--- title: "Does AI help less-experienced workers more than experienced ones? (27–46%) | The AI at Work Fact Book" description: "On well-defined tasks, the newest or weakest workers gain 27 to 46 percent from AI and the strongest 0 to 17 percent. Where the pattern reverses, with sources." url: "https://evy.so/facts/ai-helps-less-experienced-workers-more/" last_updated: "2026-10-16" --- F-12 · What AI Saves # Does AI help less-experienced workers more than experienced ones? Mostly yes, on well-defined tasks: the newest or weakest workers gained about 27 to 46 percent in support, consulting and software studies, against 0 to 17 percent for the strongest. On open-ended business decisions the pattern reversed: in Kenya, strong entrepreneurs gained from an AI adviser while weak ones did nearly 10 percent worse. 27–46% gain for the newest or weakest workers; the strongest gain 0 to 17% - Medium confidence - 5 primary sources - 2023 to 2026 - Last checked October 14, 2026 ## Cite this EVY (2026). Does AI help less-experienced workers more than experienced ones? The AI at Work Fact Book, F-12. https://evy.so/facts/ai-helps-less-experienced-workers-more/ (last checked October 14, 2026). CC BY 4.0. Copy citation [Data as JSON](https://evy.so/facts/ai-helps-less-experienced-workers-more.json) [Markdown](https://evy.so/facts/ai-helps-less-experienced-workers-more.md) ## How to read it Studies split workers in different ways, by earlier performance or by tenure, and only the support and software studies speak directly to experience. Outcomes differ: issues resolved per hour, graded quality, pull requests and customer ratings. The BCG 43% and 17% compare consultants with their own earlier work and appear in the 2023 working paper; the published 2026 version keeps the finding without printing the numbers. Noy and Zhang and Alibaba show the same narrowing without a comparable percentage, so they are in the table only. ## Every study on one scale Shaded: 27–46% One figure A range in one study Chart values as text - Scale: % gain for the newest or weakest group. Shaded range: 27–46%. - Brynjolfsson et al., support (2025): 30–46% (top skill: about 0) - Dell'Acqua et al., BCG (2023): 43% (top half 17%) - Cui et al., software (2026): 27–39% (long tenure 8–13%) **Why medium confidence:** Five independent teams in different settings find bigger gains for weaker or newer workers. Each measures a different outcome, the software gaps are not statistically significant, and a randomized trial on open-ended business decisions finds the reverse. Studies without a figure on this scale are listed in the table only. ## Sources Sources for F-12 | Study | Creator | Year | Sample | Method | Figure | Link | | --- | --- | --- | --- | --- | --- | --- | | Generative AI at Work (Quarterly Journal of Economics) | Erik Brynjolfsson (Stanford), Danielle Li and Lindsey Raymond (MIT) | 2025 | 5,172 customer support agents at one Fortune 500 software firm | Staggered rollout, compared before and after, split by earlier skill and by tenure. The tenure figure is from the 2023 NBER version. | Less skilled and less experienced agents +30%; lowest-skilled fifth +36%; agents with under a month of tenure about +46%; no significant change for the most skilled “Less skilled and less experienced workers improve significantly across all productivity measures” | [doi.org ↗](https://doi.org/10.1093/qje/qjae044) | | Navigating the Jagged Technological Frontier (HBS Working Paper 24-013) | Fabrizio Dell'Acqua, Ethan Mollick, Karim R. Lakhani and colleagues, with Boston Consulting Group | 2023 | 758 BCG consultants | Preregistered randomized experiment. Gains compare AI users with their own earlier scores on a baseline task. | Consultants below the median improved 43%, those above 17% “those below the average performance threshold increasing by 43% and those above increasing by 17%” | [hbs.edu ↗](https://www.hbs.edu/faculty/Pages/item.aspx?num=64700) [Archived copy ↗](https://web.archive.org/web/20240102034642/https://www.hbs.edu/ris/Publication%20Files/24-013_d9b45b68-9e74-42d6-a1c6-c72fb70c7282.pdf) HBS no longer hosts this version of the PDF; the archived copy is the 2023 working paper we read. | | The Effects of Generative AI on High-Skilled Work: Evidence from Three Field Experiments with Software Developers (Management Science) | Kevin Zheyuan Cui, Mert Demirer, Sonia Jaffe, Leon Musolff, Sida Peng and Tobias Salz | 2026 | 4,867 developers at Microsoft, Accenture and a Fortune 