Coinbase's ex-CPO bet on AI agents before ChatGPT—now he's closing 7-figure Fortune 500 deals. | Surojit Chatterjee, Founder of Ema

Surojit Chatterjee (00:00:00):
Every enterprise is unique. I'll give you an analogy. Back in the day, if people needed to get a trouser or a shirt, you'll go to a tailor. You couldn't go and buy a shirt. They'll build, actually create a good shirt for you because it's up to your measurement and fit. It'll take a long time and cost a lot of money. The ready made shirts, of course, you know, it doesn't quite fit exactly but it's cheaper and faster. So you're like, OK, we'll ignore. I think ready-made software today, like SaaS software, is like that. Enterprise sales process is long, takes nine months for very large deals. A lot of our deals are large seven figure deals per year, multi-year, et cetera. So, it's not like a consumer, I'll put my credit card, pay $10. Forget about it for six months. Enterprises are careful and takes a long time to go through their entire process. Top piece of advice is, look, you have product market fit when your average salesperson can go and sell your product without you being in the room.

Previous Guests (00:01:02):
That's product market fit. Product market fit. Product market fit. I called it the product market fit question. Product market fit. Product market fit. Product market fit. Product market fit. I mean, the name of the show is Product Market Fit.

Pablo Srugo (00:01:14):
Do you think the product market fit show, has product market fit? Because if you do, then there's something you just have to do. You have to take out your phone. You have to leave the show five stars. It lets us reach more founders and it lets us get better guests, thank you. Surojit, welcome to the show, man.

Surojit Chatterjee (00:01:30):
Excited to be here.

Pablo Srugo (00:01:31):
See, you've got a pretty crazy resume, VP at Google, you're a chief product officer at Coinbase, EIR at a16z, and now you've started Ema. Which is in the AI agent for enterprise world. We're going to get into all that. We're going to get into why, you know, why you started it, how you started it, how you got there. But because this is the Product Market Fit show, let me capture a moment in time. When would you say was the moment when you felt you'd found true product market fit?

Surojit Chatterjee (00:02:00):
For us, look, we are an enterprise software company, right? We are building AI employees for largest enterprises. Just to give a little bit of background, right? For a startup of our size, you know, starting out. You are trying to get to Fortune 500 and telling them you're going to touch their most sensitive data, like their employee data, their finance data. These are areas where we go really deep. That's a very difficult kind of proposition to start with. So for us, the breakthrough was really one of our clients, Hitachi, a very large conglomerate and they were trying to automate HR operations for five of their companies, and create a shared service across them. And they have around fifty five thousand employees across these companies. So it's a very complex setup. You are touching sensitive data. You are on multiple cloud platforms at the same time. The agents need to coordinate and cooperate. For me, when we could deploy and showcase that it actually works. It was really the moment I felt we have hit product market fit in terms of the scalability of the product, the governance, security, and the selling to other clients just became that much easier for us. Because it's credibility is everything in an enterprise context.

Pablo Srugo (00:03:20):
And was this when you signed the contract or once you fully deployed and you were seeing the outcome?

Surojit Chatterjee (00:03:26):
When we deployed and started seeing the outcome. I mean signing the contract is only the first step in many of the enterprise deployments. What's happening is a lot of enterprises are doing a lot of POCs and pilots. So this was not a pilot. We already did a pilot, did POC, we went through the whole process. Actual full deployments, touching production level data and by the way, now this is not our largest deployment at all. Now we have other clients that are even larger and in production. But that was, in my head, was really the beginning of the journey.

Pablo Srugo (00:03:59):
And maybe as a way, this could be a perfect example to use to really understand what it is that you do. What did they want to accomplish with Ema, with the product? How did it all work as a way for us to really get what it is that you do?

