Why Only 2% of Companies Are Ready to Win at AI with Bill Murphy

Transcript

[00:00:02] Bill: All of the AI capabilities are just making the people that can tie all the disciplines together that much more valuable. This is because the individual expertise in each discipline, whether it be coding or designing things, AI can really have amazing scaling features for each of those expert disciplines. The person who can understand your business, tie it to a technology strategy that’s nimble and future-proof, recruit, and then lead a team of individuals to go make that happen, report back in a way that everyone from the most technical to the least technical can understand, and really understand the consumer who is actually going to be using the products—those are unique individuals.

[00:00:40] Intro: Welcome to Signal to Noise by Riviera Partners, the podcast where leading executives share how they cut through the noise and act on what matters most. We go beyond the headlines to explore the pivotal decisions, opportunities, and inflection points that define their careers and shape the future of the companies they led. It’s time to cut through the noise and get to the signal.

[00:01:01] Adam: Welcome to Signal to Noise. I’m Adam Zellner, a partner at Riviera Partners in the Public Practice. We’re a global leader in executive search and the largest functionally focused search firm on the planet. We’ve dedicated what we do to the function of technology in organizations of any scale across all industries. And today, we’re joined by Bill Murphy, current Managing Partner at Cresting Wave, former Chief Technology Officer at Blackstone, and a veteran technology board member and adviser for the last three decades. Bill, I’m probably missing something else from that title, but it’s a pleasure to have you with us today.

[00:01:36] Bill: Thanks for having me. You just made me sound old, but other than that, that sounds amazing.

[00:01:40] Adam: Well, Bill, we’ve just launched our new Future of Technology Leadership Report, and there are all kinds of findings. Today, we want to talk about a few of those things and get your perspective on what we saw. The focus, as with many discussions, is around AI and where organizations are ready and one of the questions I have for you is, we’ve seen tech waves in the past, whether it’s e-commerce, cloud, or the internet, but what makes the AI wave so different?

[00:02:09] Bill: I think it’s speed. It’s happening way quicker. Everything in the past always seems rosy. So it was like, “oh, we adapted so quickly to the internet,” and it really wasn’t as quick as you remember. Remember how long people felt nervous about putting their credit card online? Right now, it just seems like that’s a quaint memory, but there were years where that happened and then the same thing with Cloud. Everyone was like, “oh, everyone moved to the cloud so quickly.” There’s actually a good chunk of companies that are still not on the cloud, and that’s been going on for 15 years plus, so, I don’t think those previous ones had expectations that were nearly as hyped as they are with AI, where everybody is like, “we have to get there tomorrow,” versus over a five, ten, or twenty-year period, as it’s been for previous ones. So I think that’s the thing that’s different, I mean, at Cresting Wave, we’re talking to, essentially what we do is we’re curating new innovations for over 300 enterprises, big and small, across many different industries. And everybody’s dipping in, everything from monitoring where we are with AI, all the way to going full bore and adopting it. So I think you have a very wide berth, and people are taking different approaches to it. But everybody sort of realizes it’s the future, and they’re certainly paying more attention than they did in the early days of the Internet or the early days of Cloud. So that speed has ramped up.

[00:03:25] Adam: Bill, when you look at the organizations, and you’ve got an interesting window into this, when you look at those organizations that are AI-ready, and Riviera, through our work, figured that only about 2% are really hitting the AI readiness button well, what makes those 2%? What have they done? Is it structural? Is it leadership? Is it, fill in the blank? What have they done to get themselves AI-ready where other organizations and enterprises are still struggling with things?

[00:03:52] Bill: Yeah. I think it comes down to will, and that usually comes down to leadership. Now, having the will at the top level to take this seriously and really treat it as something that’s business-required, and having that position cascade all the way down your organization, takes a ton of energy and leadership. Now, once you do that, you also get the foundational things right, and you run some experiments, and you figure out what works. So they progress down that path, but it always starts with the leadership saying, “this is super important for the company. We need to invest significant resources to do it, and we are going to treat it like a first-class citizen. This is not a little experiment off to the side that doesn’t matter and maybe it’ll hit. This is fundamental to who we are as a company, and we’re going to do it excellently.” There’s a quote from one of my companies—the CEO said, “if the CEO is not the chief AI officer, then they should be fired.” Now, perhaps that’s a little aggressive, but I think that is why his organization is completely on their front foot relative to every other competitor in their industry. It’s because he, from the top, is saying this is the most important thing, and we will not be left behind. And they’ve become leaders here, and I think that’s done them right in the market.

