means to improve without changing the nature of something is reformation. Or to reform something, which is interesting because in our vernacular, colloquially, because of the protestant reformation, we think of reformation being transformative. But by definition, to reform something is to improve it without changing it.
 
 Kaihan Krippendorff: Well, that's why 1 thing I loved about that introduction of that word because I think often transformation can be meaning big and important, and everything that is not transformation is not important. And yet so much of and you talk about that, you know, reformation is important. Like, like, what's more valuable transformation or reformation?
 
 Brian Evergreen: That's right. And then the path from where you are to where you wanna be, it all depends on your goals. So it would be silly in the face of a recession when your profits are down and you don't have a lot of margin. To bet everything on a giant transformation because you don't even know if the customers on the other end will have the margin to buy that from buy whatever new value you're creating. And so versus in that in that state, it would probably make more sense to focus on reformation.
 
 And how can we improve the efficiencies and the things that we already have. And, really, the path from where you are to where you wanna be in the future, depending on whatever that goal is, will more than likely involve a stitching together of we're gonna reform the data in this dataset. We're we don't need to transform it. We don't need to you know, it just needs to be improved. We need to clean it up.
 
 We need to fill it out. And versus maybe there's something else over here that we need to transform because it's not fit for purpose anymore. And then there might be a gap from where you are to where you wanna be, where you really need to create, and that's that third word you asked about, which is to reform and transform, assume that you're starting with something that you're performing or transforming. But the example I often give is if you think about creating an electric vehicle, you don't take a gasoline engine and then reform it or transform it to create an electric vehicle. You have to you have to set aside what you have and start from scratch and create something new, fit for purpose.
 
 And a lot of times, as organizations, it's easier at least maybe it seems easier to start with what you have and make it better or transform it, but there are times when you should consider at least putting it out of your mind and thinking from a white space of, okay, if I were creating something new to fit this fill this purpose, what would I create, and if that looks like something you can get to by improving or transforming what you already have even better. If not, then you'll have to come up with a different plan. Yeah. No. I like what
 
 Kaihan Krippendorff: you just said. It it's different than what I was thinking. I was thinking creation is above transformation. It is like when it's really, really big, but I hear you saying is there are lots of little things that you need to create as you move from 1 state to the other. How do you know when creating from nothing is the right thing to do?
 
 And I also hear what you say is, like, you might try creating for some from nothing and then realize, hey. What we have is pretty good or we can reform what we already have?
 
 Brian Evergreen: Great question. It comes down to your goals. So and this kind of gets into some of what I was thinking we'd probably talk about a little later, but I'm happy to jump up to it now, which is the idea of this framework that I've created called the recent driven framework, it's the first aspect or end future solving, really. They the first question is, what is it that we wanna achieve? And so if you say, okay.
 
 We wanna be carbon neutral by 20 30, let's say, which is, you know, only 6 years of doubt from the time of this recording. The next question is, okay. Well, what would have to be true for us to be carbon neutral by 20 30? And the first list of things that you document of things that would have to be true are your are your top level theories. Then from there, you say, okay.
 
 What are the things that would have to be true for all those theories to be true? Those become a layer of hypotheses, and you keep recursively asking that question. And then if you imagine a large beautiful decision tree, working down until you get to the point where the things that would have to be true are already true. Like, you get down to what's already true today. And so and then that way you have a plan of investments from where you are to where you wanna go.
 
 And so to your question around creation, if 1 of the things Like, if you said, well, we if your if your goal is to be Carbon Neutral by a given date, if 1 of the things that would have to be true is you would have to be able to source energy differently or you would have to be able to change the nature of how if you're a consulting firm, you'd have to, you know, transition x percent more calls to virtual or something. I don't know. Right? Whatever that list of things that would have to be true, you might find things you think, well, that doesn't even exist in the market at all. Or that doesn't that's with our capability.
 