Surojit Chatterjee (00:04:10):
So look, we are an AI employee company. So we build AI employees. We have a platform that allows us to build AI employees. What are AI employees? AI employees are basically teams of agents. Now a lot of people are seeing this in the real world and from a lot of other companies as well. We started working on it probably before anybody else. Two and a half years back, right out of the gate, we had the same vision. What do these AI employees do? They basically connect to multiple systems. So they have a, think of a mesh of agents, like a team of agents. Each agent is skilled in a certain thing, and they're all coordinated using our own orchestrator, and control plane. These agents will touch or operate different tools in the enterprise. It could be your Workday, your Greenhouse, your ServiceNow, your Salesforce. We'll also connect to all kinds of data sources. The data doesn't need to be cleaned. It can be anywhere, SharePoint or Google Drive, Database. Wherever it is, Data Warehouse. These agents will understand the data, understand the policies, understand your process, reformat your process. Kind of reimagine what the process needs to be, create a kind of an agentic workflow, and automate what I call the human glue between systems today. Every enterprise today has hundreds of applications, SaaS applications, and thousands of people who are acting as glue between those applications. Moving data from one, pushing data somewhere else, creating a report, sending an Email. Think about what people do day to day in an enterprise. So we are creating that intelligence and action layer across these applications and all this data. Which basically frees up humans to do more value added work rather than doing the same similar mundane work every day.

Pablo Srugo (00:05:59):
So maybe, I mean that's high level right? But just to get to the specifics, what exactly did they want AI agents to do?

Surojit Chatterjee (00:06:07):
Look, simple things to very complex things, right? For simple things, will be, I am an employee in a in a country, right? So let's say I'll take you for example of another client, right? They have a quarter of a million employees, sixty five different country, large services company. Professional services company, WiPro, actually. Their employees are very mobile because they do projects with customers and so on. Very complex HR ops because employees are asking questions about, hey, what's my vacation policy? Oh, I was hired in Romania and now I am in Germany. What does my benefits look like, right? Can I go to a doctor? Usually all of those questions will go to HR and they will answer. So that questions are only one part of it. Which is in itself is complex, because there are, you know, sixty five different level laws and policies, and so on, right?

Pablo Srugo (00:06:59):
But that's more of a, while getting the data is complex. That's more like a chatbot type

Surojit Chatterjee (00:07:03):
Then a chatbot and the reactions, somebody says, "Hey, I need to move to another location."  That kicks off a whole workflow inside HR, right? Which is, OK, arranging their move, changing their tax form. Maybe changing their comp, changing their management. There is a whole workflow orchestration that HR has to do and we are working with another large company today. It's crazy, they have sixty five thousand job changes every month.

Pablo Srugo (00:07:30):
Wow.

Surojit Chatterjee (00:07:31):
Within the company, people either getting promoted, moving to another role, whatever it is, right? Moving to another location, all kinds of things. These are massive global companies and for each one of those sixty five thousand, you imagine there will be some humans involved helping and orchestrating that change, right? Can you automate that? And that automation requires it's not just a chatbot, right? Multi-step planning, orchestration, touching different systems, records, and helping the employee through that change. So anything from like, you know, book my travel. In a lot of companies, there's some team internally helping book travels, right? Or timesheet management. So consulting companies in particular, they have to manage their timesheet very critically because that's how they charge their customers, or book a cab even, right? Because some of these companies are running large operations team that are doing overnight shifts and then company provides some benefits. So you can take a company cab and go home or whatever, right? So everything and anything all through a simple interface and imagine that you don't need to go through any human or any tools. The biggest challenge today is proliferation of tools. So there are one hundred plus tools in this company doing all this for doing all these different things and there are a thousand plus humans that you can reach out to. You also don't know who to reach out to sometimes. You're like, "Oh, my laptop is not working." See, an employee ultimately, they don't know, is this falling into HR or IT or finance? My laptop is not working. I want to change my payroll election, right? Selections, you know, W-2 selection, whatever, right? Or my deductions or I want to change my benefits. Or a lot of things fall in the fringes of employees don't even know which department they need to go to, they need to talk to. Which application they need to touch and where they need to go. All of that is automated. For Wipro, for example, they report from one thousand people, they have been able to first, you know, reduce the time taken for any of these requests. These are not just questions, requests from five to seven days to like few seconds to minutes, right? So that's a big deal for the company overall and then from one thousand people going to five hundred fifty people at forty five percent reduction. So employees are happier, higher CSAT. At the same time you are doing the same work with much less number of people. Of course, HR is always overburdened. It's not like HR didn't have any work to do. They were probably doing only fifty percent HR and fifty percent paper pushing. So all the paper pushing is gone, or most of it is gone. So you can actually do a lot more real HR work and that's, it's not just HR, that's kind of true for every function.

