Webinar: Building your AI strategy w. Philippe De Ridder & Vincent Pirenne

All right, welcome everyone. Let's kick off. Welcome to today's webinar on how to build your AI strategy, which apparently is a topic of great interest because we have over 1,600 people joining us today. And subscribe for today's webinar, where we will guide you through four foundational perspectives on AI before we dive into strategy. Then we will take you through our AI strategy framework step by step in a practical way, so you can dive in yourself. And then we have a couple of concluding notes on how to lead the transformation as a business leader before we dive in. Just wanted to say hello. So I'm Filip. I founded BOI about 15 years ago and currently its CEO. And today I'm joined by Vincent, one of the partners at boi, who's leading many of our largest client partnerships around the globe. If you're new to boi, we are the AI Innovation Consultancy and help companies reinvent how they grow. We work with large corporates around the globe, across industries and across three main capabilities in terms of AI strategy and implementation. So strategy, and obviously today, specifically, we will dive into AI. AI strategy, building AI products from product strategy to backend UX and UI as well, and then organizational transformation, which we will touch on lightly today. So we will be bringing all of that experience into today's webinar and hopefully give you some insights that you can work with as well. Alongside running boi, you might have heard that we host Autonomous, which is one of the largest AI summits around the globe. And you're very much invited to the next edition, which is on January 29th and 30th of 2025. So in about two to three months, where we gather over 50 plus speakers from the likes of Meta, Netflix, AWS, Salesforce, Coca Cola, MIT and many others. And typically we get over 10,000 attendees following this webinar. We were also happy to share with you a link for complimentary tickets for everyone here and any of your colleagues. So hopefully seeing you there as well. All right, enough of an introduction. Today we will be taking you to our AI strategy framework. But before we dive into the specifics of AI strategy, I want to take you through four foundational perspectives on how we think about AI and would love to invite you to think along with that as a grounding before you set the strategy itself. So as we are all living through AI and how that's going to transform how we work, how we live and our businesses as well, we're kind of in a liminal state from the world as we knew it before to where we will end up on the other side of it. Our articulation of that is that our world will become more autonomous. And we really see that as the next big wave of transformation, where the last two decades were very much evolving from an analog world towards a digital world which has changed how we meet our partners, how we order our food, how we are having this webinar here today and so many other parts of our lives and work. And we really see autonomous as the next big wave of transformation, which is a new paradigm beyond digital and, and applies to different principles that we will be talking about today. When we talk about autonomous, we're talking about autonomous assistance, both digitally as well as physically through embodied AI in the future. We're talking about autonomous products such as autonomous cars, which now finally in San Francisco have reached the 5% threshold, which is the critical threshold specifically for taxis in this case, to go to mainstream adoption. So our prediction is that it will get to mainstream adoption in the next decade. Autonomous interface, Sorry, Imagine your Netflix interface that autonomously tests new features, new designs, and basically forms for you autonomous agents, which we will be talking about. And you might have seen the latest releases from Salesforce with Agent Force, who are predicting that there will be 1 billion agents in the next six to 12 months on Agent Force alone. Microsoft launched their autonomous AI agents two to three weeks ago, and we see others following suit, and then also fully autonomous businesses. You might recall for those who were there on the past summit, that we have a couple of people speaking and sharing their stories on their mission to build a fully autonomous business. Think about a 1 billion unicorn run by one person with an army of AI agents exponentially doing more work and reaching that valuation. So just painting the picture of how we look at that future and how our articulation of that is more autonomous world. So today I want to take you through four foundational perspectives on AI, which I think really provide a grounding of how to think about setting your strategy around all of this quickly running through here and then diving a bit deeper. That AI adoption in my mind, will happen in three waves. The cost of anything digital will approach zero. It's really not just about AI only, but it's about the engineering of intelligence and biology. And as a last perspective, that AI in itself, or adopting AI in itself will not lead to a competitive advantage. I want to give you some pointers on where to seek real competitive advantage. Starting with the