 So we have to go create that capability. Be. That's to me, that's how, you know, it's just starting with anchoring on that goal that you have and then asking all and documenting all the things that would have to be true. And then, to me, the benefit of that is it becomes a more scientific way of making decisions because you're accounting for all the all the knowns and the unknowns. And instead of starting with it, here's all the problems we have.
 
 How can we take care of those problems? Which ones are the most painful? Which ones are the to fix. That's kind of, I think, our usual LRP or annual planning cycle, instead saying reversing that and saying, I'm not gonna focus just on the prop because some of those problems that I would have otherwise invested and spent capital to solve might just absolutely dissolve on the way to this better version of our organization that we wanna go create or better product.
 
 Kaihan Krippendorff: Guess where my mind is popping now is we take these, but what must be truths and expand them down and down and down. The other's whole, like, platform of what must be true, and a lot of those things just aren't true. But some of them aren't true because of human things. I think maybe a lot of things that maybe Elon Musk has created, they weren't true. Because, oh, that's not the way the industry works.
 
 But then he would say, well, that's stupid that the industry doesn't work that way. It will it can change. And then some of the things are it's not true now, but as whatever demand for electric vehicles increases, there's gonna be more demand. It's gonna drive down the cost. It's gonna increase the competitiveness.
 
 So and so the economics will change in the future. It will be true. So I just did is there a way that you think about when someone says, well, Brian, we did the exercise. You're right. It's not true.
 
 Brian Evergreen: Great. I've never I've never gotten that feedback. I usually, what they say is, okay. We've gotten down to the bottom of the things that are true today, and we see the map of where we would need to go. And then but it's a lot.
 
 Like, it's a lot of complexity. So where do we start? And what I something that I've come up with since writing the book, when I've gotten that feedback from different fortune 500 organizations that are that are working to implement these ideas and frameworks, is what I've come up with since then is saying, okay. Well, the next step is that you should sharpen that against your organization. So go across your organization internally and get feedback.
 
 And whatever people say, first of all, they might look and say, Well, this one's already true. And we know that because it was proven in this case study in in the industry. Or I read a Gartner report about it or what happened. So we know that that's possible. It's just not true for us.
 
 So then, okay, then we're gonna update that. They might say this 1, I think, is never gonna happen for these reasons. And then, great. Let's document that because then if we are able to prove it right or wrong, we're all gonna get to learn together about making even better decisions. And then you might also get, well, we already know that there's another organization working So the first 1 is to kind of look across your organization, get as much feedback, or they might say, you haven't thought about this.
 
 Here's another hypothesis you need to add. So get as much feedback documenting everybody's, you know, feedback along the way. And then the second step is to then measure it against your ecosystem and say, okay. All the things that are here, I'm gonna put a square on anything that is not core competency. I'm gonna market with a little square.
 
 And then anything that is core competency, I'm gonna give a dime into. And then all the stuff that's not core competency, I'm gonna go look in the in the ecosystem to see if there's is is this? Have they already submitted, especially if it's a scientific breakthrough, for example. have they already submitted funds. Is there a new research taking place from National Science Fundage?
 
 Anyway, right, it could be another organization that you have kind of a, you know, you know, competitive their competitor, but they you know, you have some friends there and you know, okay. We're trying to figure out if we can do XYZ thing. It's a noncompetitive type of project. Okay. Well, then I'm gonna mark that 1 with an eyeball, you know, for, like, the fact that I'm going to monitor the fact that someone else is already trying to prove that theory or hypothesis.
 
 They're already trying to prove or disprove it. So why would I invest my capital to do the same thing when I can just learn from them at least at the onset. And then the once you've done across the ecosystem, then another way to look at it is where would it make sense to partner back to that sort of core competency or not. So if it's what we would need this thing to be true from our tech vendors, that are supporting us. And I'm guessing a lot of others in our same industry are gonna need that.
 
