Florian Douetteau, CEO at Dataiku | Live from HumanX 2026

Yeah. So, I'm Florian, the CEO and co-founder of Dataiku. It's a company that I created more than a decade ago. My background is that I wanted to change data science. And in the early days of AI in the enterprise, I wanted to make sure that it was super easy for every enterprise to scale data and AI. And so, we built over the years this comprehensive platform where you can do everything data and AI. Prepare the data, build models, and now build agents so that enterprise can actually make it their own.

Yeah. Last year, we added robust agent capabilities in our platform, as in a full agent studio with the ability to create different types of agents, and agent testing and validation and guardrails, like all of the things. And finally, it changed the way our customers really used the platform. Meaning, the data scientists, the data people, they were using the platform to build models and a small app on top and so forth. And it's been a decade. Data, ML, data science. And the same people started to build agents. First, small ones. Instead of stopping at an app, can you automate this or that? Can you make the interface of the discovery of the information conversational for your own internal customers? Can you do the next step of action taking coming from the model and automate all of that? And so this agent framework just changed the consumption layer of our platform, making an agent one of the objects, the main object that customers are creating with the platform now. Which is, yeah, an interesting change. It's the move from apps to agents that we happen to see in real time in our own platform.

I think that is a model of a no-one-spoke model where the business logic and all of this orchestration and understanding of the data needs to be pushed on more and more to the business teams. Because what we're talking from a future of work perspective is the fact that business teams need to automate themselves in a nutshell. Because they do have the understanding of the business logic, the understanding of the trade-offs. Meaning it's not true that you can just prompt your way and put all of your data in code and say do my job. Doesn't really work like that. And those business teams need to become those enablers. Need to become able to do AI by themselves. The data scientist may stand more in the background making sure that the technology is safe, that it can scale, and so forth. So it's a change of role for them. That's a first thing. A second key thing is the way you build data pipelines, AI pipelines is changing very rapidly. In our platform already, you've got lots of AI assistants and you actually build pipelines on the fly. And our platform is now more and more kind of like a canvas for an agent to build the pipeline for you instead of drag and dropping into the interface. And so, I think those two changes are changing the role of the data scientist or the ML people or the data people at large. One, because business people need to be enabled by themselves more and more. And second, because the type of productivity you can get with data is being demultiplied. So, those two factors together will and are changing already the role of the data people.

Yeah, I think that's, if we take a step back, storytelling is part of a craft you needed to build as part of decision-making in the enterprise. You're telling a story in order to influence people so that they take a decision in practice because of that. With AI, because of the automation you can make, the process itself, the ideal process is changing. So, example, let's make it concrete. The old way of marketing and campaign management is you ask a data scientist to tell you something about your audience and the targets. The data scientist is doing data storytelling, and the business stakeholders take decision in terms of crafting their campaign or budget allocation of the marketing budget, and so on. Based on this storytelling. That's the old way. The new way of marketing, because of AI and the type of more granular automation you can make, is that you're listening more constantly to existing signals. You get way more personalized on an individual per individual basis like this type of trends. You can do it because you can identify your way to mass creation of content. And so in that particular instance, the need of storytelling is disappearing itself. Because the type of decisions you're making are way more granular and are happening differently. So indeed we have to understand that to some extent part of the role of the storyteller or part of the storytelling will be disappearing. Which is maybe sad. I mean I like stories in general.

Yeah, I think that speaking to your data is becoming a capability of the data platform. Meaning Databricks and Snowflake, you can speak to your data. And maybe that's why ultimately you should buy Databricks and Snowflake to have a free speak to your data. And it's now less about BI for that but directly you can do that directly on top of the data platforms. Now what you do with our platform, for instance, is you've got use cases where you want just to enable people in the business to speak to their data, to get point answers to some point questions. You've got other use cases where you want people in the business to build repeatable scenarios of planning and something they can do over time and on which you build a business process. And for that, you need some consistency. You still need to build repeatable pipelines or build an agent that can be fairly deterministic in terms of how it gets the data and so forth. And that's where our platform now it's new. It's no longer used to speak to your data because anyway, you can speak to your data with whatever data platform and so forth. So, it's kind of like a shift to the market. To some extent, yeah, BI itself is maybe not disappearing, but definitely is changing in terms of scope.

Yeah, but like being the agentic layer, talking about agents now, we're talking about agents in this current period now in '26 very naively, and I say that for any listener of the future because in '26, when you talk about agents, you're like people talking about websites in '97 with e-commerce or blogs not having been invented, that concept, and you're just doing HTML with no Ajax, like you're all just doing very naive stuff, not understanding what you're doing. That's what we're doing with agents today. And like everything will be agents, so yeah, if you do software stuff, you're doing agents. The question is from the perspective of the enterprise, what type of agents are built on top of which platforms? Yeah, your customer support agents are probably built on top of like very specialized platforms. Everything which can get or talk to your data is probably directly done on your data platform. At Dataiku, we're focusing in fact on lots of agentic use cases that are not token intensive. And very boring in the sense of being a back office decision heavy type of task. Supply chain inventory management, predictive asset management in a factory, clinical trial optimization, where you use agentic to optimize, you have a mix of heavy usage of the data. You are using tokens of course but like not so much compared to code or whatsoever. And what you focus on is the predictability and the soundness of the decision making. And the way I see it in the enterprise, you need whatever happens a dedicated platform for that because you need people in the business to be able to open up the thing and understand how it works. Because it's decision making, you can't just be black box on that because the core of the enterprise is to make decisions you understand that you can repeat in the future.