#288 Florian Douetteau: How Enterprises Can Scale and Adopt Agentic AI

Discover how enterprises can successfully adopt and scale agentic AI to create real business impact in this conversation with Florian Douetteau, CEO and co-founder of Dataiku. Florian shares why democratizing AI across the enterprise is essential, how to prevent agent sprawl, and what it takes to build a governance framework that keeps your data secure while enabling innovation. Learn about Dataiku's enterprise AI blueprint, its partnership with NVIDIA, and how global companies are using agentic workflows to accelerate R&D, optimize operations, and stay competitive. If you're a business leader, CTO, or data professional looking to scale AI safely and effectively, this episode is your playbook for the future of enterprise AI. Stay Updated: Craig Smith on X: https://x.com/craigss Eye on A. I. on X: https://x.com/EyeOn_AI 00:00 Intro 00:31 Florian's Background & Dataiku's Founding 03:00 Enterprise Blueprint for AI with NVIDIA 05:13 Unique Needs of Financial Services 07:09 Building Agents on Dataiku 09:22 Permissioning & Governance 11:17 Agent Lifecycle Management 13:20 State of Agent-to-Agent Systems 15:02 Real-World Use Cases of Agents 16:28 The Most Complex Agents in Production 19:01 Future Vision: Headless Organizations 21:04 Human-Like Qualities of Agents 24:56 The LLM Mesh & Model Abstraction 28:55 Guardrails & Compliance 31:12 No-Code + Code-Friendly Collaboration 36:12 Breaking Silos & Centers of Excellence 41:36 Distribution & Seat Allocation 43:34 Most Common Agents by Industry 47:02 The State of Enterprise AI Adoption

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Some of our customers, as they pushed employees to actually discover and create agents by themselves, start to have some form of like agent sprawl. As in, you've got lots of people that created small agents doing this or that. And there is a question of like, who is doing what and for what purpose? We're working with some customers on essentially helping them having a central access point for agents. And the core idea is that there is a need and an appetite for having more lifecycle management of agents. Hi Craig, and thanks for having me today. Uh, so I'm Florian and I'm the CEO and co-founder of Dataiku. And I started the business in 2013.

• 0:39Speaker 00

  Uh, prior to that, meaning I started my career more on the tech side, uh, studied math. Uh, but uh, in 2000, uh, which is back 25 years ago, I actually started to work on natural language processing and web search and enterprise as technologies and um, meaning all, but by that time or the, um, this early in the cycle, um, let's say it was very hard to get anything scalable up and running in the space of NLP. Uh, you had to build everything from scratch. And uh, yeah, I started the Taiku after having uh,

• 1:24Speaker 00

  uh, had the multiple experiences with companies that were trying to scale their data science practice and were failing to do so. Um, and that is, uh, it is that, meaning if you can democratize AI, you can actually bring lots of value to the enterprise.

• 1:44Craig S. Smith?

  Yeah, uh, and uh, and tell us, uh, in, in short, what Dataiku does? Uh, and, and what its main products are?

• 1:56Speaker 00

  Yeah, we have one product, which is in essence a no-code AI workbench. Um, a workbench where you can do everything end-to-end required to manage your AI. Uh, no code, so that Anyone in the enterprise willing to do so can actually touch it and start to work on data and AI. And it's a workbench that can work on top of any cloud, any data. And the goal is really to bridge this gap between no code, that sometimes is perceived as being prototyping for domains, and enterprise production-grade applications. And indeed, we have, our customers are, among our customers, you've got

• 2:42Speaker 00

  large enterprise that use our platform in order to build thousands of data products and models, and scale that, scale that in two ways, meaning scale the number of things you need to build, but also scale the number of people that can participate and understand what is being built.

• 3:00Craig S. Smith?

  And most recently, you have what you called an enterprise-grade blueprint for agentic AI in financial services. So on this workbench, you have, it's built for various verticals? And is this, can you talk about this blueprint and how it works on the workbench?

• 3:32Speaker 00

  So indeed, we build this enterprise blueprint for FSI within Nvidia, and it relates to the agnostic nature of our platform. As you pointed out, our platform is used equally by a large manufacturer, our life science companies, our banks. And it makes sense, actually, when you want to do enterprise AI to actually tap into all of these types of sectors, because they all have these issues of big data, complex data, diversified data, side of data, and people in the business don't understand, or you can help them. So that we want to be agnostic in that measure, or can you actually help everyone? But we also want to be agnostic in the, in the sense of the infrastructure itself.

• 4:13Speaker 00

  And so, for instance, we build our platform so that it's super easy to work with the various type of large language models and vector stores around in order to move from just doing ML to do agentic with ML, with our platform. And on that front, we connect it with the Nvidia Enterprise AI stack. So that in particular in banks that typically have a regulated constraints want to be able to host their data and all of that good stuff. So that they could actually build this type of solution in an easy manner.

