Cheap AI raises the cost of bad judgment

AI makes software easier to create, but the harder and more valuable challenge is controlling what gets built, proving that it works, and managing it over time.

In this episode of The Deep View Conversations, we sit down with Florian Douetteau, CEO and co-founder of Dataiku, to explore how large organizations can turn AI agents from impressive demos into safe, maintainable systems that deliver measurable business results.

Douetteau explains why enterprise AI models are becoming commoditized, why companies may buy 90% of their agents but build the 10% that differentiates their business, and why the emerging discipline of "agent management" will be essential. He also breaks down the dilemma facing CEOs: move too slowly and competitors may gain a structural cost advantage; move too quickly without control and one major AI failure could create a crisis.

Topics covered:

• Why the cost of creating with AI is falling toward zero
• Where value will accrue as models commoditize
• How to balance openness, innovation and enterprise control
• Why subject-matter experts must retain ownership of AI agents
• Why business problems, not perfect data, should drive data strategy
• How enterprises can prioritize transformative AI use cases without stifling experimentation
• The three qualities Dataiku now values most when hiring
• How leaders can use AI without falling into cognitive laziness

If you’re trying to move enterprise AI beyond pilots, govern a growing portfolio of agents or understand where durable value will emerge as AI creation becomes cheaper, this conversation offers a practical framework for building quickly without losing control.

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Creators and Guests

Host

Jason Hiner

Editor-in-Chief of The Deep View

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Florian Douetteau: We are entering the world where the real cost of things is not initial creation, the real cost of things is managing chaos. And so, Dataiku is specializing in this, providing a robust platform in the enterprise where everything you need to make AI safe and successful is something you can put under control. So that your data can be put under control so that the agent and the agent processes can be put under control so that you can monitor which agents do perform their work as you start. We want to provide this control layer for the enterprise that they need to use in order to actually make AI really successful in their terms, not in the terms of token burn.

Jason Hiner: In this episode, I talked to Florian Douetteau, CEO and co-founder of Dataiku, a company that started its mission a decade ago around democratizing data science, and is now focused on helping many of the largest organizations in the world to safely implement AI agents. One of the things that impresses me the most about Dataiku is that they help enterprises read their own reality and create the solutions that play to their strengths and their goals, as opposed to the old way of doing technology where companies had to adapt their business processes to whatever software they adopted. Florian talked about the best practices and most impressive business outcomes that he's seen from enterprises implementing AI today. He also talked about the three things he's looking for when hiring people now. And of course, we talked about his best tip for leaders to get maximum leverage for their time in the age of AI. The same question I love to ask every leader. All right, so here it is, our conversation with Florian Douetteau of Dataiku. All right, well Florian, tell a little bit, for those who aren't familiar, tell a little bit about what Dataiku does and your role with the company.

Florian Douetteau: So I'm the founder and CEO of Dataiku. It's a company that I created a little more than a decade ago, which mission is to help enterprise to find success with AI. And we are already serving a good portion of the 14,000 companies in terms of making them successful with AI. And well, it's a very broad mission. But the way I specifically see it is that with AI, the cost of building things is turning to zero. It's easier and easier for AI to turn any type of idea into something. And it can be a document, it can be a way to look at data, it can be a way to automate your work and so forth. We are getting into that world. But from the perspective of enterprise, the cost of managing and maintaining and controlling and governing everything being created will just get AI and AI. We are entering the world where the real cost of things is not initial creation, the real cost of things is managing chaos. And so the Dataiku is specializing in this, providing the robust platform in the enterprise where everything you need to make AI safe and successful is something you can put under control so that your data can be put into under control so that the agent and the agent processes can be put under control so that you can monitor which agents do perform their work as you start. We want to provide this control layer for the enterprise that they need to use in order to actually make AI really successful in their terms, not in the terms of token burn.

Jason Hiner: We are seeing some real moves in this area too, Florian, like in some ways you see sort of the world moving in your direction a bit, even just this week we saw, for example, Microsoft report its earnings and it had this amazing response from the market and Microsoft is really focused similarly on how do we make AI easier to deploy safer, more robust in many of the ways that you are describing and then you had the contrast was Meta who is really focused on how do we become a competitor to OpenAI and the market did not like the fact that it's spending so much money to try to become a frontier lab and so in that sense, what are we seeing the value in AI accruing more toward the companies that are putting AI into practice versus the ones who are working at this sort of this higher level of theory and research and models when the models are, so this is maybe a two part question, are the models becoming commoditized and then are we seeing the value accruing more to the companies and the teams that are putting this to work in the real world?

