Token spend is coming for your performance review

Every company has an AI strategy. Most of them are missing something critical. Adoption isn't proficiency. Most companies have zero idea which is which — and boards are starting to ask.

Walk through most companies today and you'll find the same story: leadership has invested in Anthropic's Claude, Copilot, ChatGPT Enterprise, Gemini, Cursor, or half a dozen other tools. The memo went out. The licenses were bought. The training webinar had decent attendance.

And then nothing changed.

The problem isn't motivation. Most employees are curious, and many are already building. Inside almost every company, someone is quietly transforming their workflow on their own. But companies see who has access to AI tools — not who's actually using them in ways that matter. Adoption isn't proficiency. And right now, most companies have zero idea which is which.

Token usage data lives in IT dashboards, disconnected from where work actually happens: performance conversations, manager check-ins, and how teams deliver results. That's the AI visibility gap. And it's quietly undermining every workforce AI strategy.

AI proficiency is becoming a core competency

Boards are already asking. CEOs are already tracking. The question is no longer whether employees should use AI — it's whether they are, and how deeply.

Leaders want speed. Boards want efficiency gains. Hiring plans are being restructured around the assumption that AI-augmented workers are simply more productive. But without connecting token usage to context, companies can't tell the difference between someone logging in twice a week and someone rebuilding their entire workflow from the ground up.

Token spend data matters when it sits alongside outcome data — when it informs managers about the habits, tools, and practices underneath the results. The missing piece is a signal that surfaces objective metrics about AI usage patterns, not the contents of conversations or documents — connecting adoption data to where performance conversations actually happen.

Building better conversations — not just better dashboards

Bridging the gap means more than surfacing a new metric. It means giving managers the context to act on it.

When AI adoption data lives inside the performance cycle, it becomes the foundation for constructive feedback that's specific, evidenced, and forward-looking. Not "you need to use more AI," which helps no one, but "I can see how deeply you're engaging with these tools — where is it not working for you yet?" That's a development conversation. That's how you keep strong people growing.

For distributed and remote teams especially, this signal is invaluable. Informal AI learning — the kind that happens organically in an office — rarely transfers across time zones.

Closing the gap with Engage

When token usage is visible alongside check-ins and goals, managers get a cue to step in with support before skill gaps compound. Great management doesn't require physical proximity. It requires the right data.

Today Engage integrates with Anthropic's Claude, Cursor, and Github Copilot, connecting AI token usage directly with talent management. Soon it’ll connect with Microsoft Copilot and Gemini. OpenAI enterprise analytics need some work on their end and we'll be ready when they are.

AI adoption data appears in the reviews module alongside KPIs, competencies, check-ins, and previously received feedback — so managers don't have to stitch the picture together from four different tools. Organizations get a real measure of AI maturity, not a theoretical one. And because it's built on Engage, it works consistently across every location, time zone, and most employment types.

The future of IT + HR

AI isn't going to slow down, and the expectation that employees adopt it quickly isn't going away.

The companies that close the gap between IT and HR — the ones that connect adoption to outcomes and build it into their performance culture — are the ones that will actually see the returns they've been promised. Engage makes that connection real.