472 Beyond AI Adoption: How to Reinvent Your Organization for the Age of Abundant Intelligence with Brian Solis and Dave Wright

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Welcome to Partnering Leadership, a top global leadership podcast for purpose-driven leaders with a growth mindset.
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Speaking to learn from the leadership journey of change makers and business insights from leading global thinkers.
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For additional leadership insights and bonus content, visit us at partneringleadership.com.
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Now here's your host, Mahan Tavakoli.
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Ryan Solis and Dave Wright.
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Welcome to Partnering Leadership.
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I am thrilled to have you in this conversation with me.
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Thanks for having us.
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Yeah, thank you, Mahan.
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I absolutely loved your book, Infinite, How Visionary Leaders Transform Today's Business Into AI forward companies.
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And can't wait to talk about that.
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Before we do, though, we'd love to know a little bit more about you.
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Dave, how about we start out?
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Where best did you grow up and how did your upbringing help contribute to who you've become?
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So I was born in Liverpool in England.
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Grew up there for the first 20 years of my life, moved to London, and then moved into the world of IT.
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So for me, my upbringing was very basic.
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I didn't go down the university routes.
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I ended up, I leaving school, 17, started work, ended up becoming, by the time I was 20, a mainframe systems programmer for an electricity company in London.
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And then started to work exclusively for, weirdly enough, Californian-based software companies, apart from one that was based out of Israel.
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Did that for the last 35 years.
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What really formed me was if I think about where I spent a lot of my career, it was in the world of service management.
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So I was when I was with Peregrine Systems in the mid-90s.
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I left and joined VMware, got into the whole cloud virtualization area.
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And then when I came back to join some of the team from Peregrine who work for ServiceNow in 2011, it was that combination of how do we do service management at an enterprise level in a cloud-based environment.
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So in the last 15 years, it all came together.
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But for me, I've always had an interest in technology.
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I've always had an interest in leading edge technology and what's in the future.
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So as I've progressed through different jobs, I've always been targeted as the person who can take a fairly complex idea and maybe present it in terms that people understand.
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So it's allowed me to do a lot of different jobs from chief strategy officer to chief innovation officer to head of pre-sales.
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It's been a good fun few decades.
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I love that.
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And at this point, that's even more important, Dave.
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AI is really, as you say in the book, transformative to the entire organization.
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Therefore, it is essential for every executive to really master it.
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So your background lends itself well to helping them understand the implications.
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But before we get to that, Brian, we'd love to know a little bit more about you as well.
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Where about the Yigre Up and how about your upbringing?
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Yeah, I'll get there.
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I don't want to lose this, though, because what you just said, Mahan, is what makes Dave such an awesome co-author for this.
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And also just that we work together across all of these fronts.
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Is because he's exactly the person you want in the room.
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And he's got the pedigree and he's got the experience.
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But also because of those things, he has the perspective of which qualifies him to be able to let a CIO know, for example, they're not thinking big enough, and you should probably listen to him.
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But on that side of the story, to bring a different perspective, which I grew up in Los Angeles, and yeah, I think every stereotype about LA is true beyond the traffic, is it's a big entertainment capital.
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And I was growing up as a geek.
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I I think I used to mow lawns to buy computer gear as a kid, even before I was a teenager, and taught myself basic programming and worked my way into this passion for tech where I was able to actually work, started in customer service, then worked into a database architect for a tech marketing company in Southern California, and then worked my way into a company I'll never forget this called AMS Notebooks.
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It was a Southern California, early pioneer in the business laptop category.
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And it's a company you would expect to have been in Silicon Valley, but it was working there that made me realize I need to get to Silicon Valley and moved there in the mid-90s and ended up not getting into in a tech role like I thought it was going to be, and ended up becoming this creating my own startup, essentially, which was to help startups go to market.
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And that became the go-to-market function and then also marketing and then also a lab.
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These are the mid to late 90s.
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And the one thing that became really interesting is because consumer-facing startups at that time were essentially a new type of company.
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And there was no playbook for go-to-markets for these.
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These are companies that had to gain market share in markets that didn't exist.
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So you had to create categories, you had to create markets and so everything, like crossing the chasm.
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And these, what's the Clay Christensen book too?
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These are the playbooks that you're like taking pieces from, the innovator's dilemma.
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And you're taking all these pieces and you're starting to create your own playbook.
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And this is the time in the early 2000s where everyone's learning and everybody's passionate.
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And I'll never forget, I need to start writing this down.
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And that was like pre-blogs and then blogs.
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And so I just started taking everything I was learning, everything I was experimenting with, and made it very public.
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And it turned out that there was this community of other people trying to all figure out the next playbook and sharing their ideas and sharing their thoughts.
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And this became this early online community of what would later become founders and influencers and authors and speakers.
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And that early community is what then I became an analyst because I wanted to up-level all of that research and the work I had done to now start to influence the C-suite, write research reports, write books, become a keynote, every way to attack the C-suite to help inspire them to see the market differently, to write their own playbook, to take everything that I had been learning, to recognize we're not going to help your business if all we're doing is adapting yesterday to be better tomorrow.
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And along the way, I recognized that I have two small girls at home.
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They were small then.
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And the best thing I could do was to stop being on stages all over the world all the time and in C-suites and boards all over the place all the time and to get a job.
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That's how Dave and I came together.
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That's wonderful.
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And part of what I know you have done as well, and it shows in this book that you and Dave collaborated on, is that when we are forced to observe the changes around us and the technology and its impact and communicate it in some instances in writing to others, it develops a certain discipline of clarity, which really helps and is a bridge that a lot of executives I find need built for them when people talk about technology and technology's impacts.
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Now, you start the book with the streather that many of the executives you were dealing with had and they still have, which is trying to understand what kind of organization or company they need to become when intelligence is no longer scarce.
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I wonder what that means.
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We deal with Chat GPT, Claude, you name it, we have access to intelligence.
