How Technical Do AI Product Leaders Need to Be?

"Do I need to learn to code?"

I used to get this question constantly. Now the question has shifted — even engineers are wondering if coding matters the way it used to.

The real question is: How technical do I actually need to be to lead AI work?

I've spent 20 years answering this question — first for myself, then for hundreds of product and business leaders. And the answer isn't what most people expect.

You don't need to become an engineer. But you need enough technical depth to call BS, make tradeoffs, and see what the engineers can't.

Let me show you what I mean.

The Alexa Story

In 2020, I was Principal Product Manager at Amazon Alexa. My team was building something that didn't have a name yet — we were taking real-time information and generating natural language answers on the fly.

Generative AI, before anyone called it that.

One day, our system produced this answer to a customer question:

"If you've ever wondered what it would be like to be infected with a virus, you're in luck!"

My engineers didn't see the problem. They looked at the response and said, "That's technically accurate."

I looked at it and said, "We cannot ship this."

That moment crystallized something I'd been feeling my entire career: technical people are brilliant at building systems, but they often can't see how those systems land with real humans. They don't think about the mom asking Alexa a question while her kids are in the room. They don't anticipate the headline that writes itself when AI says something tone-deaf.

That's not a criticism. It's just a different lens. And right now, in the age of AI, that lens is desperately needed in every room where decisions are being made.

How I Got Here

I started as a software developer. MIT grad, wrote code for years. But I was always the social one on the team — always more interested in why we were building things than how.

About ten years ago, I moved into product management and found my spiritual home. I got to sit at the intersection of technical, business, and human concerns. I got to be the translator.

When I joined Amazon, I thought I'd finally made it. Principal PM at one of the most demanding tech companies in the world. But even there, I saw the same pattern: smart business leaders shut out of AI conversations because they couldn't speak the technical language, and engineers making product decisions they weren't equipped to make.

The rooms where AI gets built? They're not balanced. And the products suffer for it.

The Problem I Couldn't Ignore

When ChatGPT launched, I watched my network fracture.

Some people dove in headfirst — mostly engineers and the deeply curious. But a huge number of senior leaders I respected did... nothing. They waited. They delegated. They hoped someone else would figure it out.

Six months later, those same people started reaching out. "How do I catch up?" "Is it too late?" "I feel like everyone else gets it and I don't."

Here's what I told them: You shouldn't have to go back to school. You shouldn't need a CS degree. You don't have to take a demotion and become an IC again. You have years, if not decades, of judgment, stakeholder management, and strategic thinking. Those skills are exactly what AI teams need right now.

The path forward isn't starting over. It's moving sideways — into the rooms where AI decisions are made — with what you already have.

That's why I built AI Career Boost.

So How Technical Is "Technical Enough"?

Here's what I've learned after working with hundreds of senior leaders making this transition:

You don't need:

• A CS degree (or to go back for one)
• The ability to write production code
• Deep math or ML theory
• To become a prompt engineering specialist

You do need:

• Enough architectural understanding to know what's possible, what's hard, and what's BS
• Familiarity with how AI systems actually work — constraints, failure modes, evaluation
• The ability to read technical documentation without your eyes glazing over
• Confidence to push back on engineers when something doesn't make sense

The gap isn't "technical vs. non-technical." It's whether you understand systems deeply enough to lead them.

What I Believe

I'm not here to teach you to code. You don't need to become a machine learning engineer to lead AI products.

But I am here to tell you that AI leadership isn't optional anymore. The gap between leaders who understand AI deeply enough to make good calls and those who don't is widening every month.

I believe:

• Product and business leaders bring something engineers can't. Human judgment. Customer empathy. The ability to anticipate how a system will land in the real world.

• You don't need to code, but you need technical depth. Architecture, constraints, failure modes — you need to understand systems well enough to call BS and make tradeoffs.

• Learning AI as a senior leader is different. You can't start from scratch. You need to build on what you know, not throw it away.

• The best AI products are built by balanced teams. And right now, those teams are missing voices like yours.

Who I Help

I work with senior product and business leaders — Directors, VPs, Heads of Product, Staff/Principal PMs — who know AI matters but haven't found their way in yet.

Not beginners looking for AI basics.

Not engineers who want to stay technical.

Not people looking for shortcuts or guarantees.

I help experienced leaders who are ready to do the work to build real AI credibility — not credentials, credibility. The kind that comes from understanding systems deeply enough to lead them.

What's Next

If you're reading this and feeling that "I should be further along" anxiety — I get it. I talk to people like you every week.

Here's what I'd suggest:

Start with the free masterclass. I run them regularly, and they're the best way to see how I teach and whether this approach resonates with you. No pitch-fest, just real content on how to think about AI leadership.

Watch the latest replay -> HERE

Polly Allen is the founder of AI Career Boost and a former Principal Product Manager (Technical) at Amazon's Alexa AI, where she led generative AI product development before ChatGPT made it mainstream. She lives in Bellingham, WA with her husband, who imported her from Canada, and their Rhodesian Ridgeback, Ruby. Yes, she was named (partially) after the programming language.