Fortune While artificial intelligence has become a ubiquitous topic in the business world, there is still important work to do to translate the promising experiments we see in the news to valuable and practical implementation. Large consumer Internet companies pioneered practical A.I. deployments, but their processes do not necessarily apply in other industries where A.I. projects face unique challenges.
As a result, we frequently see non-digital companies struggle with A.I. deployment. Manufacturing, for instance, is primed for A.I. transformation, but only 5% of more than 200 manufacturers surveyed by the MAPI Foundation say they have a clearly defined strategy for A.I. In a separate Accenture report that surveyed 1,500 C-suite executives in 16 industries, 76% of respondents said they struggle with how to scale the technology. This stands in contrast to the consumer Internet industry, where large A.I. systems already power everything from producing search results to language translation to targeted advertising.
For A.I. to reach its full potential, those implementing the technology must develop new techniques to enable its deployment across all industries. (My company, Landing AI, helps companies with A.I. adoption.) In particular, companies outside Silicon Valley need to overcome three challenges to increase their odds of success.
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