artifical intelligence - Martech Therapy (Page 3)

Agentic AI & the art of letting go

When control gets in the way of progress

Paul suggested the idea of doing something different. No intro. No big kickoff. Just two people talking.

That’s how this episode with Paul Meinshausen (CEO at Aampe) started and I’m glad it did. Because I fumbled at the beginning (you can skip ahead in the podcast...).

Feeling like a one-trick pony, I tried to steer the conversation toward my usual topics: CDPs, AI, use cases, and organizational challenges. But it quickly became clear I was trying to force an idea, a vision, onto something I hadn’t fully wrapped my head around yet. Paul helped me take a

If the warehouse can think, what’s left for the CDP?

A critical reflection on Databricks, data gravity, and what comes next.

Nope, this isn’t an analysis of a press release. We’ve seen all Databricks partners do that generously all over LinkedIn. It’s a reflection, a personal one (I had some spare time over the weekend), on something that’s been building quietly in the background and what it means now that someone has taken a concrete step forward. That someone is Databricks.

Last week, Databricks launched something called Data Intelligence for Marketing. And sure, you could skim the headline and file it under “AI feature drop.” But that’s not what caught my attention.

What stood out was

Are MCPs the USB-C of AI? Tealium Thinks So

A conversation with Tealium’s Sav Khetan and BJ Allmon about MCPs, AI integration, and the role of CDPs in a multi-agent world.

In this episode of Diagnosis Demo, I sit down with Sav Khetan and BJ Allmon from Tealium to explore their recent implementation of MCPs (Model Context Protocols). We discuss what MCPs mean for real-time AI integration, how Tealium positions itself as an AI enabler, and why the future might just belong to multi-agent architectures. We also demo a real use case that shows how fast and powerful Tealium's setup really is.

Watch to see:

A working demo using Tealium’s Moments API and MCP integration in a simple chatbot
The difference between MCP and A2A protocols
Why data orchestration, not

Contextual CDPs: Building the bridge

Part 4 of 6: What connects today's CDPs to tomorrow’s agent-driven context

If the first three parts of this series explored what’s possible when context becomes the driving force behind customer data, then this one asks a more grounded question:

How do we actually get there?

I’ve spoken with teams who are genuinely excited by the idea of using memory, inference, and context to make their CDPs smarter. But many feel stuck between inspiration and implementation. They see the potential, but don’t quite know where to start.

Let’s be honest, the martech stack is already complex enough. The last thing anyone wants is to bolt on a new

Contextual CDPs: Memory, not identity, is what makes context work

Part 2 of 6: How model context protocols could replace the brittle logic of user tracking.

In Part 1, I explored how model context protocols (MCPs) could shift CDPs from passive data stores into dynamic context engines. Profiles that update themselves in the moment, based on intent, tone, and history. Not just segments, but signals. And now? Let’s see what happens when those signals actually drive decisions… in real time, across multiple touchpoints.

Because here’s the next big unlock: context-aware orchestration.

Real-time isn’t just about speed

Let’s be honest, "real-time" has been one of those buzziest of buzzwords we’ve collectively abused for years. It often means “fast batch,” or “eventual personalization.

Are we overestimating AI? Or underestimating what's coming?

A Couch Confidentials conversation with Michael Katz about the illusions (and realities) of AI, CDPs, and how to survive the next data wave.

What happens when the tools we think are helping us today aren't ready for the future we’re speeding into?

In this episode of Couch Confidentials, Michael Katz (CEO of mParticle) joins me to talk about where AI is really at, why most CDPs are stuck in the past, and why success in Martech now depends less on the tech itself and more on how you think about it.

We dive into:

Why "People overestimate where we are today and underestimate how fast this stuff progresses."
The two very different paths CDPs are taking (and why one is a race

Posts tagged with artifical intelligence

Real-time isn’t just about speed