• 4:49Craig S. Smith?

  And uh the the what are some like you you were you're saying that every vertical has its uh its um. You know, it's it's particular needs. What what is it with uh financial services that that uh is different from from another uh vertical being uh built on the platform?

• 5:14Speaker 00

  I think that um for uh every vertical, there is a particular need to support uh a bit differently uh security and infrastructure constraint. I mean, like where the data is, what is the particular type of security setup you have in in place? So for instance, in financial services, it's fairly common to have the need of securing data, not just Peru has access to it, but also on a project basis. Making making sure that there is no leakage of information from one project to another. Because in financial services, there are constraints in terms of who you can use the data. Meaning you can't actually create any type of like insider um type of issues that

• 5:56Speaker 00

  uh you could get into if you don't have proper firewall between data in your system. Um the way we support that in our platform is with flexibility, but also in financial services. We provide on our platform um different types of solutions, as in way to just have pre-built projects that help you get value faster. Because there is like so many use cases of AI that many of our customers actually want to accelerate their discovery of them. It's a bit overwhelming to work in AI or to have to build AI system in the enterprise today, because you live between this uh

• 6:36Speaker 00

  high level of expectation coming from your board and leadership and the reality of the field where I'm in.

• 6:45Speaker 00

  Getting to select one platform for large language model can be an issue in itself. And so our buyers are essentially the people managing this high level of expectation with the constraints of the field and our platform help them do that more efficiently.

• 7:04Craig S. Smith?

  Yeah, and for the agents, do you have at this, is there, because this is no code, so is there, how do you build an agent on the platform? Is it, or is it something that an internal team would build and then make available on the platform?

• 7:29Speaker 00

  Yeah, that's something that the internal team of our customer would build on the platform. And in terms of agents, you can build visually agentic workflows in the platform, as in agents that would actually connect to another agent, connect to another tool, goes through various steps to achieve the task. And in this platform, you can do it by connecting natively all of those platforms to your data, or more recent data, or your more recent models. So you can actually build a fairly sophisticated thing, such as an agent that would use a predictive model to actually make a decision, instead of like hallucinating that out.

• 8:09Speaker 00

  Very useful, for instance, in banking, if you want to, at some point, agendify business impactful situations, such as, I don't know, agreeing on a loan. You don't want an LLM to decide, you actually want a good old statistical model to decide this part, for instance. And we also build the platform with lots of agentic evaluation capabilities. And in the enterprise, it's

• 8:36Speaker 00

  Fairly important to have a good level of auditability and testability of agents, as in having continuously this ability to tie them to the data, analyze the outputs, run type of like backtesting of agents before pushing them in production. When you, when you want to support like more customer-facing, not so impactful applications, you can be more like I A B test type of mindset with agents. And you can iterate in a certain way, but when you're in the enterprise, you need to have a different level of depth in terms of the test you're making, because the cost of one issue can actually be very high.

• 9:16Craig S. Smith?

  Yeah.

• 9:18Craig S. Smith?

  And, and then, um, once these agents have been built,

• 9:26Craig S. Smith?

  how do different people in the enterprise access? I mean, there must be a very strong permissioning controls.

• 9:36Speaker 00

  Yeah, meaning you need permissioning controls so that agent can only be accessed by people having access to it. You want to be able to, um, apply the access control of the person using the agent to what the agent can do. As in if, as Florian, I can only have access to this rows in this data set. When I'm using any type of regular BI tool, as said about my IT, I want the same permission to be applied when I'm using an agent, of course. And so this is the type of art things that needs to be indeed built and solved for in the enterprise. A broader question that our customer has is like, how do you discover agents?

• 10:17Speaker 00

  As in, um, some of our customers, as they, as they pushed, um, the, their, their employees to actually discover and create agent by themselves, start to have some form of like agent sprawl. As in, you've got lots of people that created small agents doing this or that. And there is a question of like, who is doing what and for what purpose? Which is, I think, another, I think, key topic these days.

• 10:43Craig S. Smith?

  Yeah, well, how do you handle that? I mean, is there, um? A master dashboard that can see every agent in the system. And not only, not only that issue, but you end up, I mean, not simply agents, probably you end up with agents talking to agents and passing data back and forth, and that creates a potential security problem.

• 11:11Speaker 00

  Yeah, but actually, I must admit that in the enterprise, the agent talking to agent type of scenario is still, let's say, very early and hypothetical currently.

• 11:21Craig S. Smith?

  Yeah.

