2026 State of the Workplace
Source: ActivTrak Productivity Lab · 2026-03-11
Collaboration surged 34%, multitasking rose 12% and weekend work increased more than 40%. Meanwhile, focus time fell to a three-year low.
The report also found that 80% of employees now use AI tools at work — up 52% from two years ago — while the average time spent in AI tools increased eightfold. Organizations are rapidly expanding their AI stacks as well, with companies using seven or more AI tools on average, up from two in 2023.
Despite expectations that AI would reduce workloads, the findings show otherwise: AI is amplifying work activity across nearly every category measured. After AI adoption, time spent across work applications increased between 27% and 346%, including a 104% increase in email, a 145% increase in chat and messaging and a 94% rise in business management tools. Meanwhile, AI users’ average daily focused time declined 23 minutes.
The findings highlight a growing AI measurement gap: while adoption is surging across organizations, most companies still lack reliable data on how AI is actually changing productivity, focus and workforce capacity.
Employees start earlier (7:48 a.m. vs 8:02 a.m.)
Saturday productive hours jumped 46% to 4h 37m daily average, with start times shifting from 8:35 a.m. to 7:11 a.m.
Sunday productive hours rose 58%, with start times shifting from 12:24 p.m. to 10:58 a.m.
“AI adoption is accelerating faster than most organizations can measure its impact,” said Gabriela Mauch, Chief Customer Officer and Head of the ActivTrak Productivity Lab. “What our data shows is that AI isn’t reducing work, it’s increasing the speed and density of how work happens. The challenge leaders now face is closing the AI measurement gap and gaining real visibility into how AI is changing productivity, focus and workforce capacity.”
Lines of inquiry this paper opens 23
Research framings built by reading the notes related to this paper — the questions it feeds into.
Does AI-assisted work increase total productivity or just shift time?- Can workplace monitoring data prove that AI caused changes in work activity?
- How much does AI actually automate versus augment in real workplace tasks?
- Do workers experience AI-driven work changes differently moment-to-moment versus in retrospect?
- Do employees spend freed AI time on better work or just more tasks?
- Can self-reported productivity surveys measure AI's real workplace impact?
- How much of employee time with AI goes to understanding its outputs rather than original work?
- Why do most organizations lack reliable data on AI's actual impact on productivity?
- Does AI assistance typically reduce support staff headcount or increase productivity?
- Do companies time productivity claims to coincide with public offerings or fundraising?
- Does checking AI output carefully eat back most of the time it saves?
- Does AI training reduce the time workers need to spend on output cleanup?
- Why do trained AI users report bigger productivity gains than untrained workers?
- How much labor does AI verification actually save compared to full manual review?
- Which types of AI tasks require the most correction work from users?
- Does receiving AI output shift workers' time away from their own productive tasks?
- Can we verify whether workers' estimates of workslop match actual quality audits?