Name: State of Martech 2026 Keynote
Uploaded: 2026-05-05T16:16:24.202Z
Duration: 1 h 8 s
Description: State of Martech 2026 Keynote

Transcript for "State of Martech 2026 Keynote": I I was burning out on Friday. I had a pipeline full of ghosts. Every campaign I've been running was the one that hurt the most. Then a curse Hello, everyone. Happy MarTech day to you, and welcome to State of MarTech twenty twenty six keynote. I'm Scott Brinker joined here on Zoom as well. Welcome. This is actually the first time you're doing one of these sessions being physically in the same studio at the same time. So, yeah, it should be great fun. It should be nice. So, wow. We are gonna cover a lot here over the next fifty five minutes. We're gonna talk about marketing and metamorphosis, which was really the overarching theme of the state of Martech report. I mean, you know this. We are we are just in a period of incredible transition transformation, for marketing in Martech. And so when we were starting the research for this, you know, we're like, okay. Clearly, like, if only there was a metaphor we could turn into about this. And, yeah, I mean, when you think about transformation, alright, one of the cliche metaphors, of course, is like the caterpillar to the butterfly. But actually, as we thought about that, we realized that's an even better metaphor than you might imagine because, you know, the caterpillar doesn't just, like, wake up one morning and be like, okay. Now a butterfly. Take off. That's awesome. You know? It goes through this intermediary stage of the chrysalis, which is this illustration, you see on the right, where the caterpillar is dissolving, the butterfly is, you know, forming. On the sounds kinda gross. You know, but, like, this this really does feel like it's representative of what we are going through in market in MarTech, where yes. We know we we know there's the AI butterfly that we are moving towards, but right now, we're in pretty much the messy middle state. And everybody's trying to figure out what to do with AI. It's changing so rapidly. So if you feel uncertain, unclear, welcome, and join the club. We all are feeling uncertainty and trying to manage it, and so much is changing. So that's what we will tap into. Alright. So as we, after we cover that, we will then dive into, I'm sure, one of the things you came here for, which is, looking at the MarTech landscape 2026. And then after that, we will talk about a fraction of the 70 use cases of AI and marketing where we're looking at, you know, which use cases are marketers using existing SaaS platforms, where they're using new AI native products, where they're building their own solutions with AI. We cover a tremendous amount of this in the report, you know, so you'll need to get a copy of that for all the details. But we'll walk through a few of those, categories and some of the themes that crossed almost all of them. And then after we finish up our keynote, we're gonna have a series of interviews with the seven sponsors for this year's report, experts and executives from each one of these companies. I'll introduce them here in a moment. But first and foremost, because you are here, tuning in to us, you get to be the first to get a copy of the State of Martech 2026 report. If you go to the documents tab that should be in the upper right of the interface here, you can download that right away. And it's free and ungated, so feel free to share this with anyone you would like. But this is where we take a moment to just say thank you to the seven sponsors who made this possible. Franz and I spent months, working on this. We have a team of people who are helping us with a lot of this research. All the beautiful design, of this is courtesy of Angelo with Datura. That's only possible because of the support of these sponsors, Growth Loop, Hightouch, NAC, MoEngage, Pega, Progress, SAS. So since they make it possible for us to do this and share all of this research with you at no cost, boy, one of the ways you can help support us is by supporting them. I understand. I'm reaching out. Have a conversation, engage with them. They're amazing companies. And speaking of amazing companies, we have to also say thank you to Goldcast, Cvent Goldcast now, which is the platform on which you are experiencing MarTech Day here now. We've been using Goldcast pretty much from beginning of, you know, running these MarTech Day events. It's a phenomenal platform, not only for virtual events and in person events, but they've evolved into an incredible video marketing operating system. So absolutely worth checking out as well too, and our thank you to all of them. But is it time to begin our metamorphosis? Absolutely. And just look at the it's it's not an illustration. It's a piece of art. I think you should frame it. Absolutely. Just, yeah, amazing work as always. And it's showing a beautiful crystal, but, actually, it's a lot of uncertainty, underneath. So that's that's the contrast that you can expect. And we're not talking about the MarTech in metamorphosis. It's marketing. It is the bigger picture of this. You know? And so often when you go through transformation, again, you you think of, like, a from to, you know, set of states. The caterpillar to chrysalis to butterfly, it is a three stage, evolution, three stage transformation. You know? And so for a lot of marketing, we're in that that middle part, but we aspire, to what the butterfly will become. And so if we take a look at, you know, both from a perspective of the market overall, what's happening with the technology and then what's happening with marketing works themselves, we can look at each one of the these three stages. And, of course, we start with the most important one, which is the market. What is changing? And and the most important question is the million dollar question. Who controls the conversation? I think this is where we see the biggest shift happening. The the control, the, overview is changing from one place to another. That's the market, and then we'll unpack more to tech and later on the org. So without further ado, let's start with the Yeah. So, you know, the truth is this is not the conversation. And actually, it was always a bit of an illusion that we thought as marketers, we could control the conversation. I mean, in terms like, the buyer's journey and journey management, You know, again, we've we've created and adopted a tremendous amount of very powerful