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Why the C-Suite is now overseeing corporate A.I. projects

Top executives now oversee most artificial intelligence projects, underscoring the importance C-suite managers place on A.I. for their companies.

In a survey of companies released Tuesday about the state of A.I. in business, 71{ce8ce7cc98bffdc4302011057a79600ea02c464c5536f1477c12acdb8bd79c00} of respondents said that their company’s A.I. projects were “owned” by C-level executives, which include the likes of CEOs, chief financial officers, and chief technology officers. It’s a big jump from last year’s survey, conducted by the data training and annotation firm Appen, that found that the C-suite oversaw 39{ce8ce7cc98bffdc4302011057a79600ea02c464c5536f1477c12acdb8bd79c00} of A.I. projects.

Appen CTO Wilson Pang told Fortune that the findings, based on responses from nearly 370 companies, highlight a “pretty significant change” for businesses that are pursuing machine-learning projects for tasks like forecasting sales and developing more powerful products. 

Pang said that top-level executives are increasingly spearheading A.I. projects because these undertakings are so encompassing, involving numerous corporate departments like finance and data analysis to work together. It’s typically the C-suite leaders who have the clout and resources to create the so-called “cross-functional teams” that can “map the business problems to an A.I. project,” Pang said.

Dell Technologies CTO John Roese, who was not involved with the Appen survey, told Fortune that, anecdotally over the past year-and-a-half, he’s noticed that more C-level executives are leading corporate A.I. projects instead of lower-level management or lone IT departments. As machine learning has becoming more prominent at businesses, Roese has also seen that executives are becoming increasingly familiar with A.I. jargon and obscure A.I. lingo.

Roese credits the Google-created TensorFlow software, used in deep learning projects, as helping popularize A.I. to the executive world. While TensorFlow was once perceived as “mad science,” it’s “no longer some weird thing,” Roese said. 

But just because corporate executives know what the words “natural-language processing” refers to (it means computers that understand language, in case you were wondering), that doesn’t mean they know how A.I. systems actually work or how they fail. 

Appen’s survey, for instance, also revealed that executives and technologists differ in what they consider to be the biggest bottlenecks to their A.I. projects. Although technologists and executives both agree that lack of talent is the top hurdle, the survey showed that technologists are more likely to perceive low-quality data and poor information management techniques as hurting A.I. projects.

Technologists, it appears, are more familiar with the adage, “garbage in, garbage out,” referring to the notion that good analysis requires good data. Pang said that many corporate executives believe that once they train their machine learning models, “they think it’s done.”

“In reality, most A.I. models are never done,” Pang said. “Data is never enough there.”

Jonathan Vanian 
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