I’ve spent my career learning how businesses actually build things.

Early in my career, I learned to understand businesses from the inside — how processes work, where they break, what people actually need, and how those needs translate into technology.

My work at Marsh gave me a very different perspective. The challenge in an enterprise isn’t simply building something from scratch. It’s making technology work reliably inside a large organisation — with governance, compliance, stakeholders, existing processes and real consequences when things go wrong.

My work has included problem discovery, product requirements, roadmap decisions, business cases, ROI modelling and human-in-the-loop workflows for critical insurance processes.

A solution isn’t valuable because it’s technically impressive. It’s valuable when it works in the reality of the business.

That matters even more now that AI makes building easier than ever.

I’ve always been interested in technology, but GenAI and Agentic AI changed the nature of product work for me. The barrier to creating something has dropped dramatically. Today, you can research, prototype, design workflows and build functioning applications with tools that were difficult to access just a few years ago.

When it’s this easy to build, how do you decide what’s worth building?

During my BITSOM programme in Product Management with GenAI & Agentic AI, I built a multi-agent product from problem definition through requirements, architecture, prototype and validation. It was a useful reminder of something years of product work had already taught me: AI can accelerate the work. It cannot make the decision for you.

If anything, the easier it becomes to build, the more product judgement matters.

I kept seeing the same gap. People know the names of frameworks — PRDs, JTBD, MVPs, prioritisation, and now GenAI and Agentic AI. But knowing about a framework is very different from knowing how to use it when the situation is messy.

• When do you use it?
• What information do you actually need?
• What are you assuming?
• What evidence do you have?
• What should you prioritise?
• Does the problem even need AI?
• And once you’ve made the decision, can you explain why you made it?

The Product Lab is my attempt to make that kind of practical thinking easier to learn and practise. Not with another course full of frameworks, but with a place to actually use them: work on a problem, make decisions, use modern tools, build something, change your mind when the evidence tells you to — and leave with work you can actually talk about.

Students are often expected to demonstrate experience before they’ve had much opportunity to get it. They may learn Product Management or Business Analysis in a classroom, but that is very different from sitting with a messy problem and having to decide what to do next.

And access to practical exposure isn’t evenly distributed. Students and professionals in major cities often have more opportunities to meet practitioners, find mentors or work on real product problems.

I want The Product Lab to help close some of that gap. Not by adding another course full of frameworks, but by creating a place where people can use them — and leave with something tangible that demonstrates how they think, not just what they know.

The Product Lab starts with workshops, tools and frameworks, but the ambition is bigger than a workshop business. Over time, I want it to become a place where people can learn, practise and apply product thinking — whether they’re a student working on their first product case, a professional sharpening their craft, a team figuring out where AI belongs, or a founder working through a difficult product decision.

As the Lab grows, I want my own work to grow with it through product consulting, startup advisory and eventually broader strategic work.

I’m not trying to build all of that today. I’m building the foundation for it.