AI Didn't Create the Distraction Economy. It Gave It a Disguise.
A colleague sends you a deck. Polished, cited, ready to present. Ten minutes in, the numbers stop connecting to the conclusions. Forty one percent of workers hit this exact moment last month. Stanford and BetterUp researchers named it workslop, work that looks finished and isn’t.
It’s not a tooling failure. It’s an attention failure, arriving as AI adoption climbs and focus efficiency hits a three year low. Attention is becoming the metric that decides whether AI actually pays off. HR is positioned to design for it, not by policing screen time, but by deciding what the work itself protects.
The instinct is to treat this as an adoption problem. Roll out more training, mandate more AI use, move faster. Microsoft’s Work Trend Index research says the opposite is happening. Focus efficiency has dropped to a three year low even as AI adoption climbed sharply, and employees now spend more time communicating than creating inside Microsoft 365 apps. Microsoft’s own researchers call this the productivity conversation outpacing the organizational design conversation.
Companies are tuning the tool and ignoring the system it landed in.
Workslop is what that gap looks like up close. Ten minutes generating a report that would have taken two hours to actually think through, then someone downstream decides whether to rewrite it, send it back, or quietly absorb the cleanup.
Output went up. Thinking didn’t.
A related study from the same research group, covering twelve thousand workers, found the effect isn’t universal. Workers who engage AI as a thinking partner are markedly more productive than those who use it to cut corners. The technology isn’t the variable, the system around it is, whether a manager rewards a fast answer over a right one, whether anyone protected the time it takes to think before generating. That’s a work design question, not an IT rollout question.
If attention is the real constraint on AI’s return, HR’s job changes shape. Less mandating tools, more auditing meeting load, review cycles, and what gets rewarded when work moves fast versus when it moves right. Start there. Pull one team’s calendar and count hours spent reviewing AI output versus producing it. Some organizations are already merging HR and IT into a single people and technology function to deal with exactly this. That’s not a threat to HR’s relevance, it’s a preview of what the job becomes if HR doesn’t claim it first. So ask what your AI rollout actually optimized for this year, faster output or better thinking. If you’re not sure, you already have your answer.