Why Most SaaS Companies Will Fail at AI (And How to Avoid It) with Intercom's CPO

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There's loads of processes at Intercom around building software that I designed and I was a part of designing. Very proud of them and love them. I've given talks about them. They don't exist anymore. You have to delete these things. They're not a part of the future. You need to ask yourself why you exist in a post-AI world. Most SaaS companies sold seats. These are seats that humans sit in. Those humans use some kind of GUI, some CRUD-type tasks to achieve some outcome. None of these things make sense anymore, right? None of these things make sense. There's no seats in a post-AI world, or at least the way seats are orchestrated is very, very different. People don't use GUIs necessarily. A lot of the product is invisible and a lot of the quality of the product is invisible. And ultimately the company with the best outcome built on the best AI, built on the best RAG system, built using custom models, they're the ones that are going to win, and you got to do that really fast.

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It will proc. I gotta talk to you about the transformation of our company. ChatGPT just had a birthday, it's 3 years old. If you look at Intercom 3 years ago versus Intercom today, they are entirely different companies. They're, it's unrecognizable. And this is really important because a lot of SaaS companies, I think, are saying that they're AI companies, but they're not really AI companies. They're not true deep AI companies. We've gone through this transformation. I'll show you what it's been like. I'll show you the lessons we've learned. It's been brutal. Like brutal. It's really hard. It's really hard. And if you're a SaaS company who thinks you're an AI company and you've not gone through brutal transformation, you're not there yet. And so I'm going to show you what we did and hopefully it'll help you kind of think about things. So, Intercom was founded in 2011. I've been there since the very start. It's been a long journey, a fun and energizing one. It's been a bit of a rollercoaster. We were a breakout SaaS company. We went from $1 million to $50 million in ARR in 3 years, which back then was really impressive. Now it's not so much. Things have changed. We really, really had these great years and they ran for quite some time. The company was going really well. We'd built a great product. We had really strong product-market fit. We've got some customers who love the product, who would tell other people about the product. But things changed. We then had 5 quarters of declining revenue growth. So revenue's still growing, but the rate at which it was growing was declining. And this was not a good period for the company. We had gone down the IPO journey. We, I think, had changed how we think, changed how we work. If anyone's done the IPO journey, it changes the company. We were in a kind of a bad spot. And Eoin, our co-founder and CEO, came back into the CEO role. And then ChatGPT showed up 3 months later. So I want to just come back. We were in this kind of tricky spot and ChatGPT showed up. We had a machine learning team at Intercom since kind of 2015, 2016, and a combination of the machine learning team and excellent people telling us this is the moment, this is a one-way door for humanity, this is the real deal, the iPhone moment, the next technology cycle. We had very knowledgeable people telling us that. We also had personal experience doing it. A lot of us had started our careers in the early internet. We kind of knew what disruption felt like and looked like. A bunch of us had worked in different scenarios. I'd worked in the mobile team at Google when the iPhone came out, which is an interesting place to be. So you could see it. We were pattern matching and we could see that this was a really big deal. So we bet the entire company on AI. We bet the entire company. It was a risky thing to do. We ripped up our strategy, we ripped up our roadmap. We made these decisions in a very small number of days. Like, you're talking 1 to 2 weeks after ChatGPT. We decided, you know, we're in this tricky spot that we had nothing to lose or felt like we had nothing to lose. We had like hundreds of millions of ARR, a lot to lose. We felt like we had to do it. So we built Fin and launched it in March 2023. So we went very, very quickly from betting the company on AI to launching Finn, and Finn was an AI agent for customer service, and people really liked it and found a lot of value in it, and it worked. If you fast forward to today, Finn has over a million resolutions a week. So every single week, Finn is resolving over 1 million customer problems. We have over 6,000 Finn customers, and Finn's average resolution rate is over 65%. So the resolution rate over those entire 6,000+ customers is 65%. So in other words, Fin resolves 65% of the queries that it's asked about. Amazing. And Intercom is on this like entirely different journey now. We have a very bright future. Fin is this groundbreaking product, transformational product. But the journey from Eoghan coming back and ChatGPT 3 years ago to today was really hard. It was really hard. And we learned a ton of things along the way. So I'm going to talk about a bunch of the biggest things for me. Uh, building a culture of change and having no sacred cows. I'm going to talk about why does your business exist in a post-AI world? How do you build software? How we build software at Intercom