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How does that impact the average organization when intelligence is not scarce?
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It's because when you designed a traditional organization over the last 50 years, it's always been based on scarcity.
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So the biggest resource cost in a company is always people.
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So what you want to do is you want to be able to design workflows that maximize how those people work.
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How do I optimize what they do?
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And that's not always by making it the most streamlined, it's making sure that everyone does things in a uniform way.
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So everyone's doing things exactly the same way.
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We've got this really repeatable thing, it's really robust.
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We can scale by adding more people to it, and that makes us a kind of a linear growth engine.
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But when you introduce AI, specifically when you introduce the genetic AI, and you've now got things that can do tasks, now you're not limited by resources.
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Now you actually face the opposite side of the coin.
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Now you're faced with abundance.
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Now you've got an infinite number of resources potentially, if you can afford the tokens.
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But you get to go out there and say, if I design this for an infinite number of people, would this be the best way to do work?
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So this is the opportunity to start to review how you do things and think, is there a better way to do it?
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And this has massive effects on a company.
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And you see it in some of the frontier companies now, but all this, this is my view of this, all this big drive now around how do we get AI to code, how do we get AI to be able to do some of the basic programming skills?
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A lot of that's being driven by the AI frontier companies because the way they look at it is if I've got 4,000 developers building this, look at the improvements I'm making year over year.
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What will it look like when I've got 40,000 developers or 400,000 or 4 million developers?
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That's why you see this big race around coding, because that's what's going to really open up the curve to be that exponential level.
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But you can do exactly the same in your company.
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You can start to look at rather than just having that steady growth rate that was based on being able to bring on onboard resources, start to think what it would look like exponentially if you started to automate some of the stuff you're doing.
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But more importantly, open up your mindset to think about what you could do that you couldn't do before.
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Not just how do I do it faster, how do I do something completely different.
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Let me jump in there, Dave.
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Does that apply to all types of organizations?
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It depends on what the nature of your business is to a degree.
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So if you were, let's say you were in the manufacturing business, for example, or let's say you're in the automotive business.
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If I went to a major car manufacturer, if I went to BMW and said, I could help you generate 10 times more cars than you generate, that wouldn't be a great business model for them.
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It's not the fact they can't produce cars quick enough to sell that limits the market.
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But if they had a vision of going into a different industry, and this is very much what the concept of an infinite company is, how do I expand out?
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It might be you could have a conversation where actually they were interested in going into, I don't know, aeronautical parts manufacturing, or they were interested in going into another diverse market that they weren't already playing in.
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You could start to isolate what the constraints were for that, whether it was people, whether it was finance, and use AI in other parts of the organization to be able to free up the resources to enable them to do that.
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But I agree, not everyone wants infinite production, which is why we say don't just do more of what you can currently do.
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I think everyone does want infinite innovation though, and that's the big difference.
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Brian, we'd love to get your thoughts also on the implication of intelligence no longer being scarce on organizations.
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Dave and I were just having this conversation.
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This is more of a philosophical take, but I promise I'll answer your question seriously.
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But we're also witnessing somewhat the dummification of thinking and strategy because of AI.
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And we see it every day in work that's proposed, idea that's proposed, language that's proposed, decks that are proposed.
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This has been studied and is being studied across the boards in academia.
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But for intelligence to thrive, let's just say human intelligence to thrive, it has to be in the driver's seat of collaborating with artificial intelligence.
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And intelligence can be abutted because the tools are there and the models are there, but it isn't how you use these things together that dictates the value that you can obtain and the value that you can create.
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And I want to just lay that down as a foundational layer because we are essentially seeing organizations led by decision makers, pressured by stakeholders and shareholders, take what could be this abundant intelligence and limit it, constrain it to the work that's being done today, to the value that's being created today, with the assumption that is infinite.
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But we all know history has taught us very well that tools aside, it is the strategy, the thinking, the vision, the innovation, as Dave said, begets longevity, begets competitiveness.
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No one's gonna write in an article or an obituary of one of these companies who's gonna probably not make it because their lack of infinite competitiveness.
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No one's gonna say we just didn't use enough AI.
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What they will say is they just lost their way.
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And that's, I think, where intelligence becomes a value-added differentiator.
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So it starts with how people perceive this moment and then how we work with these tools.
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Dave and I were having a conversation yesterday where he brought up this very important stat that I don't ever want to lose, which is that Quantum Black by McKinsey had studied 25 attributes of AI investments in terms of ROI.
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And of the 25, the number one that had impact on eBIT was the reimagination of a workflow with AI at the core.
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Meaning, and that's just a fancy way of saying, we're going to rethink how workflows throughout an enterprise with AI to extract the greatest outcome, the greatest value, the greatest potential.
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And if you do that one at a time in the areas that have the greatest impact in your organization, you're going to essentially rewire your company in the process.
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There's a lot that goes into that statement to make that happen.
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But that is the answer to your question.
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It's where are we going to focus this intelligence to help us extract greater and new value?
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What a powerful point there, Brian.
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I wanted to underline a couple of things.
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I was talking to the CEO of a telecom, and he was sharing some of his frustrations with me.
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And he said, there isn't a day that goes by where another senior executive puts up a set of beautifully crafted PowerPoints on some element of our strategy that they can't fully own or explain.
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At all levels of organizations, there is cognitive outsourcing to AI what the executives should maintain themselves.
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The other point that you made is looking at the entire workflow and how intelligence can play a role in that.
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And I would love to get your thoughts on what is an AI-enabled company versus what you call an infinite company.
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Because right now a lot of companies are becoming somewhat AI-enabled.
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What is the difference between the two?
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The book is built on the work that Dave and I do.
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So, for example, as we were writing this book, we were also doing research around what we call the four AI cultures, defining how companies are seizing this moment and moving forward with AI.
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So, on one extreme side, you have a company like an AI native.