• 11:22Speaker 00

  But, but indeed, a practical issue is first having some oversight on everything that is happening from the perspective of IT. Can I actually tell what's happening, what were the project, what are the ROI, what are the risk and so forth, and so for that, meaning it's essentially providing some governance framework and ability to gather data from everywhere. The second thing, which is funny, is that we are working with some customers on essentially helping them having a central access point for agents. And the core idea is that there is a need and an appetite for having more lifecycle management of agents. As in, it's, it's kind of like back to this question of ideation. As someone in the business, I could have a great idea of like what an agent could be doing,

• 12:07Speaker 00

  and I want to be able to prototype this quickly. So, and with agent prototyping is very easy, like you prompt, you say this and this data, and it's kind of like working. But lifecycle means that in the enterprise, you would ideally have a step before this thing becomes shared to 1,000 people in your organization, a step where IT can check it, validate it, operationalize it with like the proper data, the proper permission and so forth, before it gets and becomes something official. And doing that, you avoid two things. First is like issues in terms of accuracy, security or falseness in the enterprise.

• 12:48Speaker 00

  And second, you create more trust because you can ideate things, but having a proper vetted catalog of enterprise agents that people start to use for real. And we've seen with this pattern, I think good progress with our customers, and we think there is for us a way to actually further out there.

• 13:14Craig S. Smith?

  Yeah, um, the, um, you, you know, you said that not many people are doing, uh, agent to agent,

• 13:24Craig S. Smith?

  uh, systems yet. Um, why is that? Is it just, is it a matter of, uh, trust? Because certainly as a journalist, that's all I read about agent swarms, agent societies.

• 13:37Speaker 00

  Yeah. So, so, yeah, I think because it's, um, so, be honest with me, we have and we support and we have lots of use cases where an agent is calling to another agent. But in fact, the reality is that when you're, it's almost a development pattern where when building something, uh, significant, you start by building small things and a small agent to be able to test it. And then another one, and then another one, another one. And then your real use case, you had a wrapper on top thing, you say, you will do A, B, C, and D, and that's it. And you have built intermediary agent for each in order to actually be able to test the thing as someone building the

• 14:21Speaker 00

  system. So it's not yet agent to agent as in like, uh, uh, an agent that was, uh,

• 14:31Speaker 00

  knowing, uh, what are the best customers and another and running on its own and an agent thinking about the talent pool in the company and the two agents talking into one another to discuss and devise what should be the strategy of the company. But we're not there yet and we might never be. Uh, what we're talking about are, um, agentic systems that can automate, uh, significant tasks, but indeed, um, need to, uh, need to be built in an incremental, in an incremental manner. And we've seen good success of that in the last, um, couple of months. Uh, we've seen advertising company using our platform in order to automate their campaign management and be able to just be able to do more.

• 15:13Speaker 00

  Campaign per employee and per manager of such campaigns like by a factor of 50, which is amazing. We've seen retailers using agents in order to fluidify access to information in the shops, as in how people can actually access inventory, can provide recommendation to customer on site and so forth by having more fluid interaction and just unlocking out of this data which is into ERP and product catalog and so forth that are too hard to access by people on the ground. And we've seen manufacturers and pharmas using agents into their, some of their complex R and D cycles in order to massage lots of information

• 15:55Speaker 00

  about state of the art into multi-step process in order to refine this information and build internal drafts that serve their and accelerate their R and D practice. And so all of those things are not trivial, meaning they took some understanding of a business to be built and they are very early, meaning it's a, compared to what agents, compared to what agents will be doing, it's still very early, but very promising from my perspective.

• 16:23Craig S. Smith?

  Yeah. What is the most complex, because a lot of what you mentioned sounds a little bit like, like search, right? It's surfacing information that already exists, you know, maybe tied to a chatbot or something. But what's the most complex agentic activity that, that you've seen to date that's in production?

• 16:56Speaker 00

  And I think that in terms of, first, in terms of scope or number of employee you can touch, for instance. Lots of things that are field activities, as in supporting, for instance, a retailer, are indeed search, search as in like, can you tell me what should I recommend to this customer that is in front of me? It's search, in a sense. But like, when you dig into what you need to do to do there, you need the proper product catalogue, you need to understand the customer question, you need actually also to know what to recommend, based on prior history and all of that good stuff. So you need a supercharged kind of like Amazon recommender system with the proper product catalogue, but like being able to deliver that in front of the customer, kind of like

• 17:41Speaker 00

  in real time. And so those systems are maybe not complex in the sense of like the number of steps is not that many, but you need to connect them properly to have the level of experience you need in order to deliver the value. And so in that sense, they are sophisticated. The type of thing that we've seen that were the most sophisticated perhaps were into intellectual property, for instance, where you could start using agents in order to understand the state of the art, what was patented or not in your space, mix that with your internal databases, and use that in order to support or accelerate the management

• 18:24Speaker 00

  of your IP portfolio. And where indeed you need to understand the data and your space and the science of your space in a very sophisticated way. And again, you could say it's search, but it's not just search, it's like synthesizing all of the information of your domain in a specific context in order to understand what's new versus not new, which is indeed a complex multi-step setups, where it's in systems that are literally calling thousands of times LLM in order to answer one question.