[00:05:09] Adam: Bill, are there one or two things that boards and CEOs are missing? It sounds like you’ve got some that are progressive and are elevating AI to the top of the stack and really leaning into it. But unlike in the past with other technologies where we could run a pilot to say, “does e-commerce work?” or “should we think about a cloud strategy?”, proof of concepts work. To your point about speed, I don’t think there’s time to run pilots in the same way. Are there things you’ve seen other boards and CEOs that they need to focus on, or that they’re naturally missing, that would help them adopt quicker?

[00:05:38] Bill: I mean, I think people are quick to jump because the products that they’re using themselves are so obvious in their value, and it’s so obvious how it’s going to change the world. I think people are making this very quick leap to, “oh my gosh, this should be simple, and this should be easy.” And there’s a reason why those 2% that are really ready, I bet you if you looked at the spend on AI and the focus and all the resources put toward it, it would dwarf the other companies. So in some ways, it’s a pretty obvious answer: Are you putting enough into it? And that’s, unfortunately, usually an uncomfortable discussion to be had. Everybody wants it to be easy. They want to say, “oh, well, I’ll just spin up a quick chatbot or something, and then that’ll be good enough.” You can get pretty far with some of the very basic no-code, low-code, point-and-click-type of solutions but then that last 20%, really tying it into the enterprise systems and really making it fade into the background for your business—that’s really way harder than people are giving it credit for. So, I think people are underestimating the total cost of doing it right. I do think that the fact that the market is shifting under everybody, with new models coming out every day and new capabilities being launched, is definitely making that harder too, because you could be making good decisions today that could look stupid 90 days from now because things change so quickly. So people are nervous. And then the last one that I would add, that is a little bit more traditional, is that if you’re building on a shaky foundation, that makes moving to the cloud very hard. That makes re-platforming with legacy systems really hard. That makes everything really hard if you just haven’t kept up with the state of your data and the state of your existing systems. Then building on something that’s shaky is going to lead to pretty shaky results. So a lack of understanding of that because everyone always is like, “oh, our business is great right now,” but AI is going to shine a very, you know, data quality is one avenue where pointing an AI that can comb through all of your data and give you insights is going to really exacerbate data problems. So people may not realize that they have a bigger problem than they thought in terms of some of the basics. So those three things, that incredible rate of change, the lack of the amount of resources and people understanding that the amount of resources needs to be way higher, and then the fact that maybe people aren’t quite as foundationally strong as they thought, sort of, add up to this very difficult decision for the board and for executives. It’s like, “we want to go through this transition, but in order to do that, it’s going to be an uncomfortable level of spend to fix all three of those things and then put us on a good future.” But, you know, that’s what they get paid for. These leaders have to be bold, and the ones that are doing it are probably those 2%, I would argue, you know, it’d be great to be able to run the video forward in time and say, “do those 2% really outperform in their given industries?” And I would say they’re probably going to, but the jury’s probably still out.

[00:08:41] Adam: It feels like we’ve kind of reached the end of the “kick the can down the road” road at this point for those companies that have just put off the spend that they should have in the 2010s and 2020s. Because until you do that, you can’t really imagine a strategy with AI.

[00:08:54] Bill: Yeah. The good news is, I’d say it’s a little bit like cell phones in Africa, right? In some of these rural areas, they were able to just skip laying thousands of miles of phone lines and copper lines, and they could go straight to the cell phone. So there is a benefit: if you waited so long to clean up your mess, it is much easier to clean up your mess with some of these latest development tools powered by AI, so there is a little bit of a silver lining in some of those negatives, but you still have to put enough energy into the efforts today in order to get going.

[00:09:26] Adam: Bill, we, at Riviera, look at the world through the lens of leadership, and you said something about leaders being bold. When you think about those who are going to be successful, they’ve been bold. I’d be curious, if you were talking to a board or a CEO and counseling them on making hires for their teams, not the technical traits that you’ve seen others that have had success, but what are some of the softer skills and traits that you’d make sure they really think about when they think about bringing in the right talent to drive an AI strategy inside of an enterprise?

