Why AI Agents Make Workplace Focus Harder, Not Easier
ActivTrak's March 2026 report shows workplace focus efficiency at a three-year low while average AI tools per organization tripled. Sophie Leroy's attention-residue research explains why every agent you launch adds a cognitive switch, even when the agent itself is helpful.
TLDR ActivTrak's March 2026 telemetry on 163,000 workers shows the average focused work session has dropped to 13 minutes 7 seconds, a three-year low, while the average organization now runs seven AI tools instead of two. The Sophie Leroy attention-residue research from 2009 explains why each new agent launch is a fresh cognitive switch with a cost. The fix from the lab studies did not cleanly replicate in a recent field experiment, which is the most useful thing to know. Today’s hook In the last fortnight Microsoft made Copilot’s agentic mode generally available inside Word, Excel, and PowerPoint, and OpenAI shipped workspace agents that keep running after the browser closes. The promise is the same one we keep hearing. You hand a task to the agent, switch to something else, and come back later to a finished thing. Less work. More focus. That is the pitch, and it is worth pausing on for a minute, because the cognitive science says something different is actually happening. What the research shows ActivTrak’s 2026 State of the Workplace report, released March 11, looked at 443 million hours of behavioral data across 163,638 users at 1,111 companies. The headline number is small and damning. The average focused work session in 2025 was 13 minutes and 7 seconds, down 9 percent from 14 minutes 23 seconds in 2023. Focus efficiency, the share of work time spent in actually focused activity, fell to 60 percent, a three-year low. Multitasking rose 12 percent. Time spent inside AI tools rose eightfold. The average organization now runs seven AI tools where two years ago it ran two. This is the same gap we saw in the harness data when teams asked where coding-agent ROI shows up first . The vendor pitch is time saved. The actual measurement is time redistributed. The framework that makes this make sense is older than any of the AI tools. In 2009 Sophie Leroy at NYU Stern named the concept: attention residue. When attention moves from Task A to Task B, part of it stays on A, e