• 18:57Craig S. Smith?

  Yeah.

• 19:01Craig S. Smith?

  And in terms of agents, you know, I was at a conference.

• 19:08Craig S. Smith?

  A couple of months ago, and the guy was talking about, you know, speculating or about a headless organization someday that that there'll be agent managers in for various departments and then beneath them, you know, groups of agents doing all the various tasks that the organization needs to do.

• 19:37Craig S. Smith?

  And then, you know, maybe a an overlord agent that watches everything and

• 19:44Speaker 00

  And do you want to be the overlord agent or should I be like? It seems to be a good position to be the overlord agent.

• 19:52Craig S. Smith?

  Is that, yeah, I suppose.

• 19:57Craig S. Smith?

  But

• 19:59Craig S. Smith?

  but do you envision that sort of thing happening? And what kind of more complex actions do you see agents being able to carry out?

• 20:11Speaker 00

  Yeah, I think that

• 20:14Speaker 00

  indeed, it's an easy way to map agentic activities, to map them to the organizational structure of a company.

• 20:25Speaker 00

  Because it's make it easier to for people to discover them, to understand the role and responsibility over them. So indeed, we see our customer doing that as in in a given team. I should think about how many employees and how many agents.

• 20:43Speaker 00

  And that's, yeah, that's interesting. And it's a when you interesting agents are agents that do not just do a simple task, but that essentially accelerate or support a given process inside a company.

• 21:01Speaker 00

  And indeed, it's easier if you map, if you

• 21:11Speaker 00

  wrap all of those agents that do processes in a given domain, let's say, sales or R and D or whatsoever, in a master agent that can route to them, because then you simplify the way people in the company can actually access the information and start using them and so forth. And so it's a pattern of, well, essentially delegation, like in real life, like in human life. You've got the sales agent having different sub-processes and depending on what you ask, it will delegate to the other one, they will do its steps. So it's actually, it's actually the way people do it. Then, to maybe answer the second part of your question in terms

• 21:52Speaker 00

  of the capability, the capability space, I think it's interesting to realize that agents, when you build them, you have some expectation of their qualities that for some of them mirror a bit human qualities. As in, in some scenario, you want agents that are very good at just repeating a given task, they like to process and you want it to go through in a very predictable manner. Sometimes, and maybe you do too, I use agents for creative purposes, to give me ideas, well, generate an image, help me with a slideshow or a presentation, so you

• 22:32Speaker 00

  use it as a creative aid. Sometimes, and I'm meaning literally all the time, I use agents for more like productivity and doing some assistant type of work, as in like meeting notes and whatsoever, and it can help also with collaboration or some aspect of it. And so, and there is some agents that are more maybe at the equivalent of a management of a function or management of a process type of capacity, as in you, they, you expect the agent to support or automate an actual business decision, as in, do I resupply my stock or not?

• 23:17Speaker 00

  Do I prioritize this product or not? Do I change my prices or not? This type of things. So those, those things are different qualities. Meaning you don't expect a given person usually in an organization to be creative and be very, very good at execution and very good at taking business decision or be a great developer or be a good production or manager or a great facilitator. Meaning you don't expect the qualities, all of those qualities to be the same person most of the time. I don't actually. And so, and I'm neither of those. So the, the, the, for the same reason, I think that when we will be building agents more at scale, we will start realizing that depending on the task,

• 24:01Speaker 00

  you expect different things from agents and maybe you start using different types of system. Depending on the type of agents you, you want to use or create. And that's my, the way I think about this ecosystem moving forward. It's very early because agent is, i- it's like we're in ninety-five or ninety-seven maybe and agents is website.

• 24:27Speaker 00

  And MCP is a gopher or FTP. Not really, not really HTTP and certainly not HTTPS.

• 24:34Craig S. Smith?

  Yeah.

• 24:35Speaker 00

  So that's, that's, and so we say agent, but actually we mean

• 24:43Speaker 00

  blog or e-commerce or whatever else. Like you will have many type of agents, many categories of agents that would be actually created.

• 24:53Craig S. Smith?

  Yeah. Okay, can you talk a little bit about the, uh,

• 24:59Craig S. Smith?

  uh, data IQ, uh, uh, LLM mesh? And, and how that, uh, works with, with the agent platform, I mean, is?

• 25:17Craig S. Smith?

  Does the user decide have access to all the various open source or proprietary models and and make the choice? Or is there an agent in the data IQ ecosystem that that decides what model is best for what task?

• 25:41Speaker 00

  Great questions. Indeed, we build the.

