This article is from Jawwad Rasheed, Financial Services Transformation and Advisory Lead at agentic orchestration company Camunda,
Insurers face continuous pressure to resolve claims faster than ever, while maintaining accuracy across every settlement. At the same time, policyholders expect a modern, transparent customer experience, and regulators demand a clear account of how each decision was reached. For insurers, AI offers a route to meeting those expectations with far greater efficiency. So far, though, few have managed to embed agents into the processes where that efficiency lives.
Research reveals that 81% of insurers mainly use AI agents as chatbots or assistants to summarize information and answer questions, rather than to handle mission-critical cases. A further 45% said their agents operate in silos, disconnected from end-to-end processes.
Caution is rational, given how claims handling impacts customer retention and growth, and small adjustments in claims leakage can deliver significant savings. But without embedding agents into the processes that matter, the benefits AI promises stay out of reach.
Why point solutions aren’t enough
Claims teams often operate across disconnected systems, with inconsistent handoffs and limited visibility into how work progresses across the claims lifecycle. A sophisticated agent deployed in one part of the process could still struggle to trigger the right action elsewhere.
Pilots often demonstrate promise, but many firms cannot move past the trial stage, because nothing connects agents across the claims process while keeping the insurer in control.
Each new tool adds complexity, rather than reducing the time it takes to settle a claim or improve the accuracy of an outcome. That is the automation ceiling, and insurers meet it as soon as a claim crosses more than one system.
Prioritizing the right use cases
To move beyond pilots, insurance organizations must first identify a well-defined role for agents that improves a measurable claims outcome inside a clearly defined business process, not a use case chosen to demonstrate what AI can do. Here agents should be targeted for work that requires judgement (as opposed to rules-based outcomes) and it is bounded by the appropriate process standards and guardrails. Strong candidates should operate within deterministic rules, remain fully auditable, and establish governance models that are reusable across other claims processes or business areas.
Balancing value and complexity makes this practical. Insurers build confidence with lower complexity wins, such as document ingestion and case summarization before scaling into more complex areas, which lets them build on what works, avoid the pitfalls of what doesn’t, and carry reusable building blocks into other projects without reinventing the process each time.
What agentic orchestration looks like in claims
Those building blocks need something to hold them together. Agentic orchestration is the layer that does it, coordinating agents, people, and systems across the end-to-end process. This does not mean replacing the claim system or changing governance rules. Instead, specialized agents operate within enforceable steps that sit between an agent’s decision and its action. Before an agent issues a payment or sends a communication, the process runs the policy check first.
A single claims process might run several agents at once, each with a defined job. One classifies the loss and recommends a path, another extracts details from attachments and unstructured data to build a case brief, a third checks early subrogation indicators. Orchestration assigns the work between them, applies the deterministic steps and approvals each one requires, and records a complete audit trail of every decision taken.
The most valuable claims use cases span multiple tasks and vendor platforms and require agents to work alongside human decision makers. Each one needs a defined role, and the end-to-end process must stay traceable and auditable as the claim moves between them.
Where to start
The simplest place to start with AI agents is using them for tasks such as documents intake and extraction, claim file summarization and coverage look up. Here agents are gathering and preparing information, as opposed to making a critical decision with regards to the customer.
After that, firms can start to look to introduce agents further along the chain.Intelligent claims triage and routing is one entry point. Agents assess incoming claims and recommend a path, deterministic rules enforce policy and risk thresholds, and higher-risk cases go to a human for approval. The same pattern works for omnichannel first notice of loss, standardizing how claims are reported across channels, and routing exceptions to a handler when confidence is low.
Other entry points sit further along the claim. Communication orchestration triggers proactive, compliant updates when a claim event or a severe weather warning calls for one. In subrogation, agents spot recovery signals early and gather the evidence for a referral, while orchestration holds the deadlines. In complaints and appeals, agents reconstruct the timeline while orchestration enforces service level agreements and builds an auditable case history.
Scaling depends on the process, not just the agents
Success in claims will not come down to how many agents are deployed. What matters is whether those agents sit inside a process capable of governing them. Insurers that begin with carefully prioritized use cases build a claims foundation of reusable orchestration patterns and operational controls, and that foundation is what makes the second use case faster than the first.
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