What if most product launches fail not because of bad ideas—but because of broken processes? In today's episode of Tech Talks Daily, I'm joined by Stuart Jackson, Vice Chair of L.E.K. Consulting and co-author of the forthcoming book Predictable Winners (Stanford University Press, 2025). Stuart brings nearly four decades of experience in strategic growth and innovation, and his insights offer a much-needed reframe for businesses navigating the high-stakes world of new product and service development.
We sat down to explore a topic many leaders shy away from: why 70–90% of product launches still miss the mark. Stuart breaks down the structural weaknesses that often derail innovation and explains why relying on gut instinct is no longer enough. Instead, we discuss how a rigorous, data-backed approach—augmented by AI—can dramatically reduce the risk profile of innovation while improving speed, accuracy, and return on investment.
Stuart shares how leading organizations are integrating AI into every phase of their innovation pipeline—from idea screening and predictive analytics to launch planning and ongoing iteration. He outlines the rise of long-horizon thinking and how AI is enabling companies to identify emerging demand signals well before the competition.
What I found particularly refreshing was Stuart's take on what top innovators do differently. It's not just about having better ideas—it's about building the right systems. That means empowering cross-functional teams, systematically disaggregating risk, and knowing when to pivot versus when to persevere.
This conversation isn't about theory. It's a pragmatic look at how to move from product guesswork to predictable success—whether you're building in-house or buying through M&A. For founders, product leaders, and anyone trying to modernize their innovation model, this episode offers grounded advice and a clear framework that you can start applying today.
How are you thinking about AI as a tool for risk reduction in innovation? Are your product strategies built for speed or sustainability—or both? Let me know what you think.
[00:00:04] Innovation is the engine of progress. Quick question, why is it that 70-90% of new product launchers fail? Well, my guest today is Stuart Jackson, Vice Chair of L.E.K. Consulting, and he's the one that spent nearly four decades advising global businesses on growth, innovation, and strategy. But today, he's bringing that expertise and experience and insights to Tech
[00:00:33] Talks Daily, where he's going to be sharing insights from his book, Predictable Winners, which outlines a proven framework for reducing risk and improving success rate in product and service innovation. And I also want to learn more about how AI is transforming the innovation process, how it is helping organizations analyze vast data sets to predict market demand and systemically,
[00:01:00] hopefully reduce uncertainty. And maybe uncover what top innovators do differently. Let's try and find out that secret source in everything from managing risk and data-driven strategies to striking the right balance between those short-term wins and that long-term innovation. So if you are involved in product development, R&D, or just curious about how AI could help improve innovation, this conversation is packed
[00:01:30] with practical takeaways that you're not going to want to miss. So you've heard from me. Now it's time to bring Stuart Jackson onto the podcast now. So thank you for joining me on the podcast today, Stuart. Can you tell everyone listening a little about who you are and what you do? My last 40 years has been spent in the world of management consulting, helping companies grow and
[00:01:54] create value. That's what I've done for the... It might seem rather strange to stay with the same organization for 40 years these days when everybody changes every couple of years, but I've enjoyed it. I like the job. I think it's such a privilege to be helping people with their toughest problems about how to grow and how to navigate business. And I've had a variety of roles. I started as an analyst when
[00:02:20] Ellie Kay was just 10 people in London and then had various roles, head of Chicago, head of the US, global head for 10 years, and seen the firm grow to over 2,000 people. So it's been a blessed ride, really. I feel privileged to have been part of it. And I'm now vice chairman, which means I have a little bit more
[00:02:43] time to do what I want. But part of that includes thought leadership and developing new ideas, which is the next book that we've developed on innovation called Predictable Winners. And 40 years, you've packed so much experience, so many insights into that time. And as you said, you get to do a little about what you want to do now. But I love how you've combined both of those
[00:03:10] worlds together, all your experience that took you from London to the US. I think you're in LA today where you're talking to me. And one of the things that put you on my radar was your book, Predictable Winners, which highlights why so many product launches fail. And we're seeing this more than ever right now. So I've got to ask, before we get to the book, I mean, what are the biggest structural flaws in innovation processes that contribute to this? Because I would imagine
[00:03:36] that you've seen it all, a fair few trends there. But what are those biggest structural flaws? I think part of the problem is it's just not easy. There's a lot of ways you can get it wrong. And too often, we and innovators are hemmed in by our own biases. If you're a biologist, you're focused on the science. If you're a marketer, you're focused on the customer messaging.
