The research marks the first time the impact of generative AI tools on work has been measured outside the lab.
The results of some of these earlier experiments showed the potential, at times surprising, of large-language models in the workplace, said Erik Brynjolfsson, the director of the Digital Economy Lab at the Stanford Institute for Human-Centered AI.“Having people use it for over a year in this company, you get a much better sense of how that translates into real-world productivity,” Brynjolfsson, one of the study’s co-authors, said in an interview.
With the assistance of AI, the firm’s least-skilled workers were able to get their work done 35% faster. The productivity gains — about 14% on average — were less dramatic than in prior experiments, likely because real-world workplace processes are much more complex than one-off tasks. Top customer service agents had Excel spreadsheets where they collected phrases that they used often and that worked well, MIT’s Raymond said.
“Successful companies will have incentive and reward systems that recognize that these top performers — whether or not their performance with any given customer is demonstrably better than the less-skilled workers — create knowledge that the whole organization depends on,” he said. That could, in turn, change the employee-manager relationship, since supervisors would then spend less time with direct reports and would instead take on larger teams.
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