It’s an increasingly common tale within corporations today: The AI project performs admirably in testing during the pilot phase, gets the green light for a broader rollout…and then stops working properly; Or it fails to deliver the expected business results.
Finger pointing, recriminations, and embarrassment ensue.
The problem is not always the technology. In fact, the fault is often in the planning, processes, and expectations that companies have established—or not established—around their AI projects, according to business leaders who spoke at a roundtable discussion at Fortune Brainstorm Tech this month.
For starters, not every AI project deserves to be rolled out widely, said Amgen Chief Technology Officer Sean Bruich.
“It’s so easy with a pilot to let a thousand flowers bloom,” he said. That’s not a bad thing, since it encourages experimentation. But, he said, “the key to making pilots scale successfully is actually having a wide number of ideas, but a very tight governance on which pilots are actually greenlit.”