first perspective. AI adoption will follow three waves, very similar to how we adopted previous revolutionary technologies like electricity or digital or mobile. Where the first wave is, we tend to apply the technology on the current way of working, looking for time savings, cost savings and overall efficiency gains, which you might recognize from the AI investments currently going on in your business. The second wave typically is looking at how can we leverage the technology to get to better quality, better output from the system. And whereas the third wave is looking at driving entirely new systems and really transformative shifts in a market, as we've seen with digital, for example, creating entirely new categories with the likes of Uber, Airbnb, Netflix, Teladoc, you name them. So typically what we do when a new technology emerges, and as said, we, we've seen the same in digital, is that we often copy paste what we already have, a physical newspaper, just a PDF version on the first screen, and only later we start to think about how to shape what could be personalized news feeds, etc. Etc. So the main point that we want to make here around the waves is that businesses looking at AI purely as a productivity tool in our minds, are making a mistake. It is very much a productivity tool, but there's a lot more possibilities and that's what we would love to look at and talk through today. If we give one really practical example of the three waves, and if we think about video generation, for example, for entertainment or ad commercials or short videos, whatever the format is, is that the first wave is, can we use AI to do video editing faster and cheaper? That's very much what's currently going on. The next wave would be can we actually get better quality video, more entertaining videos out of it? And then the third wave would be inventing an entirely new model based on the new technological possibilities. For example, can we have a real time, can we have real time movie generation for an audience of one, which could be your personalized TV series, which could be a personalized stream on TikTok, on Instagram, which could be new ad commercials that are generated in the moment just for you individually. And you will see that in the third wave. Actually, the investments that you've done in wave one and wave two typically don't necessarily matter for the third wave. So being successful in wave one one is not at all a predictor of being successful in wave three. And what we want to advocate for in terms of AI strategy is to plan your strategy across the three waves with a vision around the third wave, build up towards that and have a portfolio of investments not just in wave one, but across the three waves. And you will see that coming back in the strategy framework that will take you through. The second perspective, which I think is really foundational to understand, is that the cost of anything digital in our estimations will approach zero. This is after decades of Moore's Law, reducing the marginal cost of storage bandwidth, CPU, power, etc. As you can see on the graph on the right, which has already driven down the marginal cost of distribution. What is new now with Genai is that also the marginal cost of production of anything digital will start approaching zero. Think about software with AI engineers or agent engineers. Think about creative campaigns, think about music, songs, video, etc. So it's both the production of something digital that will start approaching towards zero, as well as the distribution or at least the marginal cost of a newly generated item. And it's not just the cost, but also the time it takes to make it. So that does enable an entirely new set of possibilities if the cost of something digital is going to approach zero as well as the time to make it. So here are a couple of ideas to think about. For example, making Hollywood movies from your desk, which we see as entirely possible, where you will select actors like you now select a font. And we're already seeing that IP marketplace emerge where, for example, the author, or, sorry, the voice behind Darth Vader in Star wars licensed its voice just before he passed away. So in the future, we might not need actors to show up to make the actual movie, but we might just license and select them like a font. We will see the emergence of what we call Instant UX, where the user interface is built for consumers in the moment. Again, if the cost of generating is going to approach zero, or the marginal cost of it, as well as the time to make it, that would allow for things like instant ux. You would see ads, entertainment being generated in real time. And overall, as of next year, we will be expecting to have more AI agents than people on this planet. So these are digital AI agents. And if we could believe the likes of Elon Musk and others, by 2040, we would have more humanoid robots than people on this planet. But I said that's about 10 to 15 years out from now. So taking you through the third perspective, obviously a lot of the talk today and the investment is going what you could call the engineering of intelligence or artificial intelligence. The perspective that we want to offer you is when you think, or when I think about the next five to 10 years