Scott Brinker on How AI Will Reshape Martech in 2025

welcome to rev ops FM, everyone. Big day on the show today. As we chat with someone who really needs no introduction. It is Scott Brinker, VP of platform ecosystem at HubSpot editor at chief martech.

com and creator of the famous marketing technology landscape, super graphic. Now Scott has just released a new report called Martek for 2025. And as you might expect, it's all about how AI is reshaping the marketing and Martek environment. And it's just under a year ago that we've done our first episode on the impact of AI and marketing.

So I thought this is a great time to check in, see how things have evolved in the past 12 months, and how AI is going to change your job in the years to come. And Scott is gonna be our guide. Scott, it is a pleasure to have you on the show. Well, thank you so much for having me, uh, fun topics looking forward to this It is now Scott, I understand your cocktail party trick is that you can recite all 14,000.

Vendors from the Martech landscape graphic by heart. Is this true? and I can even draw their logos from memory. It is, it is, it is an amazing talent and my, my hat goes off to you for maintaining that now for how many years is it now that you've been, producing that graphic?

Oh my so it's over a 14 year period. We took one year off, back in 2012, I think. Yeah, but yeah, it's just been growing crazy ever since. Well, it is become an amazing sort of pillar of our, uh, of our ecosystem.

So thank you for the work that you do there. on this year's report on Martech in 2025, maybe let's just start with the bottom line up front. Like what are top one to two messages you would like mops pros to take away from this report? Wow.

Well, I think at the end of the day, we all know marketing is changing. both at a technical level of like the capabilities, you know, uh, the products we use, what AI is making possible for us as marketers to do. but it's also very clearly shifting the dynamics for how buyers are going to want to engage with us. you know, it's one of the things.

I mean, just to like pick some random thing we could look at that in previous years would have been an entire year's worth of content. Uh, and then this year we're like, Oh yeah, by the way, it turns out that maybe Google search, you know, is going to be disrupted by these other channels in which people are going to go and like, you know, just directly get answers to questions. We're like, yeah, that's one of the like 15 or 20 major disruptions that I'm looking at at the moment.

Thank you very much. But I mean, you know, again, I know this is. I don't want to sound flippant about it because I know it's a stressful thing for people when you've got all these changes and you're trying to keep up, uh, and if there's any consolation, it's that everyone's in the same boat. Everyone is trying to keep up with these changes.

Um, but I, but I always take this optimistic view that these sorts of disruptions are actually a good thing. Gift to in particular marketers, um, you know, marketing is always about differentiation. It's always about standing out from the crowd, you know, and when you've ended up in a market or an environment where sort of everything is sort of converged down to like a common playbook and common channels and common approaches, it's actually really, really hard to stand out. But when you're in these moments of disruption where things are changing, I think those sorts of marketers who are willing to be.

bit bold and like push out on the frontier and experiment with it. It becomes a really great opportunity for them to differentiate. So yes. There's a whole bunch of ways in which, you know, marketing and MarTech is changing, but it's actually a great opportunity.

One of the, risks concerns, I don't know how we want to phrase it, but one of the, one of the things that I've felt and seen other people express. Is, you know, if everyone's using chat GPT to write their copy, everyone kind of sounds the same. so it's interesting that you, you poke on it as like an opportunity for differentiation. How do you think companies can use AI, to actually be different to stand out?

Yeah, well, I mean, I would argue the, uh, yes, just using chat GPT out of the box to write my blog articles the same way everyone else is, that is that same sort of pattern we've seen of like, oh, well, I guess if you publish a top 10 listicle on keyword X, you know, I mean, there's always a formulaic approach to these things that, you know, sadly, yeah, a lot of, you know, sort of Companies a lot of marketers like fall into the trap of but very few of those things like started out That way there was someone who actually invented the listicle who like hey Wow, this is actually a way to like people this resonates with folks.

Oh, I could do a series of these Andrew Chen who? uh, has had a number of different roles, you know, as a VC up to growth for Uber. Uh, he had a phrase he had invented. The Law of Shitty Clickthroughs.

Uh, you know, which is basically this idea that, you know, marketing, particularly in the digital space. Is this continuous sequence of, okay, a market or coming up with some sort of novel way of breaking through and engaging. it works, like it's like this incredible, like return because it's not been done before. And the word starts to leak out and then other people start to do it.

And basically eventually over time, the efficacy drops and it reverts to the mean and you have to come up with another one. And so, yeah, there's definitely a lot of that with AI. I think, you know, the folks who are going to be creative. With generative AI aren't just saying like, Oh yeah, chat GPT, write my blog article for me because.

