But as obstacles go, the technological issues may be much easier to overcome than the human ones. Simply put, a lot of people need to be convinced before the AI revolution can happen in earnest.
Executives face five-year planning cycles, depreciation schedules on systems they bought three years ago, and boards demanding returns. Risk aversion in that environment isn’t irrational. Then there are the workers: People who believe they are training their own replacements aren’t going to be enthusiastic partners in making it work.
“What is being sold is this idea of productivity and efficiency,” says Kate Brennan, associate director of the AI Now Institute, an AI-policy research center, “and what that means for the people doing the actual work is rarely part of the conversation.”
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