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Being an AI product manager isn’t about knowing more frameworks — it’s about learning how to operate in uncertainty and build real products. This program started with a simple question: what does it actually take to build AI products beyond theory? A conversation with Harry Enaholo turned that into a seven-week, hands-on AI Product Management journey grounded in real work. This post reflects on how the program was built, what participants shipped, and what learning AI product management looks like in practice.

Machine learning is changing how we build products — not just what products do. For product managers, this shift requires new ways of thinking and working. In this post, I break down the full ML Product Lifecycle and show what PMs need to understand and drive at each stage to build machine-learning products.

Real buy-in isn’t created in a single meeting — it’s built long before the roadmap review, through ongoing alignment and shared context. In this post, I break down what I’ve learned from years of working with leadership and cross-functional teams on how to build that alignment early, avoid surprises, and turn your roadmap into a story stakeholders believe in.

Don’t fall for the “shiny tech = value” trap. Focus on what truly matters to your customers. If AI proves useful, here’s how to build it with purpose — what we’ve learned turning AI potential into measurable impact. This post breaks down the seven building blocks of a strong AI product strategy, from defining the right problem to delivering real outcomes.

Scaling isn’t about doing more — it’s about doing what works, better. This post breaks down how teams can move from early traction to sustainable growth without losing focus. Learn how to define what to scale, prioritize the right features, and use product analytics to turn validation into repeatable success.

This one was tough to write — I tried to untangle everything that’s changing in product management with AI. AI is reshaping product work at its core, shifting us from deterministic systems where we controlled every outcome to probabilistic ones where models learn and evolve. It cuts through the noise to show what actually changes when you build AI products: how your role shifts from defining logic to designing learning loops, why data literacy and AI evals become non-negotiable, and how to translate real business problems into AI solutions that work.

Most teams skip discovery and jump straight to building. That's a mistake. Without understanding the real problem, you're just guessing. You talk to the wrong users, build things nobody uses, and waste months fixing what could've been avoided. In this post, I break down why skipping discovery is the fastest way to waste time — and what good discovery actually looks like.

Learn how to validate your product idea before building. A step-by-step guide with real startup lessons to help teams build with clarity and confidence.

Most product teams don’t fail because of a lack of talent or effort, but because they chase speed without clarity. I’ve fallen into this trap myself. In this article, I lay out the foundations of what building products really means—and share a practical way of thinking about it: Validate → Build → Scale.