is completely different to how we built software 3 years ago. And actually building software, I think, was our strongest thing. Some companies are marketing-led, sales-led, product-led. Different companies have different strengths. For sure, building great products that customers loved based on deep customer insight was our strength. We built processes around it, and that's all gone. We've completely changed how we build software. And if you're successful at these things, you end up with kind of two companies. You end up with Intercom and you end up with Fitbit, and they look very different. So I'm going to talk about what we're doing with that. And lastly, I'll finish up by talking to you about the biggest mistakes I think you'll make, because it's certainly mistakes that we made at times and had to try and avoid other times. Okay, so let's talk about culture. This kind of sounds ridiculously obvious. To survive AI, you need a culture. Don't resist AI. And no one says that they're gonna resist AI, it's not a thing. However, look at the list of things that you have to change. You have to change everything. You have to re-found your company. If you have a SaaS company and it predates AI and it's not an AI-native company, in other words, you started the company designing an organization for the AI world, you have to change everything. Your org, your product, your roadmap, how you build, the metrics, how you measure success, how your sales team works, how you go to market, pricing, how you price, what does successful pricing look like, what does successful revenue growth look like, seats, outcomes, those two things together. You have to change everything. Everything. And it's really hard to do it. And culture is deep. I'm gonna talk about it in a second. It's deep. It's really hard to get people to change things that they do. Humans hate change. Humans, we all hate change. We're not fans of change. So we're averse to it. And it shows up in loads of different ways. If you're a deep AI company, your product looks like this. If you're truly a deep AI company, of course you have an AI layer, but you also have an app layer and a model layer. So this AI layer, this diagram, is our RAG system. And this is a gross simplification of the RAG system. We believe we've one of the best RAG systems in the world. It's been 3 years in the making from a very talented team. And it's complicated. I do not understand the depths of that RAG system at all. It's complicated. But it's necessarily complicated. We also build an app on top of that. That is, how do people access the RAG system, access your AI? And you need a model layer. We have started building custom models, training custom models. I'll kind of show you how they work in a second. But if you're a true AI company, you're doing all three of these things. You're building an AI layer, and it's complicated. You're building a model layer, you're looking at how you train your own models, building custom models, looking at different parts of the kind of space that you solve for in building models, target bits and pieces of it, may or may not work, you're experimenting a lot. And even app layer on top of that. So to get to this place, you need to change the culture. And the problem was that no one's gonna say no, but they'll say all these other things. They'll say, "Not now." They'll say, "Well, you know, we should start that next quarter. We have a lot going on. Our customers aren't ready for that. Our customers don't really, the AI thing, they're not sure about it. Last week I updated the board, I gave them these new forecasts. If we do the AI thing, it's risky, maybe we don't meet our number." They're gonna give me pressure, we'll look bad, it feels risky. Do you know what we'll do? We'll dip our toes. We'll like do a bit over here and a bit over here and a project or two, and we'll prove it out and see if it works, and then we'll change. Then we'll transform. So step one on this journey is you have to tell the company that it's happening and it's not a choice. You're changing the company, you're refounding the company. You're changing everything about the company, and it's not a choice. And the reason is because AI is inevitable. To say like AI is the next technology cycle isn't even a new thing to say. We're— it's age— that happened ages ago. We're like 3 years in now. AI is inevitable. Whatever you loved about SaaS and the last decade of mobile and social and pre— the internet before that, it's gone. It's gone already. So you have to change. You've no choice. And you can do, you can kind of like, I feel like everyone, most SaaS companies, at least SaaS companies at scale are at this crossroads. They have two paths to go down, opportunity or whatever the opposite of that is, death. If you nail it, if you truly change, you'll become a category leader like Fin is, you will grow to like $100 million in ARR very quickly. And Fin's on that path, very close to it. You'll be on the front page of Forbes, if you care about that, you'll have all the series, you'll raise all the money that you want, no problem. But if you don't do it, and if you fail, and deciding not to do it, I think is failure, you'll slide into irrelevance slowly but surely. New competitors will eat your lunch. You'll have down rounds. All your best people will quit because they'll go work at the true AI companies, and ultimately the company will die. And this is really important. We're at this crossroads, a lot of companies, and it's really important that people pick the right— the AI