Data Architecture Is Quietly Killing Enterprise AI ROI

Most enterprise AI programs are losing money to a hidden tax: the cost of moving and managing data across systems never designed for it. A 2025 MIT NANDA report found 95% of generative AI pilots produced little measurable financial impact, pointing to operational integration gaps rather than model quality. The culprit is data gravity — large datasets are expensive to move, and regulations, sovereignty rules, and legacy governance make centralization impractical. The resulting costs appear 12 to 24 months after launch as unplanned headcount, pipeline maintenance, and reconciliation work spread across teams. The fix requires a context layer, not a bigger data warehouse.