AI Agents Are Now Running the Back Office at Insurance Giants

Unlike earlier automation waves that focused on robotic process automation or narrow machine learning models, agentic AI systems are designed to ingest unstructured emails, scanned PDFs and intake forms, extract relevant coverage and risk information, apply policy rules and route exceptions to human adjusters. In many cases, these systems can trigger downstream actions such as payments, documentation requests or customer notifications.

Claims Intake and Triage at Scale

The most immediate impact is emerging in claims operations, where first notice of loss intake and triage represent both cost centers and customer experience flashpoints. In a Monday (Feb. 9) blog post, Microsoft wrote that collaborations between insurers and technology providers are focusing on embedding AI agents across claims workflows rather than layering tools onto individual steps. These systems are designed to interpret incoming loss reports, classify severity, verify coverage and assign cases dynamically, reducing manual review bottlenecks.

Sedgwick, one of the world’s largest claims management providers, last April announced it was optimizing claim workflows through its AI application Sidekick, integrated with Microsoft technologies. The company said the system supports claims professionals by surfacing relevant policy information and automating routine interactions, aiming to accelerate cycle times while maintaining compliance and documentation standards.

Major carriers are also experimenting with catastrophe response. Allianz in November described using AI to help manage post-storm claims surges, with systems designed to analyze damage documentation and prioritize cases so that claim queues clear more quickly after extreme weather events.

Underwriting and Document Intelligence

Underwriting, long dependent on human judgment and document review, is another focal point. Insurers are testing agents that can parse broker submissions, extract risk attributes from attachments, cross-reference external data sources and flag anomalies or missing information before a human underwriter makes a final decision.

Swiss Re has highlighted how AI can support more granular risk assessment, including better modeling of emerging and complex risks. The opportunity lies not only in speed but also in consistency. By standardizing data extraction and preliminary risk scoring, agentic systems can reduce variability in underwriting outcomes and help scale scarce actuarial expertise.

The Boston Consulting Group argued in a January article that agentic AI represents a new phase in core insurance modernization, moving beyond chatbots and analytics dashboards toward systems that actively coordinate processes across policy administration, billing and claims platforms. Instead of replacing legacy infrastructure outright, AI agents can operate across silos, stitching together fragmented workflows while modernization programs continue in parallel.

Governance, Model Risk and Regulatory Scrutiny

Yet as orchestration increases, so does regulatory scrutiny. The insurance industry operates under strict model risk management frameworks, and autonomous decision-making raises complex oversight questions. The Insurance Information Institute wrote in a Tuesday (Feb. 10) commentary that agentic AI is forcing a rethink of model risk management, as systems that trigger actions across multiple functions may not fit neatly into existing validation categories designed for single-purpose models.

Audit trails, explainability and human-in-the-loop controls become critical. Insurers must demonstrate not only that models perform accurately, but also that decision pathways are documented and contestable. When an artificial intelligence system routes a claim, recommends a payment or flags potential fraud, regulators will expect clarity on how that outcome was reached.