• 25:48Speaker 00

  So what? What I think in this agentic ecosystem is that from a 90 perspective, there will be more and more the the the question of abstraction of large language models. Because from a cost or management perspective, there are very peculiar type of systems in terms of their costs, their characteristic and so forth. And the fact that they you send a lot of data to them potentially and so on. It's a new type of system. And so from my perspective, that I could not enterprise, at least large enterprise, will end up having this view of having a way to abstract away

• 26:32Speaker 00

  this access to all of the L L M's. And there are at least three big ways to consume L L M's today. You can use directly the services of the OpenAI and Anthropic of the world. You can use

• 26:49Speaker 00

  them hosted into your favorite cloud platforms. As are the same models or more open source ones. And you've got lots of variants in data platforms and cloud platforms to actually host agents. And you can host them yourselves as are your own servers or services you rent on a purpose manner to just self host the models. And and you've got good reasons to do at scale. There would be good reasons to do all three of them potentially, maybe depending on security and sovereignty type of questions. So fundamentally, we said like, oh, a product should be built so that from the customer perspective, it would be very easy to manage and switch from one to the other and to have

• 27:30Speaker 00

  a more holistic view on the risks, the logs, the audit.

• 27:37Speaker 00

  So the costs, the security aspect and security for agent is not just who has access to what, but it's also the list of forbidden word or topics you should not send to this agent or that other agent. If I talk to about, maybe you want to set up your system so that if you talk to or use a customer name, you should never do it on a public server agent and always use a self hosted one. I don't know, for instance. And so that's a type of very IT mindset we use to, to, to, to build, to, to, to build our product. And I think it's actually very generic, as in like this mindset should be the, the, the type of mindset that enterprise will have when they think about those AI system at scale.

• 28:23Speaker 00

  To your second question, indeed, today, we don't think and we don't provide a magic model that is choosing the best model. And I think it's early to do that because it's more like on a pair use case basis where you choose the model when you create the use case instead of doing it dynamically. It might be the case down the line that we do that, but it's a bit early in the ecosystem to do it right now.

• 28:53Craig S. Smith?

  Yeah, you were talking about security and you have a function on the platform to set guardrails. Is, is that essentially a prompting exercise?

• 29:09Speaker 00

  Guardrails is not just a prompting exercise, it's also to have. Dynamic guardrails are about having some sort of filters that you can have on the data in or the data out, as in what you send to the LLM or what you receive to from the LLM. And this can be a mix of guardrails that are based on just keywords or dynamic list of keywords, or the guardrails that are based on topic. Guardrails on keywords could be like, I don't want my customer names

• 29:41Speaker 00

  to be sent out, or I don't want something looking like

• 29:46Speaker 00

  a social security number to be sent out, for instance, the type of guardrails. The kind of related to topics is, for instance, I don't want my LLMs to be providing back any type of else advice or financial advice, for instance. And so you typically want to set up those guardrails in a meaningful way so that either you do them universally for any type of usage within the enterprise or per category of applications. You all the one versus not the other.

• 30:17Speaker 00

  And so it's important for the enterprise, well, regardless of like whether it's a strategic use case or more basic use cases of LLM, there is a good reason for enterprise to have a good control on those guardrails from the perspective of compliance and compliance and security.

• 30:38Craig S. Smith?

  Yeah, and on the platform,

• 30:43Craig S. Smith?

  is it's, is there a conversational interface to implement these things? Or on guardrails, is there a list of checkboxes that the user can check? I mean, how you were saying it's largely no code, so how is a user

• 31:08Craig S. Smith?

  telling, you know, building this agent?

• 31:12Speaker 00

  So, so when we, when building an agent, a user would actually tap into a LLM setup by the enterprise, it would.

• 31:24Speaker 00

  Drop in a list, the one that the enterprise provided and was actually given to him. And so those LLM, when used by the platform and through the platform are already secured. As in, for instance, the someone in the IT of the company would have said, "In this company, we just use, let's say, OpenAI, and we can use it for three different reasons. One is like, super secure, and one is more like internal fluid, and one in the middle. And depending, we set up three types of GAD rels that are different for each level. And maybe for the secure one, we are super strict in terms of like no health advice, and no financial advice, and no talking about chocolate. I don't know. Whatever."

• 32:10Speaker 00

  And in the internal one, we can do whatever. And so depending on their permissions, the user would have access to level one, level two, or level three, let's say, or like what has been configured by the enterprise. And then in order to be the agent, they can prompt their way and have access also to a set of tools. And same, tools are pair a setup of a set of permissions. It is to what they can access to and what they can give access to in terms of applications and data. And so this way, a user can build an agent by prompting and sequencing prompt and accessing to tools.

• 32:51Speaker 00

  Then the user can give access, can test these agents and give access to this agent to user, other users. And then indeed, total security is about the other users when accessing the data would also have a set of security constraints being applied to them that is specific to them, so that you actually don't leak information by creating agents.

• 33:18Craig S. Smith?

  And in the background, what's happening?

• 33:24Craig S. Smith?

  Are there blocks of code that are pre-written that are being assembled? Is, is Dara Ku actually writing code in the background? Or are there?