Florian Douetteau: I have a fairly firm belief that at least for enterprise use cases, models are becoming and will become commoditized and that the value will accrue on both end of the spectrum, you need infrastructure to run those models, so almost down to data center energy and so forth where the demand will grow and grow and grow and grow like they are in front stack but from the very low level perspective and the value will accrue on the other end of the world and the value will accrue on the other end of the spectrum which is not even creation and adoption but the ability to actually have the control of the creation and adoption of AI in your enterprise because to a large extent as I said like the price of the product is not the creation itself is getting commoditized, you can prompt any idea into something, you can turn any idea into an app or into an agent but the real question is like do you do it so that you can actually keep it and run it for real and support it as a product and for enterprise that what matters, that's a difference between something you vibe code on your own over the weekend and well that forbid like everyone of us I guess is doing that too much these days and we are doing a lot of them doing this but all of the things I vibe code I don't really intend to maintain them or to have anyone but like very close friends to use them for real and I'm surprised I have like the exact opposite they would like ideally to have things only built once and be able to maintain them with a very little bit of trust and safety and so I think that's where the value will accrue at least for the enterprise.

Jason Hiner: You know Fluent there are a lot of companies that come to me now and say like we're here to build safe AI agents, we're here to help enterprises with a control plane for agents and for AI and so I hear this so often clearly that means that the demand out there is quite significant for it but it also means that everyone is sort of seeing the need and a lot of people are racing for to be that company to help enterprises do it right and small business and really everybody so but your company's been around for you know like over a decade as you said and what does that look like for you all did you did you start where did you start the journey and how did you end up here and then like what is it that that Dataiku does that's sort of really different and distinctive in all the companies that are trying to help

Florian Douetteau: my two big there is that I think we're probably fairly uniquely minded at least from that perspective is that in order to actually help large enterprise in particular for real I'm not talking about any type of enterprise that let's say enterprise with a certain level of complexity in organization you've got two big principles first your control plane needs to have this great balance between openness and control this which is one principle and the second one is well in real life you've got multiple layers to the cake so let me get to that first openness and control means that you can't just provide the control plane to allow to surprise by telling them this is over the next step everything is built fit into this template and ran with our model that's all you will make AI safe in every enterprise you've got to multiplicity of existing technologies you operate across multiple countries you've got organizational constraints you've got some users to which you want to give lots of freedom and some others where you want to provide lots of control and so you need to actually help the enterprise build that balance between openness and control that's actually what the problem is that's the helping them to map openness and control to what they actually need the second thing is that there are multiple layers to the cake as in to get to control you need to control the data because that's what fit into the agents and a good part of the problem you need to be able to have very tight control on some key processes of your business but not all of them you don't have to reinvent the wheel and you need to have a no also a view on like everything what's happening in your business where are you're not going to be able to do that? What are those agents? What do you manage them at scale? Who is doing what? So you have different ways to look at it that all need to participate to control and you don't solve the problem if you only provide a tiny portion of this.

Jason Hiner: I'm glad you mentioned control and I like to double click on that for a sec because we've lived in a world with technology where your main thing if you are an enterprise is picking the right vendor because once you pick that vendor or once you pick that vendor in sort of the traditional way then you would end up morphing a lot of your own business practices to fit the technology to fit that vendor's technology so you had to pick carefully because whichever one you pick you're going to end up having to reshape the way you operate to fit the technology like that's the way the world has worked especially in software for the past several decades. But we're moving into a world where you can instead of sort of giving over some of that control to the vendor that you pick you can have more control right you can you can create your own software the cost of making software is dropping so dramatically. So when I hear you say control what I hear and you correct me if I'm wrong what I hear is you saying that we go in and we work at the companies we help them to read their own reality and then build the thing that's going to make sense for the business processes that they have and the way that are ideal for them not go in and tell them no you have here's our system and we want you to come convert to our sort of system or template because you know we know how to we know how to run this that's a that's a pretty big shift as I understand it do I have that right.