[00:04:04] If you're a numbers guy like me, you're probably too much focused on just on the numbers than you should be. And too often, innovators fail to manage risks across the entire innovation journey, developing concepts that deliver against genuine unmet needs, targeting customers where they can really win and overcome the barriers to adoption, developing a viable business model,
[00:04:33] overcoming competitive threats, and then finally creating momentum through a very successful launch. And I recently came across, I think it was a Telegram group, and it was full of people that were using AI to create their own business. And I mean, every single aspect of that from, I've got an idea to bringing that idea down into a product or to a service from building the website
[00:04:58] to creating the contracts and marketing, absolutely every aspect was created. And people were creating successful businesses from scratch using AI. And as we're here to talk about risk in product development, AI is increasingly being used predictably to manage risk in product development too. So I'm curious, from your point of view, having seen so many big changes over the last 40 years,
[00:05:23] how do you see companies best leveraging AI to forecast market demand and identify high probability opportunities? Anything that you'd see around here or anything that excites you around this? Yeah, I mean, it's easy to forget that many breakthrough new products don't come from a truly new or breakthrough idea. Often there are multiple organizations experimenting with similar concepts at the
[00:05:52] same time. And the breakthrough really comes when an organization with the right capabilities and the right approach creates a solution that really lights up against the needs of target customers. And AI is certainly a fantastic tool for just surveying the ideas that are out there, you know, what's happening in the world, how are customers responding to different tools?
[00:06:19] It's a great tool to ensure innovators and organizations don't miss out on the next opportunities. It's a great tool for getting things on the radar. And then as you talked about, it's also a productivity tool. You don't necessarily need to, you know, when you're starting off hire a marketing team,
[00:06:43] you can check GPT and get a rough draft and some rough things, you know, good enough to get you going. You really can. And as you said at the very beginning of our conversation, many businesses, they struggle to separate those great ideas from those that just won't succeed, especially if they get too emotionally connected. So any advice on how AI driven predictive analytics might improve idea
[00:07:12] screening and testing. Anything you've seen around that? Well, I think, you know, they can, AI can survey the landscape as it were. And it can, and through that, it can tell you where is the, an established unmet need, which in other words, is a market waiting to happen. And where is your idea
[00:07:37] something that actually people aren't talking about today? And so you've got a market that may still exist, but you're going to have to create it. And in the book, I talk about the example of litter robot, which is the very, I don't know if you've seen them. It's a very high tech machine for cat litter, essentially. And it manages it beautifully. It costs $500, but it completely
[00:08:03] solves the problem in a very hygienic way. Well, you think that that will be a sort of, you know, a tough sell, you know, $500 for a thing that sorts your cats poo out. But if, as soon as this, this, the murmurs of this thing were around, there was just a massive response on social media. There were millions of people talking about it and responding about it. There
[00:08:31] was so much excitement that to me, it was no surprise that the product gained fantastic success because there was just by listening to the scuttlebutt that was out there, that can tell you what you don't really need to do a market survey to tell you that it's there. There's a lot
[00:08:56] of people frustrated about what to do about the cat mess. And over 40 years, you must have had so many conversations, seen so many great examples, seen a few bad ones as well, of course. But from all those conversations and everything that you've seen and heard throughout your career, what are some of the best practices from top innovators that consistently launch successful products or services? Any trends there or anything that you've seen that you could share around
[00:09:22] that? Well, optimistic paranoia is a term that has been used to describe the ideal mindset. You've got to believe in success, but you also need high awareness of all of the problems you're going to have to overcome. And so I think that's one term that gets you part, it partly describes it, although paranoia can also, to me, mean the wrong thing, because paranoia can mean
[00:09:51] a worrier. And in my experience, the best innovators, they're not really worriers that they have a high awareness of problems. But once they define a problem, they figure out what needs to be done and they get on with it. They don't spend a lot of time worrying because they're too busy with all the work that they're trying to do to solve the problems that they've identified.
