Why Claims AI Pilots Fail When They Reach Production

A successful claims AI pilot proves that a model can produce an answer; production must prove that an insurer can rely on it. This insight explains why curated data and controlled demos often conceal weaknesses in data lineage, platform integration, exception handling, ownership, human review, monitoring, and auditability. It also shows why automation should reflect the consequence of error: straight-through processing may suit simple, low-value claims, while complex or high-value claims require decision support and accountable human judgment. Insurance leaders should validate both the business case and operational readiness before committing to scale.