Florian Douetteau: Yeah it's a pretty big shift but it's also a and you have a combining shift which is that thing that companies are changing the way they think about as you mentioned procuring software or their software stack it's less about picking a vendor of choice that system of record and Christianian of the part of your business it's about either buying or building the best agents that will transform your business and the view of our customers is that they might end up I know purchasing 90% of their agents but like picking up the best software who are doing this not necessarily their Christian of record for sales or enterprise resource planning but like trying to actually have more competition and smart performance there and for the 10% of the agents they really need to build because it's like the specific to their business indeed they need to be able to build by themselves and the other question of control is making sure they build in a way that is safe and maintainable because you can have the illusion of building able to build very rapidly by just by putting things but the issue is the actual cost of maintenance and the quality of the business is that they need to build the business. So, you know, the data and if you have a loop and like all of the things that are important for important processes. And so that's the reality of the business and also they've got this spark of agent like 10% that they actually build themselves 90% of the procure very efficiently using more leveraging the competition between the values and others. So, the next question is like, who do I know what's happening? Do I make sure or do I make sure that continuously I challenge the existing agents that are cured or built. I check that they are actually delivering performance. I check that they are actually doing their job. I understand their cost because you've got no new science which would be about not managing human but managing agents and making them continue to be performing in your business. And it is not longer about your previous year and whatever else.

Jason Hiner: Yeah. So, there's a lot of companies out there that will put together demos of agents. They will show you all the things and either directly to you and or sort of their own videos, their own demos, their own concepts. But what distinguishes you all work with a lot of companies. You mentioned how many of the Fortune 1000, Fortune 2000 you work with. What distinguishes sort of the the agents that look great in a demo versus the ones that enterprises can actually trust and are getting deployed and are doing like real work. Because I feel like you have probably have some pretty good visibility into that.

Florian Douetteau: You need to have the right level of control in terms of our understanding of like, oh, it works. I think there is something which is a bit nuanced, but true, which is ultimately if it's doing the job of the business, the business needs to own it. And so the way we've seen most of the success is by having agent existent that can be more and more complex. Like they end all edge cases that take into consideration regulations. They improve over time. They have a memory between the executive matter and so forth. But like where more and more of those behaviors are actually understood and decided by the people in the business that there's a subject matter expert. And so the one way to think about it is that when you take the perspective of a frontier lab, you've got this perspective of self recursively learning agents that will improve magic. I think that in most of many enterprise endeavors, it's not exactly the right mindset. You should not be in the mindset of like closing the loop as in like removing the human because it's not actually the target and state for the enterprise. You should be in the mindset where you transfer and change the expertise of the subject matter expert so that they actually build an own agent existing that becomes more and more complicated but like really understand who they work to that they can actually be accountable for them and find the right way to work on it. And in practice, if you work in, I don't know, Prokure to cash or inventory planning or fraud detection use cases or clinical trial and so forth. You have people in the business are comfortable for the fact that fraud has been detected that production happens in time that you meet the guidelines and requirements of health safety and so forth. And so you need actually to keep that. And so that's the actual problem in most enterprise when you look into this deep business use cases. And so that's what I'm passionate about because I think that's where AI would actually ultimately deliver the most benefit if implemented correctly.

Jason Hiner: You also have this this atmosphere, you know, there where you all ran a survey in May that said 78% of CEOs, you know, think that I could cost them their job. Right. If they don't get it right. Actually, I take that back. I had it as 80% of see global CEOs say their job is at risk if I fails in in 2026. Is that what you're seeing out there? Are you seeing that level of tension of, you know, the leaders are they know that they or they feel like they need an AI strategy and that if they don't get this right, that, you know, there's there's sort of potentially big consequences for them and for their company. And is that created this level of urgency to do, you know, the things that you just talked about and at least for for you all. Does that let you be, you know, a good partner or is there a lot of pressure on you all too.

