Webinar: Setting your strategy for (im)possible innovation with AI

Get started now. So, yeah, great. Great to have everyone here. I'm Jeff Gibbons, I'm a managing director at boi. We're a global consulting and engineering firm focused on autonomous innovation and very excited to discuss this topic of impossible strategy. We'll talk a little bit about why impossible strategy, what that actually really means. We're going to talk about examples of previously impossible innovations that we see in the world, and then we're going to get practical and talk about how to set your own impossible strategy for your business. Before we get into that, just a quick introduction. Boi, we are the consulting and engineering firm for autonomous innovation. We reinvent how companies grow. We do that working with companies along a few different angles in terms of their strategy, their growth strategy, which is what we're going to focus on today, building products for them. So AI powered products and services that support their growth and then also transforming them to AI native operating models. And we do that globally, across different industries. And it's a very exciting field of work to be in, particularly at the moment. You may be aware, we actually run twice a year. We have another one coming up in January, the Autonomous Innovation Summit, where we bring together the innovation community to actually discuss the changes around autonomous innovation, how companies can thrive, what are the challenges they will face. So look forward to seeing you at that one in January, if you can join it and you can find out more about that, our website. So the first topic we're going to talk about is why impossible strategy? And by the way, we have some stories, some examples here to share, but please feel free to ask questions and we'll make sure that we cover them as much as we can. And we'd love to make it more of a discussion than just a presentation. Okay, so what do we mean by impossible strategy? Impossible strategy is really the strategy for AI powered growth that relies on innovations that were previously impossible. So I think probably a lot of you have seen over the last 19, 20, 21 months the massive evolution and emergence of generative AI and how that's transformed the way that people work. Obviously at the start, I think everyone like myself was astounded at what you could do with it. And you can have it write stuff for you, you can have it generate images for you, you can develop software tools that do amazing things. I think what we're starting to see as a company now is that that's great, we should be doing those things, we should be integrating that into the way that we work. But the most important next frontier for us and for our clients is Actually, so what actually can we do that's different in the world as a result of that? So if I can have a thing, an agent or a whatever that can generate text, that can think, that can generate images, that can read images, that can read video, can create video in real time, what are the types of things that we could do in the world that actually weren't possible before? And so shifting gears from a pure efficiency lens to and how do I do the same things, faster, cheaper, better with generative AI, to actually what are the new things that weren't possible before? And so that's the focus of this webinar. Okay, so we see that the next wave of growth for companies is going to come from not from digital transformation, that's sort of a wave that we see starting to wane, but from autonomous transformation. And so we see examples in the world already of companies that are growing and carving out new markets entirely by applying AI in ways that were not possible even a few years ago. So we look at intel building the first autonomous factory for chip production. We look at NotCo. They have an autonomous food generator. This is a proprietary engine called Giuseppe that they have built for their own business. And they're now monetizing for other food companies that actually thinks and understands what are novel combinations of ingredients that will create foods that weren't something that a human could imagine. So you have a plant based food that contains pineapple and cabbage in order to approximate the taste of dairy milk. And no human would ever think of that. And that's kind of the point. It's actually something that wasn't possible for humans to do. And they've now developed a proprietary engine that works in the background, thinking about all the different permutations and options of how do you create the best plant based milk. And now they're building a business around how can they do that for lots of other food companies as an engine that they then monetize? And the last example is Shein. So they actually have been one of the leaders in what we call autonomous fashion. So they have an automated model that is continually launching and testing and validating and scaling up, starting from 1000 product launches a day. And so they don't have humans analyzing all of the sales data every day. They're actually looking at how can they really think about a new approach to the fashion model that wasn't previously possible. And so the benefits of this approach to autonomous innovation, obviously there's all the things that we talked about a lot. It's faster, cheaper, but really the Focus of this discussion is how can you actually have better products and better innovations? Because really, I think as innovators, that's really kind of the goal of our jobs. It's not to do our jobs of four years ago better and cheaper. It's actually how do we do better things in the world? And we see that for innovation departments and teams, that's really going to help them to actually stay ahead and be successful in their work. And I think it's really important to make sure that we're not blinded by a lot of the current spot applications that we might see. So, like an AI tool that helps you do one small thing, an AI tool that helps you to write emails, an AI tool that helps you to generate images, an AI tool that helps you to generate voices, an AI tool that helps you to listen to things. The thing is, how do you actually