revolution cannot be negotiated. It's not something that you negotiate with people and you negotiate with staff, you negotiate with your peers. Remember all those people that will not say no, but they'll say no to other things. And here's the mistake people make when they go into this journey. They say, they make the easy, the easy fun choices, building AI features and fun things like that, trying out different models. You know, talking to customers about it. Like, that's all the fun stuff. But they'll avoid all the hard, messy ones. Like, a third of your company, you need to part ways with them. They're not fit for this new world. They don't want to build the same way you want to build. They don't like fast, crazy fast pace. They don't like chaos. It's going to feel chaotic. They're not meeting the moment. They're just going to, you know, slow things down, get in the way. That's the hard part. That's the real hard part of this. And then you'll convince yourself you've done enough. You'll do the fun stuff, you won't do the hard stuff, and you'll convince yourself that you've done enough. I see this all the time. A lot of people, it's just because it's hard and we don't like change. So what we did at Intercom was we went way too far. Because the only way to know if you've gone far enough is to go too far. The only way to know where there's a boundary is to cross the boundary. So for example, on paper, I'm our Chief Product Officer. I've always run the product team at Intercom. 6 months ago, I took over 2/3 of marketing. So I'm now actually running marketing much more than I'm running product, at least for the last 6 months. So I'm running brand, product marketing, content marketing, advocacy. Why is a product leader running marketing is an entirely different 30-minute talk. The first thing I did is I blew the entire thing up. Everything's gone. Teams, roadmaps, marketing calendar, delete the marketing calendar. People loved Coda. There was spreadsheets, planning about plans, like loads of stuff. The only way I knew how to build a marketing org that was fit for purpose for this age is to build it from scratch. Blow it all up and the right things will grow back again. So we have a calendar now again, but it's way simpler than the calendar we had before. And if I tried to iterate from the place we were, it wouldn't be the same. It just wouldn't be the same. So you have to go too far. And people hate this. People don't like this. And you have to kind of upset people and explain why you're doing it and bring them on the journey. And for those people who just aren't up for that, you have to part ways with them. Smile, shake hands, it's amicable, it's all good. They'll all go on to find different things to do that they're more happy with, but you have to do that. I'm always asking myself this question: what would a brand new startup incorporated today do here? I'm like, literally last week we're doing some more design stuff and I'm asking myself, what would a new company do? Would they have product marketers? And would they have content marketers? Or is that actually kind of the same job a lot of the time? Maybe we should have a full stack marketer, right? So I'm constantly asking these questions. What do product managers do? What do product designers do? All right, would a new company have both of those roles too? So product designer, product manager, product marketer, content marketer, all that like chained together. I don't think they'd do that. So what would they do? So ask this question all the time. And I mentioned that culture runs really deep. Culture runs really deep. So you have to be wary of the people you need to keep convincing because they're going to be a liability. You have to be wary of those people. Just not everyone's up for this. It's a fight to transform from a SaaS company to an AI company. To see the success we've had with Fin, By the way, it gets better. You know, the hard part gets easier and then it gets exciting. Today with Fin, it's, we're in a really good place, but to get to that place was really hard and was tricky. You have to delete all the parts of the process that are not part of the future. And there's a ton of these, I'm going to show you some in a second. You have to, there's loads of processes at Intercom around building software that I was, that I designed and I was a part of designing. Very proud of them and love them. I've given talks about them. They don't exist anymore. You have to delete these things. They're not a part of the future. You need to ask yourself why you exist in a post-AI world. Most SaaS companies sold seats. These are seats that humans sit in. Those humans use some kind of GUI, some CRUD-type tasks to achieve some outcome. None of these things make sense anymore, right? None of these things make sense. There's no seats in a post-AI world, or at least the way seats are orchestrated is very, very different. People don't use GUIs necessarily. A lot of the product is invisible and a lot of the quality of the product is invisible. And ultimately, the company with the best outcome, built on the best AI, built on the best RAG system, built using custom models, they're the ones that are going to win. And you got to do that really fast. You got to build this world really fast. That's how we ripped it up, took the pain because you have to do it fast. We could see new competitors entering the market. We were worried and we were paranoid about it. And we knew we had to do it really quickly. One thing you got to do early is understand how ambitious