This is the second in a two-part report series providing a comprehensive framework to guide enterprises and vendor through successful AI deployments, from pre-POC planning through pilots, scaling, and beyond. This report focuses on best-in-class AI POCs, pilots, and scaling.  

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

Enterprises that succeed with AI do so by treating POCs and pilots as disciplined, high-value stages in a broader deployment lifecycle—not as box-ticking exercises. A best-in-class POC is lean but purposeful: it validates technical feasibility, surfaces early indicators of business value, and builds organizational knowledge without overengineering or scope creep. The goal is not perfection but actionable insight, supported by clear success criteria and a structured phase gate to determine next steps.

A subscription is required to view this content.

Already subscribed? Continue Continue