• 33:41Craig S. Smith?

  A catalog of agents that IQ is pulling from and presenting to the user.

• 33:50Speaker 00

  Yeah, we have a catalog of, we have a catalog of tools that contain code. We've got, the prompts are enough to actually create, create, create the agent. And then that IQ would generate, and the tools within that IQ would generate code in the sense of, for instance, if it's about querying a database, it would generate SQL code. For instance, in order to actually support the proper, the proper activity. As a broader tool beyond agentic, meaning that IQ is also a platform to do any type of ML and analytics. In which case, it's also no code, as in you can use it to transform your data and build your models and so forth.

• 34:35Speaker 00

  And in that process, you can also decide to generate code if you want. And we see that as part of the life cycle of the platform, sometimes our users like to be able to generate code in order to actually go one step further in terms of customization and adaptation to their use case. And so, over the years, we've built the platform in a way so that it's no code, but also code friendly. And, and it's back to this topic of collaboration and lots of people in the enterprise need to work together in order to achieve goals. And we build the platform more and more so that it can be a collaboration place between the technologists and the non-technologists in any given company.

• 35:18Speaker 00

  Because I think that the lack of collaboration between both camps is usually what creates struggles in organizations. If geeks and non-geeks could be friends, world would be better. And, and so, indeed, we build a local platform where you've got a full-fledged VS Code and Python notebooks type of environment, so that coders can also thrive.

• 35:44Craig S. Smith?

  Yeah, and I mean, that's interesting. Just thinking about the organizations that I've worked in, I mean, primarily the New York Times.

• 35:58Craig S. Smith?

  The, the, the people. Yeah, there's, there's a huge divide, at least in my day, between IT and, and, and users.

• 36:14Craig S. Smith?

  It, it, how, in, in your experience, the users of the platform at enterprises, what percentage are business experts or, or, you know, operational people? And what percentage are the IT department? I mean, is this really getting out?

• 36:41Speaker 00

  Yeah.

• 36:42Craig S. Smith?

  Of the IT department?

• 36:43Speaker 00

  Yeah, in enterprise, it's typically seventy-five, eighty percent that are in departments. And twenty, twenty-five percent that stays central IT. And so the way I think about it is that, let's say, you've got IT and business, great. But in fact, in IT, you've got a bunch of people in IT that are very interested in working well with the business. And in the business, you've got lots of people that are more data experts of a form or Excel gurus or like really interesting into the quantitative side of things. And so the goal of our platform is actually to bring those two groups together.

• 37:27Speaker 00

  The people in the business that are actually more like quantitative, I want to get my hands dirty doing AI by myself type of mindset, and people in IT which don't care about building

• 37:43Speaker 00

  IT ivory towers, but actually want to build IT enabled for the business, enabling the business type of mindset, which is not all IT, like, bear with me. And so, but if you take those two groups together and you can actually have them share a platform, or you actually can achieve a great thing for the enterprise, which is to break silos.

• 38:09Craig S. Smith?

  And is that something that you advise corporations on? Because I can imagine

• 38:17Craig S. Smith?

  someone buying the platform for the enterprise, maybe, you know,

• 38:27Craig S. Smith?

  the CISO or the CTO or even the CEO and saying, "Here, I want everyone to use this." And it sort of sits idle on most people's

• 38:42Speaker 00

  Yeah, I think that there is, what we advise the enterprise is,

• 38:50Speaker 00

  in fact, a modern version of like what should be a center of excellence for AI. And center of excellence is like loaded world and like fairly old as a concept. But it's indeed effectively creating this place where you move IT as an enabler of the business and you create the dynamic where people can tribe as themselves, where they communicate, where you even have like sometimes Hackathon, real Hackathon within enterprise where they can like innovate and test and so forth, where

• 39:29Speaker 00

  instead of people being like, "Oh, my God, I don't have access to data," you create a proper desk or way for people to ask or people to help on that front. And so that's part of the dynamic we indeed helped create, creating the enterprise. And so this way, we were successful in creating like fairly, fairly large groups of people by the thousands, sometimes like thousands of employees, building models on the platform and so forth. And indeed, you need people in central IT to

• 40:05Speaker 00

  Held that by having a proper center of excellence because it does not enlarge enterprise, nothing significant happens organically. In fact, large enterprises are very good at killing organic stuff. That's just, just a fact. Like, that's a good thing, actually, because you need focus and vision and drive and rigor and discipline if you want to scale any type of organization. But so for this type of collaboration to happen, indeed, it needs to be an enlarged enterprise, a proactive, proactive initiative.

• 40:35Craig S. Smith?

