MarTech Insights and Highlights from 2025

Summary

MarTech Podcast ™ // Marketing + Technology = Business Growth

Episode: MarTech Insights and Highlights from 2025
Date: January 6, 2026
Host: Benjamin Shapiro
Guest: Scott Brinker (Chiefmartec.com)

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Episode Overview

This episode reviews the most significant developments in marketing technology (MarTech) throughout 2025, with a heavy focus on artificial intelligence (AI). Host Benjamin Shapiro speaks with Scott Brinker, a recognized authority in the field, about key trends, real-world applications, and the evolving role of AI—from content production to customer service and analytics. The discussion offers keen insights into the future trajectory of MarTech as the industry moves into 2026.

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Key Discussion Points & Insights

1. Widespread Experimentation with AI

• Adoption Status:
  • By 2025, most marketers are experimenting with AI agents in various aspects of their MarTech stack, though use cases are often limited and in early stages.
  • The rapid pace of AI experimentation and initial deployment is seen as "incredibly impressive" given how new these capabilities are.
  • Quote:
    • "Most marketers are now already using some of these AI agents... But most of them are still very early in the process. It's a lot of experimentation."
      — Scott Brinker [01:38]
• Use Case Maturity:
  • Full end-to-end automation (i.e., idea to content distribution without human intervention) is rare; AI assists more granular steps, saving time in each.

2. Real-World Wins: Content Production & Chatbots

• Content Production Pipeline:
  • AI tools are effectively used at various stages: brainstorming, evaluation, repurposing content for different channels, and engagement analysis.
  • Marketers remain in control at each step, but AI meaningfully accelerates workflows.
  • Quote:
    • "We're seeing a fair number of content production use cases where the human still has to be the one driving each step... but the time frame in which they can get those things accomplished has shrunk considerably."
      — Scott Brinker [03:16]
• Chatbots on Websites:
  • Chatbots, powered by advanced large language models (LLMs), are now capable of resolving 60–70% of customer queries without human intervention.
  • Connecting chatbots to robust customer data (knowledge bases, profiles, ticket histories) yields better self-service and efficiency.
  • Quote:
    • "For those cases where it can, like self serve, get the customer the answer they want, get them on their way quickly... those are two pretty good use cases."
      — Scott Brinker [04:40]

3. The Double-Edged Sword of Customer-Facing AI

• Mixed User Reception:
  • While more efficient, LLM-driven chatbots can feel impersonal; some users find interactions frustrating or clearly non-human.
  • The long-term brand relationship impact of these experiences is a critical area to watch.
  • Quote:
    • "I personally find the experience of talking to an LLM... very frustrating for the end consumer. It might be more profitable. I'm not sure how good it is for the long term relationship."
      — Benjamin Shapiro [05:32]
• Preference for Bots Over Waiting:
  • Despite imperfections, some prefer instant bot interaction over waiting on hold for human help.
  • Quote:
    • "The most frustrating experience for me is sitting on hold... So to the degree questions I can get answered by a chatbot right away, I'm serving the camp of... I'll take that path first."
      — Scott Brinker [06:11]

4. Future Vertical: Back Office Data Analysis

• Potential for AI in Analysis:
  • AI models are ready for deeper use in data analysis and reporting, but are held back by poor data quality and lack of integration.
  • The urgent need for clean, well-structured data is emphasized as foundational for effective AI deployment in marketing.
  • Quote:
    • "A lot of our data is just not very good, you know, but this has become one more reason for marketers to invest in... get our data layer right."
      — Scott Brinker [07:10]

5. Shift from Deterministic to Non-Deterministic Automation

• Defining the Change:
  • Previous automation was deterministic—explicit, rule-based, and fragile.
  • Current LLM-driven AI is non-deterministic: less predictable, but more adaptable, handling unstructured data and unforeseen inputs.
  • Marketers are learning to blend deterministic workflows with LLMs at specific points for optimal structure and flexibility.
  • Quote:
    • "We're starting to see, as we experiment with incorporating these LLMs... almost like the inverse trade offs... LLMs are better at adapting. You can throw things at them that would have broken one of those deterministic workflows..."
      — Scott Brinker [09:07]
• Looking Ahead to 2026:
  • The trend will intensify: structured workflows with AI-powered steps, especially for tasks previously impossible with deterministic rules.