[00:10:13] So that's some of the thoughts that come to me. Yeah, highly optimistic, but with a very firm grasp on all that needs to be done to achieve success. And when researching you, something else that stood out is you emphasised the importance of balancing short-term wins with long-term innovation. So any tips on how companies could maybe use AI to uncover some emerging trends while also maintaining immediate
[00:10:41] business growth for that long-term innovation? Any advice there? I think today, AI is less for developing the really new you. That doesn't mean to say that tomorrow it can be that. I mean, today it's more, we're seeing it being used for getting a pulse on market needs, trends, and what I would call near-term opportunities. Today, it's less good at what you
[00:11:10] might call breakthrough innovations that apply technologies in entirely new ways that customers haven't even thought of. As Steve Jobs used to say, our job is to figure out what customers want before they do. And today, we haven't seen AI doing that so much. Maybe down the road, there's opportunities for that, but I haven't seen it so much to date.
[00:11:37] And I think failure is widely seen as a big part of the innovation process. And I spoke to somebody recently, they were talking about the importance of, before even creating a product, put some marketing out there to see if there is a need for it. But how do you see companies, or the best way for companies to fail fast and minimizing costs and maximizing learning? Any tips around them?
[00:12:01] I think the term fail fast originated with startup software businesses in Silicon Valley. Let's put out a basic product to see if we even get any traction and interest. Yeah, great. Good for that. But what if you're an established company who's built a reputation
[00:12:21] over 50 years? Maybe it's a critical product like a drug, a cancer drug or an aircraft. You don't really want to just put it out there and see it flop. So I think I use the term promoting experimentation. And you can be in a critical industry or a company with a golden reputation, and sometimes they can
[00:12:50] be almost frozen by the fear of doing anything. And so you want to promote experiment experimentation, but you've still got to do it in a low cost and safe way. I'm a pilot. I like flying airplanes and TBMs are maker of high performance personal aircraft and they wanted to launch an automated
[00:13:14] landing feature, something that if the pilot became incapacitated, it could take over and land the plane all by itself autonomously. Well, how do you experiment with that? Well, what they did was they they can't just crash a bunch of planes, you know, that they created a virtual airport,
[00:13:39] 5000 feet in the sky, and then practice the aircraft and experimented with the software algorithms to essentially have it simulate landing, but still a mile high in the sky. So I think that's the mindset you've got to have is you've got to promote experimentation, but fast fail literally won't apply in for many organizations. And your book also touches on the
[00:14:07] importance of external innovation strategies and everything from acquisitions and licensing. So for any business leader listening, how can companies effectively blend internal and external innovation efforts? Well, with many large organizations, they just struggle to support anything that doesn't have a clear pathway to 100 million or in some very large organizations, it needs to have a clear pathway to a
[00:14:35] billion dollars of revenue. And so by the time something is clearly sort of in that threshold, often the technology is just too far along for them to be able to catch up, you know, developing it internally. So acquisition does make sense for those larger organizations. And it can be very
[00:14:59] successful, it's easy for us to forget. You know, we think about innovative companies such as Apple and Google, but they are organizations that, as you say, blend external with internal innovation. iTunes came from the acquisition of SoundJam in 2000. Siri was acquired in 2010. Apple Music was based on Beats Electronics
[00:15:23] that was acquired in 2014. You know, Google, Android was acquired in 2005. You know, rightily became Google Docs. You know, they had big acquisition of YouTube in 2006. So many of the most innovative companies actually are quite smart about blending internal and external innovation.
[00:15:48] So many great points. And I'm curious, bringing it back to you for a moment, what inspired you to write Predictable Winners? What was it that made you want to sit down? Because writing any book is a huge fee and it takes a lot of your time, probably more of your time than you initially expected. But what was it that made you want to sit down and get those ideas out of your head? And what was it you wanted the readers to walk away with? What was that key message? Well, I was I was actually had a chat with
[00:16:19] Richard Naramore, who just taken a new role. And I published a book with him more than a decade ago, and he taken a new role at Stanford. And he I said, I'd love to do another book with you at some point. And he said, Well, I'd love to do another book with you, you know, you know, he's the editor. And he and he said, you know, what is Ellie Kay best in the world at?
[00:16:47] And I thought about it. And, you know, we do a lot of you like a lot of consultants. You do a lot of different kinds of work. But I said, well, I think the thing that we're best in the world is if someone's got an idea, you know, they've got some science, a new molecule for treating some disease or a new idea for a new kind of transport service or something like that, then we are.