and we're doing future strategy work with our clients on that. We typically think through both lenses of the engineering of intelligence as well as the engineering of biology, which to me, combined are the biggest driving forces of societal and business change in the next decade. So when we talk about the engineering of biology, one example of what I think is a truly transformational technology potentially is GLP1. So these are the drugs like WeGovy Ozempic that you will have read about by now, which originally coming from diabetes, are now being applied to obesity tests. But week after week there are new studies that it drives down the risk for anxiety, for example Alzheimer's, cardiovascular disease. So there's a lot of different application usage and might be that that's a first step in the engineering of craving or the engineering of biology in the next couple of years. When we talk about the engineering of intelligence, obviously we've seen the whole gen AI wave with the likes of ChatGPT as the poster child of that, but it's still very much buried in a chatbot that we need to go and browse to type something or speak to it. I think it will become way more relevant and embedded in our lives once the AI systems get embedded into our AR glasses or into our AirPods or in general become more ubiquitous in our lives. Rather than being buried in a chatbot that we need to go and access, we touched upon humanoid robots, which is very much around the embodiment of AI. And then when we think about the pinnacle of where the engineering of biology and engineering of intelligence might really connect this with brain computer interfaces that the likes of neuralink are working on, you have a couple, you're. You have many other technologies that could have a place here, from life extension tech to synthetic biology. But don't want to drill too deep into that. Just wanted to provide the perspective when you're doing future strategy piece of work to think through both lenses, combine the engineering of intelligence and the engineering of biology and also how that might change our very understanding or identity of Homo sapiens. What if AI followed everything that we did and we got a near perfect memory? I think that's pretty near term. What if we all continuously had that AI assistant with us on our smart glasses or in our AirPods with the latest O1 model, already has an IQ of 120. What through that AI assistant, we all got an IQ of 120 in the way we show up to the world. What if our life expectancy increases to 120 or as per Google's predictions, to 150, etc. It might really start redefining our understanding and our identity as Homo sapiens in general, but that probably gets us a little bit too far for today's discussion then, as a fourth and last perspective is that a lot of people think that when you implement AI just because you've seen the latest gen AI tools and they're so spectacular, is that by just adopting it, that will lead to competitive advantage. I think the most important understanding here is, or at least our perspective is that is totally not the case or almost exactly the opposite. AI and Genai is very much a general purpose technology where everyone has access to all companies, but even individuals currently have access to the latest models. So just using that technology will not give you any competitive advantage and it's very much stable stakes to be driving that. So what are angles to think about how to build a real competitive advantage with AI? Here are a couple of them. So you can have the strategy of always being the first in your industry to adopt the latest evolution in gen AI and have a temporal competitive advantage each time you adopt that. That's very much possible. But obviously once you miss one step in the evolution, you're you don't have your competitive advantage anymore. What I think is the most interesting strategy to pursue is deploying AI to reinforce existing advantages. And that's for example, very much the strategy that Apple is deploying. They're actually not talking about AI all that much, but they're very much still being focused on their existing advantages, having superior products, et cetera, and embedding AI into them to reinforce those rather than to build something separately and new. The most interesting one, but potentially also the most resembling a holy grail is to build something that others can't imitate. While that is super interesting to pursue, I don't think that will be in the cards for many companies. And then the fourth strategy archetype to follow is can we use the technology to develop entirely new business models that could give us a competitive advantage? If we think back at digital and digital transformation, we had the likes of Amazon and ebay or Airbnb for example, developing two sided network business models which once you reach the network size, it gives you an enormous competitive advantage. So that's a fourth strategy to pursue. But then in this case not with digital, but with AI or gen. One additional thought to this, and we will get back to that in the framework as well. But when we think through strategy, for me it's almost equally important to consider what will not change in your industry rather than to just think through what will change. And