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This is a company that is literally inventing itself with AI from the ground floor.
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It's the DNA of the new construct.
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They're shifting from org charts to work charts.
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There's inherent human-agent ratios to accomplish work.
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Things are more horizontal across the enterprise than they are more vertical in terms of silos.
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And then you have AI forward, AI first.
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We decided to lean on the term here.
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It's even on the cover of the book.
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We want to help companies become AI forward.
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This is how we're going to think with AI.
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If that means that we can think from a native perspective, fantastic.
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But that we know that's going to be hard because you can't just stop.
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So we do want companies to recognize that infinite doesn't mean make more of what you already do today, like Dave said earlier.
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It's about becoming boundless in capacity.
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It's about being boundless in decision making.
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It's about being boundless with imagination and curiosity and to turn that into part of the culture of your organization so that you can ask questions that have maybe been a little sensitive to ask in the past.
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Or as Dave and I were talking about yesterday, we've got to be able to look at sacred cows.
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What is the legacy that we're protecting in order to be able to sacrifice what's necessary in order to compete against the future?
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So, in many ways, you could think of an infinite construct beyond where we get to in the book in terms of specifically how to do it in stages.
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If you look at Netflix, Netflix is one of the most interesting companies in that they went all in on innovation.
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So much in the same way an AI native is redesigning their organization and probably will continue to redesign their organization to be competitive.
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Netflix is unique because in order to evolve from DVDs to e-commerce, e-commerce to streaming, streaming to content creation, it is in its most latest incarnation where it's actually doing two things at once, which is streaming and content creation.
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But to get there, they've had to shed the largest parts of their business in order to take on what they knew was going to be the next S curve.
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And so with that in Silicon Valley, for those who don't know, it's called jumping the curve.
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So you're not going to see out your bell curve.
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You're just going to jump to the next as it's starting to take shape.
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And that is a mindset that you could associate with an infinite company.
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Here, where we recognize that you can't just jump your curve.
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You're going to have to see that through.
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But what Dave and I refer to it as is mode one and mode two transformation is you're using AI to maximize the curve you're on while exploring the gains from all of that work and applying it to what is next so that you're competing with and disrupting yourself as you go and grow.
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I think the other thing that's interesting is an infinite company doesn't feel constrained by where it moves to next.
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It's you've got to have that vision in the company of what you want to achieve.
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And this is one of the reasons why we always say that an AI strategy shouldn't be about AI.
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You should be looking to achieve something else with your company.
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And AI is simply the vehicle that allows you to achieve what you want to achieve.
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So it's not necessarily growing infinitely to become the biggest thing in your particular domain.
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It's always having that agility and flexibility to know that you can infinitely reinvent yourself, you can infinitely survive, you're not constrained by resources and you're not constrained by lack of innovation.
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Are there organizations that you would say have done this well or have started the process well for others to learn from?
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Look, to be honest, the reason we wrote this book is because everyone's always looking for that use case or that case study because it removes a lot of the fear and ambiguity if we can if we can go point at something and follow the model and deem it as a win.
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But that is only creating a new AI-powered status quo.
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And there are plenty of books out there and plenty of podcasts and articles out there that'll help you protect your business model as it exists today with AI.
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We didn't want to do that.
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We wanted to say if you had to reinvent your company, how would we do it?
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And so we actually did the work of what it would mean to reinvent a workflow, for example, so that we could document those steps.
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And then we thought about what it would mean to measure progress in an entirely new direction.
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So we could document those steps.
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So while they're not use cases, they are blueprints.
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And this is going to be an uncomfortable read for people who are not ready to actually change their company because that's what we're helping them do.
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So we tell stories of the snippets of progress where we see them.
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So, for example, in the book, we talk about JP Morgan Chase and how they said they wanted to be this AI superbank of 2030.
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And they detail in their strategy how they're going to do this.
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And then we dissected that strategy, compared it to an AI native strategy, so you could see where they compare and contrast, so that you can see for yourself how a company says they're going to do it, how they're tracking to a company that's literally disrupting the market.
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And then you can see for yourself where do you want to be in that spectrum?
00:21:51.200 --> 00:21:52.880
But then there's also lessons to be learned.
00:21:53.119 --> 00:21:57.119
Dave, I'm sure maybe you could tell the IKEA story, but it's not in the book.
00:21:57.200 --> 00:22:06.160
But there are stories, for example, like Ford, where you see they probably followed a use case.
00:22:06.319 --> 00:22:18.960
They implemented AI to streamline quality control and recognize that as a result, their quality control, their quality in general, was on a downward spiral.
00:22:19.200 --> 00:22:20.480
They got called out for it.
00:22:20.640 --> 00:22:30.960
They had to rehire their senior engineers that they let go in favor of AI in order to fix this, humans with AI now to improve quality.
00:22:31.200 --> 00:22:36.640
And so what we didn't want to do was say, yeah, go make the mistakes and then come back to us.
00:22:36.880 --> 00:22:38.960
We wanted to say, don't make the mistakes.
00:22:39.119 --> 00:22:40.640
There's really not time for it.
00:22:40.799 --> 00:22:42.240
Here's how to do it right.
00:22:42.799 --> 00:22:44.400
You just reminded me then, Brian.
00:22:44.559 --> 00:22:50.400
So the IKEA story is interesting because it's not what you think about an AI story traditionally.
00:22:50.559 --> 00:23:00.880
What IKEA did was they looked at all their customer service requests and they found that a lot of the customer service calls that came in were for what would look good in my room?
00:23:01.039 --> 00:23:02.240
Would this fit in my room?
00:23:02.400 --> 00:23:04.319
How could I redesign my kitchen?
00:23:04.559 --> 00:23:07.279
So they said, how could we provide a service to do that?
00:23:07.440 --> 00:23:11.599
So they looked at how many calls were coming in, they looked at how many people they'd need to do that.
