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)

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.

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]

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]

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]

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.

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