ai agents - Martech Therapy

Aampe and DOJO AI show why AI-native marketing starts with people, not tools

AI-native marketing won’t fix marketing’s problems, but it might fix how teams work. From Aampe’s change management approach to DOJO AI’s human-in-the-loop philosophy, the real transformation starts inside the organization, not the model.

As I stated on LinkedIn last week, it was a wild week of news. Lee Hammond mapped the chaos in a post on LinkedIn after his visit to CDP World, while David Raab imagined the redesign after challenging ChatGPT. And somewhere between the two, I found myself in a demo with Aampe, realizing just how hard it is to think differently.

Towards the end, I stopped Aampe’s Amaan Kulatunga and admitted, almost out loud:

I’m still trying to fit this into campaign logic.

I was still thinking like a marketer from 2015. And that, I suspect, is what

Can an AI marketing OS really think like a CMO?

Inside my demo with Dojo AI, from Lucky Saint campaigns to zebra striping trends

I sat down with Duarte Garrido and Luke Costly-White to see what DOJO AI calls an “all-in-one AI Marketing Operating System”. Big claims, but then again, these are undoubtedly “big claim” days we are living in, so I wanted to see what that actually looks like in practice.

Luke’s story already makes this demo interesting. Luke began as a DOJO power user, then became an angel investor, and now works at the very company he backed (for those who read my piece on reference calls with angel investors: this one isn’t on the sidelines anymore).

The demo itself

Agentic AI in Martech: The Handoff

Part 6: Practical experiments for teams ready to delegate

After five posts exploring the theoretical challenges of agentic AI, scale, complexity, conflicts, roles, and trust, I want to end this series with something more practical. If you're convinced that agentic systems represent the future of Martech but aren't sure where to start experimenting, what's the actual first step?

Based on the patterns I've observed and my own experiments, I think the answer lies in designing deliberate handoff protocols. Not grand AI strategies or comprehensive automation plans, but careful experiments in delegation that help you learn how to work with autonomous systems.

Starting with low-stakes decisions

The teams that seem

Agentic AI in Martech: Trust without understanding

Part 5: How to govern systems you can’t fully comprehend.

My Tesla occasionally does something that makes me question everything I think I know about driving. Since I have owned it, there have been numerous times when it suddenly slammed on the brakes while on autopilot, no visible obstacles, no construction signs, perfect weather. Just phantom braking, the phenomenon that’s been getting Tesla scrutinized by government agencies.

My wife hates when this happens. She’s made it very clear that I shouldn’t rely on autopilot when the family is in the car, both for safety reasons and because she’d prefer I not sleep on the couch.

Agentic AI in Martech: The new org chart

Part 4: Roles that emerge when AI handles execution

A friend recently asked me what a marketing ops manager actually does when AI agents are handling campaign execution, optimization, and conflict resolution. It's a fair question. If systems are making billions of decisions autonomously, what's left for us mortal humans?

My first instinct was to give the standard consultant answer about "strategic oversight" and "creative direction." But the more I thought about it, the more I realized that's probably wrong. The interesting human roles in an agentic world are likely to be things we haven't fully invented, yet.

Beyond the "Human in the Loop" fiction

Most discussions about AI

Agentic AI in Martech: When agents disagree

Part 3: The inevitable conflicts in multi-agent systems

Picture this scenario: your personalization AI decides that Sarah (Conner? 😅), a loyal customer, should receive a premium upgrade offer because her engagement scores have been climbing. Simultaneously, your churn prevention AI flags Sarah as a retention risk and recommends a discount promotion to keep her engaged. Meanwhile, your email frequency optimization AI determines Sarah is approaching communication fatigue and suggests suppressing all promotional messages for the next week.

Three different systems, three different conclusions, one very confused customer experience.

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This is the multi-agent conflict

Posts tagged with ai agents

Starting with low-stakes decisions

Beyond the "Human in the Loop" fiction