Pablo Srugo (00:10:27):
That's what I was going to ask though. Are these, are your AI agents, like, are you vertically focused on HR or employee related matters?

Surojit Chatterjee (00:10:33):
So we are a horizontal platform that can create this AI employees to what we call agentically transform your business. We are reimagining working with these enterprises, how their future org structure will look like with AI employees and human employees kind of coming together. And it's not just replacing one human by one agent, it's reimagining the entire process. A lot of times it's a broken process. You do not replicate that with AI. We go deep, so it's horizontal, you can potentially automate anything. We go deep in HR, we go quite deep in finance ops, you know, AP optimization, source to pay, go to cash and we go deep in some of the customer support use cases. Which is also for very scaled companies, companies which has very large number of humans actually running customer support today. We kind of help them optimize their operations and moving off kind of mundane work off humans to AI employees.

Pablo Srugo (00:11:36):
So now that we understand kind of what it is that you do, maybe let's go back to the beginning. You started this in early 2023. Give us a little bit of context, what were you doing before and how did you have this idea? What's the origin story?

Surojit Chatterjee (00:11:48):
Sure, I spent my entire career working for high tech companies here in the Valley, right? So fourteen years at Google, I built and led mobile ads for that entire time. Mobile ads became almost $100B plus business for Google, now even bigger, I suppose. So I had seen this massive growth and then I was at Coinbase running all our product. I was chief product officer at Coinbase and helping Coinbase rapidly expand users, and revenue between 2020–2023, and help Coinbase go public in 2021. So my experience has been running businesses, building and scaling teams, and I've hired a lot of great people in my career over time. The one thing that always comes in the way is you hire all these talented people and then you realize, oh, fifty to sixty percent of their time is just keeping the lights on. Doing the same thing over and over again. I'll go and talk to my engineering VP or engineering counterparts at Google or Coinbase and they'll be like, yeah, don't look at 3000 engineers. We have only 1500, because the rest is just keeping the lights on, and it's true for every function, everywhere. And that's to me, this was the fundamental question. Can we hire like really talented people and make them even more effective. Give them work or put them in front of work that is really innovative, creative and not mundane work like filling out spreadsheets, go into this tool, do something. That's what a lot of work today is in the enterprise. A lot of updating things across the board, updating things for your managers, creating reports. That's probably eighty to ninety percent of the work today, which I think can be automated and humans are capable of doing a lot more and a lot more innovative, creative and value-added work.

Pablo Srugo (00:13:46):
And then how did you decide? That's kind of the high-level problem, which I, completely agree with and it's been a problem for a long time. Obviously, with AI, it all of a sudden became a possibility of solving a huge component of that. When do you realize that that's possible? And what are some of the early steps that you take to see that there's a real opportunity here?

Surojit Chatterjee (00:14:02):
Look, in late 2022, ChatGPT came out and there was a lot of talk about LLMs. I started playing with it, I was deciding to step out of Coinbase. That was early 2023, I started playing with it myself, right? And one thing in my head was, can we actually use this technology as a platform? As almost like an operating system, to create ability to dynamically plan, use tools, and use memory? I mean, those are the unique human capabilities. We use tools, we have memory, that means we learn from feedback from the environment, and we adapt automatically. Without that, this technology will not work because you will keep building the fixed software, which has been the root cause of this problem. Which is, you know, we have a lot of software, but they're fixed, right? So they don't change. You have to go and keep tweaking, keep doing things for them to kind of adapt to your business. So for me, the first step was I started tweaking, writing my code, some code myself, which I haven't done for a long time. To be honest, I was an executive running large businesses and so on. But my roots are, you know, I'm a builder as an engineer, computer science, bachelor's and master's. And then I started reading a lot of research papers at that time that came out in early 2023, on how can we use this LLM frameworks, LLM systems to start dynamically planning and taking actions. This was really the root of the whole agentic thinking. The word agent was not in vogue if you read the papers. Nobody was using that word. We called it AI employee because I really wanted to model it after how humans work. That was the genesis and got together with a few other, you know, ex-colleagues of mine from Google. My co-founder is also ex-Google, ten plus years there and we got very excited about this idea. Started building some rudimentary prototypes. We worked a lot with early days, some of the PhD students who were writing these papers, almost as volunteers. Some of them joined us back later.