we'll get back to that in a minute. So I just wanted to take you through those four foundational perspectives as I Think it's super important to have that as a grounding before you get into designing your AI strategy. So when we think about AI strategy, let's first get something off the table. So AI strategy is not a sum of use cases. And we see that all too often that a roadmap consists of crowdsourced or bottom up sourced use cases from existing functions, existing teams and data get prioritized and roadmap. And that is the AI strategy. That is an AI roadmap, but in our minds not a strategy. So what is a strategy for us at least is a coherent blueprint of how to win in an AI native world. So if we unpack that for a minute, it starts with forming an idea, a vision of what that AI native world is in your industry, how your consumers will change, how market dynamics will change in your industry, then creating a coherent blueprint of how to win in that world, what role you want to play within that, and then from that, building a roadmap and a plan towards that. So if we work through this step by step, this is the AI strategy framework that we use in our work, which starts with the AI market drivers at the top, the elements of the AI strategy itself, the AI roadmap and then the AI enablers. But I'll quickly take you through all of them. Before that, just wanted to highlight two important principles for us. And the first one being that this is an outside in approach. So we see a lot of strategies starting from the current way of working and adopting an inside out approach. So we deliberately start from what are the AI market drivers, what will change in your industry because of AI, what won't change, and then using that as an outside in view to form your strategy against. And then the second principle which is super important is a future back approach. So rather than a present forward approach, where again you start from the current way of working and just deploying AI into that current way of working is that you imagine what the new system would be in that wave three for your function, for your company, or whatever the context is that you're operating in, and then working backwards from that, so that each step you take in wave one and wave two builds up towards the vision of wave three, rather than just optimizing the current way of working, which might completely be irrelevant in one or two years from now. So an outside in view and a future back approach to strategy. So when we think about AI market drivers, as mentioned before, we think through what will change and what won't change. So if we take super simple example and Think through consumer goods. We could imagine that in the future we will all have personal AI assistants that recommend and buy products for us, such as clothes in this specific example. And that might completely change the customer journey, the touch points, how brands show up in that purchasing process, etc. But at the same time there are still elements that won't change. For example, that humans will be still experiencing the packaging, the fabric, the tactile aspects when you have a drink or you buy a certain piece of clothes. Even if we don't buy that product ourselves, we will still experience that ourselves. And that might be one of the few aspects that actually remains the same in the entire consumer journey if we look at that industry. There's a framework that I wanted to show you, which is our AI opportunity radar. That is basically a tool to help you think through how your market might change. The way it works is that it starts from AI's technological capabilities and then you have a brainstorm around first order possibilities and second order possibilities. And that to me is very important, not to just look at the first order of impact and consequences, but to think through the second order of possibilities, which would get you more to the wave three type of thinking, as mentioned before. So if we just play around with this and give a couple of examples, here's a non exhaustive list of new Genai capabilities from instantly generating new things to infinite amounts digital, moving to a zero marginal cost, multi language 24, 7 etc. Etc. So if we take that through a couple of high level examples, AI more and more or the latest AI systems are enabled to take digital actions. For example, we've seen computer use from anthropic and cloud launch, I think two to three weeks ago, that can basically perform actions on our laptop or mobile, navigate websites, order things, etc. So what if that enables as a first order possibility that AI will start buying products for people, which seems like a logical next step. But if we take that through a second order possibility, that might mean the end of brand loyalty as we know it. And if you're a brand manager, that's very much something to take into account. What if AI assists distance buy products for us on more rational reasoning rather than on emotional brand reasonings, which is currently driving a lot of human buying decisions. If we start from another capability that is very much emerging, what we've called empathy simulation, which could drive personalized experiences or experiences that are modeled on how I feel at the moment, which