 So let's create a little coalition and go to and lean on our tech vendors and say, if 1 of you creates this capability, you're gonna have a growth category because we all want it. And so, partnering to create that capability. And then the last 1 is the stuff that's core competency that no 1 else is working on and that you're not gonna partner for would be the thing to really double down on saying, okay. Those are the things we're gonna specifically and
 
 Kaihan Krippendorff: We are gonna do.
 
 Brian Evergreen: Mhmm. Exactly.
 
 Kaihan Krippendorff: Gotcha. Gotcha. I love it. Yeah. We had Amy Emerson on and in her book, the right kind of run wrong.
 
 She kind of points out that often, we go in to reinvent stuff without actually looking what's already there. I think there's a huge gap between I mean, there are there are there are innovators just doing amazing stuff out there that we don't know is actually already happening.
 
 Brian Evergreen: That's so true. And it's funny that you bring up Amy because I often describe the reason driven framework as a visual dynamic strategic sort of reembracing science framework for making intelligent failures. And so because it's documented in a way that you can actually learn from it, as opposed to making basic and complex failures as she outlines them. And so I the deepest respect for her and her work, and I I think there's a there's sort of a complementary correlation.
 
 Kaihan Krippendorff: Yeah. Absolutely. Why do you call it reason driven as opposed to what?
 
 Brian Evergreen: Mhmm. Supposed to data driven. So and this 1, I often you know, anyone who's first started to snooze off during this podcast without their head up, like, with what? And so, you know, I I'm often challenged to defend it when I say that and I'll say something even more provocative, which is that the data driven method has become in practice unscientific. And so the idea of using data to make decisions sounds very scientific.
 
 Right? Because in the scientific method, their data is involved. But the scientific method is to ask questions, you know, create theories or in hypotheses, conduct experiments, analyze the results, and then draw conclusion. But in most organizations, what we do is we ask questions, form hypotheses, gather data from stuff that other organizations data that we already did, analyzing the results of that, and then drawing a conclusion, which is our investment decision, and then beginning the experiment. But instead of treating it like an experiment, we say we've proven in advance how long this is gonna take, much is gonna cost, and we're gonna tie your performance to.
 
 And so it's fundamentally unscientific. If we imagine if we use that process to try to justify cancer research, we would there would be it would stop. We wouldn't invest in it anymore because we can't prove how long it's gonna take. Right? And we if anything, we can only prove that it's not possible, which we know are reasoning as people.
 
 We really believe that it is possible so we keep trying. Right? And the and the so making so our in other words, data driven methodologies are extremely important. Precision, and I'm not advocating that that that they should go away. But we need to build we need a framework that that places human reasoning and documents it as rigorously as we account for data.
 
 Because there's limits, especially when we're trying to be innovative, we're trying to do something new, there's no data about the future as Roger Martin said. Right? And so looking out and saying, okay. Since I've reached the limits of where there's data, and I'm looking out and saying, I still think that this is worth investing in we need a way to document why and what you're thinking and then to be able to collaborate with your colleagues visually and even across your ecosystem with your partners to say, this is why we think this is worth investing in. And so, you know, if you think about Steve Steve Jobs creating the iPhone, the data said no.
 
 They like, the TAM or the smartphone after General Magic's fail failure was extremely it was projected to be extremely low. And they had data that it didn't work. Nobody wanted it or, you know, not enough of the market picked it up. And so right now, the data is that no 1 should invest in the metaverse because of the big failure that meta hat. But 10 years from now or might be logical reason or 5 year, who knows?
 
 Right? That I think that the market might be ready and the infrastructure and the way that Moore's laws, you know, brought the cost down. And so the reason driven framework allows you to account for reasoning and data as opposed to just data where your experts become computational resources that you're just saying, bring me back some numbers, and then I'll use those numbers to justify when it's there's a lot of expertise and a lot of thinking that just gets
 
 Kaihan Krippendorff: Yes.
 
 Brian Evergreen: Disappears into that.
 
