The AI Deployment Funnel – 60% evaluate, 20% pilot, 5% ship

MIT tracking of 300 AI implementations reveals a 12x attrition rate from evaluation to production: 60% of projects enter evaluation phase, 20% advance to pilots, but only 5% reach deployed status.

The gap exposes two operational realities. First, evaluation environments fail to predict pilot viability—suggesting methodological misalignment between proof-of-concept and scaled testing. Second, pilot-to-production failure indicates infrastructure, integration, or organizational readiness issues undetected earlier. This creates compounding sunk costs: teams invest heavily in evaluation and pilot phases only to face deployment blockers.

For builders, this shifts resource allocation away from initial feasibility work toward deployment architecture planning and integration testing. Organizations need to restructure pilot phase objectives to validate production constraints (data pipelines, monitoring, governance gates) rather than just model performance. Infrastructure teams become critical gating functions rather than downstream dependencies. Teams continuing current evaluation-heavy workflows will face predictable project stalls at deployment—a costly realization late in cycles.