Goodbye Excel? AI Agents for Self-Driving Finance – Pigment CEO

20:27Eléonore Crespo:And it's also to help people find delight in what they do and help them be smarter and help them be more creative and help them spend time on what matters. So that's the philosophy. Is that clear? Yes. So maybe I can go into the now the agents because you're asking me. So we have launched and we have announced our first three agents, analyst, modeler, and planner. So these agents work all together and you have to think about it as a next team of whatever you have, right? So imagine you are in finance and you have the analyst, you have the modeler, and you have the planner. So the analyst is here to actually work on the why.

21:05Eléonore Crespo:So he's here to really work on the why, explain the data, carry some recurring analysis to literally help you save time, do this analysis. That's the job of the analyst, as simple as it is in the title. The modeler is here to help you adapt models very fast. Because as I said, most of the time, that's a problem. And it's really why you really need an agile platform like Pigment. You need to adapt your model constantly because you have new countries, new product. You think about your business differently, etc. And so the modeler is here to help you build new models, whether it's financial models, HR models, supply chain models, etc.

21:39Eléonore Crespo:at scale on Pigment very, very fast and adapt them. And the planner is actually here to help you understand different scenarios. So it's really going to help you, for instance, if you want to run a thousand scenarios in parallel, the planner agent can help you do that. So it's going to help you work on the levers of your scenarios and really help you do, if you want to do a 46K forecast, you will be able to do that. So think about it really as an extended team that is going to be able to work for you on these different topics and obviously work together and we can come back to that. Why three and not one?

22:14Is that just easier for people to wrap their minds around or is that more of a technical constraint?

22:20Eléonore Crespo:So I think today these agents really need to be separated because we are still at the beginning of this agentic framework for I think any company out there. And they are working on very different things, very different sets of data and very different ways of working. The analyst is working more, like I would say, on the past and present. Think about it more as a BI analyst almost. The planner is really working with machine learning, with forecasting, and obviously handling very complex tasks to actually carry on multiple forecasts at the same time, etc. And then the modeler is really here to work on new models, on new formulas.

22:54Eléonore Crespo:Think about it as cursor or other companies that are out there, augment or whatever, for finance team, for HR team, for analysts. So it's a very different task. But over time, what we want and what the user will see is really we'll have the supervisor on top. that is really the only way that a user will interact with a platform. And the user will just give the agent a set of comments and the supervisor will then decide, okay, that is going to be done by the analyst and then we're going to pass that task to the modeler and then to the planner. And so the goal over time is that there is only one agent.

23:30Eléonore Crespo:But before that, I think we're going to launch tens of agents that are going to do different things. We want to launch a consulting agent, for instance, that is going to help during the implementation of pigment. So this is really a set of agents. And really the goal for us is to have these agents do two things. One, and I think it's going to be the same for every agent, is automate some repetitive tasks with low added value, but also work on high complex tasks that no human would have been able to do. So really enable, I would say, the impossible because these agents are more powerful obviously than any human being.

24:04So amazing. All right. So much to unpack here. So one that you alluded to is the coordination. So if you have three agents and the future dozens of agents, that concept of supervisor, is that a supervising agent? Is that a human? How does it all work together in an orchestrated manner?

24:23Eléonore Crespo:Yes, so it's a supervising agent. But I do believe that in what we do, there will always be on top of that a supervising human because we deal with very, very sensitive data. And so you want a human to always be here to make sure that we are going in the right direction and put the right galleries around the framework that we're giving to the agents. So maybe I can give an example that might be easier to understand. So if you think about these agents working together, so if you take, for instance, an analyst, so a financial analyst, let's say like a Tuesday morning 9 a.m and they have two days to run an analysis for their CFO I'm inventing that on the go so we'll see if that goes somewhere but imagine they have to do that so what you will have is they will give a prompt to the agents and and first that will trigger the analyst agent of understanding so let's say for instance they want to do budget variance analysis we were talking about that before that will trigger a first action which is understanding budget variant analysis, perhaps across 50 products, 20 countries.

25:23Eléonore Crespo:I don't know how many cost centers. So already imagine the complexity of that. It's a lot because that's things that could perhaps take you months or maybe you would not even be able to do that. So that's the first step it does. And then it will probably find some variances. And then you will have probably to take a decision because there are variances and you will need to act on that. So then that will actually trigger the planner agent that will identify the levers of these variances. and these levels of variances then needs to trigger what I would say a new scenario of data to say, okay, actually if I improve that level that's a scenario I suggest you take to actually now be back on budget, for instance.

26:03Eléonore Crespo:And so in order to do that probably the planner agent is going to call the modeler agent via the supervisor and the planner agent will tell the modeler you actually need to modify the model to make that work because now you need to create a new scenario and perhaps we'll introduce some new variable in the scenario to actually make that happen. And then at the end, it will give you scenarios and it will give you literally on you to just take the decision at the end and to say, okay, these are the different scenarios possible. We see that we do not have, we have variances here, there and there. And this is what I suggest you do.

26:33Eléonore Crespo:And here you will have obviously triggered these reagents. So that's one example. And there are many, many of them, of course, where you would do exactly the same thing, whether it's on territory quota analysis, whether it's on matching supply and demand. I could take the example of Coca-Cola today, who is using today's analyst agent, which is the first that we launched to actually understand where there are problems between supply and demand. Well, you can imagine that over time, when that runs, obviously today it runs 24-7, when that runs 24-7, then it will trigger the other agent automatically, which it doesn't do today, to actually help again take decision to say, okay, now I see that my demand and supply do not match in that particular country or for that particular product.

27:11Eléonore Crespo:This is what I'm going to do. And these are the two paths I can tell you you should explore. And the agents as of now stops at recommendation. Yes, they do. Yes. And the handoff to the human is like, okay, here's a set of options. Here's my recommendation. You do the human review and do the next step. Yes. So maybe to take a step back because we are in a very different world than any agent working on unstructured data. We work on structured data and we cannot afford mistakes. so the way we are actually using today generative ai in our agent is very different from from a lot of companies out there because the way we do it is that these agents actually interact with a pigment platform so every calculation is made on the pigment platform everything we do really like all the calculation the od trail etc is done on the pigment platform so that makes a big big difference, actually, because it means that we do not look, I would say, at audits and at, you know, like understanding really the guardrails, etc.

28:17Eléonore Crespo:The same way as an unstructured company would do because unstructured data company, because for us, we need to make sure that everything is 100 % accurate. So the most important is clarity more than really like, you know, getting to an answer as fast as possible, for instance. So you do not have a hallucination problem because you cannot afford one. So you constrain the agent in a way, just to play back what you said, where you do get to 100%. Yeah, so the goal is to constrain really the agent. And it's really in the way, so for instance, if today you work with the analyst agent, the analyst agent is really going to go pick the data directly within the framework, the labeling framework.

29:00Eléonore Crespo:we have the metadata that we have within pigment and it's going to ask you questions until it makes sure it really understands exactly like a junior analyst would do, for instance, until it really understands what data to pick from. And then it will trigger some calculation, but every calculation is done on pigment and then back to the human. And then today, what we will advise for, I think, still quite a while, is to make sure that the human does a feedback loop of validating the data. For me, the human today with pigment is here to set the framework, to say what they want to do, and then validate the data, but just forget about the converse part in the middle.

29:34Eléonore Crespo:And where we are incredibly lucky is that we had all the technology to make these agents very effective because the way they are built is that on pigment, you have obviously very, very deep audit trails. You have diagrams of data where you see all the dependencies from one data point to another, how it was calculated, et cetera. So it's actually very easy for humans to verify that. We also have our agents that verify that. So we have agents that, you know, do like first error validations, etc. But today we still recommend everybody to check the data because it's too important. And those verifying agents are behind the scenes?

30:08Eléonore Crespo:Yes, exactly. So you carry on an analysis and then the agent runs a series of tests to check if they think the analysis is correct or not before rendering the data to any human being. It sounds like UI UX is a very important part of how it all works. So just for people to understand and visualize what's happening, you expose all the steps and each time you have the human in the loop validate. Do you expose a score of, you know, X percent confidence that this is 100 percent accurate? So, yeah. So the way it works today is really every time you carry on the calculation, whether you do it with agents or without, you can see the anti-ODI trail.

30:51Eléonore Crespo:And you have to even understand that pigment, sometimes with some of our customers, we power a lot of public companies, is used as a governance platform. So we log everything and our agents log everything. So they log even more than humans in a way, because I cannot tell you the number of them. I'm sure you see that with your portfolio companies that sometimes you come to a result, you don't even know how you came to that result. With agents, that's amazing because that's the magic. you can really log everything very, very precisely and gather all that information. So that's really how pigment works.

31:25Eléonore Crespo:And then obviously we carry accuracy tests along the way and we make sure that we expose that also to our customers and that we work with them to obviously improve that over time. But the goal for us today to give a rough idea is to say we want to be more accurate than a human being, which is not hard, by the way, I'm joking. But it's really what we try to do. And this is the feedback we're getting from our customers, by the way, is that we are more accurate than when they were doing before. So you've sort of passed the Turing test of AI agent performance already? I don't know if I would call it that this way, but I would say that at least for the analyst agent first, we can really carry on end-to-end analysis and give an answer that is very, very satisfying to our customers.

32:15Eléonore Crespo:and, you know, we got like incredible feedback from all range of customers that are using it today and that all tell us that, yes, it's really like another, at least they had hired like, you know, a bunch of analysts in their team that is doing the work for them. Yeah, I'm asking because it's a truly fascinating concept, right? Because I think we all understand and sort of got used to the idea already in the last 12, 24 months that, sure, AI is going to make things faster. It's going to cut out a lot of the grunt work, the stuff that you don't want to do. But it feels like that next step is, well, AI is not just going to do that, but it's going to be actually much better than a human systematically.

32:54And that seems to be on the verge of happening, talking to a bunch of people. It's not quite benchmarked, but we seem to be sort of on the cusp of this happening. Or maybe it's already happened, but from the conversation I'm having, it seems like we're just getting there. I was chatting with some AI customer success companies where same thing, like the value proposition of AI, you know, customer chatbot was always, well, we're going to deflect X percent. And, you know, 20 percent of the time people will hate us. But, you know, and now we sort of seem to be getting in a situation where the AI is actually better than any human customer service agent all the time, which is fascinating and scary at the same time.

33:34Eléonore Crespo:Yeah, yeah. So I would say for us, obviously, you know, it's still the beginning of the revolution. So there is still so much to build and discover in terms of, you know, when, for instance, we're at the very beginning of the planar agent. So we'll see exactly how that brings us to the next level of accuracy, etc. But I would say, clearly, there are ways with technology, combining technology to actually make that happen. and I think more so also what you said is very interesting beyond accuracy it's also enabling the impossible and I think that concept is phenomenal. Yeah, Elizabeth, click on that.

34:09You mentioned automating some mundane tasks and then like doing things that humans cannot do. Cannot do, yeah. What is that?

34:16Eléonore Crespo:There are so many things that humans cannot do today and there are so many tasks that would literally take a lifetime to carry on. So, you know, if you really want to do like think about like again any large company out there have a combination of products and countries and business units and cost centers that, you know, makes the combination really, really hard to understand at scale. That's just impossible when you carry on an analysis to do that. Or if you really want to, let's say, improve your plan, you want to improve your margin and you want to actually understand all the potential levers to get there and then you want to understand the variety of scenarios, maybe you're going to literally want to run 5 ,000 scenarios in parallel on an infinite quantity of data.

35:03Eléonore Crespo:That's just impossible to do for anybody. And so there is that. And there is also, as I was saying earlier, sometimes the problem is just people are time constrained. So perhaps, yeah, maybe in three years they could get to a result, but they just don't have that time. So I think this is what is fascinating. And these are just like the first example that we see today with our customers and how they're using the product. but I think it's going to unlock a lot of use cases that we have no idea about today. We have no idea about, and that's, again, going to do things that were just not possible before.

35:36Eléonore Crespo:What I described, for instance, about Coca-Cola, they were just not able to do that before. They were just not able because it's just too complex. And so I think that's really what I love is this ability to carry highly complex tasks, do the mix of everything you need to do when you're an analyst, you know, from looking at the past, understanding the past, understanding the present, getting input from everybody at a very, very fast pace, going to think about the future and thinking about all the universe of possibilities that was just not possible before. There's a long legacy in the history of SaaS companies in the finance space that basically said, we're going to kill Excel, we're going to kill Excel.

36:20And then, you know, the irony is like, fast forward to today, Excel is still very much around. Are we at that point, though, now? Is AI actually replacing Excel?

36:30Eléonore Crespo:I have a prediction that Excel will still be here in five years. And there is a good reason. Death, taxes, and Excel are the three things that are certainly in life. You can keep that. In five years, Excel will still be there. And in 10 years, Excel will still be there. Why? Because, yes, the innovation is going at the speed of light, But there is a difference between the innovation going at the speed of light and the fact that enterprise will take a long time to adopt it properly. And I think that's what will make Excel still alive in several years. And I also think that in Excel, there are some very intellectually interesting concepts that, yes, will be replaced by AI and generative AI, etc.

37:12Eléonore Crespo:But there is still the rendering concept of what Excel is and the concept of the pivot table and the concept of the easy input, etc. that we have in pigment, actually, that we think are so important. Because if you think about the pigment experience, it's not just the agent. It is, first of all, the underlying technology, the power of the platform, the power of the calculation engine, but also the UX on top. We were talking about it earlier. How the UX is actually help you render the data. And Excel is a very good render of data. And it's also a very good way to actually input data for anybody in a company.

37:46Eléonore Crespo:So I think there will still be some sort of Excel out there. But I do think that finally humans will see that they are very, very easy way to get access to the technology that will unlock a lot of new use case and give a lot more autonomy to decision makers too. Autonomy is a really interesting word for decision makers, but also for systems. So we all understand we need to walk before we run and build reliable agents and all those things. But suspending disbelief a little bit and fast forwarding, do you think that we land in a world where enterprises are effectively automated? Can you imagine a world where Pigment does the data gathering, cleaning, storing of the data, suggests an action, the action gets validated by the supervisor, and the action actually gets taken?

38:39So hire X many people in Switzerland and launch a product in the US kind of thing?

38:46Eléonore Crespo:Yes. So actually, we are working already with some customers on an autonomous planning system. And we are working right now very seriously with, I can say, the largest transportation company in the world, I think, as of today, which wants that. And that's not far. That's not far. It's not going to be here in six months, but it's not very far. And I think they are going to work on topics around sales planning, doing automated segmentations, assigning territories, quota directly, automating that obviously for any seller out there, understanding also how that links to finance, etc. and that's our goal it's autonomous planning because the problem is that in planning there is still so much wishful thinking that this is what makes companies fail most of the time so you were talking about hiring so there are so many companies I'm sure in your portfolio they do planning and they'll tell you that they are going to be able to hire 200 people in the next six months and it's going to be easy and then I can tell you that you put any agent from Pigment looking at the data and running a couple of analysis about you, no way.

39:57Eléonore Crespo:You don't have the TA capacity. You haven't been thinking about how long it takes to hire, etc. And you're completely wrong. You're going to hire maybe 120 if you're lucky. So these are the things where you have so much, I would say, wishful human thinking in there. And this is really where the technology can bring so much value and where autonomous planning will make a huge difference in any business. and make them a lot more competitive. Such a fascinating concept to think about because presumably that gets kind of real time at some point, right? Like, so Russia invades Ukraine and next thing you know, your supply chain automatically adapts in real time and probably faster than anybody else.

40:44Therefore, you have access to like a better alternative of your supply chain because people haven't moved as fast to people that don't have the automated sort of planning agent.

40:53Eléonore Crespo:Yeah, I mean, for me, that's really the dream we are trying to build. And I think it's going to be a total game changer for any company out there. Yeah, and because you work on centralized planning, you're very much building the sort of operating system slash nervous system of any company, right? So you're ideally positioned to do that. Yeah, I think we are very lucky because we are the single source of truth. We have the data, we know how to operate on that data, model that data, and we know how to change things very efficiently. that's also the other thing is that we have a very agile and flexible platform.

41:27Eléonore Crespo:So every time you want to change something, you can change it super, super fast. And with a moderate agent, that means really making your models evolve. So you have, imagine you have a war and indeed your supply chain process change. Boom, immediately, if you need to remodel something, do something in the platform, it's super easy. So that's, it's, I think, as we were discussing earlier, the problem will not be so much innovation, but the capability for enterprise to truly adopt it at scale. I think that's going to be one of the big issues. But I do think that the companies that will do that, they will thrive.

42:00Eléonore Crespo:They will be the best companies out there in the very near future. Let's talk about adoption. That's, as we alluded to at the beginning, that's a topic I definitely want to touch on. Just going back to today's reality about how that all works. Do the agents work on top of LLMs, like the Open AIs and Anthropics of the World? Whatever you can talk about there. Yeah, so we partner with OpenAI, which has been our biggest partner since the beginning. The reasoning models or the general models? Yeah, so actually it's a mix of that and also our proprietary technology. And then we also work with Anthropic, we work with Gemini, we work with Mistral, and so over time we want to really offer anything.

42:44Eléonore Crespo:And also we have many customers, I'm taking the example of ServiceNow that is obviously pushing very hard on agents that want to use their own agents, for instance. So this is also something we want to be able to do. So for us, the goal would be really to be as agnostic as possible and offer a variety of things. Meaning to collaborate with ServiceNow agents? Yes, exactly. And collaborate and also use their own LLMs if they want to do something specific. I also think that's one thing. And then what we also want over time, if you think more about the agent framework around us, is we want to build a marketplace where all of our partners, whether it's technology partner or implementation partner, can build their own agents that can plug into pigment.

43:24That's the other fascinating part. We were just talking about like this kind of, you know, very real-time automated enterprise. Of course, the next question is how automated, real-time autonomous enterprises collaborate with one another. In which case, like all of us humans are just like at the beach, hopefully reaping the financial rewards of whatever the enterprises do. But yeah, that's that concept of a cross-company collaboration. It sounds like you're already there then in terms of at least technically being open.

43:58Eléonore Crespo:Yeah, we are open to it. We are not there, clearly, but we want to be there. We want to be there. And I think it's going to be critical because we are, I think, the best technology companies are all developing their own proprietary technology. And for us, as we are the single source of truth, we really need to integrate with what they need if we want them to be very powerful. Is that a token intensive business you're in? One of the key themes of the day is, you know, gross margins and cursor. Do you have calculations of constantly running in the background? Yeah, it's probably not as token intensive as other businesses because we run our calculation ourselves.

44:38Eléonore Crespo:So it's more like you use the LLM to actually question the platform. but then the platform does the calculation itself. Right, so it's a translation layer, which is the token part. Yeah, maybe as a last question on agents, are there areas where you do not want to build agents, where you think the agentic approach and AI in general is actually not worth the squeeze? It's really hard to tell. I would say today there are some very simple analytics where it might not be worth using an agent for. I would say. But maybe tomorrow you really want to do everything with agents. So I think there is a short term of, you know, maybe sometimes you'd better off just use a dashboard in pigment and look at your data.

45:24Eléonore Crespo:But I do think that over time, you really want to carry any sort of ad hoc analysis on pigment and that, you know, the cost will be really ridiculous compared to the value you're going to get. So that's really what we would be aiming for, especially as we go towards that vision of autonomous planning that helps you take decisions. So I would say the more you can log actually on the platform, the more information the platform will have, the better it will be at understanding who you are as a company. Because again, you see that all the time, a lot of planning discussions, a lot of what's happening has to be logged in the platform.

46:04Eléonore Crespo:So I would say the more analytics you will do with the agents, the better it will be trained and the more relevant it will be over time. Yeah, so this implied in a couple of things you said, including now, so there is a concept of constant feedback loop where the system gets better. Can I sort of declaratively input what my policies are, you know, at Coca-Cola or at company XYZ? This is how we do planning. This is how we look at data. Or does a system have to learn? Yes, so I would say it's two things. So I would think about it when you start as if you were going to train a new analyst that has just started in your team.

46:46Eléonore Crespo:So you need to train them on the concept and on your constraints as well. So I think it can be, it's not as powerful as what you described. I think it could go there. It's not there yet. I think today it's more able to really act under constraint. So to know that, you know, you are never going to be able to hire, you are maybe a 200 % company. You're not going to hire a thousand person next year. That doesn't make sense. So it's going to be able to work through that set of constraints. And I think over time, we'll be able to input even more rules around really how we work. Already today, also, you can fit them into the way you do workflows and it can follow the workflows you want.

47:23Eléonore Crespo:So today, really think about it as at first, when you're going to set up the agent, you're going to you're really going to talk to the agent and explain to him or her. I don't know, to eat a couple of things. you know there's a problem in french is because everything is a yes default to masculine masculine or feminine but the point is it's it's never it it's never it that's right but it's troubling when you when it comes to agents you might not want to use that so believe them believe them yes exactly but anyway the the point is um you you will really want to um give them as much context as possible and as many rules as possible and also obviously within the platform When you've built your model, you've already given a set of rules.

48:07Eléonore Crespo:But I think that will go as powerful as what you described over time. All right. So all of this is the perspective of Pigment as a builder of Argentic AI. Now let's switch to the customer side. So what's the vibe or the mood currently? Do people, people are certainly excited about all of this, but how do they react when you start talking about, Are we going to have a planner agent and an analyst agent? So I think I would probably distinguish two types of companies slash customers or prospects. On one side, you have people that are using pigment today, and I think they all get incredibly excited about the technology because they already love the technology.

48:54Eléonore Crespo:Otherwise, you know, they would not have adopted pigment. So I would differentiate that from like some entrepreneurs that are further away in their journey to adopting technology, where you have to start from a very different point of view. So I would say clearly today the analyst is used amongst our customers and the excitement is incredible because they really see already that not only it helps them save time. We have so many examples of customers that are already giving testimonials about, you know, how much time they save on that. I even got last week, just to, it's a funny example, but the super self-founder who is still the CEO of the company.

49:33Eléonore Crespo:And so it's a company behind the Clash of Clans and Brostars. And they have been happy customers of Pigment. Last week, he was literally walking past the finance team and he overheard a conversation of the finance team singing praises about Pigment and Pigment AI. So he recorded a video and sent it to me about, you know, how much he loves pigment and how much he's changed their life. And I think, you know, what they see is really like today they are able to automate some revenue analysis, PN analysis. They are able to really have some work that would take them so, so, so long before not even possible to do because they have quite a complex business.

50:08Eléonore Crespo:And now they do it with pigment. And we have dozens of examples of that, whether it's like some very traditional company. So, you know, like one of the largest transportation companies in Europe, very traditional or a very, very large real estate business in Europe, too, that is quite traditional. They also love, absolutely love the technology. So it's really a variety of companies, but it's different from prospect. And prospects today, I think we have to reassure them more because they really need to understand the implication of what that will mean, how we're going to help them train their teams on it, how it's going to really involve and change the workflow that they are using, etc.

50:45Eléonore Crespo:So we'd really differentiate the two. Are you seeing the drive towards AI be more top-down or bottoms-up? I think today we see it. Every CEO is pushing top-down, I want AI everywhere, and find a way to make that happen. There was a funny cartoon that made the rants on Twitter. I saw it on Twitter today. Who are we, CEOs? what do we want AI? I don't know what for, I don't know. What do we want it for, we don't know. When do we want it, we want it now. That's what I was thinking about when I was telling you that. It's like very clearly top down. And I think I'm doing the same. So yeah, anyway, I do think there is a lot of top down, but we also see, I mean, OpenAI and the other are clearly gaining the heart of the consumer business too.

51:39Eléonore Crespo:So we also see a lot of like willingness bottom up to adopt the technology. But I think there is still a big gap to bridge between the two because you might have some companies where the CEO wants to now spend millions in AI. And perhaps actually the co-workers are really scared about their job and about what it means for them and about the change management that it will require to adopt AI. So I do think there is a need and there is a paradigm here of like helping our companies adopt AI in the right way. So really helping them with change management, helping them with understanding how to train their teams to learn a new job, to learn a new way of working, etc.

52:16Eléonore Crespo:And I think all of the enterprise AI companies out there have probably still a long way to go to make that happen very well. to give you an example at pigment right now we are doing like what several AI companies are doing out there which is hiring forward deploy engineers and people to really help with change management and deploying the models in the right way and I we are also investing massively actually in customer education we have a new customer education manager that joined us a month ago to focus solely on that pretty much so there is still a lot of work to be done there if you want it to be done the right way and to really augment the human and make sure that everybody still finds joy in what they do.

53:02What do you tell customers in terms of re-skilling?

53:08Eléonore Crespo:Yes. Both for the sort of line employees, but also the managers in this new world of AI. Now, everybody in a company can become a mini CFO. so we we have everyone that can really have a team working for them and help them carry on things that they would do themselves before so it means their job is evolving and now what they have to do is to set a vision a direction and validate the data so for us for instance for and i'm talking about finance but it's the same for any team out there that is doing some analytical work. Now it's all about training people and reskilling people on really learning how to give context, how to give framework, how to validate the data, how to put the right guardrail and understand the data, and then obviously how to take a decision from there.

53:57Eléonore Crespo:So it's just, it becomes a different job, I would say, but hopefully going back to the beginning, much more interesting. And then how do, what does that mean in terms of people's careers? You know, we've had on the podcast, several great conversations with people in the context of AI coding and, you know, this tension between, okay, well, should you still learn to code? And do you need to still like study computer science in a very sort of classical way so that you can sort of deal with the output of the model? Or is that the wrong way to think about it? In the finance and planning world, like do people, how does one become a CFO or VP of finance in a context where the models do so much?

54:40Eléonore Crespo:Well, so first of all, I think if you're a software engineer today, I mean, and if you want to study, like just go, I think you still need to understand fundamentally what's happening. And I'm sure over time it will be new languages, et cetera. But I mean, the fundamentals of what coding is today, you are not able to use cursor if you don't understand coding. And I know cursor will evolve also towards probably a more advanced version of lovable or whatever. But still, you need to understand the fundamentals in order to build an app, to build a complex model. And so, for finance, I would say almost it's even worse.

55:16Eléonore Crespo:You are in a heavy regulated industry where you cannot afford mistakes, where you report your results to the street. I mean, if you don't understand the concept of a balance sheet, if you don't understand the concept of a P &L, if you don't understand these things, it's going to be very, very hard for you to know if these agents are going in the right direction. And also, you will probably use more of your finance skills because instead of going back to trying to collect the data, plug your systems, try to make sense of the data, now you will actually have the time to properly analyze it, understand it better, finding insights that you might not have found before.

55:55Eléonore Crespo:so more than ever you need to be highly highly skilled to be able to work there and combine it of course with also some somehow I wouldn't say too complex technical skills but still understand a little bit what the technology is going to do because you need to understand at least the fundamental concepts of how this all works if you want to make it very powerful so that's how I see it today and we'll see maybe over time that will evolve maybe in some years I will tell you actually maybe you don't need to be that fluent because maybe now everybody can learn about finance in three minutes because of what we've produced here all right so no vibe planning i think vibe planning is the sense that now you can build easily models on pigment so you can you can do vibe planning on like you know like building super super simple like maybe you know like a cohort analysis that would have taken you months to build now you can build in minutes so that's amazing uh so in that way yes but then understanding the concept behind to make sure your data is correct is another story, I think.

56:54Maybe let's talk about company building for a few minutes and what you learned, like one of the fascinating things about pigment other than what we just discussed is the fact that you're building a highly successful global company from inception, but you're doing it from Europe, which is a little bit of a narrative violation, as they say on Twitter. and I'm curious about how that all came about and how you've been able to do it in a context where, you know, unlike what all VCs would ask their European founders to do a few years ago, you never moved here to the US. So tell us how you pulled that off.

57:40Eléonore Crespo:Yes, so first of all, I think Europe is not a shame today because there are so many incredible companies built from Europe. you think 11 Labs, you think Lovable, you think Legora, N8N and so many others. So I think, first of all, Europe is doing extremely well today with founders based in Europe. Now, we created the company during COVID and it made everything possible, meaning that all of a sudden, all of my customers were working from their living room and so I could take meetings with the US at any point in time. So that made that really possible from Europe. I think that would not have been possible, to be honest, before COVID.

58:22Eléonore Crespo:But now, you know, a lot of people are still working hybrid, working a lot from home, etc. So it's still super easy for me to just work late hours and make that happen. I think it's clearly, what is not easy is that US is our number one market. It's 60 % of our revenue today. It's the market that we want to grow the fastest. and it's always the same it's like what do you have to do around you to make sure that even if you are in Europe because R &D is in Europe you it's as if you were a US company so I made sure that day one from the very beginning with Romain we went after US investors you were obviously part of it we've been building a full equity story in the US and all investors have been helpful since the very beginning you've been super super helpful to build our executive team, to build our customer base, to find us our very first customers in the US, etc.

59:16Eléonore Crespo:And so we've been leveraging every single lever to make it possible to build from Europe. But I want to prove that it's possible because I think it's a shame if every company has to be in the US. I think my goal is to create a global company and it's about breaking boundaries between Europe and US. And I don't think we should think in that concept. So at Pigment, my entire executive team pretty much is in the U.S. And so it's very easy today to be this type of global businesses. Yeah. Any magic trick in how to run a senior team in the U.S. when you're in Europe? Which, you know, that's Europe to U.S., but like that question applies to like anyone, you know, with a couple of offices, even within the U.S.

59:59Like what is it? A lot of Zooms, a lot of in-person meetings. How do you do it?

1:00:05Eléonore Crespo:Yes, so it's a mix. So obviously lots of Zoom, you know, I work very late hours, you have to adapt and they work very early hours too because it's impossible. We try when possible to hire leadership more like I would say East Coast, but obviously there are a lot of skills that you find on the West Coast, you know, our CMO for instance is on the West Coast. and so we really try to build around that and I think we do a lot of Zoom but it's also very important that we have quarterly catch-ups in person we also do a lot of events in person with the company we have Global SKO, we have Presence Club, we have our company off-site we have many, many moments to meet one another we try to do company trainings in person but it works super well, I have to say it works super well and you know maybe it would have been a different story with us in the US, I don't know.

1:00:59Eléonore Crespo:But, you know, just in September right now, I'm here three times in the US, just in one month. That's what it takes. So it's maybe more tiring in a way, but that's what it takes. Partners, you have some big name partners from Google, but I'm almost most interested in the SIs, I believe Deloitte and others. It's one of the recurring topics that like every board, every startup talks about. Like at some point, you want to start scaling through partners, but it's always harder than it seems. So any lessons learned there? Sure. That's a super interesting topic that is hard for a lot of companies to get their head around, I think, because it takes a lot of time.

1:01:42Eléonore Crespo:So we partner with Deloitte, we partner with EY, we partner with PwC, and then we partner with many boutique firms. What you see is it's a little bit like when you build a business is that you get velocity with the boutique firms first. It's exactly like you get velocity with your SMB customers before you get velocity with enterprise. It's the same here. So boutique firms are the first thing to focus on, I would say, at the very beginning to make sure that you have a network that is well defined with clear guard rates around you cover that geo, you cover that use case. and your friend over there, competitor friend, is not going to do the same because otherwise you guys are going to get upset or whatever.

1:02:18Eléonore Crespo:When you've done that, you need to start building in parallel the GSA motion and that takes a lot of time because you need credibility. You need to see that you are serious about what you do. And for them, the other day, for instance, I was with a Deloitte partner and they were telling me for them their yearly so-called quota is 25 million. And for those who don't know, in enterprise software, the quota is more between one and two million if you're lucky right so very different numbers so you need to understand that if you have to feed her with 25 million worth of business that's not easy because you are working with many partners at the same time and so that's why don't go too fast into these jsis and when you do it start building the relationship with one or two customers but the problem is that they will take time then to build the practice they will take time to start feeding you with leads.

1:03:10Eléonore Crespo:So at the beginning, do not wait for them also to think they're going to source leads for you. At the beginning, you are going to be the only one to source leads for them. And it's really when they start understanding your business more that they're going to start pushing you over. And that's what we start seeing at Pigment. But, you know, it took us a good five years to get there. Maybe zooming out to close the next few years. Like we talked about this amazing vision of the autonomous enterprise, which sounds like science fiction, but it sounds like it's coming pretty quickly. So, yeah, what is success in like three years, five years?

1:03:42Where do you want to be?

1:03:44Eléonore Crespo:I think, first of all, the success for us definitely will be around the amount of innovation we've managed to push. So, three years from now, I would love to really start seeing the results of what I call this autonomous planning system. But five years from now, we actually want to build in many other categories. is we have a very, very large ambition with Romain. We want to build a$100 billion business, if not more,$200 billion if we can, or even more. And so in order to do that, we are going to expand to what we do today. And so we are already thinking today about the second, third, fourth act of pigment and about where that's going to bring us.

1:04:26Eléonore Crespo:Because with the use cases that we are unlocking today, we are going to unlock actually new ecosystems that are outside enterprise performance management. so in five years I would like to see us starting to have built you know more around that going more into directions around you know what the ERP can do for instance from really like insights to action and we're going to keep pushing in that direction to hopefully you know in 10 years being able to start perhaps being really more like an SAP and an Oracle and having a really fully fledged suite of products that can help you across the board but the difference is with a lot of user satisfaction hopefully amazing well that has been a wonderful conversation thank you so much for sharing all of that with us and spending time and you know very excited about what you've been building and you know even more so about the vision that you just described so congratulations on all of this and thank you thank you thank you so much and thanks for your help again across the years hi it's Matt Turk again thanks for listening to this episode of the mad podcast if you enjoyed did, we'd be very grateful if you would consider subscribing if you haven't already or leaving a positive review or comment on whichever platform you're watching this or listening to this episode from.

1:05:41Eléonore Crespo:This really helps us build a podcast and get great guests. Thanks and see you at the next episode.