Plan for Production

Most AI pilots don’t fail because the model was wrong. They fail because nobody planned for what comes next.

Here’s the pattern I see repeatedly: a team spends 90 days in assessment and scoping. Another 60 days building a pilot. The pilot works. Leadership is impressed. Then someone asks how to move it to production, and the answer is six more months, a security review, a full rebuild, and a budget cycle nobody planned for. The pilot dies on the runway.

The problem isn’t the technology. It’s the architecture. Pilots built in sandbox environments, with governance bolted on afterward and no path to scale, are expensive proof-of-concepts masquerading as progress. By the time the real cost of production becomes clear, momentum is gone and budget has moved on.

Practitioners are seeing this firsthand. A VP of Customer Insights & Analytics in CPG Retail recently described to me environments where product assortment, pricing, promotions, and digital signals interact across large, disparate datasets…and where AI has produced recommendations that were nearly the opposite of correct. In another conversation, a  Director of Advanced Analytic Operations pointed to the same root cause from a different angle: the data infrastructure simply isn’t set up for what AI requires. Build on a flawed foundation, she notes, and everything forward will eventually crumble, along with the organization’s trust in the initiative.

Both are describing the same failure mode: AI deployed into environments that were never prepared to support it. The answer isn’t to slow down. It’s to build differently from the start.

The fix is a design principle, not a technology choice: build the pilot as if it’s going to production. Data infrastructure validated before the model is built. Governance in place on Day 1…not retrofitted. Architecture that accounts for real-world complexity, not rebuilt when reality hits. When the pilot environment is the production environment, the question stops being “can we move this forward?” and becomes “when do we expand it?”

The companies winning with AI aren’t running better pilots. They’re skipping the pilot traps entirely.