AI Marketing Strategy Gap: Pile of Parts
In my 15+ years of marketing leadership, I’ve witnessed three major inflection points that fundamentally changed how marketing works. The shift from traditional to digital. The rise of social media and content marketing. And now: the democratisation of AI.
This third shift brought me back online after years of staying relatively quiet on professional platforms. Not to celebrate AI tools. To ask why they’re not working.
Everything I Knew Was Getting Commoditised
The AI marketing conversation has been almost entirely tactical. Prompts that “write email sequences in minutes.” Tools that “generate social media content automatically.” The excitement is palpable. But so is the gap between adoption and results.
I’ve been tracking AI developments since GPT-3. What struck me wasn’t the tools. It was what was missing from the conversation: strategy.
Through 15 years of building teams, scaling startups, and managing budgets from zero to millions, one principle held constant. Strategic thinking consistently outperforms tactical tools. Yet everyone was discussing tools. Almost no one was discussing architecture.
The Real Question Isn’t Replacement
Like many experienced marketers, I faced the uncomfortable question: “Are we getting replaced?” Wrong question. The right question: does strategic experience still matter when AI can execute?
It does. But only if you understand why.
Throughout my career, I learned marketing through hands-on execution. Debugging conversion tracking. Building martech stacks from scratch. Hiring first marketing teams. Defending ROI to leadership. Each role taught me something about how strategy, tactics, and operations connect.
That connection is exactly what AI tools lack. They execute tasks. They don’t understand how those tasks ladder up to pipeline targets, attribution models, or board-level conversations about marketing’s contribution.
The “Pile of Parts” Problem
Here’s what I kept seeing: brilliant tools, sophisticated prompts, impressive automation. All disconnected from strategic frameworks. No architecture connecting them to measurable outcomes.
I call this the “pile of parts” problem. It’s like having world-class car parts without an engine block. Expensive inventory. Not transportation.
| Pile of Parts | Systems Thinking |
|---|---|
| Collect AI tools | Design architecture first |
| Chase prompt libraries | Define strategic frameworks |
| Automate random tasks | Connect workflows to pipeline |
| Measure tool adoption | Measure business outcomes |
The foundational principles of marketing success remain consistent. What changes are the methodologies and tools. The operational, tactical, and strategic thinking that got me from intern to CMO isn’t obsolete. It’s more valuable than ever.
But it needs to be applied systematically to new challenges.
What I’m Building
I’m showing up because the AI marketing conversation needs more strategic thinking. Not more tool reviews. Not more prompt hacks. Not more n8n workflow downloads.
I’m not an AI guru with the latest prompt library. I’m not selling a course. I’m exploring how AI in marketing could actually work, then building it. Documenting what works. Where I’m wrong. What I’m learning.
If you’re a marketing leader trying to hit pipeline targets with AI tools that don’t connect, this series is for you. Here’s what’s coming:
- Why most AI marketing implementations fail (and the architecture that fixes it)
- The Operator function: the human layer that makes AI systems work
- L1 to L5: a maturity model for AI marketing systems
- Building blocks: from atomic tasks to composite workflows
Next: Could AI Replace Marketing Teams?