Consultant Says 6 Enterprise AI Failures Derail Projects Before Production
Updated
Updated · InfoWorld · Aug 28
Consultant Says 6 Enterprise AI Failures Derail Projects Before Production
3 articles · Updated · InfoWorld · Aug 28
Summary
Six recurring breakdowns—not weak models—are causing most enterprise AI projects to fail, with companies often reaching production gates before discovering their organizations are not ready.
The most common problem starts upstream: teams buy a model or copilot before defining a measurable business outcome, turning pilots into expensive demos that cannot show what changed in the business.
Production efforts then stall when pilots are not wired into ERP, CRM, finance or other core workflows, and when fragmented or poorly governed data feeds fluent but unreliable answers into generative systems.
Agentic AI adds risk when firms automate undocumented or exception-heavy processes, while costs that look manageable in small pilots can jump as prompts, retrieval, orchestration, logging and availability requirements scale.
Governance, security and compliance still arrive too late in many programs, the consultant said, arguing durable AI value depends on process design, trusted data, operating controls and clear ownership after deployment.