Florian Douetteau: Meaning I think there's like pressure, cascading pressure everywhere, which is fun. But what would you to frame the let's say the strategic potential dilemma for the CEO of a large company is that imagine like you are any type of big company like a bank or a big manufacturer on top of us. And you've got in your P and L maybe 20% of your revenue or costs that are into big a V operations like the one I mentioned, like the engine of your business. And in this engine, you've got this intuition of this fear that potentially some of your competitors or peers will be able to transfer me to the AI and slash the cost by two. And the 20% becomes 10% becomes 10% of additional margin on their business, but not yours. So if you move forward, that's you say like in two years, if I'm not able to do that, my competitors would be 10, 10 point more profitable, but not me. Okay, oops. We lose my job. Like their company value will double not mine. Oh, shit, I will love. I will lose my job. That's the actual issue. And then when they send this is a flip side, they say, Oh, we will implement a in in company, but like if we do that in in those large complex cases. And if we've got an issue, if you've got something not working. If you've got a big AI era on the Wednesdays at terms that creates a scandal, I will also lose my job. And so they are like, they have this dilemma. I'm like, Oh, do, oh, should they think about the innovation risk profile of AI where they can't don't do anything but also they potentially they can be overwhelmed by the the developer frisk associated to to to to to AI. So that's what I hear in many particular large enterprise of banks or insurers or manufacturers or utilities, like healthcare companies you've got. You actually, if you understand the stakes, I think it's not you understand that this actually it's like I risk I really want necessary type of endeavor, which is why ultimately the rational way to think about it is that you need to progress but you also need to indeed have a control layer, just in the sense of like a safe model or cyber security, but in the sense of really controlling. Oh, you can go quicker and quicker, but in a control manner, deploy a in your business.

Jason Hiner: Yeah, very good. So a lot of that control that you're talking about comes down to data, right, which is where I mean, it's in the company's name. You all started as I understand it, you know, data science at the at the core of the mission. Now data, you know, when it comes to enterprise, you know, the ways that you can the ways you manage it, the ways you control it, you know, that the data sovereignty. The ways that you're be able to be nimble, you know, with your data, that feeds into a lot of the success or failure of, you know, of AI projects, right. So how has sort of the DNA of the company really focused around data. How's that impacted the way that you know, you kind of show up for customers and help them.

Florian Douetteau: Yeah, indeed we see that the data strategy or the quality of the data is one decision factor for many of those big processes, because ultimately, in big processes, your costs are coming from the fact that you've got lots of ex section, human labor, human labor, you need to cross check things. And the only way you can automate is by adding a high level of data and predictability and understanding of what's happening. And so it does need a bit of work there, quite a bit of work there. So our ability to think in terms of data and data quality and data data and infrastructure data with structure of data to talk about data from a analytic perspective as in or you look back at the data, but also from a predictive perspective as in, oh, can you use past data in order to family predict what could happen next. I think that's at the DNA of like everything we put into a agent, agent to use cases. And indeed, it's not every use case that you where you need data first to transform it. If your use case is about accelerating the creation of pages on your website to optimize for SEO, it might not be a data first approach. It might be just about like managing the content in an intelligent manner. But if you're trying to automate your inventory planning, if you try to accelerate the product of your product, you're selling if your Navy industry manufacturer, if you work in a semis and want to automate the quality of your production chain, like all of those use cases are actually fairly data. You have lots of agent use cases, but ultimately, the agents are only as good as the quality of the data. And currently, data is everywhere. Sometimes data is still in people's head or data is still in Microsoft Excel or whatever else. And so, yeah, there's a very important challenge, which is like move the data up to the agent.

Jason Hiner: When you're working with companies on their AI strategy, their agent strategy, do you start with data? Is that the first place that they have to get right in order for everything else to work?

Florian Douetteau: I've been in this business for too long to know that if you just work in data for the sake of data, you just end up doing meetings about data, but you don't get anything done. Because if you start everything with only the perspective of data, you're like, oh, what is the right data? What is the right thing about data? What is the right thing to think about the data model? And you're just getting the room, people that are patient enough, but also very brainy about data. And so you do lots of diagrams on over the place, but you don't necessarily understand, let's say, the actual business problems that drive the decision making you need to about data. You can't get perfect data. So what you need is actually perfect data for the actual problems you have. And that's all you can actually make it manageable. And so our approach is actually way more to anybody or customers and the business to discover use cases by themselves and to make the choices about their data to understand where the data is lacking to understand when sometimes you've got a lack of data that is inherently because you don't collect the data anyway. So you need part of the process to understand how you will fix that or go around it. So that's actually the way people should think about it. Data access, data equity is key, but is better driven by an actual business case.

Jason Hiner: As you go into companies today, as you go into the customers that you work with, what are the challenges that they're bringing up to you all, that they're saying, hey, this is what we need the most help with. And how are you having kind of the most success in helping them deal with those challenges?

Florian Douetteau: I think that apart from data, I've got two that are a CAC type of minor these days. One is very funny. It's okay. It's where are my agents. Among the customer we sell, so imagine 14,000 companies, a good chunk don't know anymore or many agents they've got in their business.