Pablo Srugo (00:16:12):
Oh, you're such a selfish person. I actually can't believe how selfish you are, because you've been listening to the show, you listened to this episode, you loved it. You've listened to a bunch of other episodes and you haven't told anyone about it. You haven't told any of the many founder friends that you have about it. Think about how many founders have helped you out, when you're building your startup. So don't be selfish. Tell your friends about this episode. Tell them about the show and help me help them. With these big ideas, on the one hand, they're obvious in the sense that if you can say to someone, "Hey, if I could create AI employee that costs ten percent of what a normal employee does and works 24-7, would you buy it?" Yes, of course I would. Today, people are like, "Maybe you could do that." When you were starting, it was more far-fetched and so how do you go from that huge idea to something tangible? When you start prototyping, what is the first version that you build? What's the first thing you want to test out?

Surojit Chatterjee (00:17:01):
Yeah, great question. See, the technology has moved and improved a lot in the last couple of years. But at that time, a lot of things did not work. So you have to have a belief in where this technology will be in a few years and start building with that future. It's like, you know, early days when e-commerce got built, people have a lot of doubts. I can only say they probably buy some books. Will I ever buy a dress online? let alone a mattress.

Pablo Srugo (00:17:28):
Let alone a mattress. Yeah, things have changed.

Surojit Chatterjee (00:17:31):
Yeah, I mean, look, for me, I'll give you a little bit of context. I've always worked on things that are in the frontier. So when I was working on mobile, now it is very obvious, right? I mean, my kids, they have not seen a world before mobile, so mobile phones. So they're like, "How did it look like?" But when I was starting, like 2007, mobile advertising at Google, and then I remember during first few years I struggled to convince even internally, this will be big, forget externally. I had a discussion, I remember early days, this was still like 2010 or something with, you know, CMO of Macy's advertising customers. I'm like, you know, mobile advertising will change the world. You need to make sure you have a mobile app and or a mobile site that actually works and people can transact. And he was like, yeah, I see it. But I don't think anybody will buy a dress on a mobile phone. They may just browse, maybe see the picture, but they'll come to the store. So I like your enthusiasm. So I still remember it. Because I was kind of much junior. I was given ten minutes at the end of a two hour meeting. The last guy comes in, he's kind of a mobile guy, but it's still early. And he's like, I like your enthusiasm, but I don't think it'll be true.

Pablo Srugo (00:18:49):
Crazy.

Surojit Chatterjee (00:18:50):
Crazy, right? I mean now, do I ever go to a store unless I really want to kill some time or something, right? We felt, I felt the same way about this technology when we were starting. It was not working, very broken, a lot of things were wrong, but the trends were there. We started building actually early on some customer support agents and there was a lot of problems, a lot of challenges, right? To make it work, particularly in taking actions. See, answering questions was still possible, but taking actions was super hard. Integrations with applications, making, kind of creating dynamic plans and the biggest thing is humans trusting AI also. That, oh, I'll open up my system of record for you to kind of come and do some surgery, agentically. That was a big leap of faith. But I think today we are all at that future already. We are seeing that it's very much possible and we are seeing attraction of a different kind.

Pablo Srugo (00:19:54):
So then what was the first thing that you built to show that this could be possible, to take a step in the right direction?

