FULLFACT Inc. has released a practical guide (white paper) titled "Why Do Most AI PoCs Die?" as a free resource. This handbook analyzes past failures into six distinct patterns to help organizations avoid repeatedly stalling at the proof-of-concept (PoC) stage, and outlines essential prerequisite designs for advancing to full-scale deployment. Background: Stalling at the technical validation phase is rarely due to technical issues In AI-related projects, it is common for initiatives to stall after the PoC phase, failing to progress to full implementation. This not only results in investment losses but also accumulates delayed management decisions and internal resignation—such as "AI won't work here"—which can paralyze future decision-making. In most cases, the root cause is not the technology itself. Instead, recurring issues include the lack of pre-agreed success and exit criteria, KPIs disconnected from business metrics, and undefined operational ownership. These missing foundational designs keep reappearing in different forms. This guide classifies past failures into six patterns, digs into their structural root causes, and provides prerequisite designs to prevent future bottlenecks. What you'll learn from this guide - The structural reasons why PoCs fail to advance to full deployment, and a framework for classifying them - The six failure patterns of AI PoCs and how to diagnose them by root cause, not just symptoms - Concrete methods for reconstructing "prerequisite design" across three scope axes: business, organization, and data - A 5-item template for pre-defining and formally agreeing on exit criteria - A three-option framework to decide whether to continue the PoC, move to a pilot, or go straight to production - How to structure and write a one-page summary for formal approval in executive meetings How to access the guide The guide is available for free download at the following page: https://fullfact.net/whitepapers/ai-poc-failure-patterns [For 10 companie