technology. At the end of the day, you know, the customer always was the one in control of their own journey. And now with AI, it has just become even more so. Right? Even just seeing from, you know, the what we thought we had optimized around the whole, like, you know, Google search, you know, funnel approach to life to now where, yeah, customers, prospects, consumers are just working within, things like Gemini and ChatGPT and Cloud and very much controlling their own journey, in ways far beyond, you know, what's possible before. And we're in the solid stage of that right now. You can sort of see that over the next year or two as the butterfly fully emerges that customers are gonna have a lot of just their own agents that do a lot of that on their behalf too. And so this idea of the MarTech landscape, yes, there's a lot of power and capabilities and some new innovations we'll talk about, you know, that isn't really where the story is at. And if you thought that we were, releasing now the landscape, that was the new landscape. Now he's too late. You should have taken a screenshot. Yeah. We can do that. The screenshot. Start counting because in, I think, ten, twenty minutes, we will release the new number. Yeah. You can put in the chat what you think, you know, the number is. Alright. But what we really want to focus on is what is changing. I think that is what you want to know, and there's a lot changing in in software. So, especially now with the arrival of AI, Gen AI. And before AI, it was already there. There were some SaaS vendors already focusing on use cases and what it solves for the customer, But a lot of the companies were focusing on roadmaps, features, shiny objects, or whatever. Features by the ton or the kilo or kilometer? That's right. Yeah. There's some metric issues here. Okay. So and and then we were just waiting for the next release, the the better, version off. But how does that help the customer? I mean, we still, as marketers, have to transform that into a customer experience, a customer benefit, value for the customer. And some of the outperformance we saw and observed for some years, we're already focusing on just that, the green arrow over here. And they will, you know, accelerate because they know how to use the technology Because like we said, it was never about the technology. Technology lost, not first, I would say. Customer first and then reverse engineer. So that is exactly what we've been seeing. And, I think it will also reveal that for many years, maybe even decades, we have been, optimizing for coverage and not so much cash. So do we touch every customer touchpoint or data point or integration? We have to make sure we cover for every exception, but covering for exceptions and not the rule is basically a recipe for disaster. And the rule is not something you come up with. We, as companies, don't decide what the customer rule is or the customer journey is. The customers do themselves, and they tell you through the revenue streams, through the data, through transactional data. Yeah. And that is exactly what you see the outperformance do. They optimize for cash. Yeah. You know, one of the themes of discussion in MarTech for years here has been utilization. Like, how much of utilization. Like, how much of, these different products in our tech stack are we actually utilizing, which is always been, I felt like, a little bit of, like, the wrong lens to bring the things. And now particularly with AI where, I mean, the possibilities of what AI can actually do for you. I mean, you're only ever gonna be able to, like, you know, capture a tiny fraction of that. Utilization isn't the lens that you need to start with. You need to start with impact. What are the things that actually drive impact customers? What are the things that drive impact to the business? Yeah. So, back in December when we released the Martech 04/2026 report, one of the things we looked at was, yeah, this this shift of agents entering into marketing's domain, and we looked at three different buckets of, agents. There were agents for marketers, and these tools that are really helping us as marketers do our work behind the scenes, agents for customers, which are AI agents that we as marketers or the business still control, but we're deploying them to be customer facing, things like customer service, chat, AI SDRs, AI shopping concierges on ecommerce sites. But then this third bucket of agents of customers, these are agents that the customers themselves control, things that they're doing today with, like, ChatGPP and Quad and Gemini. And we as marketers, we don't control those. We don't have a lot of visibility into them. And so as we're looking across that whole agent landscape even just a few months ago, it's like, you know, those a lot of innovation, but those those agents for marketers, agents for customers that we control, in many ways, those are the things that are helping us improve marketing and how it operates and what it delivers. Those agents of customers, particularly as we're going through this chrysalis to butterfly stage, these are actually the things that are gonna be the most disruptive to marketing as we know it. And if you're like, well, I'm not quite sure how this is gonna play out, Turns out actually the AI giants in this space, they're not entirely sure how it's gonna play out. You know, back in December, we, I remember a lot of things I remember was, oh, yeah. Hey. You know, OpenAI, for instance, has this instant check out thing, and it might, like, completely rewire how, you know, agent ecommerce works. And then, yeah, like, a few months later, they're like, I'm just kidding. No. Not that we're gonna try something different. It it's it's crazy. So, again, back to the and the state we're in, it's uncertainty. And and it's not just you. It's not just us. It's also the big guys, the giants. They play with stuff, and they try to figure out if it works. And the only way to make sure the only way out to uncertainty is experimentation, and that is what the big guys do. I mean, you see in the big in the big vendors of PDF or spreadsheet, you see, let me summarize this stuff. And then sometimes I go like, really? Is that all you can think of? You're you you invented this whole space, and now you come up with summarizes? I don't We're gonna need to be able to probably summarize the state of MarTech report. That's That would be great. Alright. So let's move to the next one, the