Nobody's going to be reading, those blog articles. I mean, like if we have, if we produce like a hundred times more, you know, blog articles, uh, we have not invented a hundred times more human attention, you know, for people to actually consume these things. In fact, if anything, there's sort of the indication now that, you know, the recipients on the other side here, they're kind of savvy on this, you know, they're leaning into like, Hey, you know, this huge stream of emails that you sent me.

Can you just summarize that in a few bullet points for me and let me know if there's anything particular I should pay attention to. So I think the creative marketers are looking at generative AI or AI more broadly, not about just how do we churn out more of the same kind of articles, but how do we do something different? on that note, your survey suggested like at least 80 percent of marketers. If I interpreted the results correctly at least 80 percent of marketers are using AI in some fashion, and it looked like at least half of them were using AI.

Daily or weekly, which I think is huge, like compared to where we were a year ago, where it felt like people were still like, what do we do with this? And what is this thing? but drilling down on on that creativity? do you have a sense?

Could be database could be intuition based, like the impact of this AI adoption? Is it mainly efficiency right now? Or are there marketers out there that you've seen who are actually being able to produce better marketing as a result of doing this than they otherwise could? there's a little bit of a blending between the efficiency and the innovation side.

for instance, like, when I think about it this way, okay, so like the efficiency would just be like, okay, well, we can do this thing faster or we can do it less expensive. Not bad. but here's where it's sort of like plays into the degree to which you're able to turn that from an efficiency into a true innovation thing is it starts with like. The questions, the hypotheses, what are the experiments that I want to run, you know, well, historically, you know, for marketers who weren't necessarily data scientists or data engineers, like being able to go deeper into their data, you know, to answer some of these questions, to get data, to feed some of their hypotheses, very often involve the cycle of filing tickets and grab an analyst, track that down, you know, and so there's a whole set of some of these, uh, you know, new AI tools.

many of them are early, but they're developing very quickly. Uh, that is democratizing the ability for marketers to be able to ask more questions of their data and have it come back with answers immediately. So we're taking out, you know, that whole like multi day or in some case, multi week, you know, analyst cycle. Now you could say, that's the thing about efficiency.

It is, you know, but to me, marketers, like one of their. is their imagination. That's just like constant, like coming up with questions and ideas, you know, the vast majority of which we've kind of been trained to just like, let go of, because we're like, oh yeah, no, I just can't take too much work to get that answer. So, you know, I don't care, you know, but as we sort of, you know, uh, reduce the number of things that feel out of reach to be able to like ask more and more questions and get answers and like chase those ideas down, I think that actually helps unlock.

New creativity and new ideas. Same thing when we talk about the actual implementation, when we compress the time and costs to be able to produce things, This isn't just about saying like, Oh, I was going to produce 10 things, you know, and I was going to spend a week on it. Now I can produce 10 things and I can do it in two days. Um, Oh, okay, great.

I'm more efficient. You know, it's really like, okay, now for that extra three days, like what do I do? Can I produce more things? And it's that produce more things, which is where you get that.

inflection from like, okay, wait a second. This isn't necessarily just about efficiency because as long as you're not saying, well, I'm just going to produce more of the same sort of thing, but you're gonna be like, oh, well, let me try some other experiments. Let me like, you I've got a little bit of time, I've got this wow idea. let me create this and put that out there.

So harnessing that time is another way. And a third one is, and we've seen this direction headed for a while with these sort of no-code tools, but now generative AI is bringing. a sort of no-code capability to so many disciplines. You know, I mean, you know, with, uh, you know, images.

We're starting to see some really cool stuff emerge here in video creation and things like this. it ex it democratizes ability for people who have an idea? To actually like, instantiate it, make it real. you know, and even if you want to be modest about it and say like, Oh, well, you know, maybe some of this stuff isn't ready to go from idea to, you know, production ready final version, if it's to go from idea to something, that's a pretty darn good representation of what it could be, you know, to be able to get reactions to that, you know, and use that as a guide for developing the production ready version one.

Again, is this an efficiency thing? Yes, because I didn't necessarily need the full team to even like put together a prototype concept, but it's that empowerment to create these prototype ideas, you know, just pretty much at the speed of thought, that is a huge unlock from an innovation perspective. Oh my goodness. It's like fuel for the imagination.

And you've multiplied your ability to iterate to your point, to try more ideas or to spend that time doing deep human work that maybe you usually would not have time for in a typically overloaded calendar. Yeah. You know what I mean? You talk about like what you could do with the additional hours in the day and certainly more experiments and more ideas, you know, but for the human work, how about spending more time just talking with customers, you know, I mean, we know as marketers, like that's a big part of our job.

I. You know, a lot of marketers who don't get a lot of time actually being able to talk with customers to be able to have that sort of like human connection and human insight that they feed in to their imagination. So I think you're right, like that sort of human work, boy, if we could get 10, 20 percent more of our week, uh, you know, allocated to that, that would be a huge gift. You know, one of the first things in your report is a graphic of the hype cycle.

