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What is AI ROI? Definition and pitfall to avoid

AI ROI, modelled on classic financial ROI, is a deceptively appealing metric for evaluating an AI deployment. Three structural confusions invalidate it: underestimation of hidden costs, lack of robust pre-AI baseline, and concentration on operational efficiency to the detriment of the other two dimensions of value.

The AI ROI metric is widely repeated in business press in 2026, but it masks three major methodological confusions. First confusion: visible costs (subscriptions, licences, API consumption) are often accounted for, hidden costs are not. Application integration, operational supervision, team time to adapt, production incidents: according to RAND 2025, production overcost reaches 380% of pilot estimate. Second confusion: the pre-AI baseline is rarely rigorously measured. Without precise knowledge of processing time, error rate, or satisfaction before deployment, the improvement measured afterwards is only an impression. Third confusion: only operational efficiency (time gained, volume processed) is reported. The other two dimensions of value (decision quality, strategic option) are passed over in silence, although they are often more durable and harder for competitors to reproduce.

Concrete example

A customer service company announces internally an AI ROI of 240% on its first deployment, calculated by dividing productivity gains (1.3 million euros over 12 months) by declared investment (390,000 euros). Three elements are absent from the calculation. The 280,000 euros of internal team mobilised for 9 months. The 90,000 euros of supervision and governance in the first year. The baseline of processing time before deployment, which had never been formally measured. Real ROI after correction: between 60% and 110%, still positive, but an order of magnitude below the initial communication.

See also

Further reading

The Root Causes of Failure for Artificial Intelligence Projects, RAND Corporation, 2024-2025 (external resource)

Sources

  1. The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, RAND Corporation, 2024-2025. https://www.rand.org/pubs/research_reports/RRA2680-1.html (accessed 2026-05-24)
  2. Generative AI at Work, Brynjolfsson, Li & Raymond, Quarterly Journal of Economics vol. 140 n° 2, 2025 (heterogeneity of gains). https://academic.oup.com/qje/article/140/2/889/7990658 (accessed 2026-05-24)

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