Fast vs. slow: the real impact of AI adoption speed [Video]

With the advent of generative AI, the pace of technological change has greatly accelerated. Is it better to take risks and experiment, or wait for proven use cases before jumping in?

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There are a lot of reasons to take a slow and careful approach to generative AI. The technology is changing quickly, so investing a lot of money in the wrong platform could end up costing a lot of money.

Generative AI still has accuracy and safety problems, and the copyright issues haven’t yet been settled in the courts, all of which could create legal liabilities or other problems. And, of course, many early projects will fail to offer any actual business value, making them a waste of time and resources.

According to a September IDC survey, 70% of CIOs reported a 90% failure rate for their custom-built AI app projects, and two-thirds reported a 90% failure rate with vendor-led AI proof-of-concepts. And Rand Corp. …