I will include a link to the report so people can can look on this, but you know, it has that peak of inflated expectations, that trough of disillusionment and the, gradually increasing up to the plateau of productivity. And I can feel riding that over the past year from like, whoa, AI, like AI, like AI SDRs, they suck. And then gradually I find myself now you mentioned about disrupting Google. I've started to ask chat GPT things instead of Googling them.

Like it's actually, I don't do it on purpose. It's just, this is a more efficient way to get my information or, I have grok on my phone and yesterday there was a battery with some words in French and I didn't understand it and I just took a picture and said, can you translate this text for me? Translated the text like it's starting to work its way in. So like, where do you think we are on that on that curve?

Where would you peg us? You know, obviously everyone's at different places personally, but as a collective, what do you think? two reasons. Um, one is I'm convinced there isn't actually one hype curve here, but like there's a multitude of different hype curves all at different stages.

I mean, again, like with, um. I mean, just take some of the stuff here with like open AI, you know, where are we at as far as our comfort level and using chat GPT, to help us brainstorm or help us like edit some of our writing. I think we're actually now pretty far into the, you know, plateau of productivity. Most people are pretty comfortable with it.

It's pretty good at that. Where are we on using it as a search engine? I don't know. Probably at this exact moment, uh, given the announcements, you know, uh, happening, you know, right now, uh, we're probably at the peak of hype of like, Oh my goodness, it's going to disrupt everything.

There'll be a trough, you know, where we had on the, you know, ability from open AI with things like soar and, you know, there's video creation stuff. Probably very early on in the technology triggers. And so it's, it's interesting because for one thing, there's just multiple hype curves at different stages. but the other thing is, this is the thing about the hype curve.

I've, I've just noticed over the years. And again, kudos to. Gartner for like identifying this pattern and like articulating it, yeah, in such a excellent way, but it used to be, I mean, I've got some gray hair here. It used to be that these hype curves were something that played out over.

Between a 5 to 10 year period, you know, in fact, this is how like the tech industry was structured. This is how venture capital was built to do this, slowly, You know, well, maybe it's more like, you know, 4 to 6 years, things like in marketing, uh, the customer data platform, you know, You know, revolution. I feel like that thing like Barely got started in like 2015, 2016, you know, and now we're already at the thing of like, well, CDPs, are they, they a thing now, or is this all the day?

And you're like, wait a second. I, this thing was like barely getting started. Like what, um, now we're on to some next new, uh, you know, architecture. It feels like an AI compressed that hype curve down to something that really is more about months than years.

I kept feeling like, you know, even in the first year or so of chat GPT, you'd be like, okay, well, yeah, this thing is really great, but it's data is, uh, you know, two years old and then they connect it up to the web and you're like, okay, all right, well, I guess that's all. Yeah. It's really good, but it can't do math problems. Uh, you know, oh, well now we connected and it can run little Python programs and.

Calculate that and you're like, okay, well, like everything I like thought was this barrier that would be this multi year thing kind of vanishes after a couple months, you know, and so I think it just makes it really hard to say like, yeah, the hype curve is still a thing, but where are we and how quickly will we move along? Boy, I don't know. you mentioned the book, the innovators dilemma, a number of times in your report. And in that book, uh, the author talks about how he studied the disk drive industry and Because of this like fruit fly effect where it was a very fast moving industry.

So you could observe the life cycle. And I just, he's passed away obviously, but he was here looking at, at these very compressed cycles that we're going through now, like it is like watching life almost on 1. 5 X. You know, the way you can with a YouTube video where you see things moving very quickly, businesses starting trends coming and going.

Yeah, no. And such a brilliant, Clay Christianson, uh, you know, the whole innovative dilemma. but this thing about disruptive innovation, boy, you just see it this perfectly thing. Like, his big, insight there was, you know, so often these disruptions would happen.

From below, where like it would end up empowering a set of people to do things who quite frankly, wasn't cost effective for them to do things the old way. Like the great example would be like, you know, coding. Okay, well, if I needed to build a software program, you know. Not too long ago.

All right. Well, I've got to hire multiple engineers. I'm going to need this. You know, it's like, oh, okay.

So if I want to do a simple little program to like, you know, go fetch me some stock market data and I'm like, no, it's just not worth doing that, you know, and then you have these disruptive innovations that come along that. They're maybe not yet at the level where they can do the sorts use Silence. cases, they work friggin beautifully, you know, and they unlock that capability for all these people who before, like, didn't even have the option, to get things done. So Big fan of, Christiansen.