  Yeah, I mean, some people have, I've heard advised that every department have an IT person embedded or a tech person embedded in the department that everyone can come to, to. So it's kind of a dispersed IT department across the enterprise. Uh, that makes a lot of sense to me because you're not going to have, you're, you're, you're going to have varying levels of skill and interest uh in the department. And if there isn't a dedicated person, uh, and everyone starts coming to, you know, Joe in the sales,

• 41:20Craig S. Smith?

  who knows how to use this stuff, pretty soon his job's going to be uh, just helping other people. So, uh, yeah, I mean, that's, that's an interesting challenge. Um, and, and you, how does, uh, how do you sell the platform? Is it by seats or?

• 41:40Speaker 00

  Yeah, it's basics.

• 41:42Craig S. Smith?

  Yeah.

• 41:44Craig S. Smith?

  And, uh, and, and how do enterprises generally distribute those seats? Is it, uh, I mean, are there some enterprises that give it to everybody above a certain level? Or, uh, is there like one seat per department? How do you see people organizing that?

• 42:07Speaker 00

  Yeah, well, there are some enterprises giving it to everybody, um, but not enough yet. Uh, but, uh, the most common pattern is that you start having a group of, uh, users into one given team, and then you expand to another by group, typically not individual users. Um, the goal being to

• 42:29Speaker 00

  Modernize the way you do advanced analytics, XML or AI into this group. And having a consistent set of use cases and data to work upon. And a consistent way to support this group. So imagine you've got a big department of 1000 people. You would have a few thousands, maybe, and kickstarting using the Taegu. That would be more for the people in various forms of ops function. And data support or AI support function within that department. And a central IT would actually support them by making sure that their first use cases and access to data

• 43:13Speaker 00

  is properly supported and that they've got a good shared view on what are the use cases and the roadmap for that.

• 43:22Speaker 00

  And at the end of the day, the goal, meaning the goal for us is just to multiply the number of AI projects that people can deliver, that enterprise can deliver.

• 43:31Craig S. Smith?

  Yeah, what's the most common agent that you see people building? And I'm sorry, I'm so focused on agents. I know that you guys are not only an agentic platform.

• 43:45Speaker 00

  Yeah, yeah, an agent, an agent, it's the shot are still to be called because we see lots of variation per sector. So to give you, to give that to you in a nutshell, for instance,

• 44:00Speaker 00

  in manufacturing, we see some appetite to use agents to help with some of the forecast and demand and maintenance schedule and accelerating those activities more realistically.

• 44:18Speaker 00

  In banking, we see quite a bit of appetite to rethink the anti-money laundering initiative, for instance, because it's aggregating lots of information and a very time-consuming activity.

• 44:32Speaker 00

  And so it depends, yeah, I think there is not like one agent to, let's say, rule them all. I think it depends a bit on the sector.

• 44:47Craig S. Smith?

  Yeah, I mean, the reason I ask is I've got an agent to help me go through my emails. And that seems like the most obvious use case, you know, I get tons of emails and I wanna,

• 45:02Speaker 00

  Oh, yeah.

• 45:03Craig S. Smith?

  you know, answer some and and ignore others and it does a very good job.

• 45:08Speaker 00

  Yeah, but my, yeah, our focus of the like, who is that? Well, what we believe is that lots of productivity agents, for instance, will be delivered by existing application or OpenAI and Anthropic down the line. Yeah, and so that's not. And so what we focus on are the agents that are related to, well, this type of core business process on which you actually need to control very well the data in, the data out, and why you took or not the decision. Not saying that your emails are not important, but I mean, for the enterprise, productivity is, let's say, an individual per individual topic, and to some extent, productivity in email is like

• 45:51Speaker 00

  general purpose type of application. The the best could be the same for every, almost for everyone on planet Earth. But for a given enterprise, or you, if you're a manufacturer, or you actually maintain or not your maintenance schedule, for instance, is very, very specific to you as a manufacturer, because that's the core of your business. So this type of agents, I think, are the ones that we need, we want to focus on, because I think that will be the one that will be the hardest to crack in the enterprise in the long run.

• 46:23Craig S. Smith?

  Yeah, yeah, you're right, because the productivity you save, you know, half an hour here, half an hour there. It's hard to convert that into a real gain. You know, the the the individual employee may not necessarily contribute that half hour saved to the enterprise, but if you focus on the core business, then you're getting real

• 46:56Craig S. Smith?

  a real impact. How you're a global company? Is this space?

• 47:08Craig S. Smith?

  Crowded, I mean, you guys have been doing this for a while. How do you see the the industry developing? Your industry, I mean.

• 47:20Speaker 00

  Yeah, I think that there is a.

• 47:25Speaker 00

  I think that the.

• 47:29Speaker 00

  It's a funny space because I still have the perspective that in the enterprise, people in the business are still underserved. Even if even after tremendous investments in AI. But lots of the investments in AI have been pretty much focused on building data centers and cloud and data platform and developer tools, various kind. But ultimately, people in the business wanting to do things themselves have not received.

