What Research Says About AI and Workplace Productivity

The best-known evidence comes from field and controlled experiments on well-defined tasks. Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied more than 5,000 customer support agents who were given an AI assistant. On average they resolved about 14 percent more issues per hour. Novice and lower-skilled agents gained around 34 percent, and the most experienced agents gained little. The researchers argue the AI spread the know-how of top performers to everyone else. Shakked Noy and Whitney Zhang, publishing in Science in 2023, found that ChatGPT cut the time college-educated professionals spent on writing tasks by about 40 percent and raised rated quality by about 18 percent. A GitHub Copilot experiment found developers finished a set programming task about 55 percent faster. The 2023 Boston Consulting Group study with Harvard and other researchers added an important caveat, which it called the 'jagged technological frontier'. On tasks inside the AI's abilities, consultants with GPT-4 finished more tasks, faster and at higher quality. On a task designed to sit outside those abilities, they were less likely to get the right answer than consultants without AI, because they trusted plausible but wrong output. More recent findings are more modest. In a 2025 randomized trial by METR, experienced developers working in their own large codebases were about 19 percent slower with AI tools while believing they had been faster. Studies of whole labor markets, such as research on Danish workers, have found small effects on earnings and hours so far. Why the gap? Experiments isolate tasks that suit AI. Real jobs mix many tasks, time saved is not always reused productively, and organizations need to redesign workflows before gains show up. Economists saw a similar lag in measured productivity after electricity and computers arrived. The common misconception is that one headline percentage applies to every job.