the system could read from my wearable, for example, which as a second order possibility might get us to us all experience a fully personalized reality through smart glasses, etc. If we think about infinite future combinations or feature combinations, sorry, think about infinite combinations of features on that Netflix interface or any mobile or digital interface as a second order possibility that would get you to possibilities around Instant ux. As referenced before, think about AI tracking the results of anything that we do or buy which would enable more outcome based business models. We're already seeing that shift in SaaS business models for example, which typically work on annual or monthly subscriptions, whereas now some are pivoting towards outcome based business models more and more as AI for example solves customer support tickets and those companies get paid on the solving of the ticket rather than on a monthly, fixed monthly subscription. If we think through that evolution of digital items moving towards a zero marginal cost, that that mean overall that the cost of goods would decline and that we might enter an age of abundance which is predicted by some. But it might also mean that we get to a deflationary economy which would be, well, fairly new to us as well. So these are just a couple of examples, but this is a practical framework to think through what the possibilities are in imagining what might change in your industry or for your company based on that outside in future vision. We would then get to defining what your ambition is with AI. And the most important point for us is that it is completely interlocked with your corporate strategy, so that it's not a standalone strategy, but really contributes to the overall corporate strategy and obviously also influences and changes the corporate strategy potentially thinking through competitive advantage. We've provided our perspective on that earlier. So an important one for us is how can you think through reinforcing existing advantages with AI and then setting your key objectives or value drivers around your AI strategy, which for example includes the choice on how much you want to go for leveraging AI for margin optimization, efficiency gains versus corporate or full company growth. So that would then take us through the roadmap and you see here that we're working through those three waves. So referring to those three waves of efficiency, better quality and new systems. And on the left side you would list the important jobs to be done as an organization in the future. So we've already given that example of using AI for video generation. If we take another example of product development is you might look at the current way of working and say we want to deploy AI to analyze customer insights faster. We might want to deploy AI to get better customer insights for product innovation. But none of that might actually matter if you think through the new system that might emerge, which could be an autonomous product engine that continuously tests and deploys new products and features which would skip the gathering of consumer insights altogether. So it might very much be that you're optimizing in wave one and two for something which wouldn't really matter in wave three. So what we want to advocate for is really to have that vision on wave three and then to think back what we're going to invest in now. Does that actually still matter in the future or should we disregard it entirely Then going through the last piece of the framework, and super important as well is thinking through the enablers for the AI strategy and roadmap, which are around the talent and capabilities and obviously they will need to evolve in the future the technology and data stack, the operating models and how they will change, AI ethics and sustainability and AI governance. That for us are the five key AI enablers. And we will be organizing separate sessions in the future to deep dive on some of them. For example, on AI ethics specifically, we're organizing a dinner in New York for executives in three weeks. So if you're interested in that, feel free to ping me. And happy to invite you in New York to the dinner on AI ethics. If we think through those enablers more holistically, there are a couple of key shifts that we're seeing. I just wanted to quickly take you through. So shifts from siloed divisions towards integrated systems both in terms of data lakes as well as in terms of organizationally, how you're structured, in which departments moving from executive decision making to much more in the moment data led decision making which is more real time and AI agents getting more decision power going forward, moving from rigid state gate process to more autonomous self steering processes. Moving from that shorter term ROI mindset in wave one towards thinking about the longer term transformation which will make you win in the long run, not just the short run. And moving from a typical annual strategy and planning process to way more adaptable processes, again with that agentic decision making. And on that note, I'm happy to hand it over to Vincent.

Great, thank you. I'll see. Potentially you can share your screen then I'll continue while you present. Actually, yes, I can share.

Sorry.