6. 2025 in Retrospect: Key Takeaway

• Host’s Summary:
  • 2025 marks the evolution from content marketing, through conversational marketing, to analytical marketing powered by LLMs.
  • The industry is now moving beyond static rules to AI that exercises judgment and handles complexity.
  • Quote:
    • "We moved beyond essentially content marketing into conversational marketing, and now we're making the migration into analysis using LLMs... now we're able to move beyond deterministic rule sets to have something that is a little bit more related to judgment."
      — Benjamin Shapiro [10:31]

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Notable Quotes & Memorable Moments

• On rapid experimentation:
  "The degree to which we've had so many marketers start to experiment and do small production releases... is actually incredibly impressive." — Scott Brinker [01:38]

• On AI's real productivity gain:
  "The time frame in which they can get those things accomplished has shrunk considerably. So that's a real world win." — Scott Brinker [03:38]

• On the chatbot revolution:
  "They started to get good for two reasons... powered by these LLMs... and connecting chatbots for customer service to the right data on the back end." — Scott Brinker [04:10]

• On adapting MarTech workflows:
  "We're starting to combine the two [deterministic automation and LLMs]... because it allows us to do things with unstructured data or generation of media... impossible with those deterministic workflows." — Scott Brinker [09:55]

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Important Timestamps

• Experimentation and limited AI deployment: [01:34 – 02:23]
• Content production & chatbot use cases: [03:16 – 06:11]
• Challenges with chatbot experience: [05:32 – 06:36]
• AI for back office & data analysis: [07:10 – 08:02]
• 2025 summarized; blending deterministic & LLM workflows: [08:19 – 10:31]
• Host’s closing reflections on progress and future focus: [10:31 – 11:49]

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Takeaways for Listeners

• The MarTech industry is solidly in a phase of rapid experimentation with AI, particularly LLM-driven tools.
• Reliable use cases include content production assistance and customer service chatbots, but data quality remains a core hurdle for widespread AI analytics.
• The future lies in merging structured (deterministic) processes with adaptive LLM steps—achieving both reliability and flexibility.
• 2025’s legacy is moving MarTech into new AI-powered paradigms—making 2026 a year to watch for large-scale, real-world deployment.

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For more from Scott Brinker, visit chiefmartec.com.
For regular MarTech updates, subscribe to the MarTech Podcast or follow on YouTube.

Transcript

[Benjamin Shapiro]

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 iheareverything.com.

[Podcast Host / Interviewer]

From advertising to software as a service.

[Scott Brinker]

To data across all of our programs and clients, we've seen a 55 to 65% open rate. Getting brands authentically integrated into content performs better than TV advertising.

[Podcast Host / Interviewer]

Typical lifespan of an article is about 24 to 36 hours.

[Scott Brinker]

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.

[Benjamin Shapiro]

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 you use technology to drive business results and achieve career success. Here's the host of the Martech Podcast, Benjamin Shapiro.

[Podcast Host / Interviewer]

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 gave got from 2025 that we could take into 2026?

[Scott Brinker]

Well, apparently AI has become a thing.

[Podcast Host / Interviewer]

I've heard of it.

[Scott Brinker]

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 agentic capabilities 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. And actually the degree to which we've had so many marketers start to experiment and do small production releases in such a tight time frame is actually incredibly impressive.

[Podcast Host / Interviewer]

Yeah, the amount of experimentation is amazing and we're all doing it and we're all trying to figure out how to this new technology which in theory is supposed to make work easier. 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 McKenzie and according to Forbes, and it always grabs onto that statistic. Now either Claude is trying to hedge expectations and using the Martech podcast to tell everybody pump the brakes on all the 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?

[Scott Brinker]

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 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 to 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? 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 time frame 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've largely sucked this year. They started to get good and they've started to get good for two reasons. One is 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 back end, 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. 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.

[Podcast Host / Interviewer]

Let me give you an n of 1. 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 relationship. So as we talk about 2026, I'm curious to see if the chatbot experience is something that gets better or starts to deteriorate better brand relationships.

[Scott Brinker]

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 questions I can get answered by a chat bot right away, I'm serving the camp of like, you know, I'll take that path first.

[Podcast Host / Interviewer]

Yeah, and please, we want to give you a survey at the end so you can 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 chat bots 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?

[Scott Brinker]

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 in. 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. 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.

[Podcast Host / Interviewer]

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?

[Scott Brinker]

Hmm. So many different things we could pick on that. I would say this. Um, we are, 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 and marketing for, you know, decade, decade and a half. But it's been what they call deterministic. You know, it's you, you explicitly configure if this, then that and this other thing 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. Okay, LLMs are not as predictable as that goes. They have variants to them. They are what we call non deterministic, you know, in 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 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 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. 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 with 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. 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 2020.

[Podcast Host / Interviewer]

6 My takeaway from this as 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 chief martech.com 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.

[Scott Brinker]

Foreign.

[Benjamin Shapiro]

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.