The AI pilot worked. Why is the insurance workflow still stuck?
Blog post from Box
Insurance operations depend heavily on documents, but manual intake, review, routing, and data entry can slow underwriting and claims work, raise costs, and affect customer experience. AI can accelerate tasks such as extracting and summarizing information from lengthy, unstructured files, yet successful production deployment requires redesigning the entire workflow rather than merely configuring an AI tool. Chris Belle of ITS recommends mapping current processes, systems, decisions, information sources, and exception paths, then assessing whether automation actually eliminates manual steps, routes information to accountable users, handles exceptions visibly, and produces measurable gains in cycle time, accuracy, throughput, or customer experience. AI should prepare and organize work while qualified professionals retain responsibility for high-stakes decisions involving coverage, liability, fraud, litigation, settlements, and sensitive communications. Effective implementation also depends on governance, integrations, data quality, access controls, retention, audit requirements, change management, and early involvement of employees who perform the work. The example of Shelter Insurance describes how Box AI is being piloted to extract claims information and reduce payout-cycle times, illustrating how automation may scale when built on an established content, integration, and compliance foundation.
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