Here we go. So I'll walk you through the case study of Walmart. So if we look at this framework and we plot some of the insights of large retailer like Walmart and take into account some of the discoveries or strategy recommendations that Doug McMillan did recently at CAS, you can use the framework to plot that strategy on here for Example at first if we look at the market drivers, we see fully automated stores, automated stock replenishment, replenishment, personal shopper bots that change the way on how the retail market could look like in the future. And what we also notice that on the other hand is that not everything will change. There's elements like convenient ordering or ultra fast delivery or cheaper products. These are inherently the elements that will likely not change in terms of consumers expectations. And therefore they've set out several strategies on the marketing, product development and supply chain side to make sure that they meet those elements that won't change. For example, adaptive retail across all different channels, E commerce and in store are fully integrated with as well. And that's the interesting piece that they can forecast what consumers want and when so that all data points from on store, but also things like social media trends, weather forecasts can be anticipated and that inventory management can happen accordingly. And in order to do that they have several AI enablers put in place covering the talent and capabilities, technology and data and the operating model. So for example, they created one intelligent integrated supply chain system across what they call discovery, purchase and delivery, both in store and online. And that all this data is integrated into one single point as opposed to actually relatively siloed data sets. And with that I think we've covered one example, but I think it's an interesting exercise to do for your organization, so definitely can recommend to do the same. We'll continue with the next and final chapter on leading this transformation because we've been talking a lot about the strategic direction you can take with your organization. What's very interesting is of course the human side and I saw some very interesting comments in the, in the chat as well about the role of humans and we'll definitely touch upon that later. So when we look at this transformation, our strong belief is that tech won't be the hard part. It's the people and the organizational change that is the biggest barrier for adoption. And I'll give you one example, one that we've used earlier as well. If you think about the design process of new products or fashion items, let's say if you think about building a system that can automate this from making the first designs, making the detailed designs and going building recommendations for production. If you build an AI system that can do this in seconds, but you have on the other hand an organizational structure that needs to go through different silos, connect divisions one to the other, have to do several checks on how this can be done. Obviously you're not capturing the full potential of the technology. So therefore working on organizational change to make these steps more efficient and have one integrated system between human and AI will be the essence to capture the value. And therefore our common recommendation is that for every dollar you invest in tech, you have to definitely do two of those in people and change. And if we then dive a little deeper into the role as a leader and how to collaborate with with teams and help people to make that transformation, we need to think about AI and humans and the different roles they have. Where we in the past or today often think as an AI, as a tool that humans use, we need to transition into AI as a connective tissue that allows humans, data, robots to truly collaborate from us using ChatGPT or any AI technology and solving a certain problem to really having an integrated system where people work together with agents and robots. The idea of humans as creators, creating things from scratch to really transforming to humans as editors and editing or curating rather than creating an example. Here is the idea of when you make a new product design, it used to be a piece of paper that you draw, your first sketch, you go into CAD modeling and then you transition through all the steps of the development. Whereas right now there's a possibility to have agents to create the first samples to create the first designs. And the role of humans, here is a curation, selection and refining of those designs. And finally, that's a more provocative point of view, potentially, where leaders used to manage people and processes, you can see that if it is a collective of humans, data, robots and different agents working together, potentially leaders manage AI native workflows and products and almost become product managers rather than traditional managers. And with that, there's obviously a lot of change ongoing, which can create some ambiguity. And in order to seize those opportunities, there's often recommendations on how to go through this. There's obviously that first step where you need to familiarize yourself with new technologies, with the tools, with truly submerging yourself in the technology and making sure that others do the same. So taking doing specific use cases, doing some experimentation is interesting, but the next step is obviously where it becomes more interesting, where you totally rethink workflows on what that could be in the long run in that wave three, and then work backwards towards what that workflow should look like in wave two or one. And then finally, once that is set up, you can really explore new business models, explore how you can rethink the system to capture new value and bring completely new experiences to your customers. And with that, I'll transition back to Phil.

Yeah, thank you. Wrapping up. And now we'll open up for questions, so keep them coming in. The Q and A section. Just wanted to wrap up with three key takeaways that I hope you'll remember and take away from this webinar is that AI transformation will happen in three waves, just as prior big technologies. And that it's so important in terms of strategy to think about what a third wave might be for your industry and anchor your strategy in that adopting AI in itself will not lead to a competitive advantage, and that it needs a deliberate strategic exercise to think through some of the archetypes that we shared on how to build a real competitive advantage with it. And then thirdly, that to build your strategy on what will change. And we've offered that imagination radar that you could use for that, but also equally importantly, to think through what won't change in your industry and build your strategy around that on equal footing. So as we dive into the Q and A, just wanted to share what could be next is that we will be sharing the slides as well as the AI strategy framework specifically that everyone can work with. And obviously if you had any questions to spar on that with us, our contact details are on here and we're always happy to dive in together with you and think through your AI strategy. On that note, super happy to open it up for questions. There are plenty already, so we will need to curate a little bit. One question that I see here or two questions around the autonomous product engine and how that could potentially work. Work without having the insight space. So maybe commenting on that. So traditionally a product development process would effectively start with doing ethnographic studies or getting human consumer insights. What AI or genai could potentially enable is that you could skip that entire step. Sorry. If you can generate multiple product features and combinations, thousands of them, on a daily basis, that would continuously be tested in your digital interface or whatever the interface is, and it would only deploy and scale up the features that work better. We've seen similar applications, for example, in food and beverage, where there are systems that we've worked with that can generate thousands of ingredient combinations and different flavors, textures, et cetera, that would test them against digital twins of consumers, rather than doing traditional consumer insights, and then actually would just work with whatever comes out of that engine. And again, you skip like the first step of traditional human insights. It's a potential future, but it's very much where we see things trending towards. Vincent, have you seen any other interesting questions to tap into?

