Name: EVERYDAY AI: BEYOND THE BUZZWORD
Uploaded: 2025-05-06
Duration: 20 min 39 s

Many still see AI as futuristic or out of reach—but what does it really take to make it useful in everyday work? We’ll cut through the hype with Florian Douetteau, CEO and co-founder of Dataiku, and discuss what it takes to build real, lasting value with AI. With a $3.7 billion valuation and over 700 customers, we'll unpack how this once Parisian startup has made enterprise AI accessible, practical, and impactful across entire organizations. 

EVERYDAY AI: BEYOND THE BUZZWORD

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Hello again. AI is still often discussed like it's futuristic or out of reach, but for many companies it's already part of everyday work, helping decision making processes, improving operations, and changing how teams function. To discuss what makes I really useful and where its value truly lies, please welcome co-founder and CEO of Data IQ, Florian Duetta. So Florian, you co-founded Dau in 2013. How long ago was that in AI time? Uh, 100 years, probably 100 years, give or take. Well, again, the reason I, the reason I bring it up is because so much has happened in AI since then. What were those early days of Data I like? In in the early days it was lots of things were similar than than today, meaning uh enterprise were facing new opportunities and technological challenges, and uh the question back then is like, what do you help people? There was enterprise to get value um from AI. And my take back then and until now is that the The roadblocks where I are usually not the one that people think. It's not that much about technology or the quality of technology per se. All of those innovation pouring in. It's about the ability for enterprise to make it theirs. And in particular, back then, what was fascinating to me is that you had the opportunity to create more value with data, of course, but many companies were like, oh my God, in order to make it happen, we need to hire a lot of experts, lots of data scientists, or what else? And this perspective would not work from my perspective because What you really need if you're an enterprise is to transform your business and actually to enable the people in your teams to make more by themselves, which led me to this venture of building a platform to make it easy for the enterprise, for people in the business to create their own AI, their own models to work on their data, to create stuff by themselves, which led to the company I know lead. Well, let's let's name it. What are some of the roadblocks that enterprises often run into as they start to adopt AI? When they start to adopt AI, um, I think the first issue they've got is the This they've got two things. They've got the skill gap, as in like what do we need to have in the business as skills to make it happen, and they've got another one which is just the fear, the fear of failure, the fact that AI is not perfect, AI is not deterministic, and so a business should run the lack a clockwork. So who do you actually reconciliate those two facts? And you're expressly running into a fear of failure. Where do you think that comes from? The fear of failure comes from the fact that indeed AI technology is RO, meaning the RO power. In order to make it work, you need to actually combine AI with your own data. You need to combine AI with things that make sense for your business. You need to put it in control before building it into a process. And it's not just about, for instance, one of our customers started to deploy agents in their shops. They are selling clothes and part of the motivation was to help people in the business to look for inventory so that they could help you faster determining whether this or that piece of item could be available or not. So instead of like going through scanning or whatsoever being To search for it faster. Great, but an agent can help you do that for sure. But in order for the agent to actually be smarter, you also want the agent to be able to suggest if the piece is missing, what else should be provided to the customer. Proactive needs to be proactive. And when you want the agent to be proactive, you don't want the agents to hallucinate. You want it, for instance, to give a recommendation based on good old AI built on models that Helps you recommend one product, one color, one type of jacket versus another, one type of pants versus another, or what else? And so any type of business, would that be in finance, would that be in healthcare, would that be in retail, would that be in manufacturing, they need to combine the power of. Les the power of GAI with the data, their metrics, they are good on models in order to create things that make sense for them, not just for the eye. Well, there's a pretty wide breadth of customers you work with. Give us a sense of the breadth of customers. What are we talking about here? Yeah, so yeah, the business is More than a decade old and we grew into uh having 700 customers, enterprise customers, um, and our business is around 300 million of IR. So our customers are for a lot of them, well 14 2000 companies and names come on, yeah, so some names include, uh, uh, BNP Pariba, Morgan Stanley, um, Fier, uh, Farms, which makes might not resonate with. UK people, because you, you don't chicken, chicken, chicken, a lot of chicken, they make so much chicken and exactly at GE Aviation, they don't make chicken, but they fly definitely not chicken, exactly. And so all of those companies have in common that they actually Generate and can generate lots of data because they are banks because they are healthcare organizations, because their manufacturing processes are very complicated and generate lots of data, and they also have in common the fact that they can derive lots of value in AI because you have lots of processes you can optimize and you can optimize them because in the manufacturing line you've got someone that may spend one hour a day going through some logs in order to write a log report of the day. And this person is paid a good amount of money just to do something in the very same thing every day, and AI can automate that and those are the type of things that our customers are building with our platform. Um, it is uh something that is valuable for them because healthcare companies, they manage lots of data with patients in order to understand what you, what kind of drug they could actually um. Uh, they could actually work on the market in order to understand what the actual use of the drug already on the market that they could