Pod Insights MarTech Podcast ™ // Marketing + Technology = Business Growth MarTech Insights and Highlights from 2025 About this episode MarTech faces an 8.6% vendor churn rate despite AI expansion. Scott Brinker, VP of Platform Ecosystem at HubSpot and founder of chiefmartec.com, shares insights on navigating the evolving landscape where AI didn't consolidate MarTech but fragmented it further. He discusses context engineering as the evolution beyond prompt engineering, combining deterministic workflows with LLM capabilities for better data analysis and customer service automation. Brinker predicts 2026 will shift focus from AI for marketers to AI for customers, fundamentally disrupting traditional sales playbooks as buyers gain information asymmetry through agentic browsers and AI assistants. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Transcript 0:00 Speaker 00 The Martech Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast? I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iHereEverything.com.
0:25 Speaker 01 From advertising to software as a service to data, across all of our programs and clients, we've seen a 55% to 65% open rate.
0:36 Speaker 02 Getting brands authentically integrated into content performs better than TV advertising.
0:42 Speaker 03 Typical life span of an article is about 24 to 36 hours.
0:46 Speaker 01 If we're reaching out to the right person with the right message and a clear call to action, then it's just a matter of timing.
0:53 Speaker 00 Welcome to the Martech Podcast, a member of the I Hear Everything Podcast Network. In this podcast, you'll hear the stories of world-class marketers that use technology to drive business results and achieve career success. Here's the host of the Martech Podcast, Benjamin Shapiro.
1:15 Speaker 04 Let's start off at the top here with our first topic. Tell me a little bit about what you observed from 2025. You just had your big report about the year in review and all things AI and Martech. What was the biggest takeaway that you got from 2025 that we could take into 2026?
1:34 Speaker 05 Well, apparently AI has become a thing.
1:36 Speaker 04 I've heard of it.
1:38 Speaker 05 You know, what we found in the research is actually most marketers are now already using some of these AI agents, they're using some of these HNT capabilities, you know, within their Martech stack. But, this is the but, most of them are still very early in the process. It's a lot of experimentation in the cases where they are deploying this product to production. It's usually very limited use cases. And this is totally rational, right? I mean, so much of this capability just didn't exist before 2025. You know, and actually, the degree to which we've had so many marketers start to experiment and do like small production releases in such a tight timeframe is actually incredibly impressive.
2:23 Speaker 04 Yeah, the amount of experimentation is amazing. And we're all doing it and we're all trying to figure out how to use this new technology, which in theory is supposed to make work easier.
2:36 Speaker 04 What I can tell you is every time I use AI to go find a stat to do my podcast intros, it almost always defaults to 95% of marketing AI experiments fail, according to McKinsey and according to Forbes. And it always grabs on to that statistic. Now, either Claude is trying to hedge expectations and using the Martech podcast to tell everybody, pump the brakes on all that experimentation, or people are really struggling to figure out something that actually goes from experimentation to deployment. So, yeah, lots of experimentation. Is anything actually working?
3:16 Speaker 05 Well, I actually, I find in the content production pipeline, this is one of the use cases where people are getting pretty good at actually harnessing AI effectively. Now, this doesn't mean like you turn it over to an AI agent and it does the complete end to end like, oh, concept, concept and production and distribution and analysis. And yeah, I went off and I got an espresso and I came back and it was all done. No, but like each step along those ways, we are seeing people productively use AI. It's like, okay, well, help me brainstorm when I want to do this or help me evaluate this. Okay, all right, now I want to create this thing. Can you take this piece of content and help break it up into different components for, you know, different channels where I may deploy it?
3:58 Speaker 05 Can you help me with the analysis of, you know, customer engagement around it? You know, again, these are all things that marketers could do before, but they were generally very time consuming. And we're seeing a fair number of content production use cases where the human still has to be the one driving each step of that, you know, but the timeframe in which they can get those things accomplished has shrunk considerably. So that's a real world win. I guess the other one I'd say is. The chatbots on websites. Now, we've had chatbots on websites for years. We can all agree they largely sucked.
4:37 Speaker 04 Yes.
4:37 Speaker 05 This year, they've started to get good. And they've started to get good for two reasons. One is, you know, they're now powered by these LLMs that are actually very good at conversational interaction. And the second thing is companies are starting to get smart about attaching these chatbots, particularly for customer service use cases, to the right data on the backend, their knowledge bases, their ticket histories, their customer profiles. So that it's not unusual now to hear companies that are deploying these seeing customer interactions with these chatbots getting a 60%, 70% resolution. You know, again, if it doesn't get resolved, you have to make it really easy to get to a human rep who can resolve it.
5:19 Speaker 05 But still, you know, for those cases where it can like self-serve, get the customer the answer they want, get them on their way quickly and be very efficient for the company. You know, again, this isn't rocket science, but those are two pretty good use cases.
