AI tokens are exposing what companies got wrong about adoption

That audit, she argues, will reveal three patterns. Some employees are building deeply valuable, domain-specific tools that represent exactly what human-AI collaboration should look like. Others are duplicating effort, building the same custom tool independently across hundreds of teams. And others are simply inefficient, running expensive queries that could be structured more cheaply without any loss of output.

Duplicated effort is one of the most common patterns. Thousands of employees independently building the same tool doesn’t just create governance headaches; it burns through tokens at scale with no organizational oversight.

“The classic HR example that CHROs share with me all the time is that in large organizations, hundreds or thousands of people have built their own tool to assist them in the process of writing performance reviews,” Dhar said.

Connor’s approach at Moxie starts with the budget. “There needs to be strict budgeting around token usage,” she said. “There is a responsibility at the leadership level to put budgets in place on a per team basis.” None of it, she added, sits with any single function. “I don’t think any of these decisions truly sits with just HR or just tech or just finance. It’s truly a cross-functional leadership transformation that needs to happen.”

Dhar’s framework for moving from adoption to accountability centers on what she calls a judgment-based policy, something between a blanket ban and unlimited access. The question she’d have organizations ask isn’t “is this expensive?” but “can you defend this use?”