ActivTrak CEO: What 120,620 workers reveal about AI maturity

ActivTrak’s Productivity Lab tracked 120,620 employees over three quarters and found something counterintuitive: the optimal level of AI adoption maturity for most employees may be somewhere in the middle between shallow AI usage and full automation.

Most leaders I know are tempted to build their AI adoption strategy as if every employee should be an AI super user.

Buy the most powerful tools, push everyone toward the deepest integration, maximize adoption maturity and assume productivity will skyrocket.

The most recent data from ActivTrak’s Productivity Lab complicates that idea.

Productivity and work-health metrics rise as employees move from little or no AI use to regular, task-level adoption, with healthy utilization peaking at 75%.

But once AI becomes embedded in workflows, healthy utilization drops about 5 percentage points — to levels statistically indistinguishable from employees who barely use AI.

The right level of AI adoption maturity depends on the work being done and the business objectives it supports.

Why moderate AI maturity may be sufficient for most Traditional AI adoption metrics track licenses or login counts, which measure deployment but offer little clarity into how AI impacts the work being done.

Typical AI maturity models measure an organization’s overall progress toward deeper AI adoption.

Our Productivity Lab takes a different approach, using behavioral data to document how AI actually changed the way people work , and categorizing them into three stages of maturity that reflect true operational progression.

The Lab tracked the same 120,620 employees across 1,009 organizations for three consecutive quarters from Q4 2025 to Q2 2026.

The data showed 27% of employees used AI like a search engine to answer questions and summarize information (Stage 1, Research Assistance).

14% used AI to draft content, generate ideas and complete routine tasks that they then validate and finalize (Stage 2, Task Execution).

Only 2% reached the stage where AI becomes an integral part of day-to-day workflows (Stage 3, Workflow Integration).