technology because, yeah, we see there is a lot happening. We covered a lot, but now looking back, comparing it to earlier research with the research we're doing now, we see there is, a balance between the traditional SaaS tooling and AI. And we want to reiterate that AI is not replacing SaaS. It's augmenting it, and that is exactly what we should understand which part is it augmenting. It's also replacing, some of the SaaS technical features in the table. Right? Particularly the features that kinda weren't actually particularly good. SaaS. The deterministic SaaS capabilities. Right? Yeah. Going back to that later in a second, but, yeah, SaaS stacks aren't shrinking. You know, so, Zylo is one of those SaaS management platforms that every year publishes SaaS, overall empirical data, the number of SaaS subscription companies have. And while it was actually starting to shrink over the past few years, this past year, It's starting to grow again. So, yeah, there's definitely not a story that, you know, the stack is, compressing. But, actually, that top line number is a little bit of an illusion, and we're gonna see this elsewhere today too, because while the stack grew a little bit, actually, if you double click on the data, there was about 20 to 25% of the stack that churned. It was old applications or things that people had experimented with, tried Verpilot, decided to move on, but then brought something else in. And so, again, this kinda just goes to that Chrysalis stage is things are dissolving and reforming, and it's, it's pretty fluid. Remember the 30% we're talking about where AI is replacing SaaS? We think it has to do a lot with personalization because if you try to personalize with if then else, it's a bit robotic. It's a bit clunky. So maybe this is a nice metaphor illustration of, yeah, it it worked. We could hammer a screw into the wall, but, yeah, I mean, sometimes they're all screwed with that. You know? Sorry. So in the AI era, we now have two complementary types of technologies. It's it's if and else, it's rule based, it's SaaS, and we have agents which is language based. And now we have an hammer and a screw, and we're good to go, I would say. Yeah. And very cool for the right job. Exactly that. And maybe they are two layers on top of the same stack. So when do you need which one? Well, we see in some of our data that SaaS is becoming more of an infrastructure layer, whereas AI agents are the value layer. They are easier in connecting and interacting, engaging with customers and getting the right data out of the infrastructure layer, the data layer, the stuff you know about the customer. And, you know, think about it. You don't wanna go probabilistic on your deterministic data like, let's guess the age of our customer today, or where they live. I mean, that's horrible. That's stuff you want to have deterministic and that will not change. So it's a nice combination, and I think, you know, we said it earlier. It's as if now, a silent movie has sound with AI. It was a silent movie, which is great and and fascinating and great we can do this, but now on top we have the probabilistic layer to AI agents. Well, and one of the things we did in MarTech for twenty twenty six was we tried to go a little bit deeper in, like, a stack architecture to really represent how we saw this playing out. And, again, the overarching thing there is not AI replacing SaaS, but AI working with SaaS. And we'll cover that a bit in some of the use cases we analyze later too. We talked about it. It's like a stratification within the static. But the key thing is, like, these core SaaS systems that we've relied on, for instance, at the data layer, you know, things like cloud data warehouses or lake houses, our classic systems of records, CRMs, CDPs, CMSs, These are very much alive and well. We we sort of use the term systems of knowledge instead of just systems of record because it, you know, feels like a broader thing there. But, like, so much of what we're trying to do with AI is dependent on that stability, like the deterministic reliability of that data layer. And then on top of that, most of the SaaS applications that we have leveraged in marketing over this past decade, marketing automation, customer engagement platforms, digital experience platforms, our ecommerce sites. These aren't going away either. You know? Now they're getting more and more AI capabilities built into them, you know, not just generated AI LLM features, but, in particular, leveraging much more deterministic machine learning, using, new things, like reinforcement learning techniques to be able to get better and better AI decisioning, you know, within these tools. But we we instead of calling these systems of engagement, we we gave it the label last time of systems of context because it's not just about engaging with humans, you know, a customer or someone on our staff, but it is now also serving agents, inside our organization, and that's moved Chrysalis to, Butterfly agents, from the customer's perspective too. So systems of context, you know. But it's AI that wraps around all of that. By the way, I just really like you know, take a moment here to appreciate how good the martech industry is at coming up with three letter acronyms. And it is, it's amazing. Yeah. It's it's a field of three letter acronyms. You know? But over the years, Franz and I have been tracking this martech landscape, and we've often framed it as the long tail where, you know, there's few dozen companies in the head, billions of dollars in revenue, you know, hundreds of companies in that torso, generally over a 100,000,000 in revenue, and then thousands and thousands of long tail solutions, not just, you know, startups that aspire to someday be in the head of the torso, but also some specialized tools, you know, in vertical markets, different regions, ecosystems, services companies, you know, who are packaging up their secret sauce. But as we started to share with you, you know, last year, as fascinating as the evolution of that commercial long tail is, where things get really exciting is the we call it the hyper tail of this more and more custom software that organizations are able to build on their own. And that was already accelerating, you know, with no code and low code platform. But with AI over this past year in particular, live coding, I mean, oh my goodness, things like Lovable and Replit, you know, it's really exploded. And often, you