Early LLM-based Tools for Enterprise Information Workers Likely Provide Meaningful Boosts to Productivity - Cambon, Alexia; Hecht, Brent; Edelman, Benjamin; Ngwe, Donald; Jaffe, Sonia; Heger, Amy; Vorvoreanu, Mihaela; Peng, Sida; Hofman, Jake; Farach, Alex; Bermejo-Cano, Margarita; Knudsen, Eric; et al. (Microsoft, 2023)
This report presents the initial findings of Microsoft’s research initiative on “AI and Productivity”, which seeks to measure and accelerate the productivity gains created by LLM-powered productivity tools like Microsoft’s Copilot. The many studies summarized in this report, the initiative’s first, focus on common enterprise information worker tasks for which LLMs are most likely to provide significant value. Results from the studies support the hypothesis that the first versions of Copilot tools substantially increase productivity on these tasks. This productivity boost usually appeared in the studies as a meaningful increase in speed of execution without a significant decrease in quality. Furthermore, we observed that the willingness-to-pay for LLM-based tools is higher for people who have used the tools than those who have not, suggesting that the tools provide value above initial expectations. The report also highlights future directions for the AI and Productivity initiative, including an emphasis on approaches that capture a wider range of tasks and roles. —– We have since released a second AI & Productivity report Generative AI in Real-World Workplaces