Innovation, Growth and Barriers to Success

In the last fifty years, Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, and and Tesla.

Total market capitalization, more than eighteen trillion larger than the stock entire stock markets of Japan, UK, and and Canada combined, year to date returns of more than fifty percent a year, and, all expanding way beyond their initial original scope. And you you can't just it's you can't just say, well, they were lucky they were in a good business. Because if you look at each one, they've successfully successfully expanded way beyond their original business concept. So Google, now Alphabet.

It started off, obviously, as a search company, but now they've got driverless taxis in San Francisco and around around the US with Waymo. They've got their Gemini artificial intelligence platform.

They have they have nests of industrial automation and and and home automation. So what you see is and, of course, YouTube, which has made them one of the biggest online, advertising platforms. So, yeah, big advancements. Amazon, the same sort of thing.

I'll go through these very fairly quickly. Yeah. You've got the the the cloud computing, the Amazon Entertainment, the Prime Video going far beyond being originally a bookseller. I have to admit that one of our partners in, I think, round about nineteen ninety nine wrote a a little newsletter saying how Amazon was vastly overvalued because they could never sell that many books.

But, yeah, that's a that's a little story for us for us.

Apple, I think we all I see, you know, dozens of those products right out in front of me. And, of course, they've gone beyond their original, which was, yeah, a computing platform, to be a communications company, to be an, again, an AI company.

Their their Apple App Store infrastructure and then their Apple TV entertainment platform. So, again, expanding far beyond what they originally started in. Same with Meta, going far beyond the original Facebook concept and, Microsoft with LinkedIn, PowerPoints, the Microsoft Teams, OpenAI.

And, NVIDIA, we don't we don't really know what they're gonna do yet. They're they're still, focused on the chips business and then finally Tesla.

Some bumps in the road in twenty twenty five for the, for the car business, but still phenomenal success in SpaceX and Starlink, the the greater, Musk sort of, organization. So, yeah, we've we've seen tremendous success in the in these seven companies. We've also seen quite a lot of success in the new business formation. So this looks at something around sixteen hundred so called unicorns, which are newly founded businesses, not publicly listed, but with valuations more than a a billion dollars.

About half of those based in the US, the rest in other parts of the world, particularly China and India.

Interestingly, when you look at their their focus, you see financial services and IT at the top of the list. Health care is all the way down in number thirteen, so we're not doing very well in terms of new business unicorns for, among the health care industry.

And in terms of returns, if you look at the S and P, you see, from twenty fifteen to twenty twenty four, twenty five percent compound returns for the magnificent seven. For the slightly less magnificent four hundred and ninety three, you see eight percent returns. So it's really been just a few a few few organizations that have just driven so much of the world's innovation and wealth creation over the last decade.

More than more than half of the of of all the growth in in the United States coming from just those seven companies.

In Europe, it's actually even worse. So if you compare the S and P to the euro stocks, a significant lag.

And, arguably, all of that is driven because Europe doesn't have one of those, you know, magnificent seven years. So if you took out that, then you wouldn't you wouldn't see that effect, but a huge difference.

And, within health care, we've actually seen somewhat slower growth and innovation in health care compared to the rest of the globe global economy. We have seen fantastic innovations in health care, but it's it's still actually less as a as a proportion and as a rate of growth than we've seen in the rest of the economy. And you actually look at the ratings for the global health care companies. They've actually come down versus the rest of, the s and p.

I I would just add that if you double click in different health care sectors, you also see similar, disproportionate value coming from a a small number of companies. So we I looked at one very recently on med tech, for example, just as it happens. And of the in the last decade, sixty percent of market cap growth in the, in the entire industry globally came from five companies. And the last five years, eighty percent of the growth came from those five companies. So it is it's been very concentrated, value creation, if you will.

So why is that? I mean, you think about businesses in Europe, you think about health care.

Yeah. We we've got plenty of smart people in health care, plenty of smart people in Europe. I'm sure there are, yeah, fantastic people in this room.

We've got access to technology to improve health care delivery. The same innovations around art AI are available to everybody.

And we've got underlying demand growth, of course, with aging population.

So we've got the demand drivers there.

We've got the opportunities.

Why can't we do better? And part of the answer is it's hard, as we'll go as as we'll see. It is hard, but our proposition for you is that with the right approach, it's actually possible to substantially increase the odds of successful innovation and get much better outcomes.

Ilya, why don't you tell us a little bit about how to do that?

Why? Thank you for asking, Stuart. Okay. Here, let me grab the clicker from you.

Well and that's where the real fun begins. Right? Identifying a problem. Consultants are very good that. Right? How do you solve that problem is the bigger question that most of us care about and where we really drive value.

A little bit more on the problem though in how and taking this more specifically to health care. Now what we'll notice these are some general themes. There are more specific nuances if you're in the in the world of, you know, provision of health care. If you're a provider, if you're in health care IT and digital, or if you're in pharma or med tech diagnostics, there's unique nuances to every single subsegment within the health care market. That said, there are some common themes that we see across the board.