Surojit Chatterjee (00:20:01):
Yeah, actually, I mean, you know, we always talk about things that work. Let's talk about things that didn't work, maybe. First thing we built, it didn't work out well. Our initial idea was, we wanted to build a horizontal platform, but we were very clear. We want to showcase that this platform works by building actual applications, actual kind of solving some problem, right? What we are doing today with HR or finance. So we were initially trying, we tried for a couple of months, like analytics area. You know, the spreadsheet jungle every enterprise has today, can you actually declutter it? At that time, it was very difficult. It was just, I mean, today, it's much better and getting there. And we thought, OK, this is not probably the right application to build today. We have the platform, we're building the platform, but we should go and build some of the other applications first. I think the two applications where we saw a lot of good resonance. One was HR, which again we didn't know or I mean we saw it in other companies. HR is very fragmented. Think of just onboarding an employee. You have to touch like twenty to thirty systems to onboard an employee, giving access to different areas, L&D, compliance training. All kinds of stuff that you have to do for that employee. It's in a large company, it's a nightmare. Look at every stage of an employee's journey from, you know, recruiting, sourcing, recruiting, onboarding, day to day, just employee experience. All the way to maybe like off boarding or any change, like the change of role, change of location, right? All the way to, you know, off boarding and even post off boarding employees have questions. Oh, I want to get my tax form from last year, right? Can you give me an employee verification letter that I worked for you for the last ten years? I mean, every employer has to fill those requests as well. We felt that there's just so many broken windows, right? In this area, agents or AI employees are the right solution and we started seeing a lot of traction. We are seeing the same thing for, say, something like finance. Where similar broken windows, lots of different things you are touching. The other big thing here is every enterprise is unique and this is a kind of a realization, right? Which is, look, I'll give you an analogy back in the day, maybe you and I may not remember. If people needed to get a trouser or a shirt, right? You'll go to a tailor. You couldn't go and buy a shirt. You'll go to a tailor. They'll take the measurement. They'll actually create a good shirt for you, because it's up to your measurement and fit. It'll take a long time and cost a lot of money. The ready made shirts, of course, you know, it doesn't quite fit exactly because every one of our bodies are unique and different, but it's cheaper and faster. So you're like, OK, we'll ignore and if we have the time, maybe we'll take it to a tailor and tweak it or whatever, right? I think ready made software today, like SaaS software is like that. Software, same thing happened back in seventies, eighties. You will get a tailor-made software, which will cost tens of millions of dollars. You'll probably call IBM or somebody to look at your enterprise and say, OK, build an HR system custom for me. It's very expensive, takes a long time and of course, you know, you can get any SaaS app today quickly, you know, up and running, but it doesn't quite fit your business or your enterprise. So then you call hordes of consultants, internal people, IT, et cetera., to do patchwork around it or train people on how to use it in the way you want to use it. I think we are going to get into the era now where the tailor made trousers can be cheaper and faster than ready made. So tailor made software, right? Is cheaper and faster to deploy, actually and that's a big step forward. I think the entire enterprise stack is going to get disrupted. That's what we are excited about. Back in the day, two years back, two and a half years back, I wrote a blog that SaaS will be dead before Satya or anybody else actually said that, you can go and check. We are at that place today. The entire enterprise software stack, I mean, by dead, they need to evolve. Some of them will be dead and some of them will evolve just like any previous era of technology. But this also presents opportunity to rethink how enterprise stack looks like and also how enterprise organization structure should look like.

Pablo Srugo (00:24:40):
One of the key things, like you're working with very large enterprise and once you have a product that works, and you have case studies. There's a motion you can get into for enterprise sales, we'll actually go deep and talk about it. But before you can even get there, you have to have some enterprises that give you the time of day for you to experiment with them in a way that's just, the bar is much higher than when you're doing consumer SMB. Because you can just run ads, get some customers, they use the product, they churn out, you move on, you just kind of keep doing that. With enterprises, you obviously can't do that. Tell me about how you got the first one, two or three customers to say, you know what? Yeah, here's access to something and, you know, let's see if you can build something useful and deliver ROI before you had a real full-fledged product.