piece together new business models and new products that take all of those capabilities together and figure out something that was completely unimaginable and impossible? Well, maybe imaginable, but impossible even just a couple of years ago? The way that we actually look at that is thinking actually future backs. So not thinking about what are the things we can do today and how do we automate those, but thinking about what are the future things we would like to do that seem crazy, and then thinking, is that possible? Can we work our way back to that? The second thing is we're not looking at individual spot applications like a tool that does one thing, a tool that does another thing. We're looking at how do you have transformative new types of things, actually drives systemic growth by piecing together lots of different pieces. You put a very good point in there. Not saying that Shein makes the world better. Just great example of innovation, not necessarily innovation for good. We're not looking at just technology and the tools. We're looking at how do we actually create technologies, but also AI native operating models that can actually really take advantage of those technologies. And then the last piece is we're not just looking at automation of the things that exist today, but also looking at autonomization. So how do we build systems and products and services where really what we're able to do is have AI agents actually make decisions and be part of the actual working of a product or a service to actually make things happen that were too complex for humans or provided too many tasks for a human to do at an acceptable cost? We really are focused on the idea that businesses looking at AI as a pure productivity tool are making a mistake. If we Think about the ways you can actually capture the opportunity around autonomous innovation. You can think about it in terms of the innovations that we're launching. Are they within our existing operating model? Are they existing types of products and services? Those would be called AI efficiencies in our terminology. So things that were possible before, but it's basically just like doing the same thing faster and cheaper, or are they things that actually offer a new AI native operating model? So are they a completely different way of running the business on the basis of the use of AI? The next thing we're looking at is are they AI native products and services? Is the actual experience that we're offering to the consumer actually different as a result of doing this? And then really where we see the biggest growth opportunity is actually how do you actually have AI breakthroughs? So how do you actually have something that is a completely new product, a completely new service that's a new experience for the consumer or the customer, but also runs on an AI native operating model that takes advantage of the benefits of that to run the business in a more efficient way, or a way that frankly just provides a much greater feedback loop into the improvement of the product over time. So to make that real, here's some examples. So you think about NotCo, they really are in the business as NotCo, of generating things like plant based milk. Like plant based milk is not a new thing, but they're doing it in a better way because they have an AI native operating model and that they have this Giuseppe engine that is really working in the background to figure out how to do that better. We then have Adobe Firefly, A number of you may have heard of this or tried this. This is really kind of an AI native version of the same thing that Adobe has been offering in Photoshop for many years. So it offers massively incremental benefits to the user using Photoshop on the basis of the fact that it can generate images, it can fill out images with backgrounds in ways that you couldn't imagine. But it's still fundamentally using Photoshop and the way the business is run is kind of the same. Then we have these things on the top, right? Showrunner is a good example of that. We'll get into that a little bit more in the next section. But really it's actually looking at a completely new type of product and service. So it's a product and service that enables me to create, like me individually to create a TV show or a TV series that wasn't possible before, but also a completely native AI native operating model. To run that in the background in terms of how they buy content, how they actually enable teams to run differently, and the business model that enables it. What we're thinking about a lot with our clients these days is this blue stuff. What is the previously impossible stuff that they couldn't have done a couple of years ago where they're actually able to do those things now? That's where we see the biggest growth opportunities over the next few years, rather than just automating the existing processes or improving slightly the existing innovations that they're already doing. Maybe to get into some more examples specifically. First one, yes, Showrunners was one we talked about. This uses generative AI to actually allow viewers to make their own TV episodes. It's a bit crazy. Obviously, teams and individuals can use it as well. You can select everything from the characters you want, the storylines, and previously that was the kind of thing that needed an entire team and a whole set of equipment and all this kind of stuff. So still in early stages, but definitely a business that was completely impossible two years ago. Noco, as I've mentioned, so they're using the engine to generate formulations that a human would never imagine. Some other examples that are very interesting and unintuitive. Materials companies have actually struggled to really implement circular reuse models. Imagine you're looking at how do you develop a business around reusable concrete, right? So concrete, if you get it back from an old building or any situation, it's very variable in terms of the composition of it and the quality. And so what that meant is if you're going to create reused concrete by kind of processing the old concrete and adding some new stuff, it was very difficult to do that because the quality was so in inconsistent. And