know, there's debate, you know, around live coding. Partly, I think it's because of the name. People like, I don't know. Do I want to rent my different sun vibes? You know? But it's not about live coding replacing a lot of this, you know, like, structured professionally engineered platforms or systems. But it's like these dimensions on which there are new kinds of agents that, honestly, was kind of hard to get software built for before. Like, if it's these simple use cases or things that's just internally, maybe for a small team or an individual to optimize their workflow. Maybe it's something like, oh, we just wanna spin this up, you know, because we're running a particular event and one app that could do this for that event. You know, the cases of, like, building things where the risk profile doesn't have to be dealing with sensitive data, you know, doesn't have to be dealing with, like, mission critical services. And perhaps even most powerful is using these AI vibe coding platforms as a way to take ambiguous ideas and really make them concrete. You know? One of the amazing things about this is you're empowering so many people who are not engineers to be able start to start to dream about, like, oh, what's possible? What would I love to see? Could we make this happen? And even if they don't create a production version of that, to be able to fiddle and manipulate and try and do these things to, like, get a prototype, to then be able to bring in professional engineers for something that then has a very concrete vision. You know, and so it's just exciting that, all of these things on the left side of this chart are the things that just traditionally we weren't getting a lot of. Maybe, just maybe, we're looking now right inside the chrysalis because this is where the magic is happening. All these things we couldn't do before, they didn't make sense, couldn't scale, what have you, and now you can try. We spoke about, you know, experiment your way out of uncertainty. This is what is happening, and we also spoke in earlier webinars about instant software. Well, maybe we'll go into a stage of instant businesses. This is a very creative, space that we're now entering, I think. You have a great talk about fifteen minutes of fortune that, yeah, after you get a chance to hear this guy's presentation on that, definitely tune in. So if you sort of step back and you look at this, it is not that the commercial martech landscape is going away, despite the SaaS pocalypse narrative out there, you know. But you are seeing this shift where for a lot of those companies, it's less and less about the application layer, and it's more and more about these platforms serving as infrastructure upon which businesses are able to tailor apps and agents that are really unique, you know. And this goes back to the, you know, your diagram of, you know, cash over coverage, is when we start really using value engineering, I know you're gonna share a bit more about that. But value engineering is really hone in on, like, okay. Well, what's the most important things for which customers? You know? You you really do wanna create something that is hyper tailored. Hey. See, that's my hyper tail and hyper tail. Alright. Sorry. You know, that's really hyper tailored to serving those customers, those needs. Horizontal platform software had a very hard time delivering that. Vice versa, yeah, a lot of these, like, you know, core systems capabilities that you need to be, you know, stable, reliable across all sorts of use cases. Again, you don't wanna Vibe code that. So we sort of see this evolution, commercial apps coming more and more infrastructure solutions, and then just this incredible explosion ahead as, you know, the butterfly starts to take shape and take flight more and more custom apps and agents. That brings us to the final piece, which is about the org, about skills, about changes of, tasks and responsibilities. So let's tap into that. And we talk about value and context engineering in this section. Let's start with the first one. What is very important to learn and to understand is that we always optimized for coverage and not cash, but the problem there is that you try to boil the ocean, all the data points, all the technology, and that is very, very hard, and it doesn't make sense. So we started looking at what outperformers do, and they are very smart in saying, okay. Yeah. But 80% of our revenue actually comes from 20% of the, the technology, the data, the content. So let's first optimize that. Then we get our hands free, resources free, and look into new, options and possibilities. And on the left hand side is where you see the marketing, and on the right hand side is where marketing ops and martech specialist should, and can thrive. Now if you talk about looking at eighty twenty, then you ask yourself which 20%, to start with. And here we make a very common mistake. This is what we see the low performers do a lot. Yeah. Let's go into the totally new market and a totally new product, and, yeah, it's a silver bullet thinking. This will change everything and we will win. But what we see the outperformers, they're far more conservative. They they first want to understand how they create their current revenue and the current customers. And have we served them completely in all aspects? Are we good? Are we done? And before they move to any other box in this matrix, this is what they do. And then once they do this, this is where innovation starts because they go like, oh my god. What are we doing in this in this market? Why don't we do it in the other one as well? We have never thought about that. And then they go to different products in different markets, and that's exactly how they expect. So if you talk about value engineering, I would say there are two rules. First of all, there's this very important notion of every customer is proven revenue, and every customer once was a lead. And now people say and they give me pushback, very often if I say this. Yeah. But, Franz, you don't understand. This kills innovation, and we need to go to new revenue streams. This is the way to innovation, and this is the way to new revenue streams because you need to figure out who's my current customer, what are they doing, what is sticky, because this is what VCs will ask. Do you have, you know, proven revenue? Is it sticky, your product and your customer base? And then if you look at those customers, they once upon were a