Surojit Chatterjee (00:25:23):
Yeah, great question. I think for us, you know, first few customers was probably the biggest hurdle. Which is exactly what you were saying, you know, why should I believe you? Is your product fully built? The challenge is, unless I touch your data, I can't really show you a product. So we had to figure out steps. OK, we'll show you the product using lots of proxy data, say we create. It's not exactly the same but it shows, then we'll do very contained POCs and pilots, right? Then we'll touch maybe a few production systems and keep the blast radius small. And so step by step, there are a few things we did early on that created a lot of confidence in our potential customers. We went and got all our certifications compliance done within the first few months. Of course, some things take more time, but like SOC two, Type one, Type two, ISO 42001, GDPR, HIPAA, anything and everything. If you go to trust.Ema.ai, you can see that anytime. A lot of people will tell me, oh, why are you doing all these certifications? But that investment really paid off. The other thing, there are architectural decisions we took that really helped and those are hard things to do. Look, if you're building a consumer application, you'll build it on your cloud, right? It's multi-tenant, it's like deployment, et cetera, is much easier. It's fully under your control, right? Enterprises are different. So we made our product in such a way that it can work, run on any cloud, fully containerized. It's much harder to do. Not only that, it can run on customers' private cloud, fully air-gapped. It's very, very hard to do. Air-gapped means it cannot access any external service while it's running. It has to be fully contained inside their cloud, right? All users, native services, and then it can run within a box kind of, right? That's what they need to know, they need to see to be sure, oh, you are not going to create a security issue for them. Those are very hard things to do. Architecturally a lot of investment is needed and very hard to do later on in your life cycle as a company. Once you have built the whole product then you have to go back and ripping it out and rebuilding. Very difficult. So those were things for more than a year we were under stealth. We didn't even build a website, that's our history. Because we wanted to build the product the right way and spend a lot of time doing all of this infrastructure. We built a whole governance layer where any PII, PHI gets auto-redacted. Every agentic execution, so our AI employs teams of agents, sometimes there are forty, fifty, one hundred agents. Every execution is completely traceable, the complete lineage of every decision, explanation of why some agent made a decision, and then ability to take feedback and automatically change their behavior.

Pablo Srugo (00:28:26):
And how did you? This is kind of all the product side stuff that you do just to be enterprise grade, which I think is obviously critical. It's a necessary condition, but then tell me about the first, like, was it just relationship based where you maybe knew somebody at some enterprise and they just trusted in you. You kind of took that credibility and said, OK, you know, I'm going to leverage the time usefully to try and build something here. And then they opened the door. Is that how you kind of got in?

Surojit Chatterjee (00:28:48):
For us, it was, I'll say a combination of some came through relationship, but many very serendipitously actually came through sometimes even a cold Email. I kid you not, I have gotten cold Email back, like reach out. Hey, I see your vision. It's interesting. Can you come and talk to us and show us what you got? One of our largest partners today came through a cold Email.

Pablo Srugo (00:29:12):
Let's dive into that. You were in stealth for a long time. What did you do for them to see what you were doing and give you that cold Email inbound?

Surojit Chatterjee (00:29:19):
Yeah, so look, we didn't spend a ton in marketing at all. It's been very little. I think what has been instrumental for us is projecting our vision out effectively. We do a lot of work with influencers, analysts. So early on Gartner and HFS Research, and Everest Group. We're in the leaders bracket in Everest Group's agentic products for last three years, right? The entire life of our company. All those things help because people will see, oh, this is a credible player. We published a bunch of articles in say, Harvard Business Review, HBR, with some of our advisors or other thought leaders in the industry and so on. I think creating that credibility is super important that, OK, you are an enterprise player. You have built technology that is ahead of others. You won't steal my data. You understand what it is to play in the enterprise. So credibility and trust and reflecting that trust in the product itself. So they can see, our customers can see everything I'm talking about manifests in the product really well.

Pablo Srugo (00:30:28):
And then to explore a different piece, let's go on tactics. When you think about closing enterprise customers, what's one thing that you've done that has worked particularly well? Whether it's an outbound tactic or inbound. But just imagine I'm an early stage founder, I'm selling to enterprise, and I'm trying to get something here that I can copy and start implementing to just get more conversations going. What would be a top one that you've done?