[00:09:53] Bill: It’s very interesting. I mean, all of the AI capabilities are just making the people that can tie all the disciplines together that much more valuable because the individual expertise in each discipline, whether it be coding or designing things, AI can really have amazing scaling features for each of those expert disciplines. But the person who can understand your business, tie it to a technology strategy that’s nimble and future-proof, recruit and then lead a team of individuals to go make that happen, report back in a way such that everybody can understand from the most technical to the least technical, and really understand the consumer who’s actually going to be using the products. Those are unique individuals, and I think they’re only going to become more valuable because the speed is just moving that much more quickly. So I think well-rounded technology leaders are going to be that much more in demand. That being said, they have to give them time to focus on the strategy, tying it to the business, and so on. I see too often that technologists are looked to as executors who just get absolutely mired in 30-day, 60-day increments and just trying to get through the next quarter and so on. It becomes a self-fulfilling prophecy that they don’t get the respect because they can’t be strategic; they don’t have the time to be strategic. So I think it’s a little bit less about the specific role, I think you could achieve things by calling people lots of different titles and stuff, but you need generals who can understand things, and then they need the space to be able to be long-term, strategic thinkers. You can’t ask one person to cut their budget 30% because we’re in cost-cutting mode and also think about the future, be AI-first, execute the near-term, and respond to crises but unfortunately, that’s oftentimes what the technology leader specifically is asked to do, and it’s asking them to be superhuman. It’s really difficult.

[00:11:47] Adam: No, it makes sense. And I think as we think about closing that leadership gap, we’ve seen titles like the chief AI officer start to pop up with a lot of different experiences underneath that title, because it’s a new class and it’s a new space. Some are incredibly technical, Stanford PhD, others to your point, are someone who knows the business intimately, and they’re supported by a whole team of technologists. In your mind, you know, we’ve seen, and the survey shares that about 20% of companies are starting to adopt that title. Does it add more complexity, Bill, to bring in a chief AI officer next to a CTO and a CIO? Does it actually move the strategy forward more quickly? What’s your view?

[00:12:23] Bill: Yeah. I think it’s sort of like a rebranding of the chief digital officer. And usually, the chief digital officer is, like, the CEO is frustrated that their CIO or CTO didn’t get it, so then, instead of firing them because they felt like they were really critical to the day-to-day, they decided to hire this chief digital officer, and they were going to fix everything. My big belief is that those roles are incredibly difficult to actually accomplish anything from because they do not have enough resources, typically because it’s not usually the case to actually get the work done. And being a strategist in an ivory tower without the execution capabilities, to me, just creates another power center in an organization, and then there’s just a bunch of infighting and political stuff that doesn’t really help, so, I am a big proponent of simple org charts and trying to have two, three, or four power centers in every organization. Obviously, product, technology, and optimization of your processes can either be one or two, and maybe it’s a COO, maybe it’s a CTO, maybe it’s a CPO. But the more that that can at least line up with a single person, I think the better chance you have of pushing the organization forward. Now, on the positive side, hiring a chief AI officer underneath a strong COO or a strong CTO, that sounds great. That’s more resources going toward this transformation. So I guess the point should be: hire people to focus on it, but don’t hire people to focus on it outside of your core operations. It needs to be part of the core of what you’re doing as a business.

[00:13:56] Adam: When you think about that hire and no matter where it sits, how does the board, you sit on a bunch of boards, how do you think about governance and accountability of an AI strategy at the board level? I think some CEOs will bring in a chief AI officer and say, “okay, done. I’ve made a move here and that fulfills.” But if you’re looking at a top-down approach through the CEO, what are the kinds of things you’re looking for, thinking about, and should the board play a role in that accountability at all anyway? I’d be curious about your perspective.

[00:14:24] Bill: It’s on the CEO to strategically prepare the company for the future. And if increasing awareness through a separate role that can help educate the rest of the business and the rest of the leadership team on the capabilities of this incredibly transformative technology, then I think that’s great. However, it is a core, we’re going to stop talking about AI companies, other than maybe people creating the models. This is just a point in time where we’re going through this tremendous transition. Remember, “Internet companies” used to be a thing, and now that seems kind of quaint, everyone’s on the Internet in some way, shape, or form, right? The same is going to be true in AI, and that’ll even be faster than it took for the Internet. So I would view it more as, first, you have a path to awareness of what AI can do to us, both good and bad, and how do we prepare for it, and then execution on choosing the path that you think is the right one given the amount that you can spend and the way you want to go as a business. And then execution should really fall to your core teams, or else it’s going to be a science project off to the side, sort of like I said before. I’m a big believer that the simpler the organizational structure, the more chance you have to succeed, however, that simple structure also, if your culture is bad and slow and there’s bureaucracy, it’s exacerbated by giving them more to do. So that’s why a lot of times you see people say, “oh, I want to keep it off to the side. I don’t want to be polluted.” I, kind of, view that as they’re being lazy about their organization in general. Fix the core. If your company is so bad that it can’t respond to a threat, maybe you should think about fixing your organization versus trying to respond to that threat over off-to-the-side in a way that’s unnatural for the given company.