Jason Hiner: Okay. Quickly, sorry.

Florian Douetteau: How many 20, 50, 200, 2000, 2000, 100, 64, but they don't know. And that's the starting question for a longer question, which is, ideally, you want to be able to have this in this control plane, the ability to understand for each agent, what is their cost, what is their value, what are their behaviors, what are their risk, who is in charge of it. Like, who start with something you have in your organization when you look at teams for a team, you've got your business units, you understand what is the cost of a business unit with the value of the key capy eyes of a business unit. If you've got any behaviors of like qualitative things you should be aware of, like you inspect your business. And if you enter a world where I know one, five, 10, 20% of your business is actually energetic, which is, I think the world we are on trying to do, you need to actually manage your part. Bad news, you need to invent what agent management is. Good news. It's likely that agent management is slightly simpler than human management is properly equipped. Because I think that as a species, we haven't solved human management yet, not really. It's been an open problem.

Jason Hiner: Yes, for sure.

Florian Douetteau: Agent management could be a solvable problem. That's a good news. That's the problem number one. Problem number two is more, maybe more into the weeds. Many large organizations have this problem where they want to transform business processes as in like inventory, planning, product quality, predictive maintenance. Know your customer like this type of big domain. And there they want to redefine processes, but at the same time, innately, those are processes that are complex where you need audits, where you've got X variants of them pair a country and business unit and so forth. And so they want to wrap the red around like, oh, do we actually govern all of that? The fact that we will have agents or other place, we want to have some full definition, but at the same time, flexibility so that things can get done for every each of our business, you need them so far. So the second problem I see a lot on the market for large enterprise again, because that's a real problem of scale of agent. Not just like, oh, do you do the proof of concept, but like when you apply it across 40 branches in your organization, or that it work in practice.

Jason Hiner: The leaders that are really using AI to drive change, when you see sort of leaders in the companies that are able to, and the teams that are able to drive change with AI and with agents, what are they doing right? Like, what are they getting right? How are they able to solve some of those problems and really get to business outcomes? And if there are business outcomes that you've seen that are really impressive, some of the any examples that you have to.

Florian Douetteau: Yeah, I've seen business outcomes that are impressive, such as you automate some popular to pay process in your company and you find a way to leverage like 50 million of cash. You automate some back end process in a bank and you get like three, four percent increase in terms of your number of transaction you can process by day and so forth. You see those business outcomes that are actually fairly impressive, so who are genetic. And yeah, I'm not talking about easy use cases, such as, I don't know, customer support or whatsoever, but I can do use cases. Indeed, you start seeing this big uplift. And I think that the commonality together is that you need to understand or to avoid the distraction of AI. And I think that's a commonality I see, which is, what do you set it up in your organization so that you have a clear understanding of the. Pick a number one, three, five, ten, maybe if you're a big company of key use cases, you really, really, really want to own. And where you understand why there is a strategic reason to do it. It's not a distraction. It's not just for learning. It's like you really need to do it. And put aside the fact that you will have in your organization 50, 100, 500, 1000 ideas associated to AI that could be worth pursuing, but that you have to do it. And so you need to have this two tier approach, which is like you give lots of freedom in your business so that people can innovate on one sense, that they can have access to the data, discover all of those use cases, pursue those ideas, get their own level of quality. At the same time, you've got clarity on the big milestone you want to eat on big projects that are really transforming your business and that you can drive from the center. I think that this operating this very dual operating model on that perspective is I think a key for enterprise at this moment to avoid the destruction and actually reap the benefit of AI.

Jason Hiner: Very good. So, you know, of course, I'm in the storytelling business. And so one of the things I and CEOs have to kind of be storytellers, you know, as well. When you're telling the story about, hey, we can come in and help you. We can help you drive that transformation. We can help you get those business outcomes. You know, what's the story that you tell to customers, to other leaders, to let them know, you know, that you could help them with some other big goals.

