# The detrimental cost of scaling AI too fast

> A widely cited pair of statistics frames one of the year's most uncomfortable questions about enterprise AI. McKinsey's 2026 global survey found that 44 percent of organizations are now scaling AI across the enterprise, while a separate MIT study found that 95 percent of generative AI pilots showed no measurable impact on the bottom line. Put together, they suggest a lot of companies are racing to deploy a technology that, so far, mostly is not paying off. Writing for The Next Web, MGKgroup founder Greg Keith argues the culprit is speed. Firms rush to roll out AI to look modern or to reassure investors, skipping the unglamorous work of fixing data, redesigning workflows, and training people, which is where value actually comes from. The result is expensive pilots that impress in a demo and vanish in the profit and loss statement. The takeaway is not that AI fails to deliver, but that scaling fast without foundations tends to. For the many organizations in that 44 percent, the harder and more valuable move is to slow down, pick problems worth solving, and measure whether the technology genuinely moves the numbers.

_Section: [Daily AI Updates](https://www.wortins.com/daily-ai) · Source: The Next Web · Published Monday, September 14, 2026_

## Wortins' read

A widely cited pair of statistics frames one of the year's most uncomfortable questions about enterprise AI. McKinsey's 2026 global survey found that 44 percent of organizations are now scaling AI across the enterprise, while a separate MIT study found that 95 percent of generative AI pilots showed no measurable impact on the bottom line. Put together, they suggest a lot of companies are racing to deploy a technology that, so far, mostly is not paying off. Writing for The Next Web, MGKgroup founder Greg Keith argues the culprit is speed. Firms rush to roll out AI to look modern or to reassure investors, skipping the unglamorous work of fixing data, redesigning workflows, and training people, which is where value actually comes from. The result is expensive pilots that impress in a demo and vanish in the profit and loss statement. The takeaway is not that AI fails to deliver, but that scaling fast without foundations tends to. For the many organizations in that 44 percent, the harder and more valuable move is to slow down, pick problems worth solving, and measure whether the technology genuinely moves the numbers.

## Source

[Read the full story at The Next Web](https://thenextweb.com/news/detrimental-cost-scaling-too-fast-greg-keith)

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