lead and they came through one or two touch points, not thousands or gazillion. And then you go like, hey. If we're using this touch point, why don't we use the other one as well? Because that's also where they are. And then maybe you double or whatever, your revenue. That's exactly the way you go from one point to another. It's almost like a Sherlock Holmes type of approach. And starting with that, you know, the answers are already in your company. They are in your data. Just look at in your transactional data. That's where I would start. You know, who's your biggest customer? What is the customer you make most revenue of? And then what products and services are we selling to the most? And and, actually, what is the margin, the cost, and the revenue? And this is where many people say, oh, Fran, it's not that simple. You know? 80% does this, but they're 20%, and then they go for the exceptions again. And then we could you know, we optimize for congress. The guts and the courage to say, no. We're focusing on just those customers. And this is what we see many, many marketers not doing. They are not able to answer these three simple questions, I would say. So now we're really, you could say, drifting off of MarTech, and what does this have to do with the company and, you know, now deep into marketing? Well, let's bring them together. Yeah. Well, I mean, again, this role of marketers to really have so much opportunity to focus on value engineering, but then having that supported from marketing ops and Martech, by context engineering. Oh, okay. So context. I think we did call this one here for 2026. We're in the year. It's gonna be context. Right? Because, boy, we're it's in everything. There's, you know, context windows, you know, for AI models. There's, context engineering. How do you get the right instruction data tools to a particular agent or agentic workflow to execute? People start talking about context graphs is like, oh, what's the history of how we make decisions? Yeah. Lots of here we are. We're talking in the context of context. So we actually have a whole chapter in the state of Martech, the 2026 report diving much deeper into this nature of context. In many ways, this is really where the Chrysalis where where things are taking shape. And we organize it into three areas of context that because we love Venn diagrams, we call it Venn diagram, but it actually makes a lot of sense as a Venn diagram. There's the customer context, which when we talk about context is usually what we, by default, are talking about. You know, what was the customer's, you know, goals, their intent, their preferences, the history we might have of engagements with them, understanding their jobs to be done. But this has to intersect with the context of the company, you know, and and the people working at the company and their goals, their strategies, knowledge, processes, incentives, you know, the governance and priorities around that. And as a Venn diagram, these things overlap, but they rarely overlap perfectly. And then even beyond this thing of, like, oh, there's the context in which the customer is operating and there's the context in which the company is operating. There is kind of this third circle of the Venn diagram of, like, okay. Well, our actual systems, how much do they actually understand and are able to action on that particular context? Because that overlap isn't perfect either. And so, like, looking at this through a Venn diagram, actually, I think becomes a very, great way of, like, just crystallizing what the mission is of the work we need to do. You know, value engineering, a framing of it is a lot of, like, okay. How do we find this intersection, you know, between customer context and company context? You know? You know, and then when you're like, okay. So as as, you know, we focus on that. Now how do we actually execute on that with our digital systems? This is where getting that systems context to have a greater overlap with company and customer context, That really is the mission of context engineering. And if you're thinking like, yeah. That's that's probably a lot of work. This is actually between value engineering and context engineering. It's one of the reasons why we're so bullish on careers in marketing and marketing operations. AI is not taking over this work. Quite the opposite, you know, as a result of AI, there is so much work to be done to harness this capability, deliver on these possibilities. And one of the things we would would leave you with is, as the aspiration, you know, is at the point where you get the intersection between then value engineering, you know, for the customer and the company and the context engineering for the systems that can actually deliver on that, that intersection of the Venn diagram is what we call the golden context. And, you know, it's a bit of a homage to the idea of the golden record, you know, that, again, one of those things we aspired for for many years with systems of record. But the golden record the customer, even that was always, you know, well, an aspiration. We never quite made it. But it was also it's always very static thing, you know, that one of the things that's so exciting about contacts is just how dynamic and fluid it is. And it's actually gonna be a lot harder to be able to get to that golden context, but that's the mission. That is the opportunity. What you see here is is exactly, I think, the game where I will have to go. If, you're a low performer, you will see that many of these, bubbles don't really intersect well. And with high performance, they they intersect much better. And maybe to give you a concrete example, we've discussed this before. IKEA, they have a chat function called Billy, and it it took over, I think, like, 70% of the workforce. Now norm normal companies, which is fire, maybe, these people, they didn't. They looked into the 30% of the questions they did not answer with AI, with the Billy chatbot. They found out it's a whole new opportunity because people were asking for interior design. So they rescale the same staff that were answering before the questions through chat and the phone into interior design, advisors, and they grew a $1,400,000,000 company, and and revenue stream on top. That is exactly what you see happening here. The intersection date, it grew closer together once more. So we won't have time to go deep in this, right now, but one of the things we cover in the report is