Generative AI in Real-World Workplaces
This report presents the most recent findings of Microsoft’s research initiative on AI and Productivity, which seeks to measure and understand the productivity gains associated with LLM-powered productivity tools like Microsoft Copilot. The report synthesizes research results from over a dozen recent studies conducted by researchers at Microsoft, with a focus on studies of generative AI in actual workplace environments. One of these is, to our knowledge, the largest, randomized controlled trial of the introduction of generative AI into organizations. Overall, the research suggests that generative AI is already aiding workers in becoming more productive in their day-to-day jobs in significant ways. However, the influence of generative AI is subject to variation by role, function, and organization and is contingent upon adoption and utilization. The report explores these variations and underscores the potential for AI to have even greater impact as individuals and organizations recalibrate their work practices to harness AI in the places where it provides the most value.
However, much of this existing literature on AI and productivity is limited in that it consists primarily of lab-based studies.
· Generative AI is already helping people be measurably more productive in their day-to-day jobs.
· As expected, the productivity story in real-world workflows is more complex than observed in lab studies.
· Productivity gains associated with generative AI, including time and accuracy, vary by role, function and organization.
· Variance in adoption and utilization influences AI’s impact.
· Early studies suggest generative AI may affect the cognitive effort required for task completion.
largest impact was on novice and low-skilled workers, with very little effect on experienced or highly-skilled workers.
Researchers found that on average, those with Copilot for Microsoft 365 read 11% fewer individual emails and spent 4% less time interacting with them, compared to people without Copilot. Some organizations saw larger effects with relative decreases of up to 20 or 25% in both emails read and time spent interacting with email.
Overall people edited 10% more documents, with heavy users of Word, Excel, and PowerPoint seeing an increase of 13% (on a higher baseline). Some organizations saw increases in the 25-30% range. One hypothesis is this is an early sign of the writing and creation assistance that Copilot provides making it easier to produce and revise output.
Lines of inquiry this paper opens 24
Research framings built by reading the notes related to this paper — the questions it feeds into.
What happens to knowledge when intelligence becomes tokenized like a commodity? Does AI assistance promote real skill development or substitute for independent learning?- Why do workers who understand AI generations learn more than those who only use output?
- What happens when AI-dependent workers must operate without their tools?
- Does constraining AI access during early task phases preserve skill formation?
- Does outsourcing tasks to AI reduce opportunities for skill development?
- Does democratizing AI access actually improve or impair human skill development?
- How does AI assistance affect human cognitive development over time?
- Why do AI-enhanced abilities disappear when workers lose AI access?
- Do workers become dependent on AI when they stop using it for the same task?
- How should professional training programs adapt to AI-assisted work environments?
- Why might AI that improves immediate task performance harm long-term skill development?
- Do salaried workers get better AI training support than gig workers?
- Do employers hire workers who learn AI skills on the job versus bringing them in?
- Do AI tools save total time or just shift work between different activities?
- Does AI assistance transfer learning gains to independent tasks without scaffolding?
- What skills do juniors lose when they skip the entry-level work struggle?
- Does AI create new skills gaps or only expose existing ones?
- Why do AI productivity gains emerge most when workers apply existing skills?
- Do AI productivity gains require existing skills or enable learning new ones?
- Does AI assistance help people learn skills or just delegate the task?
- Does AI-assisted performance predict what students can do without help?