• 47:55Speaker 00

  The same level of investments. And I think it's a space which is. Early compared to to what it should be. And so that's that's the way I think about my my my my ecosystem.

• 48:09Craig S. Smith?

  Yeah. I mean, you're you're. Quite a bit younger than me.

• 48:16Speaker 00

  I'm not.

• 48:18Craig S. Smith?

  I'm guessing you are and.

• 48:23Craig S. Smith?

  When you look at, you know, I saw. Sam Altman was talking recently and he was talking about the 2030s. And I thought that was interesting because in my life I've never thought.

• 48:41Craig S. Smith?

  You know, you can sort of have these vague. Dreams of what the future might hold, but I haven't thought very concretely.

• 48:52Craig S. Smith?

  You know, a decade ahead. Or thought of it in terms of a decade and I to me that was very interesting. What are the 2030s going to be like? As opposed to the 2020s? And do you have a view of, do you do you think in those terms like? Wow, in the 2030s, every enterprise is gonna have. Agents.

• 49:19Craig S. Smith?

  In its core business.

• 49:21Speaker 00

  Yeah, yeah, I think that that's it's a to me, there is a question of like urgency as in um and two two level of urgency. You've got the usual one, which is as you know, if you remember internet technology, there was like this five years gap or ten years gap. Between adoption in consumer land versus adoption in enterprise land. Now, the fact that you you you had access to tools and technology or had the perception of as a as a customer, as a user, um as a consumer that you did not have as an enterprise employee. And so this gap, and so there is one urgency, which is like the play catch up.

• 49:59Speaker 00

  And in the open AI, Entropic of the world and Google and even Apple, they will keep pushing great things for day to day productivity. And there will be some like catch up and like good old enterprise, great. And that's one aspect of it in the next five years, this race. The second one is also a race of, well, just sheer competitiveness, as in there was lots of discussion about like digital native overwhelmingly winning over a traditional enterprise. Which was, I think, a thing back twenty years ago. And in the grand scheme of thing, it happened a bit, but not that much. Of course, uh media is very different today compared to where it was before. Of course, e-commerce and some aspect of retail changed a bit.

• 50:44Speaker 00

  But in the grand scheme of thing, not that dramatically in most sectors of retail. But so there is this question of whether in the next five years, you have another wave, but more actually bigger wave of disruption of traditional enterprise by newcomers. Because they will be able to provide the same service, but like just thirty times cheaper. Or with thirty times more efficient with AI. And I think that for most enterprise, there is this realization that if they are not ready enough so that they can defend themselves when the real competition begins, they will have to face.

• 51:31Speaker 00

  Well, potentially, well, understanding they are not so longer relevant business. And so this has maybe not happened that much during our lifetime. It may, it may, this type of drastic change of the economic principles and fast enough might have happened to people in the 20th century, the first half of the 20th century, in terms of like dramatic changes of the economic principles that change where the companies of the regions or the whatsoever winning this or not. And this actually might happen in the next five to 10 years for us.

• 52:13Craig S. Smith?

  Yeah.

• 52:13Speaker 00

  Which is why I believe that there is urgency for all enterprise and especially traditional enterprise to be state of the art in terms of AI and to enable people in their business and so forth. Because even if it's a very, let's say, I'm making lots of fluffy remarks about an uncertain future, trying to play like sad man about being a futurologist. But I still think that enterprise should edge their bets by making sure that they can compete in AI land if they don't want to be overwhelmingly disrupted.

• 52:52Craig S. Smith?

  Yeah, yeah, yeah, I agree. I mean, it's, it's a little more fundamental than, I remember at the, you know, in the early days of the internet, there was a big divide between companies that had web pages or a web presence and those that didn't.

• 53:17Craig S. Smith?

  And, and everybody caught up and I don't think anybody went out of business because they didn't adapt to the internet fast enough. But the agentic AI and the agentic stuff, you're right, I can see newcomers in traditional industries popping up.

• 53:39Craig S. Smith?

  Uh, that can just operate far more efficiently. Uh, I've been talking to people uh, uh, in the uh, distribution space, you know, about warehouses and robotics. And you can see that if, if uh, that industry, it's all gonna be uh, the, the leaders are all gonna be those that adapt and adopt uh, rob- robotics and AI. Um.

• 54:12Speaker 00

  Yeah, we, indeed, and we've got great customers there. And they not only are using um, AI for robotics, but they're also using AI for every aspect of their business. So their uh, demand planning and real estate planning and optimize their back office function and so forth. Because the competition in that space will be acute in like every aspect, every aspect of the business. So you can't just be running the business out of Excel, even if it's real estate. You need to be uh, uh, very agile, understand about uh, the market and the pricing and the efficiency of your sales force and your back office and understanding where you can manage like hybrid use of your warehouse that could be reconfigured depending on the type of demand

• 54:57Speaker 00

  and the energy supply and like, yeah. It's um, it's becoming a different game.