This is his time. Yeah, no, it's. above, he's like, yep. but part of that dynamic that he describes about the innovation coming from above you, you start to address it a little bit in, the different segments and, maybe you can walk us through them a little bit, but the, the segments that you kind of spell out, are these indie tools, you know, bubbling up Using AI innovating very rapidly.

These challenger platforms that are kind of, threatening the big incumbents and consolidating a number of different abilities, the incumbents. who are, you know, quickly trying to add a eyes as fast as they can onto their platforms. and then, uh, won't even touch services as a software yet. But then you also talk as you just address this, this custom apps idea that actually I can just build it myself now with chat GPT, help maybe amend or add anything you'd like to to my explanation of that landscape.

And then perhaps where are you seeing the most fruitful innovation right now in all of those segments? well, I would start by saying, you know, in any, in any, uh, in any, uh, in any, uh, Period of disruption. Uh, we're used to thinking of sort of the battle between startups and incumbents. you know, and one of the reasons why we made a distinction and sort of the startup segment between this idea of these indie tools versus the challenger platforms is because so many of these new AI tools that have popped up.

actually looking to displace, the major incumbent platforms. In many cases, they now integrate with those platforms. They're complimentary to those platforms. and so it's a little bit like, oh, I mean, again, not to say there won't be overlap and exchange back and forth, you know, in many ways, there will be.

The complimentary nature there seems to exceed the competitive nature, at least at this stage of the game versus there are a set of challenger platforms that are very much looking at this inflection point of like, Oh yeah, we're gonna, we're, we're bringing down Salesforce, you know? and that's a hard. That's a hard hill to climb, but, Yep. know, this is the nature of the tech industry.

Um, some, some of those folks are actually going to climb that hill and, you know, disrupt the incumbents they're going after, you know, so that's the, you know, that's what a central, the commercial, uh, MarTech landscape, uh, you know, where we're seeing those groups. The thing about the custom software is people Because AI is really, I mean, already the barriers to entry in creating software, the cost of creating software was already like plummeting, you know, given the cloud, given frameworks, given, you know, AWS services of every flavor you can.

I mean, it was already like headed down, like more people could do it than ever, but now with these AI co pilots. And increasingly, you might talk a bit about how AI agents are able to even create code without you knowing that they were creating code for you. Um, you know, it's just further accelerated this ability for like companies to be able to create more and more software themselves. will that be a total replacement to those commercial products?

my sense is in the, Foreseeable future. Probably not. Um, you know, with due credit, uh, what was it? Klarna, you know, ripping out Salesforce and Workday and all these other ones, but you know, they're, they're in a league of their own, you know, but what I think of it is, is a more of a, again, another one of these complimentary components, where like, okay, yeah, there are certain capabilities on the market that it wouldn't make sense for me to like reinvent the wheel, right?

Like, you know, I don't need to write my own email server. At this point, that's not a comparative advantage, uh, you know, for most companies, um, but when it comes to how I want to design a particular customer experience or the way in which I want to optimize a particular workflow, from quote to cash, you know, that actually might be something that I would want to custom develop the way in which that works, the way in which that experience is delivered. Now I can do that on top of those commercial platforms, but I'm now leveraging AI.

To be able to accelerate the customization that I build on top of it. you know, incumbents have often in my experience With adding that innovation and, mean, there's many potential reasons. Part of it is maybe they just are not as laser focused on that one problem, but it's always seemed counterintuitive to me that like. You know, two developers in a bedroom somewhere can produce something that is a bit closer to the pulse of the market than some of these big legacy platforms that I've owned and used with all these resources who just seem to miss the mark.

And some of that, you know, you've mentioned, you know, they have backwards compatibility, dependence on existing revenue structures. There's a lot of the innovators dilemma stuff. And then part of it, to me, it almost feels like a mindset thing where somehow a dysfunction creeps in at larger scale where they seem unable of, of the focus or of the creative leaps that are required, to innovate at the level that sort of the young blood can, I wonder, do you share that experience?

I'll say as a notable exception, I'm not just saying, cause you're on, you're on the show, but, but, but HubSpot almost feels to me it to be an exception to that rule. And I don't know how they've. continue to innovate the way that they seem to have been able to do, at the scale that they're at, but a lot of other companies, and I come from the Marketo ecosystem, they have not been able to do that, like Eloqua has not been able to, you know, a lot of these players, um, what is your take on this issue about innovation within legacy incumbents and how can it flourish as a possible?

there's a quote and I wish I could remember who said it. Um, I believe it was a at Andreessen Horowitz. that it's like, uh, it's a race between, will the startups get distribution before the incumbents get innovation? I think this time is a little bit different.

you know, for a couple, of reasons. First of all, is this pattern of. dilemma like disruption from below. Everyone's now sort of read those books.

