Managing AI has become its own job

Managers are rushing to deploy AI for efficiency gains. Employees have to figure out how to make it work—and that’s sometimes harder than it seems. Half of organizations piloted general-purpose AI tools last year, according to MIT research. But adoption and readiness aren’t the same thing.  According to Rumman Chowdhury, former U.S. Science Envoy for AI and CEO and cofounder of Humane Intelligence, the burden is likely to fall on workers. “There’s a lot of FOMO among C-suites and high-level execs on pressure to build AI, and then they’re also incentivized to pretend like it works really well,” she says. “If and when it doesn’t, the responsibility is on the employee who had no say in whether or not this technology was adopted and used, or even often what it was used for.” For many employees, particularly those who don’t have a technical background, the promise of AI-driven efficiency comes with a catch: Useful output often requires time and effort that doesn’t always get counted. The gap between what these tools are supposed to do and what it actually takes to make them work has become its own job.  Companies are figuring out whether the fix is better training or more realistic expectations around what these tools can deliver. For now, employees are absorbing the additional labor involved in prompting AI and double-checking its outputs. “PhD-level experts in your pocket”? Kellie Romack, chief digital information officer at enterprise software company ServiceNow, suggests managing AI is a hands-on effort. During a recent session with one of the company’s AI tools, she caught the model making a basic math error. “I wrote back and said, I think your math is wrong,” she recalled. “It wrote back to me and said, ‘Oh, you’re right. I do have it wrong.’” Romack gave it a thumbs-down and flagged it for her team’s feedback loop.  The cleanup that follows is a cost organizations don’t always account for.  “There may be efficiencies of production,” Chowdhury says. “And then if y