[00:17:18] I think we're best in the world at helping figure out how can you really you know, what's required to take that to market? How big will the market be? What barriers do you need to overcome? And, you know, what's a what's a viable plan to get it there? And we've done that hundreds of times and made tens of billions of dollars of products have been launched through it. And I said, I think I think we do do that. And there's a lot of ways you can go wrong.
[00:17:45] And I think there could be a book there. So that was the nexus of it. Love it. And the book is out there now. It's on the shelves. It's in all the usual places. But now that books out, all the words are on paper. We're now looking towards the future. What emerging trends excite you right now that you're seeing? And how do you see them shaping the future of AI driven innovation? Anything that you've seen there?
[00:18:10] Well, as I said, AI today is mostly used to describe what's happening within a given industry. What are the latest trends in footwear or the new developments in cancer immune therapies? But real genius and, you know, think about Jobs or Edison or Leonardo.
[00:18:35] Real genius, I think, comes from looking across industries, looking across technologies and looking across cultures. And it will be interesting to see how far AI is able to replicate that. I mean, AI seeks to replicate the human mind, right? Yeah. It'll be interesting to see how far it's able to replicate that cross-disciplinary kind of thinking.
[00:19:06] I suspect it will require some new programming to make that work. But I wouldn't put it out of the question that it can happen. Well, we've got an Amazon wishlist where I add books for people to check out. Ask the guests to leave books on there. I'm going to add predictable winners to that Amazon wishlist. And in return, I'm going to ask you to leave a song to our Spotify playlist. It could be anything you want. Guilty pleasures are allowed.
[00:19:33] But what song would you like to add to our Spotify playlist and why? Well, I did take a look at the playlist. Some good songs there. You know, I like the Queen numbers. You've got some of my all-time favorites, including Aitno Mountain. Although I do prefer the Diana Ross version versus the Marvin Gaye version. Yeah. Brave by Sarah Burrells. Love that one.
[00:19:58] But to me, you're missing one because you don't have Hey Jude by the Beatles, which is, to me, one of my all-time favorite Beatles numbers. I love the way it starts slow, builds to that glorious nah, nah, nah, nah. At the end, it's actually got some great lines like, you know, it's the fool who plays it cool, you know, things like that.
[00:20:24] And it's actually easy to play on guitar, although not so easy for a bad amateur guitar player like me. But I remember when our kids were younger, we used to have friends over for dinner and our kids would joke the next morning that they knew the moment the party had reached its peak the previous night. You know, when they could hear from their bedrooms, dad leading the rendition of Hey Jude. So it has special memories for me. Oh, well, what a great tune.
[00:20:54] I cannot believe it is not on there, but we're going to right that wrong right now. I'm going to make sure that's added to that. And for anybody listening, they want to find out more information about LEK Consulting, about yourself. Where would you like to point everyone listening? Well, there's lek.com is the website for the firm. And then on the particular topic of innovation and the new book, predictablewinners.com is the website that will tell you all about that. Awesome.
[00:21:22] Well, I'll add links to those so people can find you nice and easily and they can find out more information about that innovation success gap. Why 70 to 90% of product launches fail. The role of AI in managing innovation risk and AI driven product development and what top innovators do differently. I urge everyone listening to check that out. But I'm going to go have a little sing song to myself now and sing Hey Jude while I get that added to the Spotify playlist. But just thank you for joining me today.
[00:21:52] Really appreciate your time. Okay, great. Great talking with you. All my best. Thanks, Neil. So from reducing risk in innovation to leveraging AI for smarter product development, Stuart has given us a practical roadmap for increasing the odds of success in a world where most ideas struggle to gain traction. And the key message seemed to be that innovation isn't just about creativity. It's about the discipline.
[00:22:19] And AI can help companies make more data driven decisions. But success still depends on structured processes, the right team and a clear strategy. So how is AI shaping innovation in your world? Are businesses becoming more data driven in their decision making as a result? Let me know as always. LinkedIn, X, Instagram, just at me or see you send me a quick DM. Let me know your thoughts.
[00:22:46] But now, yep, it's time for me to put on Hey Jude and have an almost endless sing song. And I invite you to join me. If not, I will speak with you all bright and heavy tomorrow morning on the podcast. Na na na na na. Hey Jude. Jude. Jude. Jude. Jude. Jude. Jude. Jude. Jude.