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Hey Subscriber First Name,

I've been working with AI tools a lot over the past year, building things, experimenting, pushing what's possible. And the thing that keeps surprising me is how much the results depend on what I already know. The better I understand the domain, the better the output. The more specific my thinking, the sharper the result. Every time I bring real experience to the conversation, the technology amplifies it in ways that genuinely leave me amazed.

And every time I try to use it in an area where I have no depth, the results are mediocre at best. They look polished. They sound reasonable. But I can't tell whether they're right, because I don't have the knowledge to judge them. That's the difference that nobody is talking about enough.

There's a narrative right now that AI is going to level the playing field. That anyone can do anything because the tools are so powerful. And on the surface, that's true. You can generate a project plan, a risk assessment, a stakeholder analysis, a financial model, all in minutes. But generating output and understanding output are two completely different things. The person who has spent years in a domain can look at an AI-generated risk summary and immediately spot what's missing, what's been oversimplified, and what sounds right but would never survive contact with reality. The person without that experience takes the output at face value, because they have no frame of reference to challenge it. Someone said to me recently that without domain knowledge, AI just gives you a word salad. It looks like a meal, but there's nothing in it that nourishes the decision you're trying to make. I thought that was spot on.

Anthropic recently studied 235,000 users of their AI tools and found something that confirms this. The biggest predictor of success wasn't whether someone had a technical background. It was whether they understood the problem they were trying to solve. People who knew their domain well got dramatically better results from the same tools, regardless of their job title. The skill that mattered most was the ability to specify what you need clearly, judge whether the output is right, and know when something is missing. Those are domain skills built through experience, not technical skills learned from a course.

That finding mirrors exactly what I've been experiencing. AI doesn't replace your expertise. It multiplies it. The more you know, the more you get out of the tools. The less you know, the more you're at the mercy of whatever the tool gives you.

Steven Bartlett talks in Diary of a CEO about how the shelf life of skills is shrinking. What made you successful five years ago may already be losing relevance. When you stop and think about that, you know it's true. The pace of change in our profession is faster than it's ever been. New tools, new methodologies, new ways of working. And in the middle of delivering projects and managing teams, it's easy to let your own learning slip. But this is exactly the time to lean in. To try out new technologies, experiment with emerging tools, stay curious about how the landscape is shifting. The PMs who make time for this alongside their day-to-day work are the ones who keep their edge. The ones who assume their current knowledge is enough are the ones who wake up one day and find the profession has moved on without them.

And here's the encouraging part. If you've already got deep domain knowledge in your field, all of that learning and experience puts you in an incredibly strong position right now. AI amplifies what you already know. Your years on the ground are the thing that lets you look at a dashboard and know which number actually matters, or read a report and feel which conclusion doesn't sit right. That instinct, that judgement, is built from domain knowledge. No tool can replicate it. And when you combine it with a willingness to keep learning and experimenting with new technologies, that's where the real edge lives.

It's one of the reasons I started writing this newsletter, actually. I wanted to share what I've learned, and I'm so glad I did, because had I not taken that step, I wouldn't know what I know now. I wouldn't have learned from so many incredible people, developed my own thinking in ways I never expected, or opened up opportunities in work and life that I couldn't have imagined when I started. Sharing knowledge doesn't dilute your expertise. It deepens it.

But if you're earlier in your career, or moving into a new field, the challenge is real. You're competing with people who have years of accumulated knowledge and are now accelerating it with AI. The gap feels wider than ever.

Here's the good news. Building domain expertise has never been more accessible than it is right now. The same tools that amplify existing knowledge can accelerate new knowledge if you approach them with curiosity rather than shortcuts.

Here's what I'd suggest if you're actively trying to build depth in your field:

Get close to the work. There is no substitute for proximity. Shadow the engineers, sit in on the commercial reviews, attend the site meetings. Domain knowledge is built through exposure, not through reading about it. The PM who has been on site understands constraints that the PM working from a spreadsheet never will.

Use AI as a tutor, not an answer machine. When you encounter something you don't understand, don't just ask AI for the answer. Ask it to explain the concept, then ask follow-up questions, challenge its reasoning, and test your understanding against what you've seen in practice. Stack Overflow's 2026 data shows that 64% of developers now use AI primarily as a learning tool, up from 37% just two years ago. The ones using it to learn are building depth. The ones using it to skip learning are building dependency.

Teach what you're learning. Bartlett makes a point that stuck with me. Teaching what you're currently learning is more valuable than teaching what you mastered years ago, because you remember the struggles, the confusion, the breakthroughs. Share your learning openly. Write about it, talk about it with colleagues, explain it to someone newer than you. It forces clarity and it compounds your understanding faster than any other method I've found.

Stay curious about adjacent fields. The PMs who build the deepest judgement aren't just experts in one thing. They're curious about the fields around them. They understand enough about commercial, technical, legal, and operational to ask the right questions, even if they're not experts in any of them. That breadth, combined with depth in your core domain, is what makes you genuinely hard to replace.

The formula I keep coming back to is simple. Domain expertise multiplied by AI fluency equals an edge that compounds over time. Either one alone is valuable. Together, they're transformational. And the beautiful thing is that both can be built. Neither requires permission, a degree, or a title. Just curiosity and consistency.

MAKING IT HAPPEN: Your Weekly Strategy

Pick one area of your project where you feel your domain knowledge is thinnest. This week, spend thirty minutes getting closer to it. Sit in on a meeting you wouldn't normally attend. Ask someone in that discipline to walk you through how they think about their work. Then use an AI tool to explore the topic further, not for answers, but for understanding. Ask it to explain the key concepts, then test what it tells you against what you heard from the real expert. Do this once a week and within a few months you'll have a working knowledge that most PMs in your position never build.

CHEAT SHEETS

The free cheat sheet library is here. I'll keep adding to it. Save the link and grab whatever helps.

If this one landed, forward it to someone who's been wondering whether AI makes their experience less valuable. It doesn't. It makes it more valuable than ever. The edge belongs to the people who know their field and lean into the tools.

bilaljamil.com | Newsletter archive | Hit reply and tell me how your domain expertise has shaped your experience with AI. I read every one.

Have a great week and speak soon, 

Bilal