recognizing that context is not just a singular thing, but it is a composite of many different things that are often moving at very different time scales. You know? So we look at this through the lens of pace layering. You know, years ago, Stewart Brand came up with a way of visualizing pace layering and things like architecture, fashion, maybe twenty years or so ago. Gartner once used this for, like, how you think of IT systems and how they evolve. Just the idea that there's some layers in this case of context change more slowly. They have a more stability upon which we build, you know, things like our company context, our market context, but then things like relationships with individual customers evolve faster. Particular journeys that a customer might be on evolve faster yet. Sessions within those journeys or particular, you know, moments of interaction within that session. And so, really, this mission of context engineering, once you start to realize, like, it's part of it is gonna be the synthesizing these things that are moving at different layers. So much power and potential with this, but also a tremendous amount of work, which again goes back to why pretty bullish about the future of marketing operations, martech, martech management. Oh, yeah. That was the martech landscape. Oh, Yeah. I think that was the original reason we got together here. So Yeah. Let's tap into that. Let's deep dive into that. But first, a special thanks to these great experts experts from our community. We asked somewhere in February, January. Can you help us out, because there were thousands of new tools to validate, and we need expertise. We need, scale. These people immediately jumped in. There were even more. It is so great. So thanks a ton. I think you can double click in the report on these names. Please, visit their profiles and say thank you. So did you already count all the logos? Do you know how many there are? Because without further ado, let's announce that there are now 15,505 tools in the landscape. That is not a big growth. I think. Yeah. You know, wow. So for fifteen years, the question almost every year has been, like, have we hit peak MarTech? You know, would this be the year where it finally leveled out? Well, I I gotta say, I think here we are at 2026 is the year of peak MarTech. You know, the question will be like, what happens from here? Will this, you know, commercial landscape start to compress? Will it reaccelerate? Will it sorta is this the equilibrium at which we're at? Well, we'll have to wait till next year to, you know, see. But, you know, this top line number is, again, just like with the number of apps in a particular stack in many ways, this isn't really where things are happening. It's at the next layer down of, like, okay. Well, what's what's changing within this MarTech landscape? And one of the big changes you don't see here is that almost and this is new as many tools were removed, went out of business compared to new ones. So it's not like there's nothing happening. There's no innovation. There's no new tools. Yes. There are. But there's also a lot of tools, quitting the business. That is new if you look at the division between the two, but also what is new is that we have a first of negative growth rates. It's in content. Yeah. Only for that category though, which, you know, in some ways, kinda makes sense because, you know, like, the the the content category was the one that accelerated the most dramatically back in 2022, 2023 when LLMs appeared on the scene and all of a sudden, like, oh, wow. There's all sorts of, like, content creation tools we can come up with, you know. And seeing the data, there's actually still a tremendous number. But there were maybe more than we actually needed or we're gonna be, yeah, competitively sustainable. And the reason why we have the market map, we've said it so many times, it's just research, figuring out what is going on, and it's always underneath the surface what is going on. So you could argue that it's a market thermometer because all these software vendors are doing their homework and market research. And so it's basically mirroring in a way what you as a as a brand want to buy and are looking for. So we started looking at net growth and net decline and where is the inflow. Yeah. Like, on any given category, you know, there's a question of, like, alright. Is it low inflow or high inflow? How many new folks coming in? But then, again, on the other side of that, you know, is it low outflow of the existing ones leaving or high outflow? And if you put that in this two by two, you know, when you have a category that has both high inflow and high outflow, you know, then that suggests that it's a it's a category renewal, you know, that old is removing, but new inventions and new innovation are happening. If you have a lot of high inflow and low outflow, that's that's where the growth is. If you've got a high outflow and low inflow, we can say I mean, categorically, just from a number of, vendors, it's starting to decay. And then if you have cases where it's low inflow and low outflow, those are those are largely stable categories where, you know, the products and solutions that are in market are kinda the ones that people are sticking with. So that we can apply to our data. So we do the top 24. There's more. You have to go into the report. What you see here is the the color coding we used from the previous slide. There is a bit of growth in certain areas, and, yeah, I would recommend you to double click on that. So for instance, projects and workflows, which is probably accelerating because of Gen AI and empowering the structured SaaS type of stuff. Because projects and workflow is really a bit more of a structured way than doing the collaboration one, which is one this in renewal. This is the transcriptions. This is more the Kanban boards and those kind of thing. A bit more loosely, coordinating the work we're doing. So those are shifts we see. Yeah. I thought the, you know, the growth, around, like, ecommerce and web, in particular, yeah, sort of represents that the language, like, we what we expect of these websites and the services they provide and how the agent world is changing that. We go into a bit more of that in the report as well too. Yes. So read more over there. But, yeah, maybe it's also interesting to see what market has been removed, and we checked it out by year founder. And, again, like in the