From hype to proof: how to make AI deliver measurable ROI in the real world
🔊 Over the next few weeks, I’m going to share a short series of four articles on AI with ROI.
🎯 The aim isn’t to sell you a magic bullet, but to share practical lessons on how organisations can make AI deliver measurable business value — and avoid the traps that cause so many projects to fail.
Here’s what’s coming:
1️⃣ What Success with AI Really Looks Like — redefining success in terms of ROI and business outcomes, not just model accuracy.
2️⃣ Building the Business Case (and Deciding What Success Looks Like) — how to translate business objectives into measurable outcomes, and why setting success criteria up front matters.
3️⃣ Why AI Projects Fail — and How to Avoid the Pitfalls — from poor data quality to weak adoption, and why skipping validation is so risky.
4️⃣ From Proof of Value to Production: Measuring ROI Continuously — how to validate results in a PoV and then sustain ROI once scaled.
The common thread is simple: AI success doesn’t come from hype or experimentation alone. It comes from clarity, discipline, and proof that the outcomes are real.