It’s one thing to talk about AI-enhanced maturity assessments in theory, it’s another to see them delivering real, measurable impact in the field. In this chapter, we get into the AI case studies first. These aren’t polished PR stories, they’re the behind-the-scenes accounts of what worked, what nearly fell apart, and the course corrections that made the difference.

From manufacturing firms that used predictive analytics to cut downtime in half, to consulting practices that doubled their sales pipeline with AI-driven tools, the patterns are clear. The most successful implementations start small, scale fast, and never lose sight of the human judgement guiding the machine.

Building on that, I’ll take you through an AI-enhanced maturity assessment I developed at TPE , the 8 Drivers of Equity Value in Fintech SaaS. This framework helped firms map their growth trajectories by tying operational maturity straight to enterprise value.

You’ll walk away with practical lessons you can borrow, adapt, and apply to your own work, whether you’re starting from scratch or looking to sharpen a tool you already have.

Next up: Chapter 7, applying these same principles to measure an organisation’s AI readiness. If you want to sell AI-enabled solutions, you need to show clients how ready they really are and this is the way to do it.