# 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 about 15%, strongly concentrated on the least experienced profiles. ## Definition Three real-world studies, published between 2023 and 2025, have solidly established the order of magnitude of gains. Brynjolfsson, Li and Raymond, on 5,172 customer support agents at a large Fortune 500 firm (published in *Quarterly Journal of Economics*, 2025): 15% more tickets resolved per hour on average, about 30% for the least experienced agents, marginal gains for experts. Cui, Demirer, Jaffe et al. (2025), on three randomised controlled trials (Microsoft, Accenture, Fortune 100), 4,867 developers: 26% more tasks completed with GitHub Copilot. On adoption, the Federal Reserve Bank of St. Louis (FRED series based on the work of Bick, Blandin and Deming) measures 45.2% of US workers aged 18 to 64 using generative AI for work in the second quarter of 2026. The academic consensus is now established: generative AI produces real and measurable gains, heterogeneous across profiles. ## Example Illustrative case. 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. ## For a leader 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, a deployment on juniors or operational staff will yield more substantial gains (about 30% for the least experienced agents in the Brynjolfsson study). 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). ## FAQ **What is AI-augmented productivity?** It refers to the measurable production gains (volume processed, speed, quality) a worker achieves with generative AI assistance. Real-world studies establish an average gain of about 15%, strongly concentrated on the least experienced profiles. **What productivity gain can we realistically expect?** On average 15%, according to the Brynjolfsson, Li and Raymond study on 5,172 support agents, but about 30% for the least experienced agents and marginal gains for experts. On software development, the Cui et al. study measures 26% more tasks completed with GitHub Copilot. **Why do gains differ so much from one team to another?** Because they depend heavily on workforce composition. A deployment on an experienced team yields modest gains, while a deployment on juniors or operational staff yields more substantial gains (about 30% for the least experienced agents in the Brynjolfsson study). Targeting the right teams changes the economics of the project. **How can we be sure the measured gains are real?** You need a solid business baseline before deployment. Without a robust metric established upfront, you will not know whether your 14% gain comes from AI or from other factors such as seasonality, training or staff turnover. **Beyond efficiency, does AI have a lasting HR effect?** Yes. The Brynjolfsson study documents a tacit knowledge transfer from senior to junior profiles: AI spreads the experts' good practices to the less experienced. This is a more durable strategic effect than short-term efficiency alone. ## See also - [Augmented automation](https://wyp.agency/en/glossary/augmented-automation/) - [AI agent](https://wyp.agency/en/glossary/ai-agent/) - [Prompt engineering](https://wyp.agency/en/glossary/prompt-engineering/) - [Fine-tuning](https://wyp.agency/en/glossary/fine-tuning/) - [LLM (Large Language Model)](https://wyp.agency/en/glossary/llm/) - [Workslop](https://wyp.agency/en/glossary/workslop/) ## 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) - The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers, Cui, Demirer, Jaffe et al., Management Science (the former NBER w32760 URL pointed to a different paper) (https://doi.org/10.1287/mnsc.2025.00535) - Generative Artificial Intelligence, Adoption Rate for Work: Employed Adults (series RPSGENAIUSAGESHAREWORK, Real-Time Population Survey, workers aged 18 to 64), FRED, Federal Reserve Bank of St. Louis, Q2 2026 value released 4 August 2026 (https://fred.stlouisfed.org/series/RPSGENAIUSAGESHAREWORK) - The State of Generative AI Adoption in 2025, Bick, Blandin & Deming, Federal Reserve Bank of St. Louis, 13 November 2025 (https://www.stlouisfed.org/on-the-economy/2025/nov/state-generative-ai-adoption-2025) --- Entry from the WYP.agency AI glossary · https://wyp.agency/en/glossary/augmented-productivity/ · Last updated : 2026-10-14