so you had to actually use a very large amount of virgin concrete to make sure that it was quality enough. And because it was too difficult for individual people to actually figure out the right formulation for every single batch, it's just too expensive. And what we're seeing now is that AI is actually enabling those companies to actually create new formulations every time. So. So they can analyze the quality of everything that's coming in in real time and then understand exactly how much version material to use. So they're able to reduce the amount of version material they're using, so the concrete is actually more reused and actually also available at lower cost. Something that was previously impossible just because of the complexity of those calculations and the fact that it wouldn't actually make any sense to do that as a business every single time. Duolingo number of. You might have heard of this. This is a business that's been around for a long time. They are actually now looking at how do they use generative AI to change and evolve the way that they do this. So they're looking at using generative AI to create personalized learning experiences adapted to the individual person. So you actually get real time feedback, you get customized practice. So rather than, Jeff, you're trying to learn Portuguese, here's the five or six things you need to do. First. It will say, jeff, we've analyzed how much Portuguese you know and where you have gaps, and here are the five or six things that are right for you. So it dynamically adjusts to the strengths and weaknesses and that provides a level of tailoring in their offering that was previously impossible. And then the. The last example. Talk about this is actually sort of a widely used example because it's just so. It's so fascinating and shows the benefit of the complexity management of generative AI. So AI can actually now design millions of potential drug molecules and then predict their properties and optimize them for safety and efficacy. So at Vanderbilt and Master University, they've actually been using AI to identify new treatments for bacterial infections that were previously impossible to find by traditional methods. So traditionally, you would have a design of what the bacteria looks like and you'd have a model of that. And then you'd have humans trying to figure out, how can I construct a protein that would go around that or a molecule of some sort? And that's a really complex process and it's hard to get it right. AI is actually now able to work continuously in the background, not only to generate the designs, but also predict how well they're going to work and make tweaks to them. All of that process. It's basically enabling the researchers there to do something that would have taken years, in weeks or months, just because of the entirely exponential range of options that are able to be considered. I'll take a pause here. I see a question from Jennifer. So how do you see autonomous. Good to see, Jennifer, by the way. How do you see autonomous innovation supporting the process of identifying, selecting and refining business model development? I'd say there's a few things I could think of. One would actually be how do you think about it in terms of the pure selection? So imagine you've got five new ideas for a different business model. I would see that one thing you could do would actually be to create different agents that represent different parts of that business and simulate how they might work for example, let's say you had a business that had three different types of customers and four different types of employees and different suppliers. You could actually simulate out which of those had potential in terms of was there a value proposition for each of the different people and actually have the agents kind of figure out if there was potential. But then also actually design, for example, pricing models that actually would make everyone happy, for example, by working together to solve challenges. We've been working with another company looking at how can they improve construction processes. And what we've been talking about is actually developing agents that represent different roles in the construction process and basically setting them tasks and saying, okay, we want to do this stage of the process in 50% of the time. Could you please work together to ideate and solve this issue? And so you can actually see how you could have the agents work together. Trying to see if there's any other questions. It's incredibly. And a question from niz, I think, who is in London. I believe it's incredibly challenging to urge long standing legacy enterprises to innovate their business model, do something new. What's your take on how to address this? Wow. Yeah. I mean that's a whole, that's a whole webinar in itself. I think the main thing I've seen, and I think in the last sort of 18, 19 months, I would say is because we've been having a lot of conversations with companies around, how do you address this big, disruptive, exciting, but terrifying thing that is AI? I think the main thing we've seen to work is two things. One is to find somebody who really cares about it in their career and work with them because they see it as something that they want to advance with. And then the second thing is to do something simple with them that, that they can take to others in their organization and say, look, this works. This would be valuable for us. Who wants to join me on this and can you fund this? I'd say those two things have been successful. And I think if you don't have the right person and if you don't have something that's of value that you can do quickly and cheaply, I think it's a lot harder. So I don't know if that's a full answer, but that's at least one thing that I've seen. Yeah. And I think, yeah, it comes to people. And I think one thing we see a lot of recently is that there are people in corporations who are sort of putting their hands up and saying, I want to be a leader in this new world of autonomous innovation, I see this as something I'm interested in. I see this as like part of my career platform. And we find that often those. Those are the people that we end