I didn't plan to become an AI strategist.
My career trajectory looked like this: fail out of PT school, discover email marketing, spend a decade obsessing over subject lines and send times, stumble into AI tools out of curiosity, and then realize — somewhat suddenly — that everything I'd learned about marketing was actually training for something bigger.
Here's what I mean.
What Email Marketing Actually Teaches You
Good email marketers are behavioral scientists in disguise. You're constantly running experiments: subject A vs. subject B, send time Thursday vs. Tuesday, CTA "learn more" vs. "get started." Every campaign is a hypothesis. The data either confirms it or kills it.
That's a scientific mindset. And it's exactly what you need for AI strategy.
When I started evaluating AI tools and use cases, I noticed I was asking the same questions I'd always asked: What behavior are we trying to change? What's the baseline? How will we measure improvement? What does failure look like?
Most people approaching AI ask "what can it do?" I was asking "what should we try, how do we measure it, and how do we know if it worked?"
That's the difference between technology enthusiasm and business strategy.
The Copy Skills Transfer Completely
Email marketing made me obsessive about clarity. You have about 2 seconds to earn someone's attention before they delete your message. Every word has to work. Every sentence has to earn the next one.
Writing prompts for AI is the same discipline. Vague prompts produce vague outputs. Clear, specific, contextual prompts produce useful results. The people who get the best outputs from AI tools are usually good writers — because writing is the interface.
I've watched technically brilliant people struggle with AI tools because they can't communicate what they want clearly. And I've watched non-technical marketers get outstanding results because they've spent years developing the ability to articulate exactly what they're asking for.
What Surprised Me About the Transition
I expected the learning curve to be steeper. I thought I'd need to understand how models work at a technical level before I could be useful. That turned out to be wrong.
What actually matters is understanding when and why AI works well — which requires understanding the underlying task, not the underlying technology. You need to know what "good" looks like so you can evaluate outputs. You need to understand where AI tends to fail (hallucinations, recency limits, context window constraints) so you can design around those failure modes.
That's domain knowledge plus critical thinking, not engineering.
The transition also taught me something uncomfortable: a lot of the "AI expertise" being sold right now is mostly vibes. People who've played with ChatGPT for six months presenting themselves as AI transformation consultants. The market will sort this out, but it's worth being discerning about who you're taking advice from.
What I'd Tell Anyone Making a Similar Transition
Your existing domain expertise is the asset — not a liability. The world doesn't need more generalist AI enthusiasts. It needs people who understand specific industries and business functions deeply, and who can apply AI thinking to those domains.
If you've spent ten years in finance, logistics, healthcare, or yes, marketing — that knowledge is your competitive advantage. Pair it with genuine curiosity about AI, an experimental mindset, and the discipline to measure outcomes, and you'll be more valuable than someone who started from the AI side.
The technology is accessible. The business judgment is the hard part.
David Cartolano is an AI Business Strategist. Connect on LinkedIn.