Um, you have a whole generation, you know, of, uh, executives at these incumbent companies that perhaps a number of them actually were the disruptors, you know, Okay. uh, A little bit slower, just, you know, it'd be sort of easier to say like, yeah, yeah, all right, we'll get to that next year. We've got, you know, the main thing we need to ship this year, you know, there's just. There's just no illusion of that.

You know, everyone basically looks at that and says like, yeah, this is, this is changing the game. This is going to change everything, you know, about how software actually works. And so given that, you know, forceful momentum, you know, and then, yeah, the fact that a lot of the leaders that these companies do recognize that. Yeah, you know, if, if we lose this, we're going to lose it because we let, you know, someone disrupt us, uh, you know, from below on this I think companies are like attacking this with much greater ferocity, than historically they have.

Now, that being said, there are still like, there are structural advantages and structural disadvantages, you know, that large companies have. and I don't think there's a silver bullet to it. But I think we're going to see through this next cycle of the next five years, which ones of the incumbents actually successfully made the transitions and which ones didn't. And they're going to become the, you know, business school case studies of like, okay, here's how you fend this off.

I mean, at the end of the day, it's, you know, we started with Christiansen, we end with Christiansen, it's like, you have to be willing to disrupt yourself. you know, and I mean, I'll, uh, you know, give a shout out here to, um, competitor. you know, I think, Benioff, you know, and basically taking all of Dreamforce, being like, nah, it's not Salesforce, it's AgentForce, you know, my God, was that a ballsy move. now, you know, how that plays out, how it delivers on it.

All right. You know, I'm not, not for me to comment at this point, you know, but I mean, that's sort of like willingness to like disrupt, you know, all of your existing plans, all of your existing narrative, you know, go to market. That's the sort of thing that I think Christensen would be like impressed and be like, okay, yeah, no, not shy about disrupting the previous model, uh, you know, to like make it to the next wave. Yeah.

And just to close the loop on your point, the latest edition of Innovators Dilemma has a forward by Benioff. So, um, See? Okay, yeah. So, just to underscore your point about the latest, you know, the disruptors who have internalized the message and, like, maybe we've seen sort of the end of history then of the, um, Of that pattern repeating, and in terms of the actual implementation, like I think we all saw in 2024 Oh, we need to get our AI features out the door.

Like, what are we going to do? You know, almost like a, solution in search of a problem in a way. Like we, we have AI, we need to figure out what we're going to do with it and how we're going to. we want and be able to deliver that to users.

Um, and so, and so, but we have some, we have some questions that we haven't answered. So Mike, you're welcome to jump in and get to them. and some of them. I've been really interesting.

Some of them, a few of them have been good. Some of them have been kind of like, Oh, I see what you're trying to do, but, but not so much. One of the things I've observed just personally is a bit of a last mile effect. Like, uh, we are recording this, uh, using the tool to script.

It's a great tool. I use it to, to record my podcast. They have a feature just as an example, like remove umza nos, like, Oh, amazing. This takes me hours.

I just pushed this button, remove umza nos. And it does a pretty good job, but then I don't know, 5 percent of the time, It clips a word. It makes it weird. So either like you're okay with that and you're willing to tolerate that or you still have to review the whole thing anyways and so it's almost that last mile is enough to like, it's just not quite there.

Do you think is this, a fly in the ointment and we will outgrow it or do you think there is something deeper there that companies need to address to be successful as AI software companies? Yeah, and I think this is one where it's probably incredibly use case dependent, uh, you know, on what the both. Um, what is it? It's a question of both like how much tolerance is there for error.

Um, and then. You know, what is the, uh, speed by which asymptotically you're like reducing the error down to where it falls below that threshold, you know, that you're concerned about. Um, I do think video, video, audio, any of these things that are like human representations. I mean, they have the phrase for this, like the uncanny valley.

Um, You know, we see Silence. I mean, a technological perspective, it's a frigging wonder, it's still pretty clear, like, nah, nah, this is, this is not a real person. And even if you weren't super paying attention to that, just something would seem off, you know? Will we get past that?

Kind of suspect we will. and I think in the meantime, you know, a lot of those things, yeah, like again, everyone has their own choices. Like I wouldn't use them, you know, for production purposes. you know, but am I comfortable using it to like, you know, like say clean up certain amounts of like audio background noise.

Yeah. It seems to do like in general, like a phenomenal job. I think, I think you're right. I think, no, no, I think you're right.

And it's, it's like, I can't remember if this was in your report or something else I was listening to, but it's like, it doesn't work. It doesn't work. It doesn't work. And all of a sudden it works and it's normal.

It's just like, well, yes, of course this would be the way. And I think that'll, like you said, with the, the ums and ahs or something like that, it'll, Eventually go below that threshold of acceptability of, of error tolerance. And, and then life will, you know, will never be the same. Let's talk a little bit about, agents.