One common pitfall that we see is, the companies are frankly too inner focused in terms of innovation. They think about it from a scientific or an operations lens. They don't think about the patient journey and actually solving unmet needs in that patient journey. It's too much of a focus on whatever it is that you're actually good at or you started with.

And that's very different because notably, if you look at all the the magnificent seven that we talked about, right, you know, there's obviously, there's some interesting developments for some of them. But for most of them, they identified a an opportunity, a need in the market that they expanded on and over over the years. Right?

Second is about delighting stakeholders. I don't know how many in this room would say that customers are delighted. Right? In health care, this is not that much of a concept. If we don't annoy our customers or make it too difficult to work with companies in health care, that's already typically an achievement. And you see that in all kinds of metrics of net performance scores and level of satisfaction in the health care industry, typically, certainly not what you see in many b to c businesses or cons you know, consumer businesses. Right?

But that mindset of can we truly delight the stakeholders that we serve is one that is frankly not very common in the world of health care.

A third, is around barriers to adoption. This is one of my favorite ones to talk about because even if you create the most amazing product that meets unmet needs and really does delight your customers, Oftentimes, we we see companies miss when they actually bring the product to market. There's a there's a lot around launch excellence that we'll talk about, but one of the biggest issues is that companies do not spend enough time and energy and resources on understanding the true barriers to adoption of whatever technology or whatever offering they are bringing to market before they're in the market.

You end up having to figure it out as you bring it to market, and then you figure out, oh, well, there's this problem. Now we have to figure out how to actually drive adoption. As much of that as possible that's done beforehand is is the goal, and that actually is a huge difference between the outcomes and success. And when we looked at we we did this look back study that Stuart alluded to of over a hundred product launches in the last decade leading to more than twenty billion in in revenues.

The one of the most common issues that we saw was execution of the actual launch itself, which many people kind of think about as, oh, yeah. Well, that's the simple stuff. It's not that simple, actually. Yes. It's good execution, but it needs to be thoughtfully and strategically done.

The fourth is competitive responses.

This one is interesting because mostly what happens is people just underestimate their competitors.

And this this is, the way I think about it at least, but often companies are stuck with kind of playing chess with themselves as I like to joke. You're overly optimistic around how your competitors will respond. And you don't actually think about it that well, just like you, there's somebody in in that seat who's trying to figure out how to make themselves extremely successful, build their career, and make you fail.

So that's a really important one, and many companies under invest in it. There are tools like Wargaming that we'll talk about that are actually extremely helpful for setting proper expectations and then creating mitigation plans such that you can proactively overcome ex expected responses from your competitors.

The last one, and I talked about this a little bit already around launch when we spoke about the barriers, but preparing for launch, there's actually a lot that you need to do in order to to accomplish a good launch. There's a lot in terms of configuring the organization around having the right team in place, having the right incentives in place, having the right tactics and and tools. I'll give you one specific example that I find kind of interesting that, you know, for product companies, for example, you often end up seeing overinvestment in sales teams and underinvestment in sales enablement tools that can actually make every single rep that much more productive. And that'll be things like helping with targeting of physicians, for example, and providing tools for driving conversions. Think of it like as all kinds of, calculators and ROI tools and things like that that have become critical for success in a market where value is that much more important.

These are a few of the common pitfalls that we see that the way we think about innovation in our in our book tries to address.

And one last kind of issue or kind of the problem is what we call the doom loop.

The challenges with innovation, they compound.

In fact, you can end up in a vicious cycle where because you haven't had meaningful success with innovation, it's very difficult to justify putting more money and investing more money into innovation. It's why a lot of companies who have historically not had very productive r and d don't put more money into r and d. And if you look at benchmark them in terms of, you know, r and d as a percent of sales, it's quite low, and it persists at a low level for a long period of time. Well, it how do you get out of that? Because you you end up with poor outcomes from innovation that do not kind of drive further investment.

But if you don't invest enough, you're going to have insufficient resources and end up with poor outcomes again. So you just keep basically doing the same thing. This doom loop is difficult to get out of. Yeah.

It has to start with better outcomes.

If you can get better innovation outcomes, that will earn you the right for more investment. That will earn you the right for more resources, and then you then you can start to reverse that that loop. But it it's that innovation effectiveness and innovation how outcomes. That that's that's where it has to start.

That's right. To go from a doom loop to a a Zoom loop.

And this is the way we're thinking about it in turn and and where kind of the the new thinking is for the work that we've created in Predictable Winners.

There's a lot of innovation literature. There are so many books if you go on Amazon or any bookseller, on the topic of innovation. We read many of them. The problem we run into with most of the literature is that, one, much of it is impractical.

How do you actually use it in real life? And two, much of it is focused on, you gotta get this one thing right, and then every problem is solved. And that's just not real. That's not reality.

