Turning AI into ROI: a practical framework for enterprises

Most AI pilots stall because they optimise for demos, not decisions.

When I work with an enterprise team, we start with the process, not the model. Which decisions are slow? Which handoffs create rework? Which reports get rebuilt every month by hand? Those are the places where AI compounds.

1. Pick a process with a clock on it

If you cannot state how long the process takes today, you cannot prove improvement later. Time-to-hire, time-to-approve, time-to-report — pick one.

2. Design for the workflow, not the chat box

A chat interface is rarely the product. The product is the AI reasoning embedded where work already happens: the ATS, the review cycle, the grant application.

3. Instrument from day one

Log every suggestion, acceptance and override. That data becomes your business case and your training signal.

4. Adoption is architecture

A technically sound system nobody uses returns zero. Build the change plan alongside the build plan.

The teams that win with AI are not the ones with the most models. They are the ones who removed the most friction.