 Kaihan Krippendorff: Yeah. What I'm hearing is that on 1 part and while on 1 hand, the data may support the fact that it is not possible, and it may not be possible now, but it is through the intention or this reasoning that we could find a solution that we make it possible. So I love about 1 thing I love about your work is that you're creating the impossible, intrapostable, and then I'm also hearing that we when it's something new, we may only have a little slice of the data, a little bit of it like that. You know, we might have, like, 10 percent of the data that will be there in the future. And so Right.
 
 Brian Evergreen: Yeah. And it ties into your podcast title because it's a framework for outthinking. If you're trying to outthink your competition, but you're not documenting any of your thinking in a way that your team can actually collaborate on, you know, then then you're only documenting numbers. Most organizations, it your team might spend months war rooming something, right, and trying to come up with a perfect plan. And then what happens is in the update, you know, and in that boardroom, it's okay.
 
 Here's a set of numbers, essentially. But this is the exact summary of, like, a paragraph of what we're thinking about doing. And then here's the set of numbers. And if they don't like the numbers, forget it. When, really, it's the numbers might not work, but you think about collaborating across your team if you if it the way that this this framework changes the con nature of the conversation is if imagine that same board meeting, and you're saying, okay.
 
 As we all know, we're trying to solve for being carbon neutral by 20 30. These are all the things that we've all reviewed and that we sent you ahead of time as a pre read for the things we believe would have to be true. You know, give them an opportunity to say, I disagree or agree with all things that would have to be true. If they agree, okay. This is the proposed 3 initiatives that we wanna invest in that are in the direction of that future.
 
 And if they say, well, I don't wanna do that 1 because I don't like the numbers, you say, okay. Normally, you it's just a you go to trench warfare and you throw numbers back and forth at each other, go to your homework and come back. Right? In this case, you'd stick and say, okay. So you disagree with you don't like the profitability equation on this given initiative.
 
 I'm gonna need your help here because this is 1 of the steps between where we are. This this would have to be true. We have to find a way to prove this true or false before we can move up toward the carbon neutral future that we wanna get to by 20 30. So can you collaborate can you help me come up pivots to conversation. You and me versus the problems instead of U verse.
 
 Kaihan Krippendorff: Yes. Right. Right.
 
 Brian Evergreen: Right.
 
 Kaihan Krippendorff: Right. Right. I wanna make sure that we capture this. Right. So the reason driven problem solving framework,
 
 Brian Evergreen: it's I wouldn't call it problem scoring. I wouldn't call it. You're solving.
 
 Kaihan Krippendorff: You're just solving. Yeah. Dude, can you just, like, kind of the strategy person, innovation person sitting in the board room, and they recall these steps? Could you just, like, briefly just tick off the structure so that people gonna apply?
 
 Brian Evergreen: Of the yeah. So the first thing to do is to create the vision or choose the goals you wanna that you wanna achieve. And I do have a framework for that that I've created since the book that I can share with anyone that's interested, wants to reach out, happy to share. And so they can reach me on LinkedIn is the easiest way, and there is Brian Evergreen. And but, essentially, from creating a vision, you need to make sure it's not too ambitious or not ambitious enough, but it's bold and achievable.
 
 And then having a portfolio of visions or goals that you wanna achieve that that would span across achievable within your team, achieve achievable within your organization with consensus, and achievable with a coalition. And a lot of times, we get trapped only focusing on 1 of those areas, and having a portfolio of them almost maps to the Verizon, the 3 Horizon McKinsey framework. I recommend starting there. But then once you pick 1 of those and say, okay. This 1, I'm gonna now use the reason driven methodology to try to solve for.
 
 So and that future could be We wanna have 0 workplace safety incidences. We wanna be you know, what would it take in the manufacturing setting, let's say, to create that? The next step would say, what are all the things that would have to be true for us to do that? So 1 of the things that might have to be true is well, our people would have to have longer tenure because it takes over on average, 2 years for people, so you're already using data and again. Right?
 
