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.
How should control and autonomy be allocated in human-AI collaboration?- Do self-selected heavy Copilot users show different task shifts than randomized users?
- Who decides what work a coworker agent should do daily?
- How do workers define meaningful versus pointless tasks across different occupations?
- Why did freed email time not spill over into other work activities?
- Why do workers perceive some workplace tasks as disconnected from meaningful outcomes?
- How much of employee time with AI goes to understanding its outputs rather than original work?
- Can workplace monitoring data prove that AI caused changes in work activity?
- Do employees spend freed AI time on better work or just more tasks?
- Does AI use expand work scope rather than freeing up worker time?
- Does AI use intensify work by expanding scope instead of freeing time?
- Can worker time savings exist without visible productivity gains?
- How do task shifts precede any wage or employment changes?
- How do different divisions of labor change what augmentation actually means?
- Does removing entry-level tasks increase expertise demands on remaining work?
- Does removing tasks from workers require higher or lower expertise for remaining work?
- Do employers reorganize work tasks around AI before cutting jobs?
- Why do organizations struggle to retrain workers when AI frees up time?
- Does generative AI improve immediate task performance but not sustained independent work?
- How do workers actually choose to use generative AI in practice?
- How much do people use generative AI for work versus personal tasks?
- How does nonwork genAI adoption compare to the PC's early nonwork uptake?
- What automation failure modes threaten generative AI applications in new fields?
- How does continuous feedback help users understand GenAI system behavior?