I began seeing the same dynamic in other regulated sectors as well. In a manufacturing program I supported, an equipment failure prediction model performed well in engineering pilots but struggled once connected to maintenance workflows, supplier data and plant-floor operations. In banking, a fraud-risk model delivered strong early accuracy but failed to scale because the surrounding compliance reviews and case management systems were not designed to absorb algorithmic decisions. These industries differed in context, but the readiness gap appeared for the same reason: the supporting environment could not sustain the weight of enterprise AI.
Where AI breaks when organizations try to scale
Across healthcare and insurance, the breakdown tends to happen in the same places. The first is data fragmentation. Clinical information lives in electronic records. Claims data lives in adjudication systems. Member interactions live inside CRM platforms. Pharmacy data, care management notes, eligibility information and provider relationships each have their own systems. A model trained on one dataset cannot handle the reality of workflows that cross 10 or more environments.
The second breakdown happens at the workflow layer. Pilots isolate a decision. Production requires that decision to move through people, systems and documentation requirements. A predicted risk score means nothing if it cannot be routed to a nurse, documented for compliance, recorded in CRM and tracked for audit purposes. Many organizations reach this point and realize they lack the operational foundation to support AI-driven decisions at scale.