 2 year to inform our reasoning, 2 years for them to get to the point where they're trained up enough that they're following the safety protocols perfectly. And so, okay. What would have to be true for the people to stay longer? And what would have
 
 Kaihan Krippendorff: to be true for us
 
 Brian Evergreen: to be able to give them higher salaries? What would have to be true for us to you work your way all the way down, and now you have a comprehensive sort of tree or visual decision making kind of map. Of all the things that would have to be true. You might look at that and say, that's just not like, it's not worth it for that goal. And the we do that too fast for a few.
 
 You might say, this 1 okay. Like, we so badly want to, and we're betting our whole business about creating this future. A lot of work, but, you know, we're gonna have to invest in this. And so once you've created the whole map, let's say, with your team at an off-site or something like that or just, you know, asynchronously over time, then taking it and sharing it with your colleagues and getting their feedback having them at hypotheses, having them challenge your hypotheses, documenting what they agree or disagree with, and then also if they've seen something in the market that's already proven or just proven, you know, 1 of the hypotheses. Like, if they said, oh, I just saw an announcement that GE just did XYZ thing with safety and it's changing everything.
 
 We should dig into that and see what that how that updates this lens. Right? And then once you've done that, then challenging it against the whole ecosystem and looking across what can I monitor of what is already being proven or just proven within the ecosystem? Marketing, which things are more competency for me versus which things are not. Usually, I use a square for non and diamond for poor competency, and then saying, okay, which things am I going to monitor?
 
 Which, like I mentioned, with the ecosystem, am I usually marked with an I? Which things am I going to partner with? Because either it's not core competency, but I need it, or it's not core competency. But if I build that core competency, it could be a new commercial offering because you know, not only am I gonna need it, but maybe others. And then lastly, which things am I going to invest in directly just within my patch?
 
 And I usually mark that with the money back, and the partner went with the handshake. And so if you match in that decision tree, now you have a very clear map and then regularly checking in and saying, okay. With this cadence of maybe every quarter, every 6 months, we say, these are the decisions that we've made. This is the investments that we've made. This is how it went.
 
 Hey. You know, you disagree you know, Kaihan, you disagreed with the fact that I wanted to do this. We invested in it, and you know what? You were right. You're reasoning on that.
 
 You were spot on. I thought it would be worth it for these reasons, but it ended up being that you were right on that 1. And being able to have the psychological safety as an organization to learn because that's the only way we're gonna improve. Right now, we don't account for decisions that we make in a way that we're really learning in most organizations. We don't account for the risks we haven't taken at all within most organizations.
 
 And so this creates a framework for doing that.
 
 Kaihan Krippendorff: Oh, gosh. I love your phrase. That is not worth it for the goal because it's so empowering because people could look at that number and say, 2 year, 10 year, that's the way it is. It's not possible. And other people what you're saying is, yeah, it could be, but is it worth making it 10?
 
 That's awesome. That is so empowering. I have so many more questions. The book is amazing. You have a great other podcasts that that you get to dig in more deeply here is 1 distinction I think that we would be incomplete.
 
 If we didn't cover it, it may not be so central to what we did as important with Trevor, which is what is the distinction between digital transformation and autonomous transformation because you're talking about that's the other chart of the matrix that we didn't cover. Yeah. Mhmm.
 
 Brian Evergreen: Yeah. I only and you're right. I only talked about reformation transformation and creation. So the distinction between digital and autonomous transformation is that and it's an important 1 because I think that in most organizations, digital transformation in the market as well has come to mean anything that plugs into a wall. Like, you know, that's part of our digital transformation.
 
 It's become an all encompassing I'll also make the distinction that transformation is a means to an end, not an end in and of itself. So you only need a digital transformation if you're transforming into something. So when people say, we're going through a digital transformation, I always love to say, what are you transforming into? And, you know, often, I'll get out, like, a, you know, snarky comment or something, but I think it's a good way to challenge our thinking, right, of what yeah. What are we transforming too?
 
 Because it could become just an overarching bucket that we just keep doing digital things. But it was only useful if it's creating more value for our customers than our partners. And so autonomous transformation. So digital is moving from analog to digital. That's you know?
 