up working with first, actually. So time to get a little bit more practical. So it all sounds awesome. Showrunner, not co. But what if I work in an established legacy company, how do I figure out where's the right place for me to start? One of the things we've been doing is actually trying to figure out what are some of the frameworks and tools and approaches that help you to figure out what's right for you as a business, which is exactly this. How do you figure it out, what are right for your business? The first thing is actually just to make sure that you're thinking about the fundamental capabilities of AI that enable all of these innovations. If you break it down, there's really a few things that are going on that most of them are probably working on most or all of these at the same time. The first thing obvious to all of us is the idea that you can have automated content generation. You can actually generate text, voice, video, movies, whatever it might be. The second thing is you can have massively complex simulation. With the concrete example, obviously there was a simulation going on there of if this amount of concrete is at this quality, how much version concrete do I need? That's obviously a simulation and not a hugely complex one. But I think what we found is actually the biggest value is in how can you find the most complex situations where there are the most variables, the most types of people, problems that humans just can't really solve. So, like, the construction example is a very interesting one because in a construction business, you know, constructing a huge building or a factory, you're actually looking at so many different pieces of the puzzle, so many people, so many companies, and a human can't really wrap their head around all those things at once. We could pretend we're human, but it's hard to do so. The third thing is actually mimicking human roles through agents. If you've been on LinkedIn recently, you've probably seen everyone banging on about agents the whole time. This is really about a piece of AI, really, which is simulating a human role to solve a problem, perform a task, and that takes on a specific role. So it will be basically instructed to think a certain way, act a certain way, make decisions in certain ways. And so those agents are critical in simulation, in decision making, and also sometimes in action too. So actually, you could have a purchasing agent that goes Forward. The next thing is cross modal action. So you can go from text to image, you can go from video to text. How can you think about situations in the world where you have to have that translation of something which might be difficult for a human? For example, one of the things we've been looking at recently is the idea of clothes recycling, clothing reuse businesses. There's a lot of processing of having to take a picture of a piece of clothing and see if there's any holes in it. All that kind of stuff. Kind of a complex task for human or just a time consuming task, which means it's expensive. And so how could you have AI actually use computer vision to identify if there are holes in the piece of clothing? Because it can go from a picture to a text or a number that actually would quantify the level of damage to the clothing. Autonomous improvement. This is the piece of the puzzle where it's different to automation. Actually. You can instruct AI to do something, but you can also ask agents to actually figure out things themselves and improve their own processes. I mentioned the construction example. You can imagine that there are situations in which you could say to an agent, can you please identify ways to make your job easier? Can you identify ways for you to reduce your number of interactions with other people? You have that entire process improvement that goes on and that can happen without the human intervention. Then the last thing is just instantaneous customization in the same lens that you can generate content. You can generate lots of different options of that content and you can customize it to an individual person. I could generate, you know, 300,000 emails in a couple of seconds, but I can also tailor those to individual people and their customer needs and their personalities and all those things. And so that gives you a lot of possibilities as well. Within that we will, yes, we will be offering the ability to download the presentation as well. And actually a lot of this content actually is in the, is in the playbook of how to win in the age of AI, which is actually in the downloads already. That's the first thing is think about what AI can do. The next thing is also, and we're going to go into some more details, is to think it from a future backed mindset around going beyond what's possible today and trying to find the right avenues for this. We'll go into some more specific examples. But as I've mentioned, the first thing is to find the most complex problems. Places where humans just aren't that good are a really good place to find opportunities for AI. The second is to look at what was previously impossible. So I feel like in most businesses there are situations where, you know, we tried that thing, we really wanted to make it work, but the cost was too high, things like that. And if you think about identifying opportunities that were previously impossible due to economics capabilities and lack of data or other constraints, they're often a pretty good place to start looking as well. And we generally find that most businesses have lots of those things to think about. The third thing we're going to what this means a little bit more is to imagine beyond today's AI models. So I don't think it's that easy to come up with lots of new, previously impossible innovations. If you're thinking about just like what can a chatbot do that will take you to chatbot ideas. And then the last thing is to imagine your AI native operating model. So imagine ways in which you might transform the way you work. And often that's starting to look at where are some of the pain points in your existing operating model that you would like to change. So what we'll do is go through each of these in turn as we go through that. And