[00:16:08] Adam: Yeah. That makes sense. You talk a lot about culture, you talk a lot about cost, and change management, I think, is a thread throughout all of this. Sometimes organizations resist change from traditional leaders. It’s easy not to go do the new, hard thing. But sometimes you can supercharge that by bringing somebody in from the outside who’s focused on a particular topic, in this case, AI, for a smaller period of time. Not a consultant, but somebody who’s on-site as a fractional leader. Have you seen that model work? And if so, what were some of the things you saw? If it didn’t work, what are some of the things you learned?

[00:16:41] Bill: Well, I definitely think bringing in expertise from the outside can sometimes, just like, I often joke with folks, even if internal people say it, no one believes it, and you bring in somebody with a glittering resume to say the same thing, and suddenly you have credibility for that same idea. You’ve got to think about that—it’s human nature, right? People get labeled as a certain thing, and just having a different face or somebody else to corroborate them can be immensely valuable. So it doesn’t really matter the method to get to the goal. If it helps you get to the goal, use that as a strategy. I’ve seen it work to really kind of itemize the current state because an outsider can come in with a little bit of “no sacred cows” and a completely agnostic viewpoint and sort of point out where the real problems are, where the real opportunities are, without worrying about whose idea it was in the first place or what kind of issues there were. So it’s just in addition to educating people on new things that they don’t know, just having that new perspective that people aren’t going to judge because they’ve talked to them for the last ten years about different topics is a lot of times very helpful. Now in terms of the change management to get people to actually change, this is where that last 10% or last 20% of an implementation really matters. Because right now, every business could use some off-the-shelf systems to make a lot of changes to their businesses, but that’s going to require a lot of people to change a lot. And that’s where everybody’s getting hung up. “Throw out your CRM system completely and use a chatbot,” and everyone’s like, it’s like head explode, right? It’s just too much change for people to take so quickly. I did a bunch of investing personally in older roles, and the number one correlation to the success of new solutions is the ability to get adopted quickly. And that usually comes with incrementalism, like fitting into people’s current workflow in a way that doesn’t feel so jarring but can kind of push it forward slightly. And I think that’s the hardest part for these AI solutions. They’re moving so quickly and launching new things all the time that they haven’t thought, “how do I fit into an enterprise in a way that doesn’t rock the boat completely? How do I fit into the current financial system in a way where I add a bunch of value, but I don’t require everybody to just throw out the old and start with me?” So that’s why we’re sort of in this “hurry up and wait” state, or we’re at the beginning of this major transition, but we feel a little stuck in enterprises. It’s that the solutions just haven’t been built for easy integration into the enterprise environments nearly as easily as everybody seems to think they are.

[00:19:19] Adam: That makes sense. The last minute of what you’ve just shared is probably applicable as we look backward at other technologies the same way, and it just takes time. One of the things we saw in the survey: of the 2% that show a high AI readiness, 88% of those companies had a CIO in place and clearly, there’s a correlation between a CIO and high AI readiness. From your perspective, why do you think that is?

[00:19:42] Bill: Well, I’m surprised it’s not 100%. AI readiness requires a significant level of technology spend and focus and the right people and resources and so on. So it doesn’t surprise me at all that the folks that are on their front foot have somebody in that role who’s probably pretty good and can kind of move the pieces around. It’s not surprising that the most valuable companies in the world are the ones that are investing the most in this. We shouldn’t discount that. It’s like, look at, “wow, who’s furthest along?” And then, “okay, let’s figure out their investment in AI in dollars.” And sometimes it really is that simple, and that comes down to having the right executives. And then, obviously, there are the hard investments too. But I think it’s probably more about the team than it is about any given technology.