you're going to be. So this is like really kind of simple. Value goes up, but all, you know, ambition and risk go up too. So you can decide that what you're going to do with your SaaS company is change it a bit. We're going to help humans do a single task in their job. That's pretty straightforward to do. Kind of low risk, but kind of low ambition. The value is like, you know, reasonably good, not bad. Or you might decide we're going to tackle the whole workflow. That's way harder. Now you've got all the steps in the workflow, but the ambition is greater. Maybe you can charge more for it. Maybe the company will be more successful in the long run. Or you can decide we're going to do the whole thing. We're going to be extremely ambitious. We're going to disrupt our entire business that we run today. We're going to do the work of the entire team. Today we have all these people who pay for us with seats to use our SaaS product, and we're going to disrupt the entire thing, risk all our revenue, or at least risk the revenue growth by trying to change it into a new AI outcomes-first world. So you've got to decide where you're going to be on this spectrum. And to do that, you really have to understand the job you're solving and like deeply, deeply understand it. So for example, these are like two examples of kind of classic SaaS products. We've like a meetings and project management product, we've an expense management product. These again are like GUIs that, you know, are prod tasks. People are like reading things and writing back to systems and it's very workflow-focused, like a lot of SaaS products look like this. But if you want to solve this problem in an AI-first way really well, you have to break down every single bit of these and understand them really deeply because what you're going to end up doing is pointing AI at all of these discrete things. 'Cause getting AI and getting a RAG system to accurately do these things is easier when it's discrete. You can experiment much faster, you can solve bits of the problem independently and then kind of chain them together. So you need to understand each of these steps, and you need to understand that you can reliably solve it. And the reason this matters is because all of these things compound. If you're really, really good at each of these steps, really good, like here we're really good, very reliable, you still have to multiply all these together. And by the time you're finished the entire workflow, especially workflow of an entire team, you're down into like the 90% success rate. And if you're not as good at it, you're down at the 80% success rate. It's just way harder to build a product unless you really understand it. So that's what we did with Fin. This is again, like an illustration of some of the systems, some of our AI system with Fin, where you can kind of see we have these like, custom models, um, pointed directly at steps along the journey, pointed directly at very specific things, customer service and customer experience teams. And that's how we're able to get such incredible results with Finn. This is like a lot of experimentation, days, weeks, and months of experiments, constantly trying to find a new edge, constantly trying to find the, like, tiny little increases because the reliability compounds. Each single tiny incremental improvement And each of these steps adds up to something way better, adds up to the highest performing product, adds up to something people can trust, adds up to something that people will replace their humans in places with, with Fin. But they'll only do that if they can trust it. They'll only do that if it's like credibly the highest performing. So you got to attack these things very systematically each step along the journey. And to do that, you really have to understand them. Another thing you got to think about is marketing the product. There's a thing that we call the marketing overhang, and this is like very, very, very common. I think one of the hardest— I think marketing AI products right now is extremely difficult. Evaluating them is difficult, but marketing is really, really hard. Marketing Fin is really hard because a lot of companies claim that they have a thing too, and their thing works just as well as our thing, and they have demos. And so you can kind of see here, like, they're building things and they work, and it kind of works a bit, and they can demo it. But a demo isn't a product. A demo isn't a product that works at scale. And so what happens is people market, go to market with this amazing product idea that they've real demos for. They'll do product launches of the demos. We have a rule at Intercom that we will not do a product launch that is not real. We're not going to do a product launch that isn't a real demonstration of the product, and we know that it's going to work at scale. Which kind of inhibits us a bit and it slows down marketing sometimes. The reason is the product might not work. And when you build AI products, you really, really don't know if they're going to work. You need to have them in production, experimentation all the time, trying to find edges, trying to find improvements in performance. And so you get this problem where you've promised the world, maybe you've already signed up customers, you've promised the world this incredible product, and you've got one really good demo, but you've got a huge delta between what's actually possible in reality when it really goes live and the promise you delivered. This is a great example of this. Apple Intelligence. Amazing