00:23:11.759 --> 00:23:15.440
And what they did was they used AI to build a boss.
00:23:15.519 --> 00:23:18.559
And they called the bot Billy because Billy was the best-selling bookcase.
00:23:18.640 --> 00:23:20.319
So why not name it after a bookcase?
00:23:20.480 --> 00:23:22.880
Billy automated customer service for them.
00:23:23.039 --> 00:23:31.680
But what they did was they used the headcount that they were saving off that to set up an interior design consultation unit within the company.
00:23:31.839 --> 00:23:34.160
And it was a big unit, it was like 8,000 people.
00:23:34.319 --> 00:23:39.680
But when they automated customer service, they used AI and they saved$13 million.
00:23:40.079 --> 00:23:42.960
And that is your classic mode one, AI.
00:23:43.119 --> 00:23:44.400
Look at what we've saved.
00:23:44.640 --> 00:23:50.960
The mode two side is let's take those resources and build this interior design part of the company.
00:23:51.119 --> 00:23:54.240
That generated over a billion euros in revenue for them.
00:23:54.319 --> 00:23:56.240
It increased their top line by 4%.
00:23:56.799 --> 00:24:04.240
Now, the interesting thing is if we use Brian's example before, if you were truly AI native, you wouldn't have used people to do that.
00:24:04.400 --> 00:24:08.160
You probably would have created an interior AI design tool.
00:24:08.400 --> 00:24:14.079
But what they realized was that human interaction allowed people to have a different spin on things.
00:24:14.160 --> 00:24:20.880
It allowed people to suggest things that perhaps they weren't thinking of based on a conversation rather than just processing a picture.
00:24:21.039 --> 00:24:28.000
So although they are using AI to transform themselves, it's in a different way than you'd normally tell with an AI story.
00:24:28.400 --> 00:24:33.200
The IKEA example is a beautiful example of how it can be done.
00:24:33.440 --> 00:24:50.960
That said, I'm sure you see what I see where in a lot of organizations, whether it's for senior executives or others, there's tremendous fear associated with any AI initiative, whether it is just augmentation or moving toward more of a transformation.
00:24:51.279 --> 00:24:54.480
You share an equation in your book as well.
00:24:54.720 --> 00:24:56.799
You have resistance to change.
00:24:57.599 --> 00:25:01.200
There is an element that the resistance to change is divided by.
00:25:01.599 --> 00:25:13.599
Now, therefore, the question becomes as we hear in the news about organizations that use AI and lay people off, in some instances, such as Ford, they decide to bring them back, but there are many that haven't.
00:25:14.000 --> 00:25:22.640
Boards of directors and shareholders that are expecting a return on investment on AI, in many instances, more on productivity and efficiency.
00:25:22.880 --> 00:25:27.119
They are not waiting for the new infinite organization.
00:25:27.440 --> 00:25:41.519
So, how can executives and CEOs approach this in their organizations in a way that the opportunities that you talk about can come into view rather than all the fear that has been associated with this kind of transformation?
00:25:41.920 --> 00:25:47.680
For me, the biggest things that most companies miss is building a framework of trust around AI.
00:25:47.839 --> 00:25:55.359
So there is a fear in most companies, and it's a real fear, it's genuine, it's not to dismiss it, that hey, AI is gonna come and take my job.
00:25:55.599 --> 00:25:59.039
And what happens is everyone then accepts AI.
00:25:59.119 --> 00:26:01.759
So AI does something and everyone's like, oh yeah, it's fine, it's great.
00:26:01.920 --> 00:26:11.680
You need to build a trust framework where people can question AI, or where people can say, actually, I'm not gonna go down the Emperor's new clothes routes of saying this is fantastic.
00:26:11.759 --> 00:26:18.240
I'm gonna call it out and say, no, this isn't doing it the way it should be doing it, or this isn't doing it the way you want it to do.
00:26:18.559 --> 00:26:28.799
The interesting thing I think companies need to accept around this is uh you can go in and you can just say, I'm gonna do bottom line reduction, and it is gonna be a headcount reduction exercise.
00:26:29.039 --> 00:26:41.599
But the people who realize how AI is gonna change jobs, so not necessarily replace them, but change them, they're the people who are gonna be able to survive through this a lot more.
00:26:41.839 --> 00:26:52.240
I had uh someone was reminding me of it yesterday that I'd been doing a presentation two years ago, and I had a slide up that said, AI won't take your job, AI will change your job.
00:26:52.400 --> 00:26:54.960
And the guy I was speaking to said, Why did you put that in place?
00:26:55.200 --> 00:27:06.079
And I said, Because that presentation was the first time where I'd looked at what my job is, and my job's predominantly to create presentations, to do keynotes, to talk to people, do things like this.
00:27:06.240 --> 00:27:10.720
And that was the first time I'd used AI to build the deck for me.
00:27:10.880 --> 00:27:18.880
And I realized where I used to spend ages looking for the perfect image for a slide, now I could just describe it as it appeared on the slide.
00:27:19.039 --> 00:27:22.799
And it wasn't taking my job away, but it was changing my job for me.
00:27:22.960 --> 00:27:42.880
So having that ability to have that trust framework where you can question things, but also have that company mindset where it's like, what skills have we got and where can we reutilize those skills and how are we going to allow people to be able to potentially retrain, but more importantly, look at the openings we've got in the company and find out where people fit in those roles.
00:27:43.039 --> 00:27:44.319
That's gonna be a big difference.
00:27:44.559 --> 00:27:49.200
I just played with that guy's not gonna take your job, it's gonna change your job in my head.
00:27:49.359 --> 00:27:50.799
I'm totally gonna borrow that.
00:27:51.039 --> 00:28:00.640
I remember I was on stage for MetLife's big AI event in New York, and I was challenging leadership.
00:28:00.880 --> 00:28:08.000
A lot of people who do think that AI isn't just gonna take jobs, that AI should take jobs.