let me just take a pause point for a couple of questions. Masoud asks the type of autonomous innovation you're talking about, wouldn't it require training a custom LLM, which is a very expensive endeavor? We have found that's not necessary. What we have found is that you can use much simpler, cheaper methods. Either you can, well, it's astounding what you can do with the actual public models, but also you can use rag retrieval, augmented generation, where you actually can basically provide information to the LLM on top of what it already knows. That's actually a very simple, easy way rather than actually training a custom LLM. Although I do think it's important to know that if you think about in the future, as we're talking to clients, the cost of actually creating a custom LLM is going to be massively coming down. And we're already seeing a lot of companies deploying much smaller, more focused LLMs for specific use cases. The first step we talked about was hunting for maximum complexity. So thinking about where in your industry are there problems that can't be solved just because there are so many people, interactions and stakeholders, all the coordination, where has there been issues in prediction and just data overload? And then where are there tasks in your business that have been incredibly hard to automate? So one of the areas we've seen a lot of innovation and interest among clients actually is in things like Hospitals, right? So they're massively complex systems. You have so many different types of people, so many different types of institutions, partners, pieces of equipment and such high safety needs. And so it's thinking about like, what's the, you know, what's the hospital or what's the most complex place within your business that you could look for. The second thing is looking at things that were previously impossible that people have tried a bunch of times. So this is really about finding the things where they were just hurdles to make things happen that seemed strategically good but were just super difficult. Take the example of peer to peer car sharing. There have been a bunch of companies that have tried this over the years and none of them have really managed to make the economics work. There's all sorts of issues in terms of how do you verify people, how do you, how do you know if there have been damages? Just managing all the cost of the business. And there's a few different steps in that process that you can see. Things like identity verification, things like actually logging when a car has been returned, that actually using generative AI, you could imagine how you could actually reduce the costs for running that type of business. So I don't know if anyone's going to try it again. We'll see. Maybe people are burned. The next thing is actually imagining beyond today's AI capabilities. So obviously we all know it's pretty astounding what can be done by some of the latest AI tools and feel like every week something comes out that's new, there's some kind of new model, some kind of new capability, and there's all sorts of stuff coming down the pipeline. I think it's really important just to think about how early we are in this journey. Imagine a few years from now, and I'm not going to say how many years, but a few. You can have models that would actually have 3D and spatial awareness, so they'd be able to understand what's going on around them in a room, in a factory, in a stadium. Imagine what could you do if that was possible. The second thing is people always say, oh well, AI can't really think and understand emotions. I think actually a lot of the research is showing that it's getting much better and better. And actually a lot of people actually aren't that emotionally intelligent too. So I think it's worth keeping in mind. But a lot of the research shows that in the future AI actually will be emotionally intelligent, maybe more so than humans, who knows? And we'll be able to understand sentiments and we'll be able to convey sentiments itself too. Robotics is a huge topic. So obviously a lot of what we're looking at in generative AI today is really operating in what we'd call like the knowledge realm, right? So it's text, it's videos, it's image, it's, you know, generate a PowerPoint, all these kinds of things. It's just like digital stuff, but in a software realm. The next wave we see is actually that generative AI will be shifting into the physical realm from the knowledge realm, so getting into manual work, but also day to day tasks. So this is a Google DeepMind robot here, which is learning to tie shoelaces, something that was previously impossible and is actually quite astounding if you think about the complexity of that task. It's the type of thing that people always said robots wouldn't be able to do, and now they're doing it. And then the last thing is just thinking about everything through an autonomous lens. This is actually quite simple. So if you think about the idea of like a smart home device, I would always be talking to it, right? I would say Alexa or Siri, can you do this? Can you do that? I think the mindset shift really is that we need to start thinking about, start thinking about basically what happens if they talk to you, what happens if they're instructing you, what happens if they're prompting you and reframing your role from being a user to a participant in a system. Maybe I'm going to get home and Alexa is going to say, hey, Jeff, you haven't taken your shoes off, making a mess, whatever it might be, thinking about ways in which agents are actually thinking and acting on behalf of you, hopefully, but actually instructing you rather than you instructing them. And so it kind of turns on its head the idea of everyone needing to be a prompt engineer because maybe they're going to be prompt engineering you in the future. And then the last one is thinking about imagining your AI native operating model. I think often this comes down to once you have an innovation in mind, what's the best way to operate that and how can you, what impact does that mean on the roles you need and the ways of working and how humans work with agents and all that kind of stuff. But if you think about