2026 State of the Workplace

Paper · Source
AI at Work

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 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

How does AI exposure shape employment demand and worker earnings? Why do firms choose automation over worker augmentation when adopting AI? How does task-replacing automation affect worker employment and remaining skill needs? Why do perceived AI productivity gains often fail to translate into measured business impact? Does generative AI shift knowledge work away from collaborative tasks? How do inference costs, efficiency, and market competition shape AI adoption economics? How can verification systems keep pace with AI generation? How does AI assistance affect professional skill development and retention?