in June 2024. Apple are finally here. They finally entered the AI race. They're going to have this incredible product and maybe they're going to win. They've got your device, like, you know, they've got the OS layer, they've got loads of advantages here. Customer data, all sorts of stuff. It's now next spring 2026 where we'll see Apple Intelligence finally, and then we'll see, right? This is the marketing overhang. You have to be really careful that what you're building, demoing is real and it really works in production at scale. Okay, the next thing is that how you build software is very different. So how you market it is different and how you build it's very different. Today's software is built with empirical evaluation. So running these evals, running, having a robust evaluation framework and tweaking the architecture. Yeah, using these experiments constantly to find these like little edges in performance, comparing the old, comparing the new. We've run hundreds and hundreds and hundreds of experiments with Fin, and like, obviously not all of them work. I don't know, do most fail? But certainly many of them fail, or we see an improvement in one part of the system, But back to that lit, like, kind of line of the workflows, another part of the system isn't as— and we've degraded performance there. Just gonna have to think about this thing systematically. And then the majority of product improvements are invisible and unpredictable. So, in the SaaS world, this is what building great SaaS products look like. Most of it you can see, you're building, like, really great user experiences, really great UI to help people do those jobs well. And the CRUD backend, you know, Tiny, kind of invisible, not a big deal. There's no kind of tech technology problem here. You know, you can do it. If you have like dedication, resources, you know, you can build the thing you want to build. And actually the hardest parts are in the user experience, making it easy to use, easy to learn, all that kind of stuff. AI products don't look like this at all. It's actually the inverse. Building the UI is the easy part. That's the small part. The hard part is building great AI infrastructure, building products that actually truly work at scale. This is an entirely different world. And my background's in design, originally I was a designer, and the left-hand side here was like my happy place. You know, this is an awesome world. And I had a reckoning, a personal reckoning, weeks of anxiety. What happens in the new world? You know, the thing I'm good at is like the top part, and that's really small now. And things like infrastructure, I do not. I was like, I'm way out of my wheelhouse if we get into like infrastructural layers, but I had to learn it. I had to get better at it. I had to kind of face reality and say, okay, well, I've got to get better at this kind of stuff. We had these principles at Intercom that evolved over years, and they made us really good at building SaaS product. They made us really good at it. We had like kind of 6 core principles. We had like design principles, and what we would do is we would teach new people these principles. So a designer joins the company, an engineer joins the company, we'd say, this is how we build software at Intercom. This is why we're good at it, because we follow these principles. We have a little process for each principle, like think big, start small, ship early. And there's like ways in which you would do that systematically. And we got really good at it. But these principles were designed for the SaaS era. They were designed to build a SaaS product. We had to abandon all this shit. It was, it pained me. It still pains me looking at this. It pained me to do it. You know, Des, the product leaders, the engineering leaders, it was really, really hard. People did not like it. People like me didn't like it. I love those principles. You know, I helped make them. They're part of, I like looking at them on the wall. Doesn't matter. You know, looking at principles on the wall isn't going to build a great business. And people were disempowered. They're like, this is the way I know how to build software. I'm good at building software this way. I don't know how to build software the new way, and it sounds way too technical and way too hard for me. It's not how I think about things. So there's an old way and a new way to build software. The old way, you pick a job to be done, you listen to customers. If you're very good at it, you get really good at customer insight, getting true, true customer problems down into the depths. You design a solution. You build, and you ship. And in this world, execution is certain. The technology stack is pretty stable. You know, design is one of the hardest parts of the process, not the engineering side. Users could talk about their problems. They were very easy— it was very easy to just talk about, like, all the ways in which their workflows didn't work, and their company, and their org design. It was a stable world. Technology was stable, but organizations were stable. How organizations are structured, How do you think? All stable. There was never a question of like, can this thing actually work? Is this even possible? There's an entirely new way to build software, and this is what we've changed. Again, it's painful to go through this, 'cause you've gotta delete the old way, and it's risky. It feels very risky. There's an entirely new way to build software. You start by asking, what does AI even make possible? Like, AI is a magical