It’s a sobering stat: Seven out of 10 executives whose companies had made investments in artificial intelligence (AI) said they had seen minimal or no impact from them, according to the 2019 MIT SMR-BCG Artificial Intelligence Global Executive Study and Research Report.
At the heart of the matter may be a general lack of understanding about AI capabilities and requirements. “At this point in time, many enterprises have inflated expectations from AI solutions,” says Anil Vijayan, vice president at Everest Group. “This can often create a mismatch between what is expected and what is achievable.”
But “get smarter about AI” is not the most nuanced takeaway. In fact, there tend to be some more specific recurring reasons why AI projects fail – and steps IT leaders can take to increase their chances for success. Here are eight of the most common mistakes and miscalculations that can portend AI project failure.
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