Why The Harness Is Not Enough: What You Should Be Asking About AI Deployment

InnovationWhy The Harness Is Not Enough: What You Should Be Asking About AI DeploymentByDr. Chiranjiv Roy,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)Jun 05, 2026, 08:00am EDTChiranjiv Roy, is an enterprise AI leader & fellow advising multinationals on AI that drives decisions, not dashboards. gettyA few weeks ago, I worked through a deployment review at a Fortune 500 consumer goods company that had spent $14 million over 18 months to bring commercial planning AI live across six markets. The CIO's last board update had called the rollout a success and requested another $9 million to extend it.​Then their VP of Trade Marketing pulled out a spreadsheet. The most experienced category manager in the business had been overriding the system on 78% of the SKUs she touched in the previous quarter, with similar patterns observed across 47 category managers in six markets. The system was running. The board was being told it worked. Almost no one who made commercial decisions used the output to inform their decisions.​When the CFO asked her why, her answer was the one I hear at almost every deployment review I sit through. "It doesn't really understand our business."​That sentence has changed how I think about every deployment I touch.​​The engineering explanation is that the infrastructure around the model has been underbuilt. Martin Fowler coined the term "harness engineering" in February to describe the work of fixing that, and every production system I have signed off on rests on it. But the harness keeps the system running. It says nothing about whether the output is calibrated to your business.​​Let me give a CPG example from my recent work. The system had read a 13-week Nielsen demand trend following Ramadan as a genuine post-holiday uplift for a carbonated soft drink SKU. Anyone who has...