This was a concept that I, I kind of thought that I understood. And then as I read this section, was the area where I realized my, my understanding was quite superficial. In terms of like, Oh, I actually didn't really understand what we meant by that. And about this idea of a large action model and the real implications of an agent.

So maybe assuming there's probably other listeners that are in that position as well, do you walk us through this and what it means and what the implications are maybe for how we will use them? Yeah, happy to do that with the caveat that, um, you know, if you ask 100 experts for their definition of an agent, probably get at least 120 different, explanations. I think the oversimplified version is almost like an automation. this ability to simply say like, I'm going to make a request, of this app, software agent.

It's going to go off. It's going to perform that request, come back and deliver it to me. I, I think one of the examples I pointed to here was, Dharma Shaw, uh, you know, co founder of HubSpot. He's, uh, created as one of his many projects that he goes off and does.

Uh, I've been the custom wonder of this fellow, agent AI, which is a whole bunch of these, like very. Purpose built, you know, agents that they go off and they do one thing and they do it. I think if you ask others, you know, what makes something an agent is the ability for it to have almost a little bit more of an internalization of how it goes about making decisions, you know, so you give it a higher level goal. starts to reason about, okay, well, how would I accomplish that goal?

I would break it down into these different steps. I would take this first step based on the results of that. I would adjust, move to the second step. you know, and I think this is where people get most excited because as you start looking at agents through that lens, you know, it's not just about things that are almost like these.

You know, uh, very purpose specific tools, but they start to become things that I'm not quite sure I'm ready to call them digital coworkers, you know, uh, but they become like, yeah, these more autonomous, support capabilities, for what we do, But I think this is where things get really interesting is because, okay, we're, we're used to starting to have this interaction with chat GPT, just from a conversational perspective, you know, and particularly with some of the latest frontier models on these LLMs, you can actually see them do the reasoning and you can ask them and you're like, yeah, I could kind of figure out a plan for this, the, the leap from that conversational AI to truly being an agent is like, okay, well, now I've empowered this thing to take action.

Action out in the world. and some of the most recent, announcements from folks like Anthropic and OpenAI are about, you know, giving these, LLMs, the ability to, uh, I think what Anthropic calls it like computer use, it's this idea that you can start to try and like operate some of your software for you. I think that stuff's really interesting. to me, when I look at the greatest opportunity for agents to be put into production, it's less about having them work with software the way we humans would work with software of like, Oh yes, uh, navigate to this menu choice and drop down here and pick this and fill out these three form fields.

Not that it is impossible, but it's frankly, Boy, a really circuitous way, you know, for a computer to interact with other computer systems to be able to get things done. The way in which do that is we do it through APIs, you know, and this is the thing that is very exciting for me is this rise of the AI intelligence to create agents. Is happening at the same time that we've been on a good streak for the past decade of more and more software is now built with open APIs. You know, this was a function of we ended up with these heterogeneous tech stacks.

People needed to integrate things together. How do you integrate it together? Well, you have APIs that can talk to each other. We have all software categories integration platform as a service, you know, build around that capability.

Um, And without going out on my soapbox of why APIs are such an amazing thing for, you know, I wish more software vendors did a better job with more APIs. They're at least headed in the right direction. I think having that converge with this AI intelligence for agents to then be able to take actions through those APIs is that combination is going to just unlock a tremendous amount of. Is the agent, an AI of a kind of different nature than what we're getting when we're chatting with chat GPT, or is it more just it's like chat GPT with lots of extra context empowered to take action through APIs or through access to interfaces?

I think the thing that takes it one step further is the ability for it to be a feedback loop. I mean, like, if you have an agent, you know, it's not just about like, oh, it comes to a plan. It can kind of have done that already. Alright, now the next step was, okay, it can take action.

Action on that plan. It can call, you know, these APIs or primitive sense can like manipulate the computer. UI for you. but then the step beyond that, that gets really interesting is it can then feedback into its decisioning.

What happened as a result? Oh, this is the result I got. That was not what I expected. Okay, let me adjust my plan and I'm going to try this other thing or, Hey, wow, this looks like this is a problem.

I want to flag this and create an exception, you know, for someone, you know, that sort of feedback loop. And again, this is all very early with people experimenting with stuff, but I think that's what starts to make this really powerful because. You know, we've, again, over the past 10 years, automation has been one of those themes that's made its way through pretty much every facet of the tech stack, but certainly in rev ops, marketing ops, sales ops, right? I mean, marketing automation platforms, the very name of these products, you know, are about automation at the center of it.

but a lot of it's been this very deterministic rules based automation. where things get dicey is when something goes wrong or it doesn't work quite as expected. Usually then we've had to sort of like surface that up to then the human administrator to figure it out. to allow these AI agents to be in a position where they can kind of figure it out for themselves.