00:28:08.160 --> 00:28:11.519
And these are people who feel the pressure from Wall Street.
00:28:11.680 --> 00:28:19.519
These are leaders who see that after you make certain moves, that the stock responds positively.
00:28:19.759 --> 00:28:24.480
And I said something like AI isn't gonna take your job, your boss will.
00:28:25.039 --> 00:28:28.960
Something along those lines, because I wanted to make the point it's an intentional decision that you make.
00:28:29.359 --> 00:28:34.559
And this is one of the reasons why I like the IKEA story so much.
00:28:34.880 --> 00:28:47.200
Because when you see people booing speeches at graduations, it's because that resistance is the result of a future that they were told to expect.
00:28:47.599 --> 00:28:56.400
And the trust that Dave is imploring leaders to embrace is that you have to change the narrative.
00:28:56.880 --> 00:29:06.720
If you let AI take jobs, it is because you haven't decided that AI can create new value with people.
00:29:07.039 --> 00:29:10.160
And that is most likely because you haven't explored that.
00:29:10.400 --> 00:29:20.559
And so this is why IKEA is so hopeful to me, in that it was an intentional decision to look for new ways to create value.
00:29:20.799 --> 00:29:24.079
And that research led them to those outcomes.
00:29:24.319 --> 00:29:26.480
That's how you build trust.
00:29:26.720 --> 00:29:30.960
It's not just talk, it's actually doing and showing.
00:29:31.119 --> 00:29:51.519
And I think what's most important is that when you can show that, Dave gave a construct for how you could look through service logs to see if there are any patterns, for example, to see where there's opportunities to reskill human representatives to deliver new value creation or deeper relationship building opportunities that can create and unlock new value.
00:29:51.680 --> 00:29:55.279
But now someone can say, Oh, I hadn't thought of that.
00:29:55.519 --> 00:30:02.480
And if we can do that more and more, and that's what the book is designed to do, where every page you're probably saying, I hadn't thought of that.
00:30:02.640 --> 00:30:04.480
Hopefully now you can't unsee that.
00:30:04.640 --> 00:30:10.480
And you've now start to open your mind more to think differently about how you can work with AI.
00:30:10.799 --> 00:30:13.279
I just saw one thing to this as we're going through it.
00:30:13.440 --> 00:30:21.279
I think that companies and definitely employees in companies have to not be afraid to say this is not a good use of AI.
00:30:21.519 --> 00:30:26.559
Because what happens now, what I see in a lot of companies is people are being encouraged to use AI.
00:30:26.720 --> 00:30:29.759
And Brian came up with the concept he talked about in the book.
00:30:29.920 --> 00:30:35.519
That what happens is you create this AI tax, and the AI tax is on me.
00:30:35.680 --> 00:30:37.039
So I'll give you an example.
00:30:37.200 --> 00:30:42.559
The other day, someone comes to me and they said, Hey Dave, I've got this great idea for what we could do from a business perspective.
00:30:42.720 --> 00:30:45.359
And I was like, Okay, send me an email, run it past me.
00:30:45.599 --> 00:30:51.839
So if we'd have wound back three years ago, this person would have sent me two paragraphs.
00:30:52.000 --> 00:30:53.359
Say, this is my business idea.
00:30:53.519 --> 00:30:54.480
Not now, no, no.
00:30:54.559 --> 00:31:02.960
Now I get a 14-page clawed generated PDF that I then have to go through to try and isolate what the core is.
00:31:03.200 --> 00:31:05.200
And that's not a great use of AI.
00:31:05.359 --> 00:31:12.079
People, it was funny in our industry, when AI first came out, everyone was like, hey, could we summarize this field?
00:31:12.319 --> 00:31:14.480
And I've worked in this industry for 35 years.
00:31:14.559 --> 00:31:20.400
No one ever asked me for autosummarization before, but now it's hey, summarize this, summarize that, create this.
00:31:20.640 --> 00:31:28.160
And it is, you need to part of that trust framework is to be able to say, actually, this is not a good use of AI, or we'd be better doing this a different way.
00:31:28.559 --> 00:31:32.640
Or ironically, Dave, you use AI to summarize talking.
00:31:33.039 --> 00:31:50.079
One of the VPs at Microsoft said we've reached a point where the CEO gives three bullets to an AI to come up with a 10-page memo to send to the staff, and sends a 10-page memo, and the staff gives it to AI to get three bullet points on what the CEO is talking about.
00:31:50.400 --> 00:31:52.960
So there is that friction that we go through.
00:31:53.200 --> 00:31:55.440
But I also love the IKEA example.
00:31:55.680 --> 00:32:05.200
As you mentioned, Dave, trust is critical, but it's not something that the CEO or any executive stands in front of people and says, trust this process.
00:32:05.279 --> 00:32:08.880
We need to trust each other with respect to the AI approach.
00:32:09.119 --> 00:32:20.240
I would imagine when people talk about AI use cases at IKEA, employees are not terrified because they have already seen and they have that trust level.
00:32:20.480 --> 00:32:21.599
So that is critical.
00:32:21.759 --> 00:32:31.039
Now you talk about learning velocity as the speed at which a company senses, tests, absorbs feedback.
00:32:31.359 --> 00:32:37.200
So how can organizations think about and assess their learning velocity?
00:32:37.680 --> 00:32:46.880
So for the listeners out there or the viewers, what Mohan is breaking down is our return on intelligence formula here.
00:32:47.039 --> 00:32:50.240
So resistance to change is one of the elements.
00:32:50.480 --> 00:32:52.319
And now here, learning velocity.
00:32:52.640 --> 00:33:00.160
The learning velocity sounds pretty fancy, but what we're really saying here is it's your ability to learn and unlearn.
00:33:00.480 --> 00:33:03.200
And that is both at speed and scale.
00:33:03.519 --> 00:33:11.039
And it is a simply said way of saying what you need to do in order to do what you need to do.