[00:20:28] Adam: There’s this moment where I think CIOs are in this dilemma, Bill, of, “Do I build it? Do I buy it? Do we just use AI for everything?” And it goes back to something you said earlier about jumping over the “kick the can” conversation and just laying new cable, right? And you’ve got a greenfield opportunity. What advice would you give to CIOs as they think about framing this discussion with the board? They’re going to need investment. They’re going to be sitting in front of you. They’re going to try to lay out a strategy to ask you for a giant bucket of capital. What would you counsel them to do and how to position that?

[00:21:00] Bill: And sometimes it’s not a giant bucket of capital, sometimes it’s a giant bucket of buy-in and a giant bucket of the ability to focus on this, not other things. So it’s like, “I’m asking for prioritization and focus of the whole organization.” So when they go to the CFO and they say, “I need you to change your accounting process slightly in order to enable us to get these downstream benefits,” or “I need to be able to go to the head of operations and do the same thing,” that air cover is incredibly important, and I don’t think that people talk about it enough. Every CTO, and I think I made this mistake many times in my own career, it’s like you sort of believe, “if I build the solution perfect enough, then everyone will love it, and everybody will love me, and everybody will say I did a great job,” right? We’re all striving for that validation in our role, and we don’t spend enough time and people listening who have worked with me are probably laughing and saying, “oh, Bill finally realized it.” But it’s constantly trying to bring people along even that much more than I did. I felt like I did, but I obviously didn’t do well enough all the time, right? And it’s constantly generating that buy-in from the top and below to try to get them to lean forward into the change that’s required so that we can have all the benefits of these futuristic technologies that probably will, in the fullness of time, make things pretty amazing, but the path to that is a little bit rocky.

[00:22:25] Adam: Bill, one last question for you. And we have CIOs and CTOs and vice presidents and CEOs watching this and listening to your wisdom today. If we rewind the clock and you thought about your time at Blackstone and the work you did, what lessons apply from your experiences at Blackstone to this AI build-versus-buy dilemma that everyone’s dealing with right now?

[00:22:45] Bill: I think whether it be Blackstone or any experiences in the past, we did a lot of good work both at Capital IQ and as one of the founding team members in trying to lay a solid foundation, and those are no-regrets moves and specifically, around the foundation of the data that your organization runs on, I would absolutely, I think we made it a major priority, and I think we could have even gone faster and made it even more of a priority. Clean up your data, make it right, and then the downstream stuff just becomes easier. The second thing that I always try to impress upon people is to work to find strategies that have two-way doors, because that gives you so much more flexibility in this fast-changing environment. So if you’re building a model to evaluate investment decisions, right, and you’re using OpenAI for that, you could hard-code, this is a simplistic example but you could hard-code it to OpenAI and build your prompts for that. And then, who knows? Next week, there’s another model company that’s better. And then two weeks after that, there’s something completely different. Think about, “okay, how is the world going to change?” And “let me at least prepare to be able to re-point to different models and create a solid technology strategy, a service-oriented style architecture that enables you to be more nimble and change things out.” If I was going to the board, I’d be asking for buy-in, and I’d be explaining how I’m building a strategy that is flexible. So we may not have all the answers, and we’re probably going to be wrong some of the time, but at least we’re going to be able to learn from the mistakes that we make and then adjust quickly because we put in a strategy that enables us to turn the car quickly and not get stuck in one direction.

[00:24:25] Adam: Bill, that’s hugely helpful. I think there’s wisdom in what you’ve shared today, and I think we’ve covered a pretty broad spectrum of topics that everyone’s working through right now. So with that, I want to thank you for your time. 

[00:24:36] Bill:Thank you, Adam. 

[00:24:37] Adam: And that wraps up this episode of Signal to Noise. If you enjoyed the episode, please be sure to subscribe, share, and leave a review for more leadership stories and tech-driven insights, and stay tuned for the next episode of Signal to Noise.[00:24:50] Outro: Signal to Noise is brought to you by Riviera Partners, leaders in executive search and the premier choice for tech talent. To learn more about how Riviera helps people and companies reach their full potential, visit rivierapartners.com. And don’t forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.

Outro: Signal to Noise is brought to you by Riviera Partners, leaders in executive search and the premier choice for tech talent. To learn more about how Riviera helps people and companies reach their full potential, visit rivierapartners.com. And don’t forget to search for Signal to Noise by Riviera Partners on Apple Podcasts, Spotify, or anywhere you listen to podcasts.