this as a tool for ideation, often it starts with where are the biggest opportunities to save time and money and what are the most tedious tasks that no one actually enjoys doing but actually have to happen? So you think about the world of museum archivism, archivists Archives in museums. I was talking to a friend recently who works for a museum and a lot of museums have had a lot of issues recently with staff stealing stuff because a lot of the stuff is actually not cataloged properly, it's not secured. And the reason for that is because nobody wants to do that work. Like nobody actually wants to catalog these things. It's super boring. And a lot of the people who work on the archives, they're kind of not interested in doing rote manual work and they're not willing to pay people to do that. So it's things like that. If you could imagine how you could actually automate or semi automate some of that process, that would actually be a good way of figuring out some ways into what an AI native operating model will be. I'll take a pause here for questions. So Jennifer had a question around. What are your go to's for tools or platforms for creating these various agents? Yeah, great question. I think it can be quite a wide variety. It's quite easy. I mean, depending on the exact boundaries of how you call an agent, you can do everything from building a GPT in ChatGPT. They can't really interact with one another. There's a very good platform called Autogen and then often also just working in custom development too. So I think there's a few different levels of complexity that you can think about using whatever LLM is your sort of go to within your business. Carolyn has a question. What kinds of AI capabilities have you encountered within the SaaS industry and are worth mentioning? I think. I mean this is an interesting example of where I think we're seeing more of the custom LLM development actually. So. So there's a bit of a shift we've started to see in terms of companies developing very small custom LLMs for very specific tasks that might be customer management, for example. So if you think about using OpenAI to do your customer response management, when it's really just doing very focused tasks that need very specific data, it's kind of like using the hugest objects in the world to try and squash a fly. You're actually using something that's so enormous and so general purpose to do a very small specific thing. And so one of the things we started to see in SAS is actually the development of very small LLMs that are very focused and much cheaper to train and also to use and faster that are specifically focused on things like customer management responding to requests as well, because those things are quite predictable. Joanna has a question. How do you work with your client to identify opportunities in the capabilities and offerings matrix. And what role do you play? We'll get into that a little bit at the end. But in general, what we do is we, you know, we kind of do like a sort of like a strategy sprint, often with clients looking at using some of these frameworks and doing research on their business to understand where are the opportunities, develop a range of, like a portfolio of different options and then prioritize those within them. So from the strategy realm, and then we actually work with them to build those products and help them to figure out their sort of new operating model around that. And then Nada has a question. So do companies replace or supplement innovation development process with Gen IO tools? To what extent? Great question. And it sort of comes a bit back to Jennifer's question at the beginning. So I would say currently we're in the phase where it's not about replacing the innovation process, but supplementing and kind of changing the way you need to do things and the speed at which you can do things. And we've also worked with companies to actually develop custom tools that automate their innovation process. So, you know, you log into a website, you can use it to identify opportunities and generate concepts and validate those. So we've been, I guess that is a bit of a replacement, I guess you could say, sort of depends the level of investment that you're interested in making. Okay. And so, yeah, I guess the last couple of things. So to help companies get started, we've implemented a new approach that like uses these things. So to the point of the question earlier from Nadia, so we have been putting into practice the last few months what we call the impossible strategy sprint. So this is basically where we actually work with clients to set the vision and understand their growth objectives. We do some competitive analysis, industry analysis, looking at a lot of these different lenses we just talked about. We actually work with some of the experts from our Autonomous Innovation Summit to bring an outside perspective and then work through some of the steps that we've just talked about to actually identify what are the some of the revenue growth opportunities. First of all, to actually figure out ways to optimize your existing revenue, identify new adjacencies, but then also actually figure out what are those previously impossible innovations that you can launch and then figure out what's the blueprint for the organizational changes that you actually need to make in order to capitalize on the value of those. If you're interested in learning more about that, you can get in touch either by hello at Border Innovation or through our contact page. We found these sprints to be very valuable, obviously. Well, obviously I would say that. But in actually helping companies to figure out both the efficiencies in so the optimizations of their existing revenue, but also the new things that were previously impossible. And that was everything that we had prepared, I guess. Want to see if there's any more questions from the group? Looks like no. Okay. Awesome. Well, thank you very much everyone for your time. There is a deck you can download in the Documents section, which is your playbook to win in the age of AI, which has a lot of this content. And thank you very much and hope you all have a great day day. Thank you.