Why more than half of AI projects could fail in 2026 (www.techradar.com)

Industry research and analyst forecasts warn that 60–90% of AI projects are at risk of failing by 2026 (Gartner predicts 60% of orgs will miss expected AI value by 2027). The story isn’t poor model choice — it’s messy data and fractured governance. Without provenance, context, consistent metadata and clear ownership, pilots stall, cost overruns and “shadow AI” proliferate, and models produce unreliable or noncompliant outputs (the Air Canada chatbot case is a cautionary example). New agentic tools and copilots amplify these problems because they inherit existing access controls: overshared or over‑permissioned users can expose sensitive data at scale. The path to success is practical and technical: treat data governance as an enabler, not an afterthought. Make data “AI‑ready” — discover, classify, secure, retain and continuously test both structured and unstructured sources so inputs are trustworthy, versioned and contextualized for LLMs/agents. Remove ROT (redundant, obsolete, trivial) data, apply retention schedules, minimize sensitive fields, and audit access/permissioning before deploying platforms like Copilot. Establish a centralized governance control plane and continuous metadata management to declare and enforce policies uniformly, provide audit trails, and keep models predictable and compliant. Do these things, and organizations are far more likely to deploy AI that delivers measurable business value with lower operational and regulatory risk.