100 electronics maker | Three randomized company experiments, split by tenure and job level. The splits are not statistically significant. | Short-tenure developers +27% to 39% completed tasks; long-tenure +8% to 13% “less experienced developers had higher adoption rates and greater productivity gains.” | [doi.org ↗](https://doi.org/10.1287/mnsc.2025.00535) | | Experimental evidence on the productivity effects of generative artificial intelligence (Science) | Shakked Noy and Whitney Zhang (MIT) | 2023 | 453 college-educated professionals | Preregistered online randomized experiment on writing tasks | The gap between stronger and weaker writers halved: low scorers improved in grade and speed, high scorers kept their grade and got faster “ChatGPT compresses the productivity distribution by benefiting low-ability workers more.” | [science.org ↗](https://www.science.org/doi/10.1126/science.adh2586) | | Generative AI in Action: Field Experimental Evidence from Alibaba's Customer Service Operations | Xiao Ni, Yiwei Wang, Tianjun Feng, Lauren Xiaoyuan Lu, Yitong Wang and Congyi Zhou | 2026 | 5,940 new customer service agents, about 2.56 million chats | Four-week randomized trial run by Alibaba, split by earlier customer ratings | Ratings for the lowest-performing fifth rose 0.81 points on a 5-point scale and fell 0.28 for the top fifth “Lower-performing agents benefit the most, indicating that gen AI can narrow performance gaps.” | [arxiv.org ↗](https://arxiv.org/abs/2603.29888) | Where it reverses ## When does AI help strong performers more than weak ones? On open-ended decisions, where people must judge which advice fits. In a five-month trial with 640 entrepreneurs in Kenya who got a GPT-4 business mentor on WhatsApp, low performers' revenue fell about 9% while high performers' rose about 18%, a gain the authors treat as tentative. They trace the gap to weaker owners acting on generic advice such as price cuts. When does AI help strong performers more than weak ones? | Study | Creator | Year | Sample | Method | Figure | Link | | --- | --- | --- | --- | --- | --- | --- | | The Uneven Impact of Generative AI on Entrepreneurial Performance: Evidence from a Field Experiment in Kenya (HBS Working Paper 24-042) | Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning | 2026 | 640 entrepreneurs in Kenya, five months | Randomized field experiment: a GPT-4 mentor on WhatsApp or a standard business guide | No average effect; low performers about 9% lower revenue, high performers about 18% higher (p = 0.07) “low performers did nearly 10% worse due to the AI assistant” | [hbs.edu ↗](https://www.hbs.edu/ris/Publication%20Files/24-042_567db548-5b24-4182-b703-9d5ef1328043.pdf) [Archived copy ↗](https://web.archive.org/web/20261009165007/https://www.hbs.edu/ris/Publication%20Files/24-042_567db548-5b24-4182-b703-9d5ef1328043.pdf) | ## Numbers you may have seen These figures circulate widely. Here is what the original source says. - **AI mostly helps top scientists (Toner-Rodgers, MIT, 2024).** MIT said in May 2025 that it has no confidence in the paper's data and asked for it to be withdrawn. - **Below-average BCG consultants improved 43% more than the control group.** The 43% and 17% compare consultants using AI with their own scores on an earlier task without AI. - **Novice support agents gained 34% with AI.** That is the 2023 working-paper figure. The peer-reviewed 2025 version says 30% for less skilled and less experienced agents and 36% for the lowest-skilled fifth. - **AI levels the playing field for every worker.** It holds in studies of well-defined tasks. In the Kenyan trial on open-ended business decisions, AI widened the gap. ## More from What AI Saves - [F-08**3–5.4%**How much time does AI save knowledge workers?](https://evy.so/facts/time-saved-by-ai-at-work/) - [F-09**25–40%**How much faster do people finish writing tasks with AI, and is the work better?](https://evy.so/facts/ai-writing-speed-and-quality/) - [F-10**14–15%**How much more do customer support agents get done with AI?](https://evy.so/facts/ai-customer-support-productivity/) - [F-11**21–26%**How much faster do developers code with AI assistants?](https://evy.so/facts/ai-coding-speed/) - [F-13**14–25%**When does AI make work slower or worse?](https://evy.so/facts/when-ai-makes-work-worse/) [The whole chapter](https://evy.so/facts/what-ai-saves/) · [Chapter poster](https://evy.so/facts/what-ai-saves/sheet/) · [All of The AI at Work Fact Book](https://evy.so/facts/)