previous, report last year, same time, this section, of vendors that were founded, between 2010 and 2019, yeah, are hit hard. Yeah. I mean, in many ways that twenty ten to twenty nineteen period was sort of almost the heyday of classic and smart tech. You know, I mean, literally just hundreds and hundreds of, you know, platforms that received a significant VC investment. But, you know, you always ask, hey. Shouldn't these things consolidate? And the truth is they do consolidate. They always have been consolidating. It's just sometimes that consolidation is masked by the fact that they're continuing to be new start ups and new innovators coming into the space. But when you look at through these lines, you see, like, yeah, you know, from that 2010 to 2019 cohort, that is now where calling is happening. And those who survive, you know, we think actually have a very strong future as infrastructure, in this next generation. But, yeah, musical chairs, not enough chairs for number of fast flyers. Yes. And we can double click on this and see that it's the smaller companies that, you know, they tried for maybe a decade and then it it didn't work out. So I think with that, we are coming to a very exciting part, which is deep diving into 70 Genya use cases in marketing. Yeah. And, there's no way we're gonna be able to cover all of those here in the next, like, ten or twelve minutes that we have, but we dive very deeply, for this into the report. And one of the things you did is you got all this data back as you came up with a bit of a a bonus item that's available just for you here, you know, joining us today. Yes. We did this, one and a half years ago when we ran a, Genii use case of 50 use cases, and we thought it was a good idea to rerun it so we can do a a bit of a comparison, and it didn't work out. I I wouldn't say we failed, but it didn't work because it was a totally different sort of thing. It's evolving. It's changing. Yeah. Back in the days, one and a half year ago. Yeah. All year. The old times, it was more about AI assisting humans. So right now, it's more about agentic. So we have to reword the whole survey until we're like, this is not even comparing, which is great, which is also information in itself and an insight. But, yeah, we, we tried to look into this and came to some great altitude view insights. You know? And one of the things that is probably not surprising is just across use cases, overall AI adoption and usage by marketers has grown pretty consistently. You know, in many cases, yeah, like, you know, ten, twenty, 21 points. And I have to say, well, at some level, like, it's not surprising, like, well, obviously, you know, we're all using a lot more AI. I think just recognizing how much adoption across, how many use cases have accelerated in, you know, eighteen, twenty four months, like, this is crazy. This is why if if you're feeling a little bit exhausted, this is this is why. I mean, the Internet, you know, when digital marketing was, you know, really first getting formed. You know, that happened over fifteen years of evolving that. The speed at which, AI is being adopted and incorporated into marketing, it is it is truly remarkable. Alright. So we won't dive into all of the categories here. They're all in the report. But one of the things we wanted to share with you is a bit of an overview of these we saw across all of these different categories and use cases. And maybe the first one I'll jump in on, which is, build versus buy. So when we actually did the survey instrument, you know, one of the things we were curious to see was like, in my cases, are people like buying commercial solutions versus where are they building something on their own? And, the answer have to be turned out to be, yes. Both, you know, and you actually see very it varies from use cases. You see, there's a lot of use cases where people are doing both. They are both, you know, buying commercial solutions and building their own capabilities as well too. Yes. And if you look at this stack, it's stratifying it. You see a lot of experimentation on the one hand, duplication with, existed, which is already something we discovered a couple of years ago with a composability survey that, yes, you have a laboratory type function and a factory type of function. Yeah. You know, that stratification is maybe just a a slightly more elegant way of wording what we were describing earlier, which is, you know, these SaaS platforms that serve as the deterministic bedrock, you know, as this infrastructure upon which things are building. They are actually thriving, you know, SaaS pocalypse be damned. They are thriving in those capabilities, you know, but it's on the layer on top of that, you know, specialized solutions that companies are now able to create for their operations, their workflows, their customer experiences, that's where you see so much of this new AI native and AI home built solutions. And in B2B, it's leading on breadth, whereas B2C built for depth. And what is really interesting, we always saw that b two c was kind of leading in the adoption of SaaS. Here, it's almost like the opposite. So a, AI is really well adopted in b two b, especially buying and building, whereas b two c is building for depth. I think it has to do with the fact that they need to make it very contextually Sound expert. Specific for for their brands and the brand experience. So, the trust ladder, you know, one of the things we were able to look at was across these different use cases. The use cases where marketers are essentially using AI to just help them understand and interpret and discover what can happen, you know, more analytical approaches. Like, that's not widely adopted because, again, there's very low risk to that. You know? The next stage up on the, you know, trust ladder is using AI to create things, but still with a human in a loop, you know, and there you see slightly less adoption, but growing, you know. And then, like, this this third layer really feeling comfortable enough that we can trust AI to turn over certain tasks in a more autonomous fashion. That's where the data shows, yeah, you know, a number of these use cases are still early on. So that kinda makes sense, you know, the stage in which you would climb up the rungs of the ladder. The other thing we saw was in use cases that tend to be more focused on governance. Things like, hey. Are we, you know, properly enforcing, you know, compliance here? Are