Boy, that's a huge breakthrough. Maybe if you'll indulge me in a thought experiment, because this is, it's just a really interesting thing. something in marketing operations that is A common task, you know, building out some kind of campaign or program or email within a marketing automation platform, whether that's Marketo or HubSpot or something else, quite often we have internal service resources or teams that do that. So a marketer says, Hey, I want an email.

They do a ticket, someone on the marketing ops team will go and build that for them. what would be involved if we wanted to make an agent to replace that, which it sounds like we could, whether the agent was using APIs, which many of these tasks are possible through APIs or had to do it through the interface. Like, how do you even get started if someone wanted to do that? Like, is that a set of steps that an agent could actually, you know, take over?

All right. Well, so I've got to like, you know, let, is it getting full copy or is it only getting sort of a framework? Does it adjust it? Does it check it up against, uh, you know, brand voice?

Okay. Now, like, do I think about like, okay, who am I doing for like audience selection? You know, who's the list? What should be the criteria for that?

Um, okay. Now I've, you know, got my email, like, you know, what's the right delivery schedule, you know, for these folks, or there are other competing, you know, messages. How do we make sure, you know, we've got the right throttling on that. I don't know.

Maybe there's things of like a check of like the accessibility, you know, there's like a whole series of things that you could imagine us doing that with more traditional automation. It's just. Some of these things were so qualitative in how they needed to be evaluated and like traditional, you know, machine learning, automation AI just wasn't quite able to capture that as well as, you know, these LLMs have almost like the mirror opposite. You know, they're particularly good at some of those like squishy, qualitative things.

I think if you mirror those two things, it wouldn't all surprise me, uh, you know, to have more and more of these agents show up that can, yeah, kind of like take a ticket from a marketer, basically roll together, you know, the whole campaign. Presumably for at least some time here, then present it to a human who will review it, make sure, like, okay, yeah, this is, you know, what I wanted, but if that reduces, the work and the time, involved in putting one of those campaigns together from again, like three days to three hours, a huge, huge leap.

the fascinating thing about that for me is like every tool is like putting their own little. AI features in place. Like I use Zapier and you can go and build his app the traditional way, like drag your little things in and configure them where you can type something in and be like, and do it. And it's doing an okay job of doing that.

But quite often as a professional in that I'm maybe more inclined to just do it myself a lot of the time. But if every tool has those little things, you're still needing a human there to stitch it all together. But now you have this agent that is kind of outside of all those systems, but can interact with all of them. It's trained on your brand and company specific policies and procedures and can chat with the marketer on your behalf and then go out and execute and take feedback and make changes like that's fairly revolutionary.

Like, are we, are we, do you think that all the systems are in place to do that today? Or are we still like a step or two away from an agent being that autonomous? Yeah, I think a lot of the ingredients are there. again, this is one of these things where, just like we were talking earlier here about, you know, the uncanny valley of, you know, uh, AI video editing and generation and whatnot.

you know, it's particularly when you're talking about like actually running these operations, you know, for multi billion or multi billion dollar, marketing organization, your tolerance for error in there. it's probably such that we're probably a little bit of ways, you know, from wanting to let, you know, the AI agents run that stuff autonomously. They've got some work to do to prove that, the reliability is going to be there. But I think directionally, yeah, it makes a ton of sense.

Yeah, I mean, you know, it's an interesting question as to will this be a different layer of the tech stack? I think one of the things that's really Interesting right now is there's not one agent platform out there. There's not like there's one home for agents. pretty much that every tool seems to be developing capabilities for its own agents.

There's a standalone agents, the LLMs themselves are creating agents, you know? And so I think one of the most interesting things is going to be like, okay, yeah, we've got all these like different. Agents out there, what's the, what's the orchestration, will that be the existing, uh, in common platforms, you know, that have served as the orchestrators and sort of current go to market automation, orchestration, uh, Uh, I'm certain everyone who is an incumbent there, uh, wants that to be the case, you Yeah.

but I think it would also be again to, you know, disruptive innovation. Like, yeah, are there other places where people might be trying to, like, serve as the ultimate orchestrator, of these different agent capabilities? Personally, I would keep an eye on that. I mean, so to an extent, you've just asked yourself the question that I wanted to ask next, which is, you know, you, you referred to the importance of this orchestration and you kind of called it this coordinating centers of gravity.

I thought that was a really good phrase. In this big ops environment that provides cohesion and governance to the plethora of apps and agents and automations. And what does that look like? is it just a workado on steroids that kind of is able to provide the API pathways for agents and other apps that are on the periphery to interact with each other and to take action.

