The Hidden Costs of AI Proof-of-Concepts: Why 85% Never Reach Production

Gartner's figure — that 85% of AI projects fail to move from experimentation to production — has become a cliché precisely because it's true. The organisations we work with have typically run two or three proof-of-concepts before they call us. Some of those PoCs were technically impressive. None of them survived first contact with the production environment.

The PoC Trap

A proof-of-concept is optimised to demonstrate that something is technically feasible. It uses curated data, runs on a single machine, has no error handling, no monitoring, no security review, and no integration with the systems that real users rely on. When it works in the demo room, it's genuinely impressive. When you try to run it on real data in a real environment with real users, every one of those omissions becomes a blocker.

• Data quality: PoC data is cleaned manually; production data is messy by default
• Integration: PoC runs standalone; production requires integration with ERP, CRM, and existing workflows
• Security: PoC has no access controls; production operates inside a security perimeter
• Scale: PoC handles 10 test cases; production handles 10,000 edge cases per day

What to Do Instead

The alternative to a PoC is not a full build — it's a production-ready pilot. Scope it to a single, well-defined use case. Use real data from the start, behind real security controls. Build the integration layer early, not last. Define success metrics before you start, not after you've seen the demo. And involve the people who will maintain the system in the design process — not just in the UAT.