technology. I think people are like, we've kind of lost ourselves in it. There's so much hype. So much news, no one can keep up. There's all sorts of mad stuff happening. Is it like hyped, underhyped, overhyped? Is there a bubble? Is there not? Like, no one knows. But if you step back, we have this like crazy magic technology. Like, it's magical. When you actually step back and think about AI and think about the fact it's only 3 years old, it's kind of wondrous. You know, I think if you went back 5 years ago and looked at us today and the things we can do with ChatGPT, with Claude, like with all the coding tools. I cannot code. I never could code in my life, but I can build things now. Like, it's an entirely— it's a magical thing, and we lose that. So there's a new way to build it. You start with what does it make possible? What might the product be? And can we build it reliably? I don't know. Let's find out. Let's prototype. Let's try stuff. You then later build the kind of CRUD part, the UX, the UI. You build that later. You ship, and then you look at scale. You look at like, does it work in the real world at scale for thousands and thousands of companies? So there's way more possibilities, but it's way more ambiguous. It's a way— it's more chaotic. Execution's uncertain. You got to throw stuff away sometimes, start again. And you learn the most when it's live. There's also like these new muscles to grow. These are blog posts. We have a research blog. Again, like our machine learning team has been around at Intercom for a long time. It's now way bigger than it was. It's now called the AI group. We rebranded it, and the amazing team, and I've learned so much from these things, but this is like way out of my wheelhouse, way out of our product team's wheelhouse, I think way out of some of the engineers' wheelhouse too. There's like empirical evaluation, it's way more scientific, there's like scientific rigor required. You need to like really understand causality and like how things change other things. There's a whole new world to learn. And design has changed as well. This is, you know, design, when we used to build software, design was kind of the hardest part. Design was often the bottleneck. Design was often the difference between a great product and a good product. Like it's because it was better designed. This is what we'd do. We would like sketch out the wireframe, we'd sketch some ideas, we'd pick one, we'd iterate that a bit, and then we'd ship. And the reason we work this way is because design is expensive. This is the new world. You don't work like that anymore. Design is now cheap, and you can do loads of stuff quickly. PMs can design stuff, engineers can design stuff, people can prototype stuff. You can give these systems, like, principles and rules and guidelines and product design rules of thumb, and you can just build stuff really, really quickly. You can explore really, really fast, and so the possibilities are much, much more open. And that, of course, then leads to changes within your company. Well, if design was expensive and now it's cheap, what does that mean for designers? If PMs can design, do PMs also PM still? Like, how does this, how should all this work? So there's changes we've made, big changes we've made. The old way was, should designers code? If you're a designer like me, oh my God, this question, like for years and years, existential, like navel-gazing about whether designers should code. Yes, designers should code. In fact, they do. And I'm going to show you that in a second. Designers should code, of course. Now they can. And they should. What about engineering velocity? Like eking out incremental improvements? We are 2x-ing our engineering velocity. Hackathons, playing around with stuff versus saying AI engineering is now the default. Here's examples of this. This is Domingo from our product design team, just talking about him kind of shipping some front-end changes. Every single designer in Intercom has shipped code to production. Like a year ago, maybe 18 months ago, no designer had shipped to production. We went from zero to every single designer shipping code to production because we said we're going to do it. Remember, AI is not negotiable. We said every designer is now shipping code to production. It is now part of your job. If you don't like that, we had a great run, but we should part ways and you should go somewhere where designers don't have to ship code and we'll hire designers who love the idea of shipping code. This is really empowering for our people because what it means is that all of these designers can now fix all the front-end bugs that they hated for so long, right? The old world was like annoying engineers, please fix that stupid 2-pixel thing, right? Now designers can change all of this stuff. So we started small, little changes like that, like little UI bugs, and it's going to grow. And the design team is loving this. If you go on LinkedIn, you'll see all our product design team shipping these code changes to code and loving it. We've also doubled engineering productivity, or on the path to doubling engineering productivity. And the only way we did it is the same as design. We said, it's happening, we're doing it, it's not negotiable. If you like that, you're going to love this. And if you don't like it, we're probably not the right match for each other anymore. It's— you got to pick a hard-to-hack metric. So 2x-ing productivity, you have to, like, enable the team internally. You got to explain why we're doing it. You got to