Florian Douetteau: I think I want to, I usually stop by actually signing, saying them that they, well, they actually have the solution. They have the solution in their red. If not in their red, they are a team. They probably have people in their team that actually know the solution and that with the state of technology, it's actually very realistic for them to actually build things quickly. And so they might underestimate their need to get external help of a certain nature and underestimate the quality of their own team of themselves. That's one. So again, I acknowledge that in order to scale or do anything AI at scale, they can't be a cobalt and just imagine that by having unlimited the cloud tokens to everyone, everything will be rose and work better in their company after three months. I have to admit that they do that. It probably be quite a bit of chaos and that everyone will go crazy. They have not understanding what's happening in the business anymore. What is this? Oh, it just likes a new app by every scenario that is providing fake data and we make all of our business decisions based on its. And so I'm telling them like what they want to do. I need to do is to trust their business. I have a clear approach, especially on this big project. They need to move. I'm poor that seem to do so. And but indeed leverage. Some ability to control what's happening on the data level on the process level on like overall of the and the overall, agent, part of for you to make sure that they are continuously going in the right direction. And that's what we want to provide. So if they can trust themselves, they can work with us. If they believe that control is required, like, realistically, they can work with us. If they don't believe either of that, well, maybe we actually need not a good fit.

Jason Hiner: Makes sense. Okay. How about, you know, hiring Florian, you know, there's a lot has changed even in the past year, you know, in the in the space. There's a lot more that these tools can do. There's a lot more that, that, you know, teams can do that they might have needed like an intern or an entry level employee to do. And we're seeing this, you know, again, and again, that AI can do some of those kind of tasks. And so entry level hiring, you know, has hit this kind of speed bump. What are you looking for when you all hire? Are you all hiring entry level folks in hiring in general more broadly as well? How has what you look for when you hire, you know, changed? And, you know, what, what would you say to people out there that, you know, might want to work for your company, right? What, what can they do to prepare themselves and to stand out?

Florian Douetteau: I think that I know probably over index when I ring on meaning three things before that I was like indexing on before, but maybe less so a certain type of curiosity, a certain type of judgment on a certain type of motivation. So the type of motivation is easy, which is like, AI is so complicated and so maybe are changing that you want people that are passionate about it. And let's say self motivated about AI to an extreme. Why? Because you're entering your world where you can't really guarantee that everything will go every time, like up to the right. By design, it's AI. Things will change. You will have market collapse. You will have redefinition of the market. So are you someone that is more, like, say, way more self motivated than the average? The question of curiosity, meaning of course, everyone wants people to talk curious and wants to learn in their business, but now it's like very down to the bottom of it, actually, it's like, have you vibe coded something? What was the last thing you actually created by yourself? If you have not, even if you're like a service person, if you have not created something by yourself, it's kind of strange. Why do you want for an AI company if you're not like doing things by yourself? Like, why? So I think that this level of curiosity of like adoption of technology in the context where we are living every hour with technologies that will change completely the world. Like, if you don't have this level of curiosity, it's a problem for me. And the last one is the topic of judgment, which is probably the order to actually test for. But here the idea is that even then before, it's not about like, oh, fast, can you type? Oh, fast, can you even assemble ID? It's whether you can have some judgment of taste in terms of what's important versus not, what looks real versus not, and so forth, in all you think about problems. What you want to solve for here is you don't want to get into your business, more people that will just generate five pages of AI slop and send it to everyone believing that it's okay. This is actually very up to police. And so you need to bring people that will be just like, whatever happens, even if you end up having like five pages from Claude or ChatGPT, get it to one and focus on what matters and understand what is slop versus not. And so those like things like judgment, motivation and curiosity are from my perspective, even more important now than two years ago.

Jason Hiner: Are you still hiring entry level folks into your company?

Florian Douetteau: Yeah, we do. We do in select position. I think that there is a, my rump guest here is you're in a market where by design, you have lots of people who are in a market where you're in a market, you're in a market where by design, you have lots of people who are in a market. Entering your career in particular in engineering that are actually lots of time to actually find a job. So I'm maybe a bit cynical here, but I think that there's a case for hiring at every level still.

Jason Hiner: And you all are global too, right? Like where, what are the places that you have offices and where, you know, do you have teams located? What are some of your kind of your main locations?

Florian Douetteau: Yeah, main location include Tokyo, Singapore, Dubai, Madrid, Paris, London, Amsterdam, Berlin and New York. So yeah, working at Dataiku, you can actually work in a variety of great cities. I don't know which one is my favorite, but which one is your favorite on the cities? Do you have a favorite city?

Jason Hiner: Do I have a favorite of those cities? London, I would have to say. London and New York probably. That's a little bit boring. I've been to most of those cities, but yeah, London is a very humane city compared to the US city. So yeah, how about you? What are some of the ones that you spend your most time?