The what we believe is that you actually have to get a lot of things right in order to be successful.

So the approach that we've taken is is thinking about the, the innovation journey as a journey.

First and foremost, you need to configure your organization for success, and we'll we'll share some thoughts on it. But some of this is around leadership mindset and the culture you create in the organization, but some of it is also around structures and incentives to support innovation and enable risk taking, even if you're a larger organization, bringing some of the magic of the startup world into corporate settings without the kind of fluffiness that you usually end up having with kind of entrepreneurship programs.

That's a starting point in the foundation. But the, these bottom four chevrons, you know, every consultant loves their frameworks. Right?

This is actually a helpful way to think about the innovation journey. And the goal that that that we set is that you need to be thinking about the the fact that you have many steps on this journey. At every step, there are ways for you to improve your odds of success, and you're, in many ways, retiring risk as you go. You want to kill concepts that will fail as early as possible and make sure that you're tilting investment towards the things that have better odds of success. Because if you do that, you improve their odds of success even further. You're not wasting investment into things that will never end up getting anywhere, and you're losing on the opportunity cost of investing in something and making it much more successful.

So hence, this green band at the bottom. But there's four kind of steps in the journey that we'll that we'll that we talk about. The first is around developing product concepts. This is about methods for identifying and kind of and and and finding the highest value, highest potential product and service concepts.

How do you take early signals, as input? And you see a lot of this in, you know, in the digital world, lean start up kind of concepts around testing it, getting feedback from customers early. And we often think, well, that's not possible in health care. That's not true.

There's actually many ways to get early signals of and market feedback that you're on the right path. This is a meaningful unmet need and or by the way, people are willing to pay for it.

The second step is once you actually have the product concept kind of, gleaned and and really have significant conviction that this is the right concept to go after, you need to actually build the business case around it. We call it forecasting revenue here because, actually, if you think about it, if you wanna develop a a a robust revenue forecast of any product or service, it needs a lot of a lot of inputs. You need to understand the market potential. Who are your customers going to be?

You need to understand the competitive set and what kind of share you can expect to get. You need to understand your pricing. There's a lot that needs to go into it and requires significant diligence. And frankly, going through the exercise of creating a business case that's quite rigorous will force an organization to ask the questions that are required to be successful down the road.

And those who don't invest sufficiently at this stage, they end up discovering surprises down the road that are not particularly pleasant that lead to the seventy to ninety percent failure rates.

The third step is around launch. We already talked about this one. There's a lot that needs to be done when you actually prepare for launch, and launch can look very different. Right?

It can be, you know, it can be like a proper launch, like in pharma or medtech that you see, but it can also be something that's a little bit softer in terms, like, if you're a provider and offering a new service and, you know, trying to, you know, bring it into into clinics. Or if you're a digital platform and you're, you know, converting from one version of your product to another that, you know, offers a whole range of benefits or might be a different pricing model. There's a lot of things that you that that you can kind of think about in the launch step. This is one that's very execution driven and where we see a lot of stumbles.

The last one, the last step is it's not enough once you've brought it to market. And even if you're successful. What happens next is actually quite important. And a lot of the difference that you see between organizations that were just successful versus the magnificent seven is what happened later.

Can you create an enduring franchise built around your innovation? And often, this this comes in different flavors. This comes from using m and a as a resource for building around it because you're never gonna be the only source of innovation. You need to find it wherever it is.

It also is where you find, where you find value from, balancing breakthrough and incremental innovation. Many of the biggest innovations are breakthroughs. There are significant departures from what was happening in the past, but to get the full value from that, you need to innovate around it with incremental innovations, tweaks. I mean, how many iPhones have we have we had yet we keep getting the next one.

Right? Well, there those are incremental innovations, but the first iPhone was a breakthrough innovation. Right? Twenty years ago now.

Almost. Anyway, so this is our predictable innovation framework that we talk about in the book, Predictable Winners. Great book, by the way. We can sign a few copies for you all if you'd like. It's particularly rare if you get both of us.

We'll talk, we'll talk now about a few case examples. I'm gonna hand it over to Stuart to share an example of a provider pharma kind of collaboration led to a a pretty remarkable outcome, and then I'll share a different example.

Yeah. We'll just talk about two examples. First one is, a university. So, I'd just been made chairman of LEK in two thousand and twelve and met with Ralph Muller, who was the new CEO of University of Pennsylvania health care system.

And, we were chatting, and he said, we've got this thing. We've, we you know, some of our some of our oncologists have developed this this procedure, and we've given it to three patients, obviously, all no option patients, you know, expected to expire in in in weeks. And of those three patients, two of them survived, with this advanced lymphoma.

Two of them survived against all the odds, gone into complete remission. We think we might have something here. It was quite a complex process. It was not a normal drug that you just give somebody.

