Last reviewed: May 25, 2026

What is AI-augmented productivity? Definition and business implications

AI-augmented productivity refers to measurable production gains (volume processed, speed, quality) that a worker achieves when they benefit from generative AI assistance. Real-world studies show an average gain of 14 to 15%, strongly concentrated on the least experienced profiles.

Three real-world studies, published between 2023 and 2025, have solidly established the order of magnitude of gains. Brynjolfsson, Li and Raymond, on 5,179 customer support agents at a large Fortune 500 firm (published in Quarterly Journal of Economics, 2025): 14 to 15% more tickets resolved per hour on average, 30 to 34% for the least experienced agents, marginal gains for experts. Cui, Demirer, Jaffe et al. (2025), on three randomised controlled trials (Microsoft, Accenture, Fortune 100), 5,000 developers: 26% more pull requests completed with GitHub Copilot. Bick, Blandin and Deming (Federal Reserve St. Louis, 2025): 35.9% of US workers used generative AI by December 2025. The academic consensus is now established: generative AI produces real and measurable gains, heterogeneous across profiles.

Concrete example

A 1,200-employee European insurance company measures its gains over 9 months after deploying an AI assistant in four trades. Customer service (180 agents): 13% more cases processed, consistent with the Brynjolfsson study. Software development (45 developers): 22% more features shipped, close to the Cui et al. result. Legal (12 lawyers): no measurable gain, consistent with the hypothesis that expert profiles benefit little. Marketing (28 people): 35% more content produced, exceeding the studies due to less constrained creative work. Total: 28 full-time equivalents of capacity freed, reinvested into business growth rather than headcount reduction.

Three implications

AI productivity gains have become measurable and reproducible. Three implications for the executive. First, these gains are not uniform. For a given AI investment, the return depends heavily on the composition of your workforce: a deployment on an experienced team will yield modest gains (5 to 10%), a deployment on juniors or operational staff will yield substantial gains (25 to 35%). Targeting the right teams changes the economic equation. Second, AI acts as a tacit knowledge transfer from seniors to juniors, as documented by the Brynjolfsson study. This is a more durable strategic HR effect than mere short-term efficiency. Third, measuring gains requires a solid pre-AI baseline. Without a robust business metric before deployment, you will not know if your 14% gains are real or attributable to other factors (seasonality, training, turnover).

Sources

Generative AI at Work, Brynjolfsson, Li & Raymond, Quarterly Journal of Economics vol. 140 n° 2, 2025. https://academic.oup.com/qje/article/140/2/889/7990658 (accessed 2026-05-24)
The Effects of Generative AI on High Skilled Work, Cui, Demirer, Jaffe et al., 2025. https://www.nber.org/papers/w32760 (accessed 2026-05-24)

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What is AI-augmented productivity? Definition and business implications

Concrete example

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Sources