we looking at the the validity of, you know, content being aligned with brand standards, things like that? Those those adoptions, they they were lesser, than the other ones. And so, it's still like a theme along folks are talking about, humbly, rightfully so, As we move from this pilot experimentation stage to trying to actually make a butterfly that's gonna take off and, you know, not, you know, end up in legal trouble, yeah, there's a governance gap to close. Yeah. Nobody gets promoted for implementing a governance model. That's cute. Yeah. That's good. You know? Alright. And then the last one was, of course, you know, just this rag everywhere, which, you know, that was the term that, you know, had been used last year. I think at this point in time, we can simply say, this is really a part of the overall context engineering, mission. It's being able to make sure we can get the right data to the right place at the right time, the right governance. So, again, we can't go down time to get into all the categories here. But, you know, if you dive into this report, you'll see for each one of these, like, content experience, we go through the different use cases, where people using AI in existing SaaS, using an AI native tool, building their own, or just even not using AI in a use case yet. We kinda have a nice heat map for that. Yeah. And then we did this great way of, like, actually splitting out, you know, the b to b versus b to c's. Number of these graphs, that's the way you interpret them is the y axis is where we see people doing more and more building. The x axis is where they do more buying of solutions and, again, not necessarily either or. And then the color of these circles, is reflective of when they do buy solutions there, how much of it is more, you know, classic platforms evolving, how much is more AI native on the sides of the bubble. It's also a bit of just like total adoption there. And we took this one because, you know, initially, this was the category most, impacted by Gen AI. There's interesting stuff happening in advertising as well on social, so you have to have a look in the report. What you see here in content and experience, especially in DTC that, for instance, briefings, you know, one of the most important elements to start and kick off an ATL campaign in b two c is is very high up there, and then it's content creation and maybe prototyping a little bit. Even translations are high up there, but they're building it themselves. This has been a bottleneck for decades, and this is where you see now AI coming in and they're playing with it. Very cool. And then maybe one other category just to be a sneak preview for data because, yeah, I mean, like, what? You know? It's, you don't have an AI strategy if you don't have a data strategy. Yeah. And this one we picked because initially, generative AI was developed to interpret unstructured data. So here you go. And you see here that it's using very different, variety. What strikes me here, and we spoke about the infrastructure layer, stuff like lead scoring and dashboarding and audience identity management is not something you do in a probabilistic way. You want to have a deterministic approach. So this is where we started thinking, hey. This is the divide or the the the division or specialization of roles and tasks in probabilistic and deterministic fashion. Very, very interesting. And, also, you see what I think is very striking, very important also for value engineering, chat with your data. Mhmm. So the customer talks to you through the data because they click on something, they purchase something, they don't purchase something, they read something, they don't open some, and this is something you need to interpret quickly and rapidly, and this is where GeniEye excels. Anyways, there is so much more in the report. Hopefully, you've already downloaded a copy of it. As we wrap up this keynote, I do hope you're gonna stick around because the discussions we're gonna have with experts from each of our seven sponsors, this was our opportunity to bring a lot of our questions to them, our questions of, like, hey. How are these things changing your business? How are things changing, you know, what you're seeing with your customers? You know, so we're in chat with, John Moran of, SaaS about the middle layer, of the MarTech stack. You know, this idea between systems of record and systems of engagement. Kept up Oh, long. We're kind of missing a pretty important, middle layer within that. We're gonna have a wonderful conversation here, I'm sure, with Tayshia Spannohar, the co CEO of Hightouch, how AI agents are reshaping the marketing workflow. You're hearing a lot about agentic marketing platforms. There's a growth loop, Anthony Rodeo, co CEO. We're gonna talk about Causal AI in the future of marketing. Turns out causation and correlation, not the same thing. Our returning champion, Sarah Paz, we're having a wonderful conversation on the future of CMS in an agentic world. Some of these things you might have been hearing about are CMS three point o or generative CMS. We're gonna, like, pull back the cover on that. We'll then go chat with, Ravi Doda, who's the CEO of MoEngage about open and explainable AI marketing, you know, making that black box a little bit more translucent. We then get a talk with, Tara Dezeo about the self orchestrated customer journey in real time and also a bit about the responsible AI. I think she's got some phenomenal insights on that. And then we wrap up, with a conversation with Brendan Farnan, who's the cofounder and CCO of NAC on the campaign creation renaissance. You know, that whole content and experience space Yes. Turns out we're actually just getting started, in what's gonna be possible on that. So, wow, with this first of all, thank you, my friend. Thank you. Such a lovely collaboration. Thank you for coming and joining us here today. A huge thanks once again to our sponsors, Growth Loop, Hightouch, NAC, MoEngage, PIGA, Progress, and SAS, wonderful platform providers here, Goldcast. It's a super exciting time. I think, you know, closing out where we we started that we're in that Krasala stage. Things aren't perfectly formed. It's okay. We're we're on this journey together. We're on this journey together. And maybe it's a good idea if you go into the report webinar or whatever and share it online and let us know what stands out for you most. Thank you so much. Thank you. Tokens run out.