persuade people, you got to get people on side, explain why this is valuable to the company, explain why this is amazing for their careers. You know, since, since Finn's success, one of the hard parts being a leader at a company that's now succeeding is everyone wants to hire all your team. So it's really good for your career if you're a designer who can code. It's really good for your career if you're an engineer who says, we 2x productivity, I can tell you how to do it. So we're publishing all this stuff externally too. We have this Ideas blog, and you'll see a lot of the design team on the Ideas blog posting kind of frameworks for how they're doing this stuff. Okay, next is GTM. So how do you do go-to-market with an AI product? Again, there's a ton in this. I'm going to just cover some small things. Customers do not know how to buy AI, especially if your customers are not technologists like everyone in this room is. They don't know how it works. It's, it's a black box. It's a mystery. It's also scary. You know, we living and breathing this thing can't keep up. So like, God help anyone outside of technology. And most of our customers are people outside of technology for the most part. So you need to help people understand AI. You need to really invest in education. Customers don't know how to evaluate AI products. They don't know how to run good evaluations. Oftentimes we look at Fin, we lose a deal, there was a head-to-head, we look at the eval and you're like, shit, they didn't set it up right. You know, like they just, if they'd learned a bit more, maybe we still would have lost. but I think we would have done way better if they'd set up the evaluation properly. You got to market your strongest differentiator, which might not be like this big screenshot UI. And then you got to really invest in happy customers and successful customers. The people are looking out, people are crying out for education, they're looking for like, show me, I don't know what to do. Show me people like me who've done it already, and I'll learn from them. So customer advocacy is just enormously important. The product marketing has changed too. Like, all these products look the same. A lot of AI products do look the same. A lot of them are now AI agents. They all kind of look like some kind of chat interface. They all do different things, but they all kind of look the same. It's hard to tell them apart. Look at 5 competitors, they all kind of look the same. So actually, the pitch is actually much more about infrastructure than it is about features. You know, we talk at length about Finn's RAG system. At length about this empirical evidence, our scientific rigor, why Fin is better at scale across these like hundreds and thousands of customers. That's like a different way to market a product. That's not how typically SaaS products are marketed. And the buyer has changed. So in the old world, this is our example from Fin, but I think this is like fairly universally true. In the old world, what happened with us was we had a customer service product. So we have a customer service leader buying the product. We have an ops leader, but they're not the buyer. They have influence. We need to kind of get them on side, talk to them, market to them. And the team. The team are like good vibes, you know, good for loyalty. They'll tell the boss which one to pick, but ultimately there's a single buyer. Doesn't work like that anymore. There isn't a single buyer anymore. So for example, with Finn, we have, yes, the customer service leader, and they're still heavily influential. They have a lot of clout. But they don't make the decision. And the reason is because there's two other people involved. There's typically a C-level executive, because their job is AI transformation. They've been told, hey, my job as a C-level executive is AI transformation in our company. We're changing how we work. We're going to start with customer service. So I'm sorry, but I'm going to pick the tool. Actually, I'm going to decide. You can help me, inform me, but, you know, and then there's the AI person. There's like, you know, IT buyer here. It's often an AI kind of someone who's very fluent in AI, maybe they run an AI team, and they're going to evaluate if the thing is actually good. Again, these AI products are invisible. It's hard to remember the iceberg, hard to know if it's good or not good. So we have a whole different kind of setup here. It's good at marketing to these people, selling to these people. A CEO and a customer service leader live in different universes. They don't go to the same events, they don't like the same things, they don't read the same things, they operate at different Zoom levels. So I'm doing like dinners with like CEOs, I'm going to like trade shows for the customer service leader, we're doing all sorts of different types of activities, because they're like the Venn diagram here isn't even overlapping at times. So if you do this really well, and you're really successful at it, you get a different problem. And that's where we're at today. We now have two companies, we have Intercom, the SaaS company, the SaaS product, seats, and we have Finn, the AI product outcomes. And with Intercom, we have a really easy product domain to understand, easy to roadmap, easy to talk to customers about it. They've done this thing for 10 years. They know how to talk to us about it. They know what features are missing. We've got predictable metrics. We've got history for these metrics for a decade. We've got like a really clear