5:32 Speaker 04 Let me give you an N of one. I hate the chatbots because, yes, they are lowering costs and they're able to execute 75% of queries successfully. And then eventually they bounce to a human. I personally find the experience of talking to an LLM, A, it's very clear when you're talking to an LLM. And B, it is very frustrating for the end consumer. It might be more profitable. I'm not sure how good it is for the long-term relationships. So as we talk about 2026, I'm curious to see if the chatbot experience is something that gets better or starts to deteriorate brand relationships.
6:11 Speaker 05 No, it's a great thing. You know, I guess for me, it probably depends on what the very specific use case is. And I say this as a person that the most frustrating experience for me. Is sitting on hold, listening to people tell me on a recorded voice how important I am to them. And like, you know, you are 74 in the queue. So to the degree that there's questions I can get answered by a chatbot right away, I'm sort of in the camp of like, you know, I'll take that path first.
6:36 Speaker 04 Yeah, and please, we want to give you a survey at the end so you could tell us how much we stink. Yes, I agree that the automated phone processing, I'd actually rather talk to a bot, I think, than type to it. Okay, so you mentioned the content production workflows are getting pretty good. The chatbots are getting all customer-facing experiences, essentially. Are there any other verticals where you're seeing, or you predict that in 2026, we will see better use of Agentic AI?
7:10 Speaker 05 I think the back office use of analysis of data. Right now, actually, a lot of these AI engines are very capable of doing a good job with that. The reason they're not used very much for that is because we aren't connecting the right data to them. Or the data we have is, surprise, surprise, this is a theme that's been in martech since there's been martech. A lot of our data is just not very good, you know? But this has become one more reason for marketers to invest. And like, you know, we really got to get our data layer right. Because everything we want to do, and AI is just sort of the latest incarnation of this. Everything we want to do is kind of predicated on having good data and being able to get that data to the right place at the right time.
7:56 Speaker 05 I'm not saying that's easy, but at the end of the day, that really is the mission. If you want to harness what this technology makes possible.
8:02 Speaker 04 So if you looked back at 2025 and had to summarize in one sentence, I'll even give you a paragraph. What is the big takeaway from the adoption of AI? What did we learn this year that will be applied moving forward?
8:24 Speaker 05 So many different things we could pick on that. I would say this.
8:30 Speaker 05 We are coming to grips with the fact that AI is very, this sort of generative AI, this generation of AI, is very different than the kind of automation we've had in the past. You know, we've had automation in marketing for, you know, decade, decade and a half, but it's been what they call deterministic. You know, it's you explicitly configure if this, then that, and this other thing, which is great. It's very reliable, predictable, explainable. It also tends to be very fragile. We tend to create all sorts of complex exceptions and whatnot, and quickly can get a mess. On the other hand, we're starting to see as we experiment with incorporating these LLMs into our workflows and our automations, it's almost like the inverse trade offs.
9:15 Speaker 05 Okay, LLMs are not as predictable as that goes. They have variance to them. They are what we call non-deterministic, you know, and how they're going to respond. And that has challenges, but it also has a set of benefits that actually these LLMs are better at adapting. You can throw things at them, you know, that would have broken one of those deterministic workflows of I'm expecting X, you didn't give me X, what do I do with it? You know, you can throw more things at an LLM. You can throw things at it that are going to change, and the LLM can adapt to it. And so where things are getting really interesting is you're seeing marketers here in 2025, and I think we'll see a lot more of this in 2026. They're starting to combine the two.
9:59 Speaker 05 They're like, okay, we're going to have a workflow, and that overall workflow is going to be deterministic and very well structured because that's what we need to run our operations as a business. But we're starting to incorporate the use of LLMs at specific steps along that workflow because it allows us to do things with like unstructured data or, you know, generation of media or things like this that were impossible to do with those deterministic workflows.
10:25 Speaker 05 We've started to experiment with that in 2025. I think we'll see a lot more of that and start to move it more into production in 2026.
10:32 Speaker 04 My takeaway from this is we look back at 2025. It was the year where we moved beyond essentially content marketing into conversational marketing. And now we're making the migration into analysis using LLMs. And as you said, we have had marketing automation for 15 years or so. But now we're able to move beyond deterministic rule sets to have something that is a little bit more related to judgment. And that wraps up this episode of the Martech Podcast. Thanks again to Scott Brinker, the godfather of the martech industry for joining us. If you'd like to contact Scott, you could find a link to his LinkedIn profile in our show notes or on martechpod.com. You can always visit his website, which is chiefmartec.com.
11:16 Speaker 04 It's chief, M A R T E C dot com. If you haven't subscribed yet and you want a regular stream of marketing and technology knowledge in your podcast feed, hit the subscribe button in your podcast app or on YouTube and we'll be back in your feed every other week. All right, that's it for today. But until next time, my advice is to just focus on keeping your customers happy.
12:02 Speaker 00 Thanks for listening to the Martech Podcast and I Hear Everything production. Looking to launch or scale a podcast like this one for your brand? Then visit iHearEverything.com.
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