Is it something else that we don't really understand yet or haven't conceived of yet? What will it be? Yeah. Uh, and this would be one of the things I will hesitate to make a prediction.

because I think there's old model, you know, the way in which we would historically have thought of this is something that is a very clear conductor, to the orchestra, uh, your example of like, you know, workado, you know, as a version of that, I think what's interesting as a parallel is to consider what has happened if the data layer inside companies. so for a while, what we had was we had all these little mini databases attached to every single individual different app that lived in their own little silos.

you know, now with the, More and more of those silos opened up. We started to, in particular with things like cloud data warehouses and lighthouses and things like that, be able to get, you know, data, you know, flowing more freely. but then the question became like, okay, we've now got all this data being generated or being used by different things throughout our organization, but how is this being orchestrated? you've now seen a multiple different architectures, of how people think about dealing with that.

You know, do we do it entirely through one central authority? Do we do it through this concept of data products, you know, where different teams, like, you know, manage things as a product, you know, interfacing to others, how much do you allow things to be federated, you know, uh, where do you allow there to actually be. redundancies or, desyncs, because in the grand scheme of things, what you're losing, you know, from that redundancy is more than gain for, you know, by the efficiency or the speed on some other level.

I suspect it's going to be, if I had to take a guess, it would be something more like that where, boy, this ability at the. Services layer, not the data layer, but the services layer. it is going to be a free flowing mechanism where like, hey, it's all in the cloud. Anything can call anything else.

So it's going to be then about the architectures we put around that. Will some of them be centralized? Yeah, I think those will be the simplest models. But could you imagine one where people start to actually design them as decentralized, systems?

Yeah. Uh, could you could see that? In fact, actually, again, like the larger, larger you get as a company, it's almost like, I mean, we see this in the data products, you know, side, it's just at some level, you know, a hundred percent centralized solution just becomes incredibly unwieldy, you know, at this massive enterprise scale. un, unmaintainable.

And to your point about like digital coworker, I had this, this chat with another founder who's working on an AI product, like an AI that has all the same context as you, you know, that reads every email. That is in your project management system that's getting all the tickets that's getting the meeting notes or is attending the meeting could become could take your job or could become this incredible resource to sort of dialogue with, like, all right, what are we going to do?

You know, like this, this kind of always on partner thought partner working through problems with you, like every person could have one. how much does that actually amplify the ability? Of a person and it's, I mean, it's hard to think of that being centrally coordinated. That's kind of like your, your AI mini me in a way, sort of following you around and partnering with you.

but that, sort of surrealistic kind of futuristic idea doesn't actually seem that far fetched now, given everything we've talked about. No, in fact, actually, this is, I would argue, one of the use cases I talk to who are now doing a lot with AI, like it's one of the most reliable use cases is having these AI again, like whether it's feeding it all of your own stuff, or quite frankly, having it feed. Things of like, oh, well, this is everything I've ever received from my boss, you know, you know, and being able to, like, have these sorts of dialogues, you know, it's like a Socratic method, almost Yes.

uh, you know, just how it changes our ability to look at things from, like, a different perspective. because it's both got that combination of, uh, we won't say it's omniscient, uh, you know, yet, but it's like, boy, it, it, it does have more data than pretty much any other human you would, you know, talk to about that. but also again, in an environment here where there's, there's no emotion on the other side, there's no judgment, there's no, you know, like. Privacy thing of like, yeah, now the AI is going to go like, you know, whisper behind my back about that conversation.

I mean, it just, it's this amazing thing, I think, to give people a lot of freedom to just like creatively dialogue and explore. Different ways of looking at things, coming up with new ideas. It really does feel to me that we were creeping closer and closer to having like the onboard ships, computer of Star Trek, just available to us where it's like, you're talking to them, like computer, do this, do that. Help me solve this problem, become this, and an assistant.

do you ever sort of take a step like sometimes when I think about these things, I'm like, this is like, yes, it could be weird and dystopian and there could be bad outcomes, but it's also just incredible like that. This stuff exists right now. Like what? What timeline did we wander into that?

This is happening. It is so weird. Yeah. you know, again, probably, probably reaching too far back into the archives, but you know, a very early Star Trek movie was a Star Trek four where they travel in time to like what at the time was, you know, 1980s, you know, earth.

and was it Scotty, you know, he has to interact with one of the computers and he's like trying to talk to it at the computer. You know, the guy hands him the mouse. He's like talking to the mouse, like, this is a huge, big laugh line. Like, well, of course.

I think that's probably all that we have time for today, but Scott, thank you so much for chatting with me. Super interesting. Super exciting. Again, I encourage everyone to read the full report, which is obviously a lot of detail that we didn't get to cover, but thank you for, uh, for putting that out and being such a great resource to the marketing ops community.