It was this CAR T cell therapy, which essentially involves extracting your the the patient's blood, treating it to essentially dial up the patient's white cells so that they can be reprogrammed to attack the cancer cells in their own body. So you have to withdraw the blood, process it, put it back into the patient, and then it allow it to do its work. But, and so a tremendous number of questions of how do you how do you monetize this? How do you how do you take this forward? This was a university. They didn't they they didn't have that kind of experience.

And so we helped them work through what was needed. It's it quickly became obvious that well, is it a device? Is it a process? Is it a drug?

The way it works in the US, if you wanna get high reimbursement, it has to be we needed to position it that way. We needed to find a way to it needed to be a very high priced drug because the cost of doing the processing was gonna be tens of thousands of dollars per patient, just the cost, never mind what you need to actually pay back all the development and, actually make a reasonable profit on the thing. So we helped them work through all of that. They ended up partnering with Novartis to bring the bring the product to market.

And it, maybe if you wanna just click through some of the the the the the the keys here, you know, identifying the patients. They started with a very defined disease class and, and the pricing sort of, you know, relevant for that.

Engaging the the the the delighting the stakeholders. Well, I think this was, I mean, the picture in the in the in in the slide before was someone who the the young lady there had was one of the first children to receive this treatment, and you saw her as a, successful woman twenty years yeah, fifteen years later.

Addressing the barriers to adoption, very difficult to to commercialize this product, which had very different it's not like the normal pharmaceutical, which has you know, ninety eight percent gross margins and, you know, you make it for a few dollars per pill and then sell it for thousands. Yeah. It was it it was gonna be a different proposition, but we we worked through that, worked through getting a number of hospital centers on board as centers of excellence to to develop the, the therapy and, and and, obviously, you know, worked with Novartis. Or Novartis took over, you know, leading the the manufacturing and launch.

I mean, there's there's actually a part two to the story because University of Pennsylvania, this was very successful, but for them, it generated tens of millions of dollars of royalties for the university. And interestingly enough, when they got another, concept, they said, you know, we'd like to actually take it further and actually develop it ourselves. So what they did was when they the the next thing they had was a treatment for pediatric blindness. So, a a a very niche disease affecting young babies where they were eventually gonna lose their sight in the next few years. They they they use some of the, if you like, the winnings from, from Chimera and in and and help to fund a new a new commercial entity, Spark Therapeutics, which then was the organization that developed this this second drug, which was called LUXTURNA.

So, I think it's just an interesting example of that what Ilya called the zoom loop of successful innovation spawning more successful innovation outcomes.

So again, yeah. So the first one, they made tens of millions. In the second one, they made hundreds of millions.

Yeah. Well, I think that they spun that out. It's a multi billion dollar. Yeah.

Yeah. Spark Therapeutics was the name of the company.

But but I do think that, Stuart, to to your your points, it was interesting because in this case, you know, this is a provider organization that knew they had something interesting, but they knew they didn't have all the answers and they needed to find partners to work with, which is which is sometimes the right answer too. And sometimes the problem is that we try to own everything. And we and by hanging on too long or or not bringing in partners that could be substantially valuable in in in unlocking the full potential, we end up missing out on what it could have been. And that's actually I know at some point, we we share a statistic in the book around kind of one of the main differences between successful serial entrepreneurs and first time entrepreneurs.

Serial entrepreneurs tend to be more willing to dilute earlier because they wanna get in sufficient investment to derisk it, and then they they kinda think about it as a smaller piece of a bigger pie is a better outcome than no pie or a small pie, but big piece of it.

So we'll go on to the to the second example I'll share here is Intuitive Surgical.

I'm sure many of you've heard of Intuitive.

They're, for me, kind of a fun one because I've seen them develop over the last two decades in quite the I mean, it's quite the journey. I mean, their their valuation is astounding.

But they've also created a market where nobody has been able to enter despite enormous efforts in the last fifteen years. I mean, the the herculean efforts, you've got companies like J and J and Medtronic pouring billions to try to get in, and they have not been able to do it successfully yet.

The the company, just for maybe a little bit of context for those that are less familiar, in the late nineties, this actually was, it started with a there was a Stanford program with the, the Department of Defense. They were trying to figure out how can you do surgery on soldiers in, you know, field hospitals or or or, you know, basically, you know, as close as possible to a a battle zone. And, you know, can you do remote surgery? Well, that didn't quite work out as planned, but they basically created a technology that was basically a a robot that assists with surgery.

I don't I think I probably don't need to explain to to the group exactly how it works. But suffice it to say that by the early two thousands, they had created a commercial product where a surgeon was able to use, a robotic tool to help them with laparoscopic surgery. And the in the beginning, it was interesting because they just kind of they thought they would be used for cardiothoracic surgery, and that was where they had started with some of their KOLs and some of the development.

And, you know, we'll talk about this in a in a minute, but there was a different surgeon type that was like, oh, this could work quite well for this particular application, and that took off. But the you know, if we look back today, you have, you have an organization that has dramatically kind of altered the standard of care for certain, procedures.

