The hidden tax on enterprise AI: Why data architecture is the ROI problem nobody budgeted for

What happened

A 2025 report from MIT’s NANDA initiative found that 95% of enterprise generative AI pilots produced little or no measurable impact on the profit-and-loss statement. The report points to gaps in how companies integrate AI into their operations, not simply to model quality.

Many large companies have spent the past few years investing in artificial intelligence infrastructure, software and implementation. This isn’t the kind of failure that makes headlines yet.

UPDATED 09:00 EDT / OCTOBER 11 2026 BIG DATA The hidden tax on enterprise AI: Why data architecture is the ROI problem nobody budgeted for GUEST COLUMN by Gaurav Chawla Many large companies have spent the past few years investing in artificial intelligence infrastructure, software and implementation. There is no major outage, breach or recall.

Instead, it is a quiet, compounding drag that appears as unplanned headcount and slipping timelines. Talk to enough infrastructure leaders about what happened and you’ll hear the phrase “data gravity” more than once. The scale of the problem is visible in the numbers.

Key facts

• A 2025 report from MIT’s NANDA initiative — found: that 95% of enterprise generative AI pilots produced little or no measurable impact on the profit-and-loss statement