How to measure the ROI of AI projects

"What's the ROI?" is the first question every executive asks about AI. Here's how to answer it: baseline before you build, define success early, and track more than cost savings.

"What's the ROI?" Every executive asks it, and it's the question AI projects most often struggle to answer. Here's how I measure and communicate the value of an AI investment.

Beyond cost savings

Cost reduction is the easiest metric to track. It's rarely the full picture. AI projects typically create value in several places at once.

Direct metrics

Time saved on specific tasks
Lower error rates
Higher processing capacity
Direct cost savings

Indirect metrics

Employee satisfaction and retention
Customer experience
Better decisions
Competitive position

The measurement framework

Baseline before you build. Document the current state before implementation.
Define success criteria early. Agree on what good looks like before the project starts.
Track leading and lagging indicators. Don't wait for the annual review.
Account for learning curves. ROI usually improves over time.

Common pitfalls

Mistakes I see when companies measure AI ROI:

Counting only the obvious wins
Ignoring implementation and maintenance costs
Forgetting the opportunity cost of the alternatives
Giving AI credit for outcomes it only partly caused

Communicate value continuously

Don't save the ROI story for the annual review. Regular updates build confidence and keep support for continued investment.