I recently completed an industrial market mapping project for the US Construction & Engineering sector: identifying and categorizing 100 significant companies with detailed data on each.

Normally, this kind of market mapping takes me 2-3 full days. I'd spend hours digging through industry databases, visiting company websites, categorizing players, and building spreadsheets. It's essential work, but it's tedious.

This time, I decided to let AI do the heavy lifting. Seventy-five minutes later, I had a comprehensive market map with all three categorization frameworks and detailed company data ready for review. Here's exactly how I did it.

The client needed more than just a list of companies. I decided to approach this by building a framework that would help PE firms and strategics understand how money flows in this industry. That meant three layers of categorization: first by company type, then by primary service offering, and finally by commercial logic. The last one was key. How do buyers actually think about these vendors? Where does budget come from? Is it CapEx or OpEx? Government contracts or private development?

To make this more useful, I added detailed data:official websites, current market caps or revenue figures, key services in 3-5 words, and operating regions. For 100 companies. All verified with 2024-2025 sources.

I've done this kind of research before, and I knew what I was up against. Even with my industry knowledge, manually gathering and organizing this would take days. But I'd been experimenting with AI tools for research tasks, and this felt like the perfect test case.

I started by spending 30 minutes with ChatGPT and Claude just thinking through the approach. This turned out to be the most valuable half hour of the entire project. Without a clear framework, AI just gives you data. With one, it gives you insights.