 And if you're if it's transformative, then it's changing the nature or structure of the value that you're creating. Right? Netflix is a great example or Uber. And then autonomous transformation is moving from analog or digital to autonomous paradigms, where the system is able to autonomously handle a process and make decisions. And we're in that transition right now.
 
 People are you're seeing lots and lots more talk about AI agents, for an example. I saw that back at Microsoft Research, which is why I wrote the book on it. And I knew it was coming, but it's now getting more and more popularity and more visibility. And so as an example, if you think about from autonomous reformation where you're improving something, but you're moving from digital to autonomous, or analog to autonomous, you can look at Amazon's investments in robotics and their warehousing. They're using tons, you know, they're the biggest purchaser of robotics in the US, and they're doing lots and lots in robotics and warehousing to make things more efficient.
 
 But they're not transforming the nature of how they deliver. They're just doing it more efficiently, which is still extremely valuable. And that so that's autonomous reformation. Autonomous transformation, the best glimpse we have into that future right now would be and an example would be if let's say that I worked for a construction company, and I wanted to get some lumbar delivered to Seattle where I'm based. And I call a local, you know, Acme Lumbar, and I say, hey.
 
 Will you will you get some lumbar delivered to Seattle? And they say, well, the only lumbar we have that meets what you need is in is New York. We're gonna charge you x amount to, you know, have a truck driver, bring it across the US, etcetera, etcetera. And then let's say you you're based in Florida, usually. Right?
 
 Let's say, you own a construction company, and you're like, hey. I'm calling instead of Acme Lumber, I'm calling AABC Lumber. Hey. I need some wood here in Florida. And they say, oh, well, the only lumber that we have is based in Seattle.
 
 So we're gonna pay charge you, and we're gonna have a truck driver drive it from Seattle to Florida. In the nature of today, that's just that's just how it's gonna be. We're gonna be spending extra money. There's gonna be truck drivers that are driving extra time to take you know, the same lumbar across the country to 2 different locations. In the autonomous transformation paradigm, when we move into this world of agents, instead, when I issue a sort of request to an agent from Acme Lumber, that Acme Lumber agent can reach out to a network of agents that represent in in milliseconds of time, all of these other lumbar companies.
 
 And ABC, lumbar can say, we actually have some in Seattle right now. And so then that's just fulfilled. And then when you reach out, then, you know, even it's okay. The nearest we have is in is in New York. And so you're not only tapping into 1 network, and so they're trading customers between them in a way that we would never have time for humans to make all of those phone calls.
 
 And this is an example to me of it's important distinction is that doesn't take away any person's job that adds a new underlying value, a new version I often say agents are gonna create a new type of Internet. That we don't even need to see because it's just happening behind the scenes and adding all of this value that we can't currently unlock. And then it'll refocus. It'll transform the nature of the way that we as people work in the value that we bring.
 
 Kaihan Krippendorff: Beautiful. Yeah. I love it. I mean, that that's a whole other side that we're only touching on a little bit here, but I encourage people to listen to you talking about it and read about you talking about it. And as you said, the a good way to reach up with you is and we connect with you some LinkedIn.
 
 You've all got a great website as well. Any last comment you wanna leave with us? I know there's so much more to talk about, but this is the time we have?
 
 Brian Evergreen: There is. I can tell I can tell we have a lot to talk about. I think the only other thing I'd say is that the trouble with solving problems is that, you know, that there there's a problem with solving problems. It gets rid of what you don't want, but it doesn't get what you do want. And so that's why I advocate for solving for the future instead, picking that future you want, and solving for that instead.
 
 And that's sort of the foundation of the whole reason
 
 Kaihan Krippendorff: I love it. I love it. Well, Brian, thank you so much for the work that you do thinking so deeply and actually applying it and sharing that with us and a little bit of it with us here. So appreciate your time. Thank you.
 
 Brian Evergreen: My pleasure. Thank you so much for having me.