AI Proof of Concept to Production: Essential Foundations and Critical First Steps

Summary

Catalyst

The efficiency and value of AI proof of concepts (POCs) and pilots remain hotly debated—intensified by MIT’s Networked Agents and Decentralized Architecture (NANDA) report, which stated that only 5% of generative AI (GenAI) pilots reach production (July 2025). This has been interpreted by some as evidence that GenAI is struggling to scale, that failed POCs signal unmet expectations, and that we may be heading toward an AI bubble. However, the primary reason POCs fail is not because of intrinsic flaws in AI technology, but because enterprises and vendors underestimate the complexity of AI deployment. To address this, Omdia has developed a comprehensive framework to guide enterprise and vendor partners through the full AI deployment lifecycle—from pre-POC planning through pilots, scaling, and beyond. This two-part report series tackles a critical gap in enterprise AI implementation strategy.

Omdia view

A POC, put simply, is a small-scale feasibility test to determine whether an AI technology solution can support a desired use case and its associated business goals. The focus is on concept feasibility. A POC is often followed by a pilot, which is a more comprehensive evaluation of an AI solution’s performance and value under real-world conditions, albeit still bounded by time and scope, with the lens on deployment readiness and ROI potential. POCs and pilots are separate but connected steps that provide evidence-based progression, helping enterprises validate, refine, and operationalize AI solutions. Taken together, AI POCs and pilots reduce risks, prevent costly failures, and optimize business outcomes, and as such are critical steps on the path to deployment.