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The article examines non-technical barriers to AI adoption, specifically focusing on executive risk aversion and workforce resistance rather than algorithmic advancements or hardware infrastructure. Since the primary subject addresses the societal impact, labor dynamics, and implementation challenges surrounding AI deployment, it falls under the intersection of technology and social systems. This analysis is relevant for readers interested in how organizational behavior influences the trajectory of artificial intelligence integration.
Here’s How Long It Will Take for AI to Reach Its Potential
“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.” The post Here’s How Long It Will Take for AI to Reach Its Potential appeared first on AI Now Institute.
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.”
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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