There was an interesting question from Dominic on the Idea that based on a study that happened, that only 30%, 13% of digital transformation actually leads to successful transformation or leads to success. And then like posing the question whether this whole AI wave, will it be in the same trajectory? And, and should actually companies be the first mover in this technology transformation? And I think there's the. The first thing is of course that you don't necessarily have to be the first mover in an industry. Look at Apple or others that are not necessarily being the pioneers in this domain yet. What's different in comparison to the digital transformation of the past decade is that the technology is advancing this fast and has so broad applications. We see it. If you take the comparison with Web3, it's very different. People were imagining what use cases could be. Whereas in this case, the widespread adoption of the technology allows you to have so many applications and it's also very easy to apply them. If you think about how coding can be done, the experimentation and use cases are going very, very fast. So I think the threshold to do the first exploration and that first step of submerging yourself in the technology isn't that big. So the only thing I can encourage you is to do take that first step and take it from there.

One question I'm seeing here as well is what level of leadership should own the development of the AI strategy. So I think given the massive transformation, that at least we are seeing this from this as the new paradigm of shifting towards a more autonomous world. We're definitely advocating to having this as a CEO responsibility in terms of corporate strategy, but obviously in terms of more of the execution of that, in most companies we see a chief AI or chief transformation officer type of role that would really own the roadmap. So that in terms of levels of leadership, but beyond that, obviously I think this is so transformational that it's important to take the full leadership on board and for them to be bought in in that future vision of where is our industry going with AI then what's our strategy? And then what's the roadmap? And each of the bus or whatever your organization is then driving the items on that roadmap, but to make sure that it's that coherent strategy rather than just a sum of individual use cases. Vincent, have you seen other questions?

I see an interesting question from Doug which is potentially difficult to answer, but the question is synthetic change will be even more complex or will it? Is there a real opportunity to solve more wicked problems? And I think that is a very relevant question. I think the world is becoming much more complex and the setup of these systems isn't always straightforward. Yet the movement towards more agentic model where the systems are self learning and actually perform objective oriented tasks so they work towards solving a challenge rather than following orders from humans or from humans. I think there's a, there's a big opportunity for an agentic AI system to solve very, very difficult challenges, some that we were not able to solve in the past. So personally I'm optimistic about systemic change becoming much more possible through an agentic system.

Yeah, that's a good one. And I know we're at time or slightly over time we haven't been able to go through all of the questions. So if you still have a burning question, feel free to reach out to us on LinkedIn or over email. You have our email addresses here. So as said like we will be sharing the slides the framework would be great to hear what you think of it and if you want to spar on using it maybe as a, as a last note. So as mentioned like you're invited all to Autonomous and I saw somebody asking for the link as well. So in the chat I will be sharing with you the link for complimentary tickets and we'll make sure to include it it in the email as well. Well, there you go. So for now thanks a lot everyone for being here. Was great to all have you here and also to get those great questions which I hope made for a good discussion and I hope this has given you some inspiration and some practical tools as well to think through what AI is and what the foundational perspectives are and then a practical tool to set your AI strategy Gene. So thanks for today and see you next time. Bye everyone.