differentiator. We know what not to break. We can kind of look at like different types of activities. But Fin is a different beast entirely, a new product domain. Customer service teams, customer experience teams, if you sell to sales teams, if you sell to marketing teams, everything's changing in their world, chaotic. They don't know how to even talk to you about what they need. They don't know what they need. Everything is changing so fast, no one really knows what the future looks like. So it's really hard to articulate those jobs to be done. People actually don't really know what the job to be done is. So it's really important to help them. The metrics are really unpredictable. What will this outcomes revenue do to the seats revenue? And how should we think about that? What's the— is it— what is this trajectory? Good? Is it not good? Jeez, I'm looking at like all these Lovables of the world, you know, Waze were just here, like, holy shit, look at their growth chart. Should we have a growth chart like that? Or is ours okay? Ours is really good, but is it, you know, Finn's growing 300% year over year. Is that bad? Right? So everything changes. The differentiator is often performance, evaluations, head-to-heads, and you got to do startup stuff. We kind of say it's a bit of a kind of cliché maybe to say like you're a startup reborn, or we're trying to run Intercom like a startup. Intercom's 1,200 people. We are trying to run it like a startup, which means it's a bit chaotic. But I think a bit of chaos is actually good. It means we move fast, we ship things, and then we end up in this situation, which is like Intercom, Fin, We're trying to work out the brand, what's the brand relationship to these two. But I'll take it. It's a good, it's a good problem to have. Okay, so let me finish up with just mistakes I think you might make along the way. We've made some of these, I've seen other people that I talk to make them, and I'm telling you them, so I hope you don't make them. One is you won't reimagine your product. You'll add AI to your product. That is not reimagining your product. Fin is totally different to Intercom. It's a completely different product. The two of them are like, they've almost nothing in common in some ways. It's a completely different product. I'm like, yes, Fin is— Fin usage is eating Intercom usage at times. They're totally different products. You design them totally differently, think about them totally differently. So you won't reimagine your product. And again, you do the easy stuff. You add a bit of AI to it. It's not the same thing. You won't make self-harming decisions to win. You'll protect revenue. You don't want to piss off the board. You don't want to piss off your sales leader. Like, there's loads of reasons and loads of places where you won't say, do you know what, we're going to take a 10% revenue hit to do the right thing for the long term, or even the medium term. These self-harming decisions are critical. I remember I said at the start, it's brutal. It's a really hard transition. It's really hard. It doesn't feel really painful. You're not deep enough. You're not going for it enough. You got to refine your company to fight properly. You need people who are going to fight for success, fight for these amazing outcomes. And they're there to be had, by the way. If we can do it, any SaaS company can do it. There's nothing special about us. There's nothing unique about us. We just decided to do it. We decided to refine the company and we decided to fight for it. And it was hard. There was tension. There was a lot of disagreements. People fight with each other. Positively fighting, you know, not actually fighting. So you'll avoid that because people hate change. And lastly, these are the things you will do. You'll dilute down the vision. You know, you're like, oh, you know, we'll just kind of dial it down a little bit. We have a vision, it's kind of new stuff. We'll kind of dilute it down or we'll delay. Hey, you know what? Q1, we did have a great Q1, so we'll do it in Q2. You'll convince yourself you've done enough because you did the easy stuff, not the hard stuff. Big company habits and slowness will creep in. You listen too much to customers who say no to AI. We had lots of this. We had loads of people saying to us, we're not ready for AI. We don't want to do AI. Actually, we're going to differentiate ourselves on human service forevermore. And now they use Fin, right? So you have to really think about who you're listening to and what they're saying and why they're saying it. Because all of those businesses that you sell to, they don't like change either. And they don't want to transform either because it's really hard to do. And then you'll make these mistakes and deny it to yourself or deny it to your team. You got to be really honest. We've had the most honest, soul-searching conversations in our exec team. We still do today. You know, every day in our exec team, there's like some version of some existential question that might mean we do or don't succeed greatly. Or, you know, it's— you really have to like look each other in the eye, build these like deep relationships with your peers and colleagues. So that's how we did it. That's what we did. You can learn more on our Ideas blog. You can learn more about our research, our AI research, on our Research blog. If you want these slides, I'm very happy to share them with people. Just email me and then we'll send you on the slides. All right, thank you.

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