Surojit Chatterjee (00:30:52):
The answer will be different for every company. For us, close, small group conversations have been very useful. Which is, you know, we host dinners with industry leaders, say CHROs and so on, right? In different cities and these are small group conversations. Actually, our sale is processes to not sell. We are trying to get people to think about agentic business transformation and through that conversation, we get to know them, build relationship, and then that turns out into a commercial relationship over time. Enterprise sales process is long. It may take nine months for a very large deal. A lot of our deals are large, six-figure or large seven-figure deals per year, multi-year, et cetera. So those things don't happen. It's not like a consumer, I'll put my credit card, pay $10, forget about it for six months. I may or may not use it. Enterprises are careful and it takes a long time to go through their entire process and so forth. So you have to have that patience. So to me, building credibility, getting your name out, understand where it's taking. For example, a lot of people spend a lot of time in large conferences. For us, that was not the right approach. For example, if you go to a very large conference, twenty thousand people showing up. You as a small company, probably not noticeable at all. A lot of the attendees of these conferences also are not necessarily decision makers, right? They may be junior folks in enterprises. For us, the level of contracts we are signing requires a C-level buy-in, and the C-level folks are not necessarily just walking around in a large conference floor. They're super busy. So how do you get to the right people in the right place is important. One thing that I personally try to avoid is kind of FOMO and replicate what somebody else is doing, right? Oh, somebody else is doing this, so I should go and do it as well. You have to find your own process and own method. Of course, it will change over time. Look, early days to today is a little bit different. Today, we do see kind of mid-size conferences that are useful for us, but very large conferences probably not yet that useful now, right?

Pablo Srugo (00:33:21):
Let me ask you just a market-related question because you're on the frontier of this. As you're talking to these enterprises about AI and AI agents specifically, where are you finding most are landing, right? All the way from the extreme of, because of AI agents, I'm going to not hire any more humans or fire all the humans I have and just basically purely AI agents. That's one extreme, but unlikely, but just to draw the spectrum and then on the other extreme. Because of AI agents, I'm just going to do way more and I'm therefore going to hire more humans and more AI agents, and just the output is going to grow exponentially. Where on that spectrum do you think things land for most large enterprises based on your conversations?

Surojit Chatterjee (00:33:58):
See, most enterprises are not, unlike maybe what people think in Silicon Valley, are not looking at just letting go of people. They are looking at how do I get more efficiency, revenue efficiency. My people are already overburdened. How do I create a better work culture, atmosphere? I mean, if you look at any examples, say healthcare, right? The average nurse is first of all, there are not enough nurses available to hire trained nurses. Second is fifty percent of their time is in paperwork. So they would rather spend time with the patient care, but they have to do all this paperwork, right? So even when a hospital hires a nurse, they're only getting like half a nurse or something. So I think the first level is efficiency, productivity. Even some of the VCs are saying the same thing. I see with all these AI tools, it's not like people are working a lot less. They're working on things that they like to work on, but they are excited to see, oh, I can do this also. I can get so much more done, right? That's what I am hearing. By the way, one more thing I'll mention in our go to market approach. For us, a big thing has been partnership. We partnered with some of the largest consulting firms, GSIs. These partners have very large reach, like whether PricewaterhouseCoopers or KPMG and so on. Because what we are doing requires C-level folks to think differently and transform their organization. And a lot of these partners have been doing that for a long time, have been advising clients. So that has been another thing for us and even in this kind of sales process, we are often going with partners and helping draw this picture in front of the client that, what would your organization look like in a few years with your current headcount? What can you do more? I'm sure there'll be restructuring. I'm sure some of that will also happen in some areas like customer support. I do see there is more radical shift happening. Other areas, it's more efficiency gains.

Pablo Srugo (00:36:11):
Perfect, well, let's stop it there. Let me ask the final question, which is, if you think about an early stage founder looking for product market fit. What is a top piece of advice that you might have for them?

Surojit Chatterjee (00:36:20):
Your piece of advice is, look, you have product market fit when your average salesperson can go and sell your product without you being in the room. I am continuously looking at that and saying, OK, can I train my sales team to go and sell without me being in the room? Sometimes, of course, for larger deals, et cetera, you still have to show up and it's different for different types of products. Which is like, look, some products are more complex than others, right? But can you get to a point where somebody can articulate that is simply what the product does and what benefit it will bring for you.

Pablo Srugo (00:36:58):
Perfect. Well, Surojit, thanks so much for jumping on the show, man. It's been awesome.

Surojit Chatterjee (00:37:01):
Thank you so much, take care.

Pablo Srugo (00:37:03):
Wow, what an episode. You're probably in awe. You're in absolute shock. You're like, that helped me so much. So guess what? Now it's your turn to help someone else. Share the episode in the WhatsApp group you have with founders. Share it on that Slack channel. Send it to your founder friends and help them out. Trust me, they will love you for it.