They're continuing to do that in many other procedures.

There's clinical value from it, especially when you're moving from open to, minimally invasive surgery where it's really not an option. But if you actually talk to their management team, the real kind of value in many cases is it's about reducing variation. If you think about the normal distribution of outcomes, it's cutting the tails off. Because in many and in many instances, you see pretty dramatic differences in what the capabilities of of clinicians are. And this isn't exactly democratizing, if you will, which is an overused term for the for the concept, but it is about reducing that variability and creating, more consistent outcomes and more consistently good outcomes.

The success of the company is quite impressive. They have a hundred and fifty billion dollar plus valuation. They've been growing at double digits, twenty percent plus CAGR for more than a decade. Doesn't seem to be slowing.

They've got almost ten thousand surgical robots placed, globally at this point, and that is growing substantially. And there's still a long runway and a lot of white space in terms of the the the number of procedures that can be addressed successfully with surgical robotics. It's also spawning just a broader surgical robotics field. We see that now in heart tissue.

In orthopedics, you've got robots being used. You've got a whole bunch of different robotics being developed for biopsies and for all kinds of procedures now, as well as flexible robotics.

But a few lessons and kind of interesting points for this one.

You know, one of the one of the things that's a real a real struggle is creating especially when you're trying to provide something new to to health care providers is creating compelling evidence, compelling claims. Everybody wants clinical trials that will be useful.

Not as many organizations actually spend time upfront figuring out what will actually be com considered compelling by their customer. This is going back to the customer centricity. Right? Well, if I'm gonna go try to sell to doctor Smith, what what will doctor Smith need to see from clinical evidence to actually say, yeah, I want this product, or I wanna use this in my practice.

And that's actually a best practice you saw in Intuitive's developed very strategically in every single specialty, an an enormous base of data. And it's not just kind of initial kind of FDA and CE mark claims, but also all the the real world evidence generation post market data afterwards and incredible amount of publications that they've supported with their KOLs.

A second one is that I find really interesting is around the the customer runway. And customer runway is think of it as who are the customer groups, the customer segments, and in what order of priority. It's it's interesting. One thing that a lot of organizations struggle with is this concept of early adopters.

What I like to joke about is, like, anybody can sell stuff to their friends and family, but eventually, you run out of friends and family to sell it to. And if you haven't chosen your early adopters wisely, those early adopters will not be champions, will not be advocates who go say, you have to use this. This is unbelievable. So whether you're selling a a digital health platform, excuse me, or or or offering or something very different, figuring out who those early adopters are is really quite critical.

But then you also need to have a plan for who next. And typically, that is a function of two things. One, strength of the value prop. For whom is it valuable, truly valuable?

And second, access. That one is less thought about. It's like, yes. Okay. Who else would love this?

Who can I reach successfully? Because sometimes that's actually quite challenging depending on what market you are. If you're trying to sell to GPs, for example, there's an awful lot of them. Right?

But in some cases, it's not that hard.

Regardless, in the intuitive case, the the story goes on from these early cardiothoracic surgeons to urologists.

Many of you probably kinda know this story, but urologists figure out that, you know, there's a procedure, radical prostatectomy, so removal of the prostate, typically the prostate cancer, that you could not do in a minimally invasive, way. It's just the anatomy was too complicated. Very few people in the world were able to do it laparoscopically.

Lo and behold, if you actually have a surgical robot, you can do it. So the clinical value proposition was enormous because, you know, for obvious reasons, moving from an open to a minimally invasive procedure has enormous benefits, both clinically and economically.

So this became the killer app. And but but they didn't stop there. What's been really interesting is their approach for adopt for driving adoption by specialty as an s curve. So they they understood that you need that you need to create the right dynamics where for a surgeon, you you provide the right set of the right set of tools, the right set of data, and they'll start using it for a broader set of their procedures. So your urologist will start with prostatectomies, but then they move on to nephrectomies. They they they they move on to other types of procedures that they would not have even thought of doing originally with the robot because they already have it. They get used to the tool, and they and they end up loving the experience of being able to control the surgical, theater in a different way.

You see the same thing kind of happening now. They're they've moved from urology to gynecology to general surgery, colorectal surgery, and there's an s curve of adoption that they have pursued in a very interesting way that comes to the, the barriers to adoption because they took pains to really understand their customers. And this is important because think about, like, surgical robotics is it's a pretty hard sell. Twenty years ago, when people were looking at this market, they're like, no way.

No way. I mean, think about it. You're gonna spend a million and a half on a piece of capital and then more than a thousand dollars per case in some in many cases, for for a procedure. Like, would you ever use it for old cholestostectomy?

You could do it in forty five minutes or less with a good lap surgeon, and it's not gonna cost very much.

Today, it's being adopted for for coles quite extensively.

