How KPMG's AI Dashboard Backfired

KPMG built a dashboard to encourage its people to use AI.

Within weeks, employees figured out how to game it.

It's not a glitch. It's the natural byproduct of an accounting culture that rewards 'looking busy' over achieving results.

David Leary and I broke down what happened on Episode 491 of The Accounting Podcast.

Token Maxing Is the New Billable Hour

KPMG rolled out an AI usage dashboard in late 2025. The target, according to reporting we covered on the show, was roughly 75% of the week. Basically, four out of five days a week, you need to be in an AI tool, doing something.

So what did KPMG's people do?

They had AI summarize emails they'd already read. They asked it to make drawings. Anything to show activity on the dashboard.

This includes employees who actually believe in AI. People who want to use these tools well are now wasting time performing usage for a metric.

There's a name for this now: token maxxing.

It's not just KPMG. Amazon ran an internal leaderboard tracking AI usage across its workforce. They shut it down for the same reason. People were burning tokens to climb the rankings rather than to solve problems.

This is the billable hour in a new disguise.

For decades, accounting firms have measured value by counting hours. Not outcomes.

You know what that produces. Staff who pad timesheets. Staff who work an extra fifteen hours a week and call it normal. Staff who burn out and quit, which is a big reason we don't have enough senior accountants today.

Now, firms are taking that same approach to AI. But it doesn’t work.

What Software Companies Already Figured Out

Picture a software company telling its engineers, "I want you writing code with GitHub Copilot for six hours a day. We're tracking it."

What happens?

Engineers accept every suggestion Copilot offers, even the bad ones. They generate code they don't need. They inflate their "AI-assisted" stats because that's what gets measured, and what gets measured gets rewarded.

Meanwhile, another engineer uses Copilot for fifteen focused minutes to solve a hard problem, then spends the rest of the day thinking, reviewing, and talking to the team.

They look lazy on paper. But that’s the person making a difference.

Jeff Seibert, CEO of Digits, said he hasn't written a line of code himself since December.

This is a guy who taught himself to code at age 12 and says his passion is coding.

Claude Opus writes almost all of it now. His job changed from doing the work to guiding the agents and reviewing the output.

Nobody tracks how many hours Jeff spends "in Claude." Nobody needs to. What matters is whether the software shipped and whether it works.

The metric is output, not activity.

Firms that measure activity will train their people to fake activity. Firms that measure outcomes will actually benefit from the tool.

Measure the Work, Not the Tool

If you're a firm leader who's already built (or is tempted to build) an AI usage dashboard, here's your first step.

Throw out the usage metric.

Don't ask "how many hours did this person spend in an AI tool this week?"

Ask "What got done that didn't get done before?" instead.

Otherwise, you'll get the appearance of progress and none of the substance.