Insurance companies are quietly deploying a new generation of artificial intelligence systems that go far beyond automating simple tasks. These “agentic AI” platforms are beginning to orchestrate complex workflows across traditionally siloed functions like claims processing, underwriting, and policy servicing, even interfacing with legacy systems never designed for autonomous coordination.
The shift represents a significant departure from earlier automation efforts focused on robotic process automation (RPA) or narrowly defined machine learning models. Agentic AI is designed to ingest unstructured data – emails, scanned PDFs, and intake forms – extract relevant information, apply policy rules, and route exceptions to human adjusters. Critically, these systems can also trigger downstream actions, such as initiating payments, requesting documentation, or sending customer notifications.
The most immediate impact is being felt in claims operations, where first notice of loss intake and triage are both significant cost centers and critical touchpoints for customer experience. On February 9, Microsoft highlighted collaborations with insurers focused on embedding AI agents directly into claims workflows, rather than simply adding tools to existing processes. These systems interpret incoming loss reports, classify severity, verify coverage, and dynamically assign cases, aiming to reduce manual review bottlenecks.
Sedgwick, a global claims management provider, announced in April 2025 that it was optimizing workflows through its AI application, Sidekick, integrated with Microsoft technologies. The company stated that Sidekick supports claims professionals by surfacing relevant policy information and automating routine interactions, with the goal of accelerating cycle times while maintaining compliance and documentation standards.
Major carriers are also experimenting with catastrophe response. Allianz described in November 2025 using AI to manage post-storm claims surges, with systems designed to analyze damage documentation and prioritize cases to expedite claim resolution after extreme weather events.
Underwriting, historically reliant on human judgment and painstaking document review, is another key area of focus. 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 emphasized the potential for AI to support more granular risk assessment, including improved modeling of emerging and complex risks. The benefit extends beyond speed to include consistency; by standardizing data extraction and preliminary risk scoring, agentic systems can reduce variability in underwriting outcomes and help scale limited actuarial expertise.
The Boston Consulting Group argued in January 2026 that agentic AI represents a new phase in core insurance modernization. This moves beyond chatbots and analytics dashboards toward systems that actively coordinate processes across policy administration, billing, and claims platforms. Rather than requiring a complete overhaul of legacy infrastructure, AI agents can operate across silos, connecting fragmented workflows while longer-term modernization programs proceed.
However, increased orchestration brings increased regulatory scrutiny. The insurance industry operates under strict model risk management frameworks, and autonomous decision-making raises complex oversight questions. The Insurance Information Institute noted on February 10, 2026, that agentic AI is prompting a re-evaluation of model risk management, as systems triggering 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 are becoming critical. Insurers must demonstrate not only that models perform accurately but also that decision pathways are documented and contestable. Regulators will expect clarity on how an AI system arrived at a particular outcome – whether routing a claim, recommending a payment, or flagging potential fraud.
The implications extend beyond operational efficiency. By automating routine tasks, agentic AI frees up human employees to focus on more complex and strategic work. Underwriters, for example, can concentrate on specialized tasks requiring nuanced judgment, rather than being bogged down in manual data entry and verification. Claims adjusters can dedicate more time to handling complex cases and providing empathetic customer service.
The successful implementation of agentic AI will require careful attention to data quality and governance. The accuracy and reliability of AI-driven decisions depend on the quality of the data used to train and operate the systems. Insurers will need to invest in data cleansing, standardization, and validation processes to ensure that their AI agents are making informed and accurate decisions.
insurers must address potential biases in AI models. If the data used to train an AI system reflects existing biases, the system may perpetuate those biases in its decision-making. Insurers will need to proactively identify and mitigate potential biases to ensure that their AI systems are fair and equitable.
The move towards agentic AI is not about replacing human workers, but about augmenting their capabilities and enabling them to deliver better service to customers. The challenge for insurers will be to successfully integrate these new technologies into their existing operations, while also addressing the regulatory and ethical considerations that come with increased automation and autonomous decision-making. The industry is entering a period of significant transformation, one where the intelligent orchestration of workflows promises to redefine how insurance operates and delivers value.