00:33:11.279 --> 00:33:14.480
However, that is part of the resistance to change.
00:33:14.799 --> 00:33:20.319
So Dave and I talk about this and probably comes up almost every day.
00:33:20.559 --> 00:33:21.599
Self-awareness.
00:33:21.920 --> 00:33:31.359
Like this you have to be so humble to recognize that you're both not learning and you're not unlearning.
00:33:31.599 --> 00:33:33.920
You're essentially just adapting.
00:33:34.079 --> 00:33:37.759
You're taking this stuff and putting it on top of what you do.
00:33:37.920 --> 00:33:40.480
And you're probably making it faster.
00:33:40.640 --> 00:33:45.119
I can promise you, you're probably making it worse for people who have to experience it.
00:33:45.200 --> 00:34:01.279
So whether that's a memo or a deck you're creating, an email you're writing, or if you're trying to create a customer experience, if you're throwing a bot in front of a customer instead of a service agent, a human service agent, for example, you're probably going to impede the experience that they have.
00:34:01.519 --> 00:34:10.639
So taking a step back, learning velocity is really about understanding what this moment is all about and what you can do about it, what you can do differently moving forward, and then scaling that.
00:34:10.800 --> 00:34:13.440
So resistance to change is just that.
00:34:13.760 --> 00:34:18.320
To learn or to unlearn, those are elements of change.
00:34:18.559 --> 00:34:22.559
And people want to throw change management at this.
00:34:22.639 --> 00:34:34.079
And I can assure you, and so can Dave, that there's two things about change management that people just vehemently resist, which is change and management.
00:34:34.320 --> 00:34:37.199
And yet that is the construct that we're throwing at this.
00:34:37.280 --> 00:34:41.039
And so this is why we also talk about in the book the need for leadership.
00:34:41.199 --> 00:34:43.440
What we're trying to do is arm a new kind of leader.
00:34:43.519 --> 00:34:46.800
And Dave and I could talk about this at length.
00:34:47.119 --> 00:35:00.639
In order to lead change, trust becomes part of it, but so does vision, or what Dave and I call visionary vision, which is having this vision that is so different, like JP Morgan Chase.
00:35:00.719 --> 00:35:03.519
We are going to become the AI super bank of 2030.
00:35:03.599 --> 00:35:05.280
And here's what it means and what it looks like.
00:35:05.519 --> 00:35:09.360
You have to give something to give people to believe it.
00:35:09.599 --> 00:35:12.400
People have to see themselves in that story.
00:35:12.559 --> 00:35:15.920
They have to see themselves on the other side of what this all looks like.
00:35:16.159 --> 00:35:18.000
And then they have to believe it.
00:35:18.079 --> 00:35:20.480
They am empowered and they are safe.
00:35:20.719 --> 00:35:25.360
There's a trust word, to go and learn and unlearn in these directions.
00:35:25.679 --> 00:35:28.880
Then they see the role that they play in this and know how to get there.
00:35:29.039 --> 00:35:31.199
We're making it sound maybe simpler than it really is.
00:35:31.280 --> 00:35:39.679
But this is why leadership is so important and that you can't attack the institutions and the methodologies and the legacy thinking of the past.
00:35:39.920 --> 00:35:48.639
This is a really wonderful opportunity that we believe is not just taking another technology revolution and adapting yesterday to it.
00:35:48.800 --> 00:35:51.440
This is really a control alt-delete moment.
00:35:51.760 --> 00:36:00.079
And you can move forward in whatever direction you want, but one of them is going to give you the exponential returns that keep you competitive.
00:36:00.400 --> 00:36:05.920
The one word we haven't talked about that it's not going to be big, it's going to be small, but at the end of this is transparency.
00:36:06.079 --> 00:36:14.880
From a leadership perspective, if you can go in with transparency and explain why you're doing something, where you want to get to, this is what the plan is, that allays a lot of fear.
00:36:15.039 --> 00:36:18.639
If someone just comes in and goes with a plain AI and people go, why?
00:36:18.719 --> 00:36:19.760
And you go, you'll see.
00:36:21.599 --> 00:36:25.920
But to be able to go in and paint that vision of why you're doing it gets rid of a lot of fears.
00:36:26.079 --> 00:36:26.239
Trevor Burrus, Jr.
00:36:26.320 --> 00:36:28.239
That transparency is essential, Dave.
00:36:28.719 --> 00:36:32.320
Now, Brian, you said this is pressing control alt-delete.
00:36:32.800 --> 00:36:35.519
Who needs to press control alt-delete?
00:36:35.840 --> 00:36:42.320
I always say that when people ask, hey, what industry is ahead of everybody else, or which industry is getting this right?
00:36:42.480 --> 00:36:44.639
You can generalize if you want.
00:36:44.800 --> 00:36:49.840
But the the answer to your question is it is all going to be dependent on the culture of the company.
00:36:50.079 --> 00:36:52.400
That's why culture is so important to this.
00:36:52.880 --> 00:36:54.239
It could be the CEO.
00:36:54.480 --> 00:36:55.760
It could be the board.
00:36:56.079 --> 00:37:04.639
It could be someone who is a minus one, minus two from the C-suite who says, hey, can we have an honest conversation?
00:37:04.960 --> 00:37:11.199
Maybe they're not the ones who hits control alt-delete, but maybe they're the ones who raise the need or raise the awareness for it.
00:37:11.360 --> 00:37:19.840
Somebody's going to have this conversation because right now, pushback is probably at an inflection point, meaning that not pushback itself, but transformation is.
00:37:20.000 --> 00:37:35.360
Dave and I were having this conversation this past weekend about the pressure that companies are facing to embrace AI and their lack of ability to demonstrate ROI from doing so.
00:37:35.679 --> 00:37:41.039
And that pressure's coming from boards, and pressure's coming from CEOs downward.