better understand and that they do lots of surveys, lots of texts, and a good amount of their workforce is working on analyzing patient interview and patient data in order to trans that into something they can then use in order to make some determination, and this can be automated with AI. And so those things are small, each of those, but like those are the hours and hours that AI will actually automate, and I think that is the concrete version of AI that would transform the enterprise. Well, one of the things I find really interesting about all of these different use cases is pharmaceuticals are very different from aviation applications, which is very different from chicken, right? So what is the thing that binds them all together? What is true in every case, if anything? What is true in every case is that the people that actually in order to actually create a good AI project, you need first to understand the context because many of those companies are building fairly sophisticated products. Why? Because they exist for decades and decades. There is a way actually to do chicken, a way to build an airplane engine. There is a way to do finance. There is a way to actually push a drag on the market and They actually have lots of knowledge and they need to translate this knowledge and so you need the context of the business in order for the EI implication to make sense. And so for that you need to enable people that are those domain experts. The second thing they've got in common is that they've got large operations that do work, so you don't want actually AI to break things up. So the main risk of AI is that you flip the switch, you build a model, you let it run something, you build an agent and so forth, and if it's not good enough. Uh, best case just distract people, which is never good. No one loves distraction. I do love distraction, but not when I work. Or actually I love distraction when I work, but I should not. Whatever. You want the right amount of distraction at the right time. Exactly. So worst case is just distraction and Best case is discussion and worst case they actually break something. They make a customer service poor. They actually create an issue that you wouldn't have before. And so consequences which yeah, which should have practical consequences. And so the the the thing that is key for them is that. When they run AI systems and when they get more and more sophisticated, they actually need to just build more and more tests, essentially speaking to focus a lot on the quality of those systems and the issue when using generative AI at scale or using agents in the enterprise is today not so much the relative quality between 1 and 03 or whatever else. Of course it matters, but it's. And from my perspective and not just that is the fact that in order to use them for real, you need to build them, test them, connect them to your actual data and so forth in a way that is. That require some work. Now, one of the things that is talked about a lot is AI being hyped or overhyped. How do you help customers differentiate between what will really work and what's noise? I think that the perspective from our customer is winning. The, the, um, well, a couple of months ago, um, we gathered some of our customers and that would be mostly chief data officer for instance of various, um, large organizations, um. And one of their concerns was that essentially speaking in their company, lots of people were talking about AI. The management team of the company was talking about AI. The board of the company was talking about AI, and so they were like, OK, we actually have to do more AI. Everyone's talking about it, so we have to do it or not, blah blah, to manage it. And so one of our next step is that. In order to understand better, we actually try to survey CEOs themselves again from various organizations across Europe and US and various sides of the company, but like, OK, what do they actually think about AI? And indeed part of the eye and part of the thing that we learned from the survey is that many CEOs realize that they have to do things about the eye and are almost to some extent fearful of losing their job or their position. Because of AI, because of not doing enough, like 80% were actually fearful of that. And at the same time they were conscious about like regulation and the impact of regulation, and we're aware that for 30% of them that some projects of AI were canceling the company because of regulations. And so what I, what I think is that part of the hype is because there is this realization that it's a moment, it's a moment where a lot of organizations will actually transform their productivity. Where the one that will go faster to AI will actually potentially be a big winner, but also where people that go too fast and actually create a risk in their business could actually also be the first losers because they would just essentially open themselves to the critique coming either from their customers or shareholders or whatsoever to the risks and consequences of AI. So from the perspective of leadership in the organization, depending on. The way you look at life, it could actually be either a win-win type of situation or a lose-lose type of situation because you might lose on both and if you go too fast, you lose because people will yell at you that you're a responsible user of AI or if you go too slowly, well, exactly. And then, well, you could be replaced as as a leader by the next generation of CEOs that would be AI first, whatever that means. Now, I'm gonna come to you all for questions in a moment, but Florian, the IU has reached quite a bit of scale and has long been rumored as an IPO candidate. Anything I need to know? In front of all these people. Oh yeah, of course, that's the type of place where you make revelation about IPO. Yeah, I guess it's been the question all day. Yeah, yeah, meaning we built as many companies, we, we, we've built ourselves to our public readiness and we brought together in the last few, um, in the last few quarters, actually, um, quite a few seasoned people in the next, uh, uh, a CFO, a CRO, a chief people officer, a GC coming from uh Twitter, Zoom. Cisco and so forth, and which is I think what you need in order to scale and grow a business. Uh, a related question uh can be uh in which country and those kind of things that you haven't asked, but I can see you please continue, but indeed as a company we, um, as a company and I think for many companies of our generation, we decided fairly early on to, um. Uh, essentially get, uh, US investors