A Glean survey finds that for every hour an employee spends getting a useful output from AI, they spend another hour making it usable: giving agents context, checking work, flagging mistakes, and cleaning up answers. Workers reported nearly six and a half hours a week on this maintenance, more than a third of AI sessions fail outright and require starting over or heavy rework, and 69% said they review outputs, which means a meaningful minority do not and let errors slip through.
Why it matters to the HR leader: the productivity story most boards are told, that AI removes work, is on this data incomplete. AI is shifting work from visible production to invisible supervision, and that hidden shift lands hardest on the employees and managers least accounted for in the ROI math. Workforce plans built on assumed AI time-savings will overstate real capacity.
The signal underneath: Glean’s Rebecca Hinds, who heads its Work AI Institute, frames adoption as a “vanity metric” problem, where seats and prompts get measured but usable output does not. The companies that win, she argues, build human infrastructure around AI: training employees on when and how to use it, setting guardrails, and reinvesting reclaimed time into higher-quality human work rather than chasing maximum usage. Her test is concrete: AI use should be “grounded in the right context, measured against real outcomes, and governed in a way that helps employees move faster without lowering the bar for quality.” Measure outcomes per task, not prompts per seat.
Source: HR Dive.
Related on this site: Wells Fargo CEO Discusses the Complex Impact of AI on Employment and HR Strategies.
