Why Is It So Hard to Measure the ROI of AI?
CIO, Wednesday, July 8th, 2026
Complex processes, missing baselines, hidden costs, and a productivity paradox make AI ROI notoriously hard to quantify.
CIO explores why organizations struggle to measure AI's return on investment. Complex, multi-step processes make it hard to isolate AI's impact, and many firms lack baseline metrics before deployment.
A METR study found developers estimated 20% productivity gains while data showed a 19% slowdown, and efficiency gains rarely translate into headcount cuts.
Hidden costs beyond subscriptions - infrastructure, data prep, API calls, security audits, and training - compound the problem, with agentic AI adding unpredictable consumption.
Structural obstacles like conflicting billing models and organizational silos further obscure end-to-end ROI visibility.