and uh focus our business in the US even if, uh, as a company we are very nuanced because we, uh, operate our product from Paris. I'm based in London and the company investor based in the US. Any questions for Florian? It's not, I can keep going in the back. Oh, hello there. I think then, co-founder and CEO of Nana where we do home-based digital diagnostics using AI and applied machine learning. Lauren, I have a question because you guys are kind of horizontal across the enterprises like an AI developer of different project needs. So I imagine. Your success with your enterprise customers actually comes down to their data sets and what the project they want to develop. How do you take those learnings from one enterprise and one sort of industry vertical to others, because obviously you can't take that data with you for your next project. Oh yeah, of course not. And because our product enable our customers to differentiate by building their own models, their own AI and so forth, it's not desirable. And actually many of our customers operate their IT in a very nuanced way with things on the cloud, but they're on VPCs, things on Prem, and parts of our value proposition must be actually to be very agnostic to that, and then keep the data where it is, whatever cloud, whatever data platform, like, yeah, keep it, keep it, we don't want it and uh. So what we, what we do though is uh focus on the ease of use so that people in the business can actually use the platform and actually build the solution and accelerators so that the most common business needs can actually be done faster on the platform, uh, for instance, um. Well, marketing mix modeling, that's something that every company needs to do, that every pharma company needs to do, every retail company needs to do, uh, and in the grand scheme of things, no one really cares to differentiate that much because of that, because it's a bit of plundering. Um, if you're honest, so, well, very smart, but like still, and so yeah, they are very happy to take something that is off the shelves if they can adapt it to their own data because many of the situation, the issue of like of the shelf solution from the perspective of the enterprise and especially the larger one is that it does not really work for the mix of their data, their constraints, the code increase the operate in, and that's kind of like something they can do with our platform and the way we think about it. Any other questions for Florian? I can keep going. What do you think Is the most misunderstood thing about enterprise AI right now. Hm. I think that's the If I, if I reflect about the AI in the last 10 to 15 years, one thing that has been constant is change, as in if you're the, if you're an enterprise, um, you can look at all of the technology popping in and lots of these core tech technology is actually coming from the, from the Bay Area. And in fact this technology keeps changing, meaning the best database, the best models, the best, I don't know, open source, whatever that can be useful, you've got something new every 2 or 3 years. And so the issue for the enterprise is that an enterprise is also something that is stable and needs to accommodate change in a very specific way. So something that is misunderstood is that the The reason why entropy succeed in AI and so forth will not be that much based of that technology, but whether they build a culture, they build a framework, they build a way to think or integrate those technology so that they get it slightly faster than the other and slightly better for their own business. And that relates to a little bit of framework or platform like ourselves perhaps, but also to the culture of the company, and I think that's what's potentially misunderstood about this. ultimately, the success will be cultural. Well, and sort of in this vein, what does every single person who's an executive at a company need to know about enterprise AI in 2025? Um, I think that's what they. What they need to know is um hm. I think they need to understand what's magic versus not. They need to try it for themselves. I think they need to understand who is in the business doing it versus not. Uh, in the survey we did, I think that more than again 80% of the CU had the perspective they had a good grasp of what was happening in AI in their company, but I'm sure, I'm not sure I believe it. The same CEO were also saying that. So they're reporting to you, they have a good grasp. You're sort of sitting here saying what is a good gras just they've got the perspective of like the things that were coming to them. And they also have the perspective funnily enough that uh and I think it was a very high number like 90% of them plus were considering that AI could do a better job as at least one of the above member. Yeah, well, well, I don't know, maybe they all have like very, very big board, so law of the statistics say that in a very big group you always have a stupid person. I don't know. Yeah, say that sentence did not. I thought it was going to end. I'm not sure if they understood what the answer to this question because they also participate with the board, so it could be applied to. But so I think that it's also understanding what what we actually delegate or not we are having a good understanding of the limit of this technology and how to, how to think about it is probably something they should do and by getting a eye to their business, not just using it casually through the GPT in order to prep their next interview or their next meeting. Now we're gonna end somewhere that I know is central to JI2. Everyday AI. What does it mean and why does it matter to the future of AI as a whole. Yeah, we, we focused uh uh quite a bit of messaging or even build events around the topic of everyday AI as in the moment where AI and then we started that back like 7 years ago, back to. This idea that at some point AI would be so pervasive in our day to day lives that we should stop talking about it because today we talk a lot about it because it's new, but like ideally at some point we stop talking about AI because it would be an everyday thing. I think that with the advent of a generative AI and so GPT and so forth, we made a step toward this because indeed uh 12 years ago AI was a funny word of mad scientists, so. I was very, very careful before saying AI today like everyone probably in this room has done some. GPT today we got a we got a full room of hands earlier on. So a lot, a lot has changed and you've watched that trajectory. Thank you so much for taking the time, Florian.