You are under pressure to reduce cycle time, control leakage, and improve customer experience, without taking on regulatory or reputational risk.
Many carriers have already proven AI value in pockets of claims work. The problem is AI agents aren’t embedded within core processes. AI assistants or chatbots that summarize a document or draft an update do not, by themselves, take action autonomously within a set of predefined guardrails.
This is not just an insurance issue. Gartner notes that as organizations pilot agentic automation, many struggle to orchestrate agents built on different vendor platforms, and they commonly worry about observability and compliance in critical processes. Your claims organization feels that reality every day with fragmented tools, inconsistent handoffs, unclear accountability, and audit trails that are hard to defend when a claim is disputed.
Prioritizing the right agentic insurance use cases helps you avoid the value trap
McKinsey has described insurers getting stuck in pilot mode with gen AI when scaling mechanics and domain rewiring do not keep pace with experimentation.
Prioritization, and focusing on concrete jobs-to-be-done that power your organization is how you avoid that trap. The goal is not to pick the most exciting use case. Your goal is to pick use cases that:
- Improve a meaningful claims outcome within a defined scope
- Can be governed with clear deterministic processes, thresholds, and auditability of every agentic action or decision
- Create a foundation you can reuse across other lines and regions for both internal learnings and process elements
This is why a value-versus-complexity view is practical. Start with lower-complexity wins (meaning processes with low to medium levels of complexity) and move into higher complexity areas of work once your orchestration layer and governance model are proven successful. This way you can scale what works and avoid the pitfalls of what doesn’t. Additionally, you can take reusable building blocks to speed up other projects without reinventing the process each time.
Defining multi-agent orchestration isn’t as complex as the goals they can achieve autonomously
Forrester describes adaptive process orchestration as combining AI agents and nondeterministic control flows with traditional deterministic flows to meet business goals and perform complex tasks.
In claims terms, that means you do not replace your claim systems or workbenches, authority rules, or controls. Instead, coordinate specialized agents around these rules and build deterministic processes that the AI agent must follow as part of the “tools” they can access.
A simplified example of multi-agent orchestration in claims
The complexity of claims and regulatory oversight is where orchestration becomes a necessity. You can coordinate multiple agents across tools and vendors while enforcing consistent policies, visibility, and controls. Gartner explicitly recommends maximizing agentic investments by choosing orchestration platforms that operate across multiple vendors’ systems, and it emphasizes the full traceability and auditability of agent actions.
For example, a multi-agent claims process will have:
- A triage agent that classifies the loss, flags complexity signals, and recommends a path
- A document agent that pulls first notice details, extracts key entities from attachments, notes, and unstructured data, and produces a brief for the case
- A recovery signal agent that checks early subrogation indicators and recommends a referral as needed
- Agentic orchestration that lets you apply deterministic steps for an AI agent where required, the AI agent assigns work, triggers required approvals, and records a complete audit trail of every decision and step taken.
Six agentic insurance use cases to prioritize first
Let’s quickly cover six common starting points that create a credible path to bringing autonomous agents into high-value processes for insurers.
1. Intelligent claims triage and routing
Use agents to assess claims and recommend a path based on the context of the claim. Use deterministic rules to route and enforce thresholds aligned with internal policy and regulation. Keep humans in control for elevated risk paths and automating lower risk with AI agents.
- Owner: Claims leadership and operations
- Key stakeholders: Transformation; technology, and architecture; data, AI, and analytics
- KPI examples: Time to triage decision; reroute rate; adjuster capacity
2. Evidence ingestion and case summarization
Create a structured claim brief and a checklist for missing information from documents, images, phone calls, and notes. This reduces administrative load without changing authority.
- Owner: Claims leadership and operations
- Key stakeholders: Data, AI, and analytics; technology and architecture; transformation
- KPI examples: Admin time per claim; time to first meaningful action; completeness of initial file
3. Omnichannel first notice of loss (FNOL) intake
Normalize intake across channels and prefill structured fields even with unstructured data sources. Route exceptions to humans when confidence is low, or the risk is higher than threshold.
- Owners: Claims leadership and operations
- Stakeholders: Transformation; technology and architecture; data, AI, and analytics
- KPI examples: Time to acknowledgment; straight-through rate; inbound call volume per claim
4. Customer communication orchestration
Trigger proactive, compliant updates based on claim events or potential catastrophes. For example, a catastrophic weather event is anticipated, and proactive communication to help policyholders is sent ahead to ensure safety and reduce emotional impact.
- Owners: Claims leadership and operations
- Key Stakeholders: Transformation; data, AI and analytics; customer experience
- KPI examples: Status call volume; complaint rate; customer satisfaction
5. Subrogation and recovery from FNOL
Agents identify recovery signals early and gather the evidence required for referral, while orchestration enforces deadlines, partner handoffs, and status tracking. This is where multi-agent execution can pay off because recoveries often fail on missed timing, incomplete evidence, and inconsistent follow-through.
- Owner: Claims leadership and ops
- Key stakeholders: Transformation; technology and architecture
- KPI examples: Time to referral; recovery identification rate; recovery cycle time
6. Complaints, disputes, and appeals workflows
Use orchestration to enforce SLAs and produce an auditable case narrative for every complaint, dispute, or appeal to a claim determination. Use agents to reconstruct timelines, collect evidence, and summarize actions.
- Stakeholders: Claims ops and customer advