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

Today we live in a world that keeps accelerating. The sea level executives,

accelerating. The sea level executives, they have to act fast, faster than ever.

Maybe you're going to literally want to run 5,000 scenarios in parallel on an infinite quantity of data. That's just

impossible to do for anybody. These

agents work all together and you have to think about it as an extended team of whatever you have.

Welcome to the Mad Podcast. I'm Matt

Turk from Firstark. My guest today is Eleonor Crespo, physicist turn entrepreneur and CEO of Pigment, a company that has raised $400 million to

bring AI agents into the CFO's office and already powers dozens of customers like Enthropic, Figma, Snowflake, Uber and Coca-Cola. We talked about building

and Coca-Cola. We talked about building errorproof AI agents. We deal with very very sensitive data. We have launched first three agents, analyst, modeler and

planner. These agents really need to be

planner. These agents really need to be separated because we are still at the beginning of aic framework.

Why self-driving finance is closer than you think.

We are working already with some customers on an autonomous planning system. That's not far.

system. That's not far.

Building a global category definer from Europe.

My goal is to create a global company and it's about breaking boundaries between Europe and US and whether AI will finally kill Excel.

I have a prediction that Excel.

Stick around for this excellent conversation with a truly remarkable CEO.

Good to see you. Thanks for being here.

I'm super happy to be here with you, Matt.

You are building an extraordinary company that uh people may or may not have yet heard about called Pigment.

What's the simplest way of describing what Pigment does?

Yeah. So maybe I will start by setting up the scene of what's happening today in the world and I think that will explain clearly what Pigment does. So

today we live in a world that keeps accelerating because of AI but also because of macroeconomic events. So we

see in the world so many things happening inflation tariff wars etc. So like supply chain shortage and because of that the sea level executives they have to act fast on that they have to

act really really fast and faster than ever and if they don't have the right technology to help them take fast decisions it's very difficult for them to adapt to these fast macroeconomic conditions. So there is this element and

conditions. So there is this element and there is also the proactive element of this macroeconomic condition which is I want to potentially hire very fast because of AI. I want to potentially

invest in some products etc. And again you need to make fast decision and so pigment in a nutshell is what we call an AI enterprise performance management platform that helps you take better

faster decision based on the right data.

So we collect the data from all of your system so source system whether it's your CRM your ERP HIS etc. We put them in one platform and we help you model that data to help you take these great

decisions. So think about it very simply

decisions. So think about it very simply as what a GPS is compared to a compass.

The goal here is to say for instance if you want to go fast and you're on the highway, we will help you take the highway. If you actually want to be cost

highway. If you actually want to be cost conscious, we'll help you take another road that perhaps will waste less gas.

And and this is really what we are trying to do with our customer is to help them adapt very fast and take very fast decision to react to the world.

Fantastic. So I mentioned extraordinary company. So maybe give us some some

company. So maybe give us some some stats. U so you're growing very fast. Um

stats. U so you're growing very fast. Um

you've raised close to 400 million.

Yes.

In in venture capital. How how big is the company now in terms of number of employees or any stat you can share?

Sure. So today um we are 500 employees at Pigment. uh we are still growing uh

at Pigment. uh we are still growing uh 2x uh we are so we've been growing very fast since the beginning there is real demand because of of everything that I just explained uh we raised 400 million

thanks to you Matt you were one of our first investors and uh and I was so happy actually to to to get to know you now almost six years ago um we have 60%

of our revenue in the US um we serve uh more and more fortune 500 customers because our platform is really for large companies so um we have really expanding there And we are growing forex the

number of large customers that we have at pigment. And so you know that's just

at pigment. And so you know that's just the beginning of the story but very exciting for for the future.

Yes. And maybe some uh customer names again like for general situational awareness.

Sure. So we serve different type of customers. So we serve large tech

customers. So we serve large tech companies in the likes of service now Uber um and snowflake etc. And we also serve uh companies AI companies. So many

companies such as entropic for instance.

And we serve companies then that are uh traditionally literally any single uh industry. So we have retail companies,

industry. So we have retail companies, we have financial services, we serve Coca-Cola, we serve Uni Lever, uh we serve uh some of the largest uh payment providers such as Aden for instance etc.

we really serve a very uh wide um range of customers and and really I think um pigment starts to be useful when you start to be you know approximately a thousand employees and so this is really

our uh what we try to tackle and I seem to remember one of your earliest customers was Figma indeed yes so we are powering a lot of usapos and I hope a lot more this year we are powering cla we are powering

Figma we are powering sh and many other so um you know Figma has been an amazing one of our very first customers And I think uh what what I love about the Figma team is that they have really

started to use Figma to the full extent from finance to sales to HR and I think and I hope it's been quite helpful for them to prepare this incredible event.

So tell us about yourself. What was your founder journey uh to yeah that led you to here?

Yes. So um I I'm an engineer uh by background actually. So um I I studied

background actually. So um I I studied engineering. I studied fundamental

engineering. I studied fundamental physics. Um, and I think I had always

physics. Um, and I think I had always had a passion for creating and understanding the world and and trying to see, you know, um, how could I have myself an impact on the world. So trying

to really learn as much as possible. Um,

and I also always had a passion for freedom, I would say, and and and very large impact. And so when I did my

large impact. And so when I did my engineering studies, I actually studied entrepreneurship in parallel. Um,

because I knew that at some point I wanted to create a company. And so fast forward, I spent most of my career abroad. uh came back to Paris for

abroad. uh came back to Paris for pigment and I actually discovered the work of enterprise performance management during my time at Google uh when I was working as a data scientist

for the CFO of Google and the CFO of Alphabet and uh I discovered how much enterprise performance management could be difficult when it's managed on spreadsheet and that's how we started uh

I did index ventures after that and I saw I would say the other side of like founders uh struggling uh with everything uh with everything uh planning related, performance management

related and that triggered uh my uh my uh my my real like the the the willingness to actually start start the company.

Yeah. What was the journey from uh index venture? So famous venture capital firm

venture? So famous venture capital firm to being a founder and in what way did that help you or not help you to become a founder?

Index was fundamental in everything I learned. uh you have to understand that

learned. uh you have to understand that you know when when you do engineering studies and uh and uh you you don't really understand what what a great business look like and what it takes and that was for me the reason why I joined

Google actually was to learn amazing management practice and actually at index I was the luckiest person on earth because I was literally every day talking to the best founders on the planet we're talking about Figma Figma

was one of them uh oi the founder of data dog that is still a mentor today was one of them and many other companies at index power um uh index has been an incredible experience for me to

understand every single business model and it also helped me understand what it took them to get there and how difficult it was in the journey but at the same time it triggered uh the fact that I

understood that I could do it too if I wanted to I could at least try and I I I would see their past from literary C to series A to you know building post IPO companies that were thriving and for me

that was just phenomenal so I've been incredibly lucky and grateful to be part of that adventure.

And when you were in in school studying quantum physics, did you did you consider doing this as a career or was it always clear that you'd be doing something else?

Absolutely. Actually I I thought first uh that I was going to do probably a research career. Um and at the time um

research career. Um and at the time um so I think he so I studied that in France and at the time we had one of the best I think master of uh of um machine learning in France called MVA and I

actually wanted to take that role but then I realized that um the problem with research especially fundamental research is that the time lines are very very long um and usually takes you you know

more than 10 15 years to start seeing the results and the problem is that I think I don't have the patience I have too much energy to wait 10 15 years to actually get to see the results. I

actually think that maybe today there is never better time to do a PhD because I actually think that now probably in two years you can actually actually achieve things that you would have done before in 15 years. So that's quite exciting

actually. It's a good time to be in

actually. It's a good time to be in fundamental research meaning with with AI or what what what accelerates the timeline of a PhD.

For sure for sure it's AI. Uh definitely

I think you know AI will trigger so many ways to discover great things you know whether it's in biology in physics anywhere else. It's like you're going to

anywhere else. It's like you're going to be able to have like the fastest feedback loop to discover a new process, a new protein, a new way to do something. And that's so exciting. I'm

something. And that's so exciting. I'm

pretty sure today like I I would imagine, you know, you know, I was thinking about something is that one of the reason I actually went to Google I think was because of Deisa was very early stage of what Deep Mind is today.

But I was so impressed by everything they were doing and I think that really triggers my my willingness to join Google. But the fact is um I do think

Google. But the fact is um I do think that when you hear him talking today for instance about the power of AI and on what potential AGI will bring to the world. I do think that every timeless is

world. I do think that every timeless is going to decrease and perhaps we're going to go back to a time where you know Nobel Prize will be won by people that are like 25 years old like Einstein or whatever just because you are able

now in a PhD to discover the unknown and that's so fascinating and amazing.

Like I wonder if the bar is going to just like keep raising and like people will still have to do four years because that's sort of the way you're supposed to do it. you'll just be expected to produce something even more mind-blowing.

It's possible, but I think the brilliant mind will probably uh be able just to accelerate their thinking and uh and actually push through perhaps like you know 10 ideas instead of one. And that's

incredible. That's just incredible.

Except if AI becomes an autonomous scientific discoverer uh which it seems to be on the path of doing in which case maybe there will not be any PhD. It's for sure going to

become discovering like many many new things in science in every domain. But I

still think that at least for the foreseeable future, you will still need some some sort of human supervision to guide them to oversight them to you know give them a framework into what what

they should be looking and and you know I think um today um AI has been incredible at knowing what they know and and you know fetching the web for whatever is known. But um we still need

um AI to prove how much they can help with the unknown. And I think that's going to be the difference in the world too is people that are able to push AI to do the unknown. And I I I I don't see

it coming completely naturally. I'm sure

that over time it will, but I think there are some some incredible years right now to actually push AI to to to start working on the unknown. You

mentioned uh impatience which uh I I love as a term u you know I obviously as a as as a VC but a lot of people that listen to this podcast and other

podcasts are fascinated by the the founder persona you know what makes great founders. Uh so what what kind of

great founders. Uh so what what kind of kid were you growing up? It sounds like you're a combination of like being deeply thoughtful but impatient like I what kind of kid were you?

I don't know. You should ask my mom.

Uh on the next episode of the Mad Podcast, Elenor's mom. Yes,

Elenor's mom. Yes, that's a concept.

It's a great idea. This

I think you would learn a lot more.

This uh this is the birth of a spin-off of this podcast happening live right here.

Indeed. Indeed, cuz you will get the secrets of, you know, I call my mom when I have an issue. She's the only one that knows some of the things I'm worried about. So

about. So anyway, so I don't know. I think uh I was just as I said like super curious. too much

energy probably I needed to put it somewhere and uh and um just I love to learn so I've always tried to learn and I think also quite competitive to be honest I think I was always very

competitive and uh um in a way that might different from like a an athlete you know but like I think I always wanted to just push myself the hardest and still today I think that's that's

what I need to do as a as a CEO I think you know it's a trade that you keep having and I think it's what we try also to hire for at pigment like It's for me the the the the people that uh can be

successful in a fast-paced environment.

They are learning all the time. They are

trying to coach themselves to be better.

They're never satisfied with what they've done. They are just trying to go

they've done. They are just trying to go faster to you know execute things at the moment. And I think that's uh that's

moment. And I think that's uh that's really uh yeah that's that's that's really the only way to to to to make progress I would say in a fast growth environment. And it's clearly not for

environment. And it's clearly not for everybody because it's really hard every day. Do do you now also select people

day. Do do you now also select people based on sort of AI fluency? Is that is that a thing at Pikmin? Obviously, there

was, you know, the Shopify memo and uh various other founders that started talking about this. Is that something that um is now part of your criteria?

Indeed, for sure. I mean um in in the interview process whether we are hiring for an executive or whether we are hiring for a seller whether we are hiring for an engineer um if they do not have the curioity and I haven't do do

not have like a sful idea around uh what how AI is changing their job today how AI is revolutionizing the world of what we do today etc. uh for me that they're not going to be very happy. They not

because we're going to keep pushing them every day. And I I do think also that

every day. And I I do think also that you could you can still find it depends what you call AI fluency because you can still find people that perhaps in their company were not exposed to AI as much

as you will be at pigment both from an internal process standpoint and also product standpoint. But then what what

product standpoint. But then what what you need to look for is people that are naturally curious and if you ask them in the case study to work on everything AI and if they don't come back with a good answer then you know that they are not

able to act very fast and learn. Um but

clearly I think uh it's not going to work um and as everything is accelerating it's not going to work if you if you are not able to adapt to the technology. Yeah, actually while we're

technology. Yeah, actually while we're on the topic, um presumably u you guys are heavy users of um AI productivity tools like coding and other things. Uh

maybe one or two thoughts on on on that.

What what do you use and what do you find helpful, not helpful?

Yes, so we try to push it. So we have an internal AI committee. So we really try to push it across teams and we actually have a committee to make sure we know what we are doing within the company because we could end up buying 10 times

the same tools and you know every team trained. Exactly. Yeah. So we try to put

trained. Exactly. Yeah. So we try to put guard rails and also as you can imagine with pigment we uh handle very um important data and strategic data for our customers. So we need to make sure

our customers. So we need to make sure that it's okay to use an AI tool and and bite kind of bottom up when you writing a blog post but it's not okay to use on pigment data or on a on a customer call

for instance. So we we have this

for instance. So we we have this incredible committee now and so I think you know we really use it AC across every single team. So obviously from code generation in uh in engineering to

everything in marketing like generating content, generating SEO, helping us with literally every team in marketing. Um we

have built also our internal AI tools with the growth team that we have internally to help actually feed the right leads to the right seller at the same time know exactly when someone might be ready to buy.

Build that internally versus working with a vendor. Why? So we we and what we've built internally, don't get me wrong, is a combination of internal API and also like connecting with some

vendors, but we could not find exactly a vendor that was doing exactly what we wanted. So we decided to build it

wanted. So we decided to build it internally and really fit with our own process.

Um we obviously use you know um um call recordings technology extensively that's so helpful to coach everybody that's really for me like such a big big big game changer. uh we use it obviously

game changer. uh we use it obviously everything note takingaking now I I I think we it's it's not acceptable anymore at pigment if you you leave a meeting and you don't know what what's

been talking about that's not acceptable and so in every team we we are trying some technology obviously in legal etc I think there is really power everywhere and guess what pigment everywhere as

well is helping a lot on everything data so it's on unstructured data we use a lot of technologies across teams and for structured data a lot of it is on pigment so all right so for for the the core of

this conversation, I'd love to talk about uh AI agents. So, from two perspectives, so first from you guys perspective as builders of of AI agents,

what you've built, what you've learned, and then from your customers perspective, what you've seen work, not work. Obviously, AI agents is like the sort of the hot topic of the

last 12 months. Uh but I think it's very hard for people to parse what's working, what's not working, what's reality, what's hype. So I'd love to get into

what's hype. So I'd love to get into some uh some some details there. So

maybe to set the stage starting with what Pigman is building. So you've

you've launched three agents. So talk

about what those are and what they do.

Yeah, first three agents. So maybe also just to go back to a bit of context around what we were saying because that will I think explain a bit the philosophy behind these agents. I think

with Roma, my co-founder, we were trying to solve for two things. The first thing was what I said earlier which is around acceleration of the world. Uh companies

needed to act very quickly and needing to think about how to improve their margin very quickly, how to adapt their plan very quickly, how to reorgarch very quickly, how to do all of these things that normally take a lot of time. So

that's on one side. On the other side, I think you have everybody that works that do not necessarily love their work. They

love what is it about their work. So

they love ownership, they love impact, they love autonomy, they love collaboration, but do they really love uh gathering data? Do they really love

like cleaning data? Do they really love like reconciliating data from one source system to another? Do they really love doing budget variance analysis every month? Do they really love thinking

month? Do they really love thinking every month about reorging their territory and quota if you're in RevOps?

Do they really love like matching supply and demand when you are in supply chain?

No, what they love is finding insights as soon as possible to actually trigger action. And the most important is what

action. And the most important is what it is is it's like find time to decide to be smart to look smart in front of your CEO to have the right answer to feel very secure about that answer to

collaborate with other stakeholders to actually make that answer stronger and also on top of that obviously to take actions and most of the time the problem uh with what we do is that you have a

lot of finance teams you have a lot of HR teams revops team etc that were working in rigid very complex tools they were working also on Excel and they were

not at all able to do what I said. And

so if you use for instance like legacy tools that you know are very rigid and complex, you were working really on trying to um I would say get accurate data but you were not able to go at

speed. You were not able to go fast. If

speed. You were not able to go fast. If

you were working in Excel it was a bit the contrary. And so that's a huge huge

the contrary. And so that's a huge huge problem for any team out there because obviously that triggers the first problem that I described which is if you spend your time then collecting data,

gathering data, how smart are you in front of your CEO? How are you going to really improve the company trajectory imagine your plan is falling short and uh you don't have the aid that you were waiting for like what do you do like how

can you act fast on that? There is no way. So you have to understand that the

way. So you have to understand that the concepts of our agent is to help literally on these two topics is to help company be better, thrive more, be more

competitive, be able to react faster and to really improve their trae trajectory on the fly. 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 a philosophy.

Is that clear? I guess. So maybe I can go into the now the agents because you were asking me. So we have launched uh 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 an extended team of whatever you have right so imagine you are in finance and you have the analyst, you have the model and you

have the planner. So the analyst is here to actually work on the why. So he's

here to really work on the why. uh

explain the data uh 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 that's a problem and it's it's really why you really need an agile platform like pigment is 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. at scale on pigment very very fast and

adapt them. And the planner is actually

adapt them. And the planner is actually here to help you um um understand different scenarios. So it's really

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

do that. So it's going to help you work on the levels of your scenarios and really help you do if you want to do 46k forecast, you will be able to do that.

So think about it really as an extended team uh 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? Is that just easier for people to wrap their minds around or is that more of a technical constraint? So I think um today these

constraint? So I think um today these agents really need to be separated because we are still at the beginning of of the 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 on with machine learning with forecasting and obviously uh

handling very complex tasks to to to to actually carry on multiple forecast at the same time etc and then the modeler is really here to work on your models on your formula think about it as cursor or

other companies that are there augment or whatever for finance team for HR team for analysts so it's very different task but over time what we want um and what the 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 the platform and the user would just you know give the agent um a set of comments and and and the

supervisor will then decide okay uh that is going to be done by uh the analyst and then we're going to pass that task to the the modeler and then to the to the planner and so the the goal over

time is that there is only one agent but before that I think we're going to launch uh tens of agents that are going to do different things we want to launch a consulting agent for instance that is going to help uh during the implementation of pigment

So this is really a set of agents and really the goal for us is to have uh these agents do two things. One and I think it's going to be the same for every agent is automate uh some

repetitive task with low added value but also work on high complex task that no human would have been able to do. So

really enable I would say the impossible uh because these agents are more powerful obviously than any human being.

So all right amazing. All right. Uh so much to unpack here. So one um that you alluded to is the coordination. So if

you have three agents and in 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?

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 uh we are going in the right direction and put the right gall around the framework that we're giving to the agents. So maybe I can give an example that might be easier to understand. So um if you think about

to understand. So um if you think about these agents working together so uh if you take if you take for instance an analyst so a financial analyst let's say like a Tuesday morning 9:00 a.m. and

they have today to run an analysis for uh their CFO. Um I'm inventing that on the go. So we'll see if that goes

the go. So we'll see if that goes somewhere. But imagine they have to do

somewhere. But imagine they have to do that. So what you will have uh is they

that. So what you will have uh is they will give a prompt to the agents and and and first that will trigger the analyst agent of understanding. So let's say for instance they they want to do budget variance analysis. We were talking about

variance analysis. We were talking about that before. That will trigger a first

that before. That will trigger a first action which is understanding budget varian analysis perhaps across 50 products 20 countries. 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 um 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 this the these levers of variances then needs to be um

uh needs to trigger what I would say a new scenario of data to say okay actually if I improve that lever that's a scenario I suggest you take to actually now be back on budget for instance and so in order to do that

probably the planner agent is going to call the model agent uh via the supervisor and 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 uh we do not have uh we have variances here there and there and this is what I suggest you do 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 um 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 uh 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.

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 and that the handoff to the human is like, okay, here's this set of options.

Here's my recommendation. You do your human review and do the next step.

Yes. So maybe to to take a step back um because we we are in a very different world than any agent working on unstructured data. We work on structured

unstructured data. We work on structured data and we cannot afford mistakes.

So the way we are actually using today generative AI um in our agent um is very different from from a lot of companies out there because the way we we do it is

that these agents actually interact with the pigment platform. So every

calculation is made on the pigment platform. Everything we do really like

platform. Everything we do really like all the calculation the OD trade etc is done on the pigment platform. So that

makes a big big difference actually because it means that um we do not uh look I would say at uh at audits and at at at you know like understanding really the guard rate etc the same way as an

unstructured uh 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

100% accurate. So the most important is clarity more than uh more than really like uh you know getting to an answer as fast as possible for instance. M so you do not have a hallucination problem

because you cannot afford one. So you

constrain the agent uh in a way uh just to play back what you said that just uh where you do get to 100%.

Yeah. So the goal is to constrain really the agent to u and and it's really in the way. So for instance if today work

the way. So for instance if today work with the analyst agent um the analyst agent is really going to go pick the data directly within the framework the labeling framework we have the metadata

that we have uh within pigment and it's going to ask you question until it make sure it really understands exactly like a junior analyst would do for instance until it really understand what data to pick from. Yeah.

pick from. Yeah.

And then it will do it will trigger some calculation but every calculation is done on pigment and then back to the human. And then today what we will

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 you know forget about the conversant part in the middle. 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 you on pigment you have obviously very very deep audit trails

you can you know you have like diagrams of data where you see all the dependencies from one data point to another how it was calculated etc. So it's actually very easy for humans to verify that. We also have our agents

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 those verifying agents are behind the scenes.

Yes. Exactly. So you you carry on an analysis and then uh the agents uh runs a series of tests to check uh if they think the analysis is correct or not before rendering the data to to any

human being.

It sounds like UIUX is a very important part of how it all works. So um just for for people to understand and visualize what's happening you you expose all the

steps and each time you have the human in the loop validate do you do you expose a score of I'm I'm you know x% confident that this is 100% accurate. So

yeah, so the way it works today is um really every time you carry on the calculation whether you do it with our agents or without you can see the entire OD trail and you have to even understand that pigment sometimes with some of our

customers. We we power a lot of public

customers. We we power a lot of public companies is used as a governance platform. So we log everything and our

platform. So we log everything and our agents log everything. So they log even more than humans in a way because I 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 uh

information. So that's really how uh pigment works and then obviously we carry accuracy test along the way and we 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 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 it's it's a it's a it's it's it's really what we try to do is to and and this is the feedback we're getting from our customers by the way is that we are more accurate that when they were doing before.

So you've sort of passed the touring test of AI agent uh performance already.

your your I don't know I don't know if I if I would call it that this way but I would say that at least we we at least I would say 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 and um 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 a another uh as if they had hired like you know a bunch of of analysts in their team that is doing the work for them. So

yeah, I'm asking because it's a truly fascinating concept, right? Because I I think we all understand and sort of got used to the idea already in the last 12

24 months that um 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. And that seems to be on

systematically. And that seems to be on the verge of happening. talking to a bunch of people. Uh it's not quite benchmarked but like we seem to be um sort of on the cusp of this happening or

maybe it's already happened but like from the conversation I'm having like 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% and you know 20% 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 uh customer service agent all the time

which is fascinating and scary at the same time.

Yeah. Yeah. So I would say um uh for us uh obviously you know it's still the beginning of the revolution so there is still so much to build and and discover in terms of you know when for instance

we're at the very beginning of the planer agent so we'll see exactly how uh how that brings us to the next level of of accuracy etc. But I would say clearly there are ways with technology uh

combining technology to actually make that happen and I think more so also what you said is very interesting um beyond accuracy it's also enabling the impossible and I think that concept is phenomenal.

Yeah, let's double click on that. You

mentioned automating some mundane task and then like doing things that humans cannot cannot do. What is that?

There are so many things that human cannot do today and there are so many tasks that would literally take a lifetime to carry on. Um so you know if you really want to do like a think about

like again um uh any large company out there have a combination of of products and 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. more if you really want to let's say um improve your plan, you want to improve your margin and you want to

actually understand all the potential levels 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. That's

just impossible to do for anybody. And

so, um there is that and there is also uh as I was saying earlier, sometimes the problem is just people are time constraints. So perhaps yeah maybe in

constraints. So perhaps yeah maybe in three years they could get to a result but they just don't have to that time.

So I think this is what is fascinating and these are just like the first example that we see today with with with our customers and how they 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. What I described for

possible before. What I described for instance about Coca-Cola they were just not able to do that before. they were

just not able uh because uh there it's just too complex and so I think um that's uh that's that's really what I love is this ability to carry highly

complex task do the mix of everything you need to do when you're an analyst you know from uh looking at the past understanding the past understanding the present getting input from everybody at

at at very very fast pace um uh going to think about the future and thinking about all the universe of possibilities that was just not possible before you know there's a long um legacy in the

history of um SAS companies in the finance space uh that basically said uh we're going to kill Excel, we're going to kill Excel and then you know the irony is like fast forward to today

Excel is still very much around. Are we

at that point though now? Uh is AI actually replacing Excel?

I have a prediction that Excel will still be here in five years.

Yeah. And there is a good reason taxes and Excel are the three things that are serving 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 uh in uh in in several years and I al also think that in Excel there are some very

uh interesting concepts that yes will be replaced by AI and generative AI etc but there is still the 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 not it's not just the agent it is first of all the underlying

technology the power of the platform the power of the the calculation engine but also the UX on top we were talking about it earlier um 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 uh way to actually input data for anybody in a So I think there will still be some sort of excel out there but I do think that uh finally humans

will see that there are very very easy way to get access to to to to the technology that uh 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 for systems. So we all understand we need to walk before we run and build reliable agents and and all the things but like

suspending disbelief a little bit and fast forwarding. Uh do you think that we

fast forwarding. Uh do you think that we land in a in a world where enterprises are effectively automated? Can you

imagine a world where uh 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. So hire x many people in Switzerland uh and launch a product in the US kind of kind of thing.

Yes. So actually uh we are working already with some customers on an autonomous planning system and we are working right now very seriously with I 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 uh here in six months but it's not very far

and I think um um they are going to work on um uh topics around uh self planning uh doing um um automated segmentations uh assigning territories kota directly

automating uh automating that obviously for any seller out there understanding also how that links to finance etc and that's our goal it's automutonomous

planning because the problem is that in planning there there is still so much wishful thinking that this is what makes company fail most of the time so you you were talking about hiring so there are so many companies I'm sure in your

portfolio they do planning and and they'll tell you that uh um they are going to be able to hire uh 200 people in the next six months and it's going to be easy and then I can tell you that you

put any um uh any agent from pigment looking at the data and on and on and and running a couple of analysis will tell you no way you don't have the TA capacity you haven't been thinking about like 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 um uh uh you you I would say yeah wishful

human uh human thinking in there and this is really where um 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 uh 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 in real time and probably faster than anybody else.

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

Yeah I I I mean for me that that's really a dream we are we are trying to build and I I think it's going to be totally game changanger for any company out there.

Yeah. And uh because you work on centralized planning like you you're very much building the sort of operating system nervous system of of any company right so you you're ideally positioned to do that

yeah I think uh 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 so every time you want to change something you can change it super super fast and with a moderate That means really making your models

evolve. So um you have imagine you have

evolve. So um 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 that's um I think uh as we were discussing earlier the 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 issue but I I do think that uh the companies that will do that they will thrive. they will be the best companies out there uh in the very near future.

Let's talk about adoption. Uh that's um as we alluded to at the beginning, that's a a topic I definitely want to touch on. Uh just um going back to

touch on. Uh just um going back to today's reality about how that all works. Uh do the agents work on top of

works. Uh do the agents work on top of LLMs like the open eyes and anthropics of the world like whatever you can talk about there.

Yeah. So we partner with OpenAI which has been our our biggest partner since the beginning. the reasoning models or

the beginning. the reasoning models or the general models.

Yeah. So, so actually it's a mix of that and also our proprietary technology and then we also work with entropic, we work with Gemini, um we work with Mistral and

so we we over time we want to re offer anything and also we have many customers I'm taking the example of service now that is obviously pushing very hard on agent that want to use their own agents

for instance. So this is also something

for instance. So this is also something we want to be able to do. So um for us the goal would be really to be as agnostic as possible and uh and offer various meaning to collaborate with service now agents.

Yes. Exactly.

Yeah. And and collaborate and also use their own LLMs if they want to do something specific. I also think that so

something specific. I also think that so that's 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 where all of our partners whether it's

technology partner or implementation partner can build their own agent that can plug into pigment that's that's the other fascinating part we're just talking about like this uh

kind of um uh you know very real time automated enterprise of course the next question is how automated real time autonomous enterp rises collaborate with

one another. In this case, like all of

one another. In this case, like all of us humans are just like at the beach uh hopefully reaping the financial rewards of whatever the enterprises do. But

yeah, that's that concept of a cross company um collaboration. It sounds like you're

um collaboration. It sounds like you're already there then in terms of uh at least technically being open.

Yeah, we we are open to it. We are not there clearly, but uh we want to be there. we want to be there and I think

there. we want to be there and I think uh I think it's um it's going to be critical because uh we are I think the best technology companies are all developing their own proprietary

technology and uh for us as we are this single source of truth we really need to integrate with what they need if we want them to be very powerful. So

is that a token intensive uh business you're in? One of the key themes of the

you're in? One of the key themes of the day is you know gross margins and cursor. Do you have calculations of

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. So it's

more like you use the LLM to actually question the the platform but then the platform does uh the calculation uh itself.

Right. So it's a translation layer which is the token part. Okay.

Yeah. Maybe maybe as a last question on on on agents are there areas where you do not want to build agents where you think the uh aentic and 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 you you'd better off just use a dashboard in pigment and look at your data. Um, but I I do think that

your data. Um, but I I do think that over time you really want to carry any sort of adoc analysis on pigment and uh and that you know the cost will be

really uh really ridiculous compared to the value you're going to get. So um

that's really what what we would be aiming for especially as we go towards that vision of autonomous planning that helps you take decision. So,

so I would say the more you can log actually on the platform, the more information the the platform will have, the better it will be at at understanding who you are as a company

because again like you see that all the time a lot of planning discussions a lot of what's happening has to be logged in the platform. So I would say the more

the platform. So I would say the more analytics you will do with the agents the better it will be trained and the the more relevant it will be over time.

So yeah. So this um implied in in a couple

yeah. So this um implied in in a couple of things you you said including now this so there is a a concept of constant feedback loop. Yes. Where the system

feedback loop. Yes. Where the system gets better. Can I can I uh sort of

gets better. Can I can I uh sort of declaratively uh input what my policies are you know at Coca-Cola at company XYZ. This is how

we do planning. This is how we look at data or does a system uh has to learn?

Yes.

I would say it's two things. So I would think about it when you start as a as if you were going to train a new analyst in your that has just starting in your team. So you need to train them on the

team. So you need to train them on the concept and on your constraints as well.

So I think it it it can be it's not as powerful as what you describe. I think

it it could go there. It's not there yet. I think today um it's more able to

yet. I think today um 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 person 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

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. 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 and explain to and and to him or her I don't know to eat a couple

of things. I would say to um um uh you

of things. I would say to um um uh you know that in French is because everything is a yes default to masculine is that 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

use that. So believe them. believe them. Yes,

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 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 but I think that will go as powerful as what you described over time.

All right. So all of this is the perspective of of pigment as a builder of um Gentic AI. Now let's switch to uh

the customer side. Uh so what what's the vibe or the mood currently? Like do do people people are certainly excited about all of this but uh how do they

react when you start talking about are we going to have a planner agent and the analyst agent? So I think I would

analyst agent? So I think I would probably distinguish uh uh two two types of uh companies slash customers or prospect is 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 otherwise uh uh you know they would not have adopted uh pigment.

So um I would I would differentiate that from like some entrepreneurs that are further away in their journey to adopting technology where uh you have to start from a very different point of view.

So I would say clearly today the analyst is used amongst our customer and the excitement is incredible because they really see already that not only it help 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 uh last week just to it's a funny example but um the supercell founder uh who is

still the CEO of the company and uh so the company behind the clash of clans and bro stars um and they they 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 you love pigment and how much it's changed their life and I think you know what they see is really like today they are able to automate

some uh revenue analysis PN analysis they are able to really have uh some work that would take them so so so long before not even possible to do because they have quite a complex business now they do it with pigment and we have uh

dozens of examples um of that whether it's like some very traditional company so you know like one of the largest transportation company 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 uh but it's different from prospects and prospects today I think we have to

reassure them more uh because uh they really need to understand the implication of that what what that will mean how we're going to help them train their teams on it how it's going to really uh involve and and change the

workflow that they are using etc so we really differentiate the two are Are you seeing the drive towards AI be more top down or bottoms up?

I think today we see it uh every CEO is pushing top down. I want AI everywhere and and and find a way to make that happen and it was a funny cartoon that made the

rants on the I saw it on Twitter today.

Who are we CEOs? Exactly.

What do we want AI?

I don't know. Yeah. 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.

So that's what I was thinking about when I was telling you that's like very clearly top down. Um and I I think I'm doing the same. So um yeah um anyway I I

I do think um uh there is a lot of top down but we also see I mean um open AI and the other are clearly gaining the heart of the consumer business too. 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 core 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 um and

AI. So I do think there is a need um and there is a paradigm here of like um helping our companies adopt AI in the right way. So really helping them with

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. And I think um all of

the entrepreise AI companies out there uh have probably uh still a long way to go to make that happen very well. To

give you an example, at Pigment right now, uh we are doing like what several AI companies are doing out there, which is hiring forward deployed engineers and people uh to really help

um with change management and deploying the models in the right way and I uh we are also investing massively actually in customer education. We have a new

customer education. We have a new customer education manager that joined us a months ago to focus solely on that pretty much.

Mhm.

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 and and and make sure that uh that everybody still finds joy in what

they do. What do you tell customers

they do. What do you tell customers uh in terms of reskilling?

Yes.

Both for uh 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.

Uh so um we we have everyone that can really have a team working for them uh 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

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 guard rail

and understand the data and then obviously how to take a decision from there. So it's just it it becomes a

there. So it's just it 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 we've

we've had on the podcast several great conversations with people in the context of AI coding and um 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 uh deal with the output of the model or is that the wrong way to

think about it in in the finance and planning world like do do people how how does one become a a CFO or VP of finance in a context where the the models do so much.

Well, so first of all, I think if you're a software engineer today, I mean and if you if you 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 etc. But but I mean the fundamentals of what coding is today you are not able to use cursor um if if you don't understand coding and I know

cursor will evolve also towards uh probably a more advanced version of lovable or whatever but like it's still you need to understand the fundamentals in order to build like an app to build a

complex model. Um and so for finance I

complex model. Um and so for finance I would say almost it's even worse. you

are in a heavy regulated industry where you cannot afford mistakes where you report your results to the street where you 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 if you don't understand these things you are it's going to be very very hard for you to know if these agents are going in the right direction and and also it will

be you will probably use more of your finance skills because instead of of going back to trying to uh collect the data uh 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 insight that you might not have found before. So

more than ever you need to be highly highly skilled to to to to to be able to work there and combine it of course with also some somehow I wouldn't say too complex technical skill but still

understand a little bit what the technology is going to do because uh you need to understand at least the fundamental concepts of how this all works if you want to make it very powerful. M

powerful. M so that's that's how I see today and we'll see maybe over time that will evolve maybe some 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 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 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 another story I think. Maybe let's uh talk about company

think. Maybe let's uh talk about company building uh for a few minutes and what you learned like one of the fascinating things about pigment other than what we

just discussed is uh the fact that you're building a highly successful global company from inception but you're doing it from Europe which uh is a

little bit of a narrative violation as they say on Twitter. uh and I'm I'm curious about uh how that all came about and how you've been able to do it in in

a context where you know unlike uh what uh all VCs would ask their European founders to do a few years ago you never moved here uh to to the US. So like tell

us tell us how you pull that off.

Yes. So uh first of all I think uh Europe uh uh is not a shame today. There

are so many incredible AI companies built from Europe. you think 11 Labs, you think Lovable, you think Legora, N8 and so many others. So I think first of all Europe is is doing extremely well

today with founders based in Europe. Now

we created the company during COVID and it made everything possible. Uh 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's that's uh that that that made our that that really

possible from Europe. I think that would not have been possible uh to be honest before covid but now uh 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

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.

market. It's 60% of our revenue today.

It's a it's a growing the market that we want to grow the fastest and it's always the same. It's like what do you have to

the same. It's like what do you have to do around you to make sure that even if you are in Europe because our R&D is in Europe uh you it's as if you were a US

company. So I made sure that day one

company. So I made sure that day one from the very beginning with Roma we went after US investors. You were

obviously part of it. We've been

building a full equity story in the US and our investors have been helpful since the very beginning. You've been

super super helpful uh to build our executive team to build our our our customer base to find us our very first customers in the US etc. 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 uh be in the US. I think

my goal is to create a global company and it's about breaking boundaries between Europe and and and US and I don't think we should think in that concept. So at pigment my entire

concept. So at pigment my entire executive team pretty much is in the US and so it's very easy today to build this type of global businesses.

Yeah. any magic trick in how to run a senior team in the US when you're in Europe which uh you know that's Europe to US but like that question applies to

like anyone um you know um with a couple of offices uh even within the US like this what is it a lot of uh zooms a lot of uh inerson meetings how do you do it

yes so it's a mix so obviously lots of zoom you have uh you know I work very late hours you have to adapt and they work very early for us to because it's

impossible. Um we try when possible to

impossible. Um 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 on the west coast you know our CMO for instance is

on the west coast um and so we we really try to to build around that and I think we do a lot of zoom but it's also very important that you know we have quarterly catchups in person we also do

a lot of events in person with the company you know we have a obviously global SKO we have presence club we have our company offsite we many many moments to to to to to meet one another. We try

to do company trainings in person. Um

but it works super well. I have to say it works super well. And you know maybe it would have been a different stories with us in the US. I don't know. Um but

the you know just in September right now I'm here three times uh 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 some um big name partners from Google, but I'm almost most interested in the SIS. I believe Deoid and and 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. Um it's um so we

partner with Deoid, we partner with DY, 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

enterprise. It's the same here. So

boutique firms are the first thing to focus on I would say at the very beginning uh to make sure that you have a network that is well defined with clear guard rails around you cover that

go you cover that use case and the your friend over there competitor friend is not going to do the same because otherwise you guys going to get upset or whatever when you've done that you need to build start building in paral the gsi

motion and that takes a lot of time because you need credibility they need to see that you are serious about what you do and for them the The other day for instance I was uh with a deoid

partner and they were telling me for them their yearly so-called quota quota is 25 million and uh 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 gsis 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 to start feeding you with with with leads. 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

for you. At the beginning, you are going to be the only one to source lead for them. And it's really when they start

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 to to close the next few years. Like we talked about this amazing vision of the autonomous enterprise, which sounds like science fiction, but like it sounds like it's

coming pretty quickly. Um so yeah what what is success in like three years, five years? Uh where where do you want

five years? Uh where where do you want to be?

I think uh first of all um the so 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 um to 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. We have

a very very large ambition with Roma. We

want to build a you know hundred billion dollar 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 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 because with the use cases that we are looking today we are going to unlock

actually new ecosystem 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 uh 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 in 10 in 10 years uh being able to start perhaps being really more like an SAP and an Oracle and having a really fullyfledged suite

of products that can help you across the board but the difference is with a lot of user satisfaction hopefully Amazing.

Well, that um has been a wonderful conversation. Thank you so much for uh

conversation. Thank you so much for uh sharing all of that with us and spending time and uh you know, very excited about what you've been building and uh 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

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