But it it took a while to get there. But they have they they were able to figure out in a multi pronged approach that there's actually there there's a that you can kind of overcome a lot of these adoption barriers. And, you know, I mean, one, there was a in the US, there was a whole marketing thing. Right? It was like, well, that health system put up a billboard to say we have sir a surgical robotics program.

Don't you want one? How are you going to get patients to be interested in going to your center? Or are you really the place the the academic center that's training the best surgeons in the world if you don't have a robotics program?

Every academic medical center in the US and in most in Europe as well have robotics programs because of this. It's expected in certain specialties at minimum if you wanna be a a player.

But this took a lot of time and effort for them to figure out, kind of step by step for each specialty. And there's some of his technical things, like a product development road map. Well, you need different set of instruments for a different procedure. So you had to figure out kind of how to overcome that for every single procedure for every specialty.

The last thing I'll talk about is competitor moves. This one's interesting.

And, you know, this is this is a market where you have a ninety five percent plus share of one player, right, depending on how you define it, of course.

So be careful about how we talk about competition or or lack thereof. But they've been very, very thoughtful about how to create moats where it's really challenging to to compete with them. Because first, they created a a a truly an experience in surgery that delights customers. Like, if you talk to surgeons who use in Intuitive's products significantly, they love it.

It's not just a, oh, this is a nice tool. They love it, and they advocate for it. But they've also invested in the whole host of things. And right now, the frontier is around digital, which is very relevant for many of the the the the organizations here because it's all, of course, perf you know, it's it's, you know, diffusing everywhere.

But if you think about it, where they're going is kind of like where Tesla went. Right? Like, you had twenty years ago cars that, you know, it would beep if there's, you know, someone in your blind spot kind of thing, or you have a camera that shows where you're reversing with little lines that show you where you can go and, you know, etcetera. But you're moving towards self driving capabilities in Tesla.

Right? That is where surgical robotics is going. And if you think about the application of AI to a massive body of, of of data around surgical videos and what that can do in terms of automating certain tasks, reducing risks, flagging that there are structures that you need to make sure you don't hit when you're doing surgery that create adverse outcomes.

This is a massive kind of departure and it further, changes the value proposition. So very interesting example, a pretty different one than the one Stuart shared, but I think there's common themes around being, being thoughtful about how you bring a new innovation into the world and, and kind of systematically think about, that, that journey from when you have a concept all the way to when it's in market and beyond that as well.

So those are the examples that we had. We did save a little bit of time for q and a. We have about fifteen minutes or so.

And we do have a couple of books with us.

So we've got, four books for which, my assistant, Jayshree, will hand out to the first four questions. So oh, yeah.

You've got a job to do.

Yeah.

Yeah. So, yeah. We'd we'd love to hear from you. First of all, any reactions? Any questions? Go ahead.

I think there might be a mic running around, but let's see if we can or just maybe Let's see let's see if we can hear you.

A loud voice.

Try without a mic.

Oh, boy.

We can hear you.

We can hear you.

Yes. If you take the example of running a prostatectomy, my experience is is a very good surgeon will be very marginally, more comfortable with a robot.

An average surgeon will become better.

If you're a patient and you're given a choice between robotic and not robotic, we'll say, well, if if I have a chance to, even by a fraction of percentage, increase my, outcome with preserving my erectile function, yes, I will pay the money in pocket, in my own from my own pocket.

We could discuss that on the side if you want. My question, sorry, is really, there's I'm I'm surprised there's one point you don't mention at all in your in in your explanations is, we are not in a free market. We are not in a consumer market, and and the payer is often the real issue with innovation.

I think it it could be I I I would say there probably is a correlation between the fact that the health care market is not growing as fast as the the rest of the market because there's only so much money you can spend on health care.

And and I think that's also a very strong barrier to success and, and and innovation.

And nowadays, probably, it's different from one country to another. I'm French, so we're really impacted by that.

But I think that's a very important consideration, in in in this industry.

It's great.

I think that's a absolutely, it's a valid concern.

I think the the impact of payers can be a challenge, but in the right circumstances can be an opportunity. But for sure, the the near term wins will be we will be if you can make we we're seeing a lot of the innovation in in health care today, applications of AI, for example, in improving efficiency of of of of care rather than more value added. That's that's some of the the near term wins.

But, obviously, if you actually are saving lives, then you can you can get, yeah, you know, the right reimbursement and and and the right value.

I would just add, I I appreciate you bringing it up because I I think look. This is actually one of the challenges, I would say, in health care more broadly because it's not just the challenge with reimbursement.

The constraints around both reimbursement and regulation create, also a mindset of this is actually where I think it affects us the most. It creates a mindset that's that says, well, we can't innovate faster or we can't do that. There's too many I can'ts. Right?

Like, this is where a lot of you see astounding innovation in in the consumer world. Right? Because there aren't that many I can'ts. There's like, why can't I do that?

Oh, that's a problem. I wish I could get my kid to to to soccer practice, football practice without having to drive back and forth ten times. Right? Or maybe I can is there a kid version of Uber, right, that I I mean, it's like, is there a problem to solve?

