Reflection — An AI pilot that works does not scale on its own

A pilot that works does not scale on its own. In S&P Global Market Intelligence's survey of more than 1,000 enterprises, the average organization scrapped 46% of its AI proofs of concept before production, and the share of companies abandoning most of their AI initiatives jumped from 17% to 42% in a single year. The cause is not the technology: scale is engineered, it does not emerge. What carries a pilot into production is not a better pilot but reusable foundations, data contracts, governance and shared building blocks, laid down from the very first project.

For a small or mid-sized company the implication is direct: treating each use case as an isolated experiment programs its abandonment. Before you put your first AI project into production, lock in a reusable data-governance base (contracts, lineage, cycle-time and error metrics) that your next projects will reuse as-is.

Source: S&P Global Market Intelligence, 2025 enterprise AI survey, March 2025 (more than 1,000 companies across North America and Europe).