00:37:41.280 --> 00:37:50.880
And so essentially, then with that pressure, the metrics of that become things like fluency, adoption.
00:37:51.280 --> 00:38:00.559
But one of the things we were talking about was what is the cost of a task and what is the value of an outcome?
00:38:00.880 --> 00:38:12.320
And deconstructing the economic units of this in order to build it back up in order to then make the case for some kind of change with specificity.
00:38:12.559 --> 00:38:21.360
Because otherwise, you're going to leave it to someone to just get a zap of lightning from the sky and say, whoa, I got to hit control alt delete.
00:38:21.679 --> 00:38:24.239
That's a very rare trait in leadership.
00:38:24.400 --> 00:38:36.159
But the people who can make the case to make the stories find the differentiated use cases that like IKEA or like the lesson that Ford learned and say, hey, we're not going to make that mistake.
00:38:36.239 --> 00:38:39.440
Let's try A, B, and C, it can come from anywhere.
00:38:39.519 --> 00:38:41.360
But just people have to feel safe.
00:38:41.519 --> 00:38:45.440
They have to trust leadership to be able to speak up.
00:38:45.760 --> 00:38:48.159
I think it has to come from the top for a number of reasons.
00:38:48.320 --> 00:38:57.679
It's got to come from the top because if it's not a directive from the top, what you'll end up with is 150 pilots where everyone tries it in their own division.
00:38:57.840 --> 00:39:05.519
And then everyone, because there is no strategy around it and everyone's not got the authority to reallocate resources, no one gets any ROI.
00:39:05.679 --> 00:39:07.039
So everyone's deployed AI.
00:39:07.199 --> 00:39:08.400
The teams are still the same.
00:39:08.559 --> 00:39:09.920
They're doing stuff faster.
00:39:10.159 --> 00:39:14.079
Great quote from a guy called Tim Hogarth, who's the CCO of ANZ.
00:39:14.239 --> 00:39:16.480
Asked him once what his AI strategy was.
00:39:16.559 --> 00:39:18.719
And he said, we don't want to let the dogs win.
00:39:18.800 --> 00:39:20.639
And I said, explain the strategy.
00:39:21.039 --> 00:39:23.840
He said, everyone deploys AI everywhere on their own.
00:39:24.000 --> 00:39:26.639
Everyone gains 20 minutes a day productivity back.
00:39:26.719 --> 00:39:28.320
They take the dogs for a longer walk.
00:39:28.480 --> 00:39:29.599
I spend millions of dollars.
00:39:29.679 --> 00:39:31.119
The dogs are the ones that are happy.
00:39:31.280 --> 00:39:39.519
And if you've got something, if you've got something coming down from the top, what it allows you to do is it allows you to have a holistic vision for the company.
00:39:39.679 --> 00:39:44.880
Because what happens, let's say you didn't get that view from the top, let's say you're doing things departmentally.
00:39:45.119 --> 00:39:51.280
Sales goes in, deploys AI, uses it to save a lot of time on internal sales.
00:39:51.440 --> 00:39:55.280
So emailing people, coming up with trying to get leads, trying to get meetings.
00:39:55.440 --> 00:39:56.800
Let's automate all that.
00:39:56.960 --> 00:39:59.360
And you save 25 people because of that.
00:39:59.519 --> 00:40:03.119
And then you look to say, what are we going to use those 25 people for in sales?
00:40:03.199 --> 00:40:04.559
And you create some kind of job.
00:40:04.800 --> 00:40:07.360
Or perhaps you do want to reduce bottom line.
00:40:07.519 --> 00:40:15.760
But if you can drive this from the top, that gives you the capability to say, those 25 people, we're going to move from sales to marketing.
00:40:15.920 --> 00:40:21.840
And we're going to start a different initiative on the marketing front, or we're going to go for a different segment in the markets.
00:40:22.239 --> 00:40:26.960
If you don't drive it from the top, you don't get that flexibility to move the resources around.
00:40:27.119 --> 00:40:29.199
Everyone holds onto the resources.
00:40:29.280 --> 00:40:31.440
And then that's where you see this lack of ROI.
00:40:31.519 --> 00:40:33.119
So for me, it has got to come from the top.
00:40:33.519 --> 00:40:52.559
There are lots of cases of organizations, including one of the largest consulting firms in the world, where they measure the wrong things, where the CEO said, we now mandate all of our managers to use AI and track those for promotions.
00:40:52.960 --> 00:41:03.920
So what would be some of the measures that the organization is moving in the direction of becoming more of an infinite organization?
00:41:04.320 --> 00:41:08.800
For me, one of the important things is making sure that you measure the whole workflow.
00:41:08.960 --> 00:41:17.519
So what I find people do with AI is they deploy AI and they measure it at the point of inception rather than looking at the whole workflow.
00:41:17.760 --> 00:41:22.239
So a great example would be I'm going to measure how many lines of code are generated by AI.
00:41:22.320 --> 00:41:26.159
And our target is to get to 10 million lines of code generated by AI.
00:41:26.400 --> 00:41:33.039
But what you really need to measure is what's your defect rate, what's your QA rate based on the fact you have deployed AI.
00:41:33.199 --> 00:41:35.920
Because you don't want to just do more of something, you want to do it better.
00:41:36.079 --> 00:41:47.199
Or if you're using AI for deflection, customer service deflection, employee call deflection, don't just measure how many calls you're deflecting, measure the reopen rates as well.
00:41:47.360 --> 00:41:53.280
Because I could deflect 100% of calls tomorrow just with one simple agent that says, here's your answer, close your call.
00:41:53.440 --> 00:41:54.719
Not great for anyone.
00:41:55.519 --> 00:41:57.599
But you have to think about that entire workflow.
00:41:57.760 --> 00:42:08.079
If at least you've taken the stance of saying if we want to be infinite based on what Bryant was saying before with the carbon black stuff, we have to redefine our workflows to be more AI-enabled.
