Hungry for Data

Failing to feed Al tools with company knowledge can create a costly learning gap, experts tell Newsweek

- BY ADAM MILLS

WHEN MIT SLOAN RESEARCHERS EXAMINED why so many companies were failing to derive value from generative AI pilots, the problem wasn't the models, it was the data. The study found that most enterprise Al agents—advanced systems utilizing artificial intelligence to make decisions and perform tasks—lacked the necessary context to make sound decisions, often because the institutional knowledge they required was scattered, outdated or simply missing.

“Organizations risk falling into the ‘AI learning gap’ where tools don’t learn from feedback or fit with existing processes,” Paul McDonagh-Smith, senior lecturer at MIT Sloan School of Management, recently told Newsweek. “Many pilots use generic AI models that work in isolation but don’t readily adapt to existing and emerging workflows, resulting in employees abandoning AI when outputs prove unreliable or unworkable in their daily operations.”

A report from advanced knowledge management platform Bloomfire, “The Value of Enterprise Intelligence,” puts numbers to that blind spot. After surveying more than 10,000 professionals across 115 companies, the firm concluded that inefficient knowledge management affects a quarter of annual revenue, roughly $2.4 billion for a typical Fortune 500 company. That's money lost not to bad algorithms, but to bad information hygiene.

“Despite massive investments in digital transformation, many organizations still struggle with the ‘last mile problem’— connecting employees to the knowledge they need, when they need it,” Philip Brittan, CEO of Bloomfire, told Newsweek. “Information remains scattered across siloed systems, slowing decisions and frustrating teams.”

In other words, the hidden asset is knowledge itself; the connective tissue linking people, data and decisions.

Turning Knowledge Into Capital