Let me try to solve it. Right? And I think, I think there are real constraints in health care that prevent us from being able and for good reasons too, from being able to take the same approach that consumer companies can. But I think we often use it we too often use it as an excuse for not innovating fast enough or focusing on solving real problems.

Because both of the examples that we gave, like, there wasn't a clear path to reimbursement, initially. They had to create and forge a new path because they had created an innovation that they knew was valuable.

And so I think if you could create real value, you can find ways, around some of these constraints. But the bigger lesson is that we need to challenge the mindset that that these are constraints we can't overcome. Because it's possible. We just we need to work within them, but but it's a great question.

Great. Any other yeah.

Hi. I'm really keen to get one of your books. Alright.

I've got a two pronged question.

The first is really in reference to your earlier slides about getting into the market. You talked about a very well established strategy. You talked about, getting your product right, your your market preparedness night, your market marketing material right, etcetera. I've been launching a number of businesses over the last few years, and actually, we've been doing completely the opposite, which is just really going with an MVP, a minimally deployable product, modeling your way through the market, finding out what works, finding partnerships, getting payers on side who are willing to, go the journey with you, and then finding a model that works and emerging from that cloud of fog with a product that actually has product market fit and getting investment along the way, to find your way to that clarity rather than having that clarity at the outset. So I'd be interested in your observation on the different methodology. And the second point part of the question is about overreach.

So, very large multi unicorn health care companies, have failed very publicly. Obviously, Babylon overreached with trying to do total patient capitation, and, and more recently, twenty three and Me, with trying to do drug development.

They could have been great, but the magnificent seven have never ridden in health care, because of these sort of quite public failures. So your observation on those two points.

I I think, maybe I'll take this one, and you can take the next one, Ria. But I I think the first of all, on the on the concept of so called fast fail, as a way of developing concepts, we do talk about that in the book. You know, that I think that was that's an idea that was developed a lot in Silicon Valley with the I when you were developing new new software, new apps. You know, let's just put a minimum viable product out there, see if there's there's some take up. And then if if there is, we can have a version two, version three, version four.

Makes sense. Doesn't make sense. If you wanna develop a drug for cancer that increases five year survival rates from fifty percent to eighty percent, you can't just throw something out there. You gotta wait five years. You gotta do a thousand patients.

So you've got to adapt. So the way we we think about applying that is we've kind of a broadened fast failure to think about promoting experimentation because we we think there is always ways in any business to to try to promote the idea of direct experimentation.

Can you at least test part of the product? Can you at least get some signals? So promoting experimentation, we think, is a is a great thing. Fast failure directly may or may not be able to apply, but promoting experimentation always does.

And then the second part of your question was, overreach.

Yeah.

Well, I mean, fundamentally, you've got to have the value proposition, and twenty three and me didn't have the value proposition. Yeah. They had the hype, but not the value prop you've got to have the value proposition. I think the, you know, the counter would be someone like Tempus who who developed that, essentially, an integrated database around clinical trials and outcomes to help guide guide clinicians in oncology and and related areas.

They actually started with some, a more narrowly defined but clear benefit, and I think that's that that's a lot of what you got to look for, which is actually similar to yours, I think, in the in robotic surgery. It could easily have been one of those failures if you launched it as a well, here's a platform. It costs a million dollars. Who wants to buy it to get your surgery a a little bit more efficient?

Well, you they they did it right by starting with a very narrow, extremely strong value proposition as was talked about in, prostate cancer and and ED.

So that was I think that was what they did right there. But great questions.

I'll let you say the next one, Ilya, if there is another question.

Hopefully, it's not a hard one.

On I just wanted to say that on the fast fail, like, the the framework that we shared isn't it's actually not incompatible with fast fail or, like, the lean start up approach.

We've taken elements of that and tried to explain how it fits in because to Stuart's point, we think it's actually really valuable and important to to support direct experimentation and to get those market signals. But in many contexts, it doing exactly kind of if you you you know, the the lean startup type of approach doesn't actually work. So you have to adapt it to the specific context. But the the principles are actually compatible with the approach that we've structured. We actually talk about it directly in the book because we knew that would be a question.

So Good morning.

My question is about, AI usage.

I've seen so many, programs or which is pretty much noise and not a a case for using as a benchmark to really differentiate our service.

So, thinking about a hospital provider, you have any good examples on AI usage that, we can benchmark?

So thank you for for the question.

How I I was kinda wondering when would we get a question on AI. Right?

Could could we have a session without mentioning it, right, since, you know, in the last year and a half? And it's a great question. So I think there's there's multiple kind of facets to it. First of all, is there actual value from AI?

Right? There's a lot of hype. There's plenty of companies that have made up stuff where you're like, you just that's clearly a valuation bumper. That's that's it.

