A B2B technology company believed it had achieved AI adoption.
More than 95% of employees had accessed AI tools, according to the company’s software license data. For an organization with ~200 employees, that number signaled broad uptake and steady progress. Leadership treated it as a reliable indicator of adoption. Except it wasn’t.
Activation did not reflect how employees actually used AI.
The company had no visibility into what happened after initial access. Any interaction — even a session lasting only a few seconds — counted as usage. A brief application open registered the same as sustained work tied to real tasks.
When measurement shifted from activation to engagement, the gap became clear. More than 30% of users showed minimal usage, with sessions so brief they barely registered. What appeared to be widespread adoption was, in part, incidental access.
That gap limited decision-making. Leadership could not identify where adoption was progressing, where it was stalled or whether enablement efforts were producing measurable change. Training investments lacked precision, and coaching efforts could not target the teams that needed them most. The organization lacked visibility into how teams actually used AI in their day-to-day work.
To establish a clearer baseline, the company deployed ActivTrak across all its teams.
ActivTrak’s AI Insights captured time-in-application data at the session level, enabling the company to distinguish between incidental access and sustained engagement. This gave the company objective, behavioral data on how teams use AI across the organization, helping them measure the true impact of AI adoption across teams and workflows.
The company then applied ActivTrak’s workforce intelligence framework to translate usage patterns into structured maturity levels, mapping users from Stage 0 (non-users) to Stage 5 (end-to-end AI orchestration). This gave leadership a consistent way to evaluate adoption across teams. It shifted the focus from tool access to how AI integrates into real work.
Within six months, the company replaced assumptions with measurable insight.
92% of users demonstrated meaningful AI engagement, and go-to-market teams averaged 3.6% of time spent in AI tools — 5x higher than the 0.6–0.7% benchmark across ActivTrak’s customer base.
The data revealed significant variation across functions. Solutions and Engineering showed consistent, intentional usage, whereas Customer Service and Operations showed the largest gaps between initial activation and ongoing engagement.
With this visibility, leadership identified which teams were progressing toward Stage 3 maturity — recurring, workflow-integrated usage — and which required additional support. Benchmarking added context, showing how the company’s AI usage compared to broader adoption patterns.
We thought we had strong AI adoption. ActivTrak showed us we had strong AI activation. Those aren’t the same thing.
The company moved from a single metric to a measurable system of adoption.
Leadership now sets team-specific AI maturity goals, ensures team accountability for progression and measures whether enablement efforts drive behavioral change. Decisions about AI adoption now reflect how work is performed — not just whether tools are accessed.
As a result, AI adoption shifted from a proxy metric to a behavior-based signal that leadership can track over time, establishing a clear baseline while highlighting where further measurement would refine understanding. The company can now distinguish between access to AI and its role in day-to-day execution.
“We thought we had strong AI adoption,” said the chief technology officer. “ActivTrak showed us we had strong AI activation. Those aren’t the same thing.”