00:42:08.239 --> 00:42:16.320
If you're measuring that entire workflow and AI is improving that workflow, then inherently by that logic, you are improving the company to some degree.
00:42:16.559 --> 00:42:21.440
And why it mightn't make you an infinite company, it means you're on the journey to become an infinite company.
00:42:21.679 --> 00:42:30.079
I love that because it's approachable, it's tangible, it's different, but it gives me an understanding of what I need to do and how to think differently.
00:42:30.239 --> 00:42:38.559
And I think the other thing, coming back to something you said, Dave, around the dogs, which as an abstract or obscure, maybe is a better word story as that is.
00:42:38.800 --> 00:42:41.519
I also heard someone say that recently.
00:42:41.840 --> 00:42:42.239
Oh, really?
00:42:42.639 --> 00:42:43.440
About the dogs.
00:42:43.519 --> 00:42:51.360
And I was like, Damn, you must have somehow, someway influenced this person because that's only the second time that I've heard that.
00:42:51.840 --> 00:42:54.719
And of course, I had to tell how I first heard it.
00:42:54.880 --> 00:43:13.039
But to that point, okay, if you're not going to let the dogs win, that means that you are going to cognitively understand what you're saving and now use a judo move and take those resources, energy, whatever that is, towards something new.
00:43:13.199 --> 00:43:14.960
So we're not going to put them towards the dogs.
00:43:15.039 --> 00:43:16.639
So what are we going to do with that time saved?
00:43:16.719 --> 00:43:18.800
What are we going to do with those resources saved?
00:43:19.119 --> 00:43:21.920
Because now this is where leadership can step in.
00:43:22.079 --> 00:43:25.119
This is where a culture of experimentation can come in.
00:43:25.360 --> 00:43:32.079
This is the root of how innovation can be born because you're now going to experiment in a new direction.
00:43:32.159 --> 00:43:33.920
You're going to invest in a new direction.
00:43:34.079 --> 00:43:36.000
This is the IKEA story again.
00:43:36.079 --> 00:43:38.800
This is how you action all of this.
00:43:39.039 --> 00:43:47.519
Because otherwise, you'll just have stats that say 445 hours saved or percentages of things saved.
00:43:47.760 --> 00:43:50.320
But now we could say saved plus added.
00:43:50.400 --> 00:43:51.440
And this is the mindset.
00:43:51.679 --> 00:43:52.880
This is mode one, mode two.
00:43:53.039 --> 00:44:00.079
And we get very specific in the book on the questions you can ask and know which mode you're in, so that you're working towards both of those directions.
00:44:00.400 --> 00:44:18.079
One of the great examples I use that always makes people just stop mid-conversation is if you've got a 10,000-person company and you can save half an hour a day for everyone, and you can reallocate those resources and compress the half hour, you get an extra 600 people working for you.
00:44:18.239 --> 00:44:24.639
So to say to a leader, if I give you 600 heads for free, what would you do with those 600 heads?
00:44:24.880 --> 00:44:31.119
Then it opens a whole new world of wow, we could build these new products, we could build this new plant, we could move to this new country.
00:44:31.360 --> 00:44:33.679
But you've got to think of it holistically.
00:44:34.000 --> 00:44:35.599
It's an outstanding question.
00:44:35.679 --> 00:44:39.199
And your book opened a beautiful new world for me.
00:44:39.599 --> 00:44:44.880
As I have had one reading and I know I will read it again to better understand it.
00:44:44.960 --> 00:44:47.440
And I'm sure the audience will want to as well.
00:44:47.679 --> 00:44:52.800
So where can the audience follow your work, find the book as well?
00:44:53.119 --> 00:44:55.039
Mahan just funny though, Dave.
00:44:55.119 --> 00:44:55.920
I don't know if I told you this.
00:44:56.159 --> 00:45:00.400
Someone just told me that they're on their fourth read of the book.
00:45:00.719 --> 00:45:01.760
Brian, I love that.
00:45:01.840 --> 00:45:05.519
And here's part of what I say, both about podcast conversations and books.
00:45:05.679 --> 00:45:09.920
It's a heck of a lot better to reread a great book.
00:45:10.079 --> 00:45:14.639
By the way, as an aside, if you haven't read Sangeet Paul Chowdhury, I love this book, Reshuffle.
00:45:15.360 --> 00:45:16.239
I've read it twice.
00:45:16.400 --> 00:45:17.840
I'm going to read it again.
00:45:18.079 --> 00:45:26.719
It's much better to reread a great book to truly understand what the author or authors were trying to get across than read a hundred other books.
00:45:26.880 --> 00:45:30.400
Anyway, so can the audience follow your work and find your book, Brian?
00:45:30.639 --> 00:45:35.199
The book is at www.infinitecompany.ai.
00:45:36.159 --> 00:45:40.239
And I think the best way to connect with us individually is probably on LinkedIn.
00:45:40.400 --> 00:45:44.159
Dave Wright is Dave Wright2, if I'm not mistaken.
00:45:44.719 --> 00:45:45.760
Tragically, I am.
00:45:46.480 --> 00:45:47.360
On LinkedIn.
00:45:47.519 --> 00:45:53.599
And I'm Brian Solis on LinkedIn, but I'm also Brian Solis on any one of your favorite social platforms.
00:45:53.679 --> 00:45:59.039
And that's just because I can't sacrifice 20 years worth of investments in communities.
00:45:59.360 --> 00:46:03.440
Thank you so much for joining the conversation, Dave Wright and Brian Solis.
00:46:03.760 --> 00:46:04.400
Thank you.
00:46:05.199 --> 00:46:06.320
Thank you, Babnus.
00:46:07.519 --> 00:46:11.679
You have been listening to partnering leadership with your host, Mahan Tavakoli.
00:46:12.320 --> 00:46:27.920
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00:46:28.159 --> 00:46:34.239
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