There's nothing else. It's like, we've we've added this AI capability, and, you know, you they just changed the way they talk about digital health to AI. And, you know, for discerning investors and discerning customers, it's like, yeah, that's that's not really there. Right?

And it does create, that kind of fog, right, where it's very hard to actually figure out what's real, what's not real, and it creates troubles for a lot of us. And if you're a health care provider and you have fifty companies coming to you that all say, oh, I've got this AI solution for you, it's very hard to tell which one's real and actually valuable and which ones are just fluff.

Smoke and mirrors. Right? So I think that is a a real challenge, but I would say that there are actual meaningful, like, applications of AI that are emerging. The one that I think in health care is clearest at the moment is around clinical decision support.

There's clear value in certain applications already that are available commercially where, you know, you can in like, for example, like, Radiology would be Exactly.

Like, imaging is one of the earliest kind of spaces where we've seen real value because there's there's there's an ease to training around this. And you have, like, in in a lot of these, in a lot of imaging modalities, you're looking for the the anomalies. Right? You're looking for the small percent of of reads where you actually have something to look at.

And by the way, if you're a radiologist, you know, looking at a hundred scans and you really need to be focusing on two, it's really helpful if you have an AI tool that will help you say, yep. You can sign off on these eighty out of a hundred because, you know, I've checked it for you. Just take a quick glance. Does this work or not?

There's the whole, like, help clinicians work at the top of their license kind of concept, right, which is very appealing for any health system where you're trying to deploy scarce resources, and you are all trying to figure out how to get more with less. Right?

So I think those applications, they're going to stick. They're gonna be meaningful. They're gonna be part of care delivery for sure.

The challenge right now is monetization for a lot of these AI companies. I mean, one, there's all the noise that causes doubt. And so you kinda worry about some of the start ups and some of those that, you know, are they gonna survive even with a great product?

But two, you know, I have a client that developed a pretty cool tool like that that helps triage patients that show up to the ER with, chest pains. Well, their trouble is different because they figured out that, you know, forty percent of the patients that get admitted shouldn't be. So what happens? Right?

Okay. You have that information, but then, like, in the US market, for a lot of health care providers, I mean, one, there's a liability concern. Is the is the clinician gonna say, yeah. You're fine.

Go home. But then what happens if they're wrong?

And the second piece is, well, utilization of of the hospital goes down. Right? Do they act you know, if they're able to fill, as one health care provider I know in the US says, beds with heads, you know, if they're able to do it because there's a queue out the door, great. But if they're not, they're they're saying no to revenue.

And, obviously, that is a perverse incentive that that's not kind of the the way it should work. But the a lot of these AI tools are falling into a, you know, a system where the the monetization, the reimbursement, it's just it's not structured to support it today. So there's complexity, but I think AI is not it's not gonna go away. It's gonna get we're we're definitely gonna see kind of a lot of the stuff that smoke and mirrors fall out over time.

Let's just hope that the good things really survive because they will change care delivery for all of us. I think some of it's overstated, though. Right? It's not the solution for everything.

Right? It's just it's a tool like any other.

Maybe one more question if there is. Yep.

It's it's it's a follow-up on the on the on the point that you mentioned regarding the the the over legalistic environment that we may find in health care. What is its role in stifling innovation? I mean, it's a this is not a mere it's not a mere footnote. You have to address a lot of elements there for you to actually be successful. And the case in point is Google Health. They've been struggling with just to provide to provide the benefit that they were promising in using this accumulated information for for actually facilitating decision making in medicine.

Yeah. It's a good point.

I can't but the the the answer is always around the best answer is always around segmentation.

Who are the clinicians, the patients, the customers?

Who are the the few never mind the the millions that you can maybe help. Who are the few that you can absolutely help and will have a clear value proposition from this innovation? So finding the starting small, starting with a very targeted approach as, you know, da Vinci did with with prostate cancer, but, you know, as you painted with, yeah, advanced lymphoma, You know, finding finding that target that target group of customers that you can really make a difference for. And I think that's what a lot of these if you don't if you're not doing that, if you start too broad initially, then that that makes it much more difficult.

And that and I would just add that for the kind of liability, the legal, these are real constraints for sure as well that I I think look. We we didn't come here to say, look. It's easy to innovate in health care. It is harder. But our our our point is to challenge and say, it's not impossible.

We can do better in in innovating, and there are examples that have succeeded. There's a lot of, you know, a lot of these tech companies who've tried to come in and be quote unquote disruptors, and they have struggled because the approaches that have made them successful in the kind of markets that we talked about do not necessarily translate into our market.

But that doesn't mean that you can't innovate and challenge the status quo because I think reality is we have to change it. Like, we we we given kind of the demands on our health systems and the level of unmet need. I mean, for the sake of all of us and for the sake of all of our families and every I mean, we need to continue to innovate. So it's more about how do we within the constraints that exist, how do we push the boundaries and not let them, you know, prevent us from innovating?

Thank you. Thank you very much.

Thank you, everybody.