Florian Douetteau: I mean, I love London too, meaning that's mine. Yeah, I feel like I feel quite a bit like a Londoner, which you could not tell, I guess.

Jason Hiner: For sure. For sure. The company started in Paris, right? Remember that correctly? Yeah. Yes. Indeed. Very good. Very good. Well, one of the things that I like to do, Florian, is sort of end with the same two questions with, um, all of the other things that I like to do. So I think it's kind of a very, very, very important thing. Right. So I guess it's sort of end with the same two questions with, um, with all of our, our guests. And the first is, you know, there was this promise and AI that AI was going to do sort of the, a lot of the, the grunt work for us, like the things we didn't want to do. And then we'll be able to focus more of our time on, uh, you know, the higher level bit kind of tasks. And the other thing that we're trying to do is, is they're more stretched for time than ever. They're moving faster. They're focused on how can they get the maximum leverage for their time. And so, um, my first question as we wrap up is, you know, what's your best tip? How do you get maximum leverage for your time? And what's your best tip, you know, for other leaders who are trying to do the same? Uh, my, my, my.

Florian Douetteau: The best tip for using the AI is that, uh, yeah, use it with, uh, use it in a way that makes yourself smart in order to not get into cognitive laziness. Uh, at some point in the last year, I discover myself that I was getting into cognitive laziness. I was, I was like, not reading as much or not thinking as much before asking the question to the system. I was becoming the number number. Uh, and I realized that you need to actually keep for yourself this ability to actually think for like multiple minutes and ideally multiple dozens of minutes. Uh, without asking the system to make sure that you get the best of it. Uh, because ultimately your ability to think for a long time and to come up with some ideas is what anyway, is not really good for yet. So where you need to keep an edge and for that you need to keep your concentration. I think that's when that's when that's when that's one tip.

Jason Hiner: I love that. It's funny. And I actually have a story that's that's coming up soon on cognitive atrophy and how, you know, we have to be really intentional about what we use AI for and what we're okay, sort of getting away from. Right. There are things throughout history that people have no longer been good at right over time, riding a horse or lighting lamps or things like that. Um, but we should be really intentional about what we, you know, are okay, given over to AI and what we need not to and some of those things like just as you said, some of the like really deep thinking considerations, sitting with problems for a while. Like some of those things are really interesting and uniquely human. So great love that one. All right. The second one is what's the tool that you're using right now that's making a difference for you. You know, in your day to day that you recommend people take a look.

Florian Douetteau: So, so of course, I use that. That's a bit. I use it quite a bit. I use it quite a bit, which is a bit self-promotional. So, and of course I'm passionate about it. So maybe I won't get into it too much. I think that as part of that, the part that I do that I find the most interesting, which I think could be applicable, even if you don't have it yet, is the fact that as a decision maker, it's interesting to be able to build and go for yourself your own little data brain of like, oh, you transform data and you actually build for yourself. So, that's a very, let's say, a structured brain. You've got lots of tool sets to do that, like having an structured brain for your for you build on your notes or whatsoever else that you've got lots of tools like this on the market. But I think that adding some of them, which are quite a bit about your let's say quantitative decision making is interesting. And that's something I use that I could for which I think is probably under served in the market today, generally speaking.

Jason Hiner: Very good. So for a company that wants to get started and try, you know, the tool, what's the best way to do it?

Florian Douetteau: Get online and you've got, you actually have like a free version of Dataiku, which is usable by individuals. I think up until three, which is fairly popular and is actually fairly flexible in the sense of enabling you to connect to a variety of system and like. So that can be a very good way to actually start, start leveraging Dataiku.

Jason Hiner: Yeah, very good. So for those who want to give it a try, you know, Dataiku is spelled D-A-T-A-I-K-U. So getting the spinning right may be the hardest thing.

Florian Douetteau: I thought of the spelling that it's easy.

Jason Hiner: Very good. So you could go and find it, you could Google it and get a hold of it very easily. So once you know how to spell it. Very good. Florian, thank you so much for the time. Great. And get it a chance to catch up with you. Enjoy this a lot. And I'm sure we'll get the chance to talk again. But until then, yeah, keep doing what you're doing, helping all the companies out there that you're helping. And yeah, we'll talk again soon. Thanks for your time. Thanks for watching.