Organizational Coherence Decides Whether AI Pays Off Or Not At Scale

Madhu Shashanka is the Cofounder, Chief Scientist and CTO at Concentric AI and author of the forthcoming book, Coherence.

Why is it that individual team dashboards show successful AI deployments and wins, but the impact barely shows up at the organizational level? McKinsey’s 2026 “state of AI” report shared that 80% of respondents said that AI improved their individual productivity. But the share reporting any enterprise-level EBIT impact stayed flat at 37%, unchanged year over year. And “AI high performers”—companies who can attribute 5% or more of their EBIT to AI and see significant value—accounted for only 6% of the organizations.

More companies are scaling AI, but business outcomes haven’t caught up. Individual performance differs from enterprise performance, and only the former is watched at most organizations.

The Problem Is Structural​

In July 2026, BCG researchers reported that about nine in 10 CEOs see benefits from AI in targeted areas, but most can’t scale those gains. “The core problem,” BCG analysts said, “is not technology. It is execution.” The same report points out that although two-thirds run pilots, only about one-quarter embedded AI in a real transformation.​

Agentic AI has made it easier for employees to set up an individual autonomous system using plain English and without expert help. The end result is many systems, deployed independently, for specific local goals. W. Edwards Deming observed decades ago that improving each part of an organization independently doesn’t necessarily improve the whole and can often degrade it.

Organizational Coherence​

There’s already a name for part of this: agent sprawl. According to SAP LeanIX’s 2026 survey, 98% are deploying agents or planning to. But fewer than half can inventory all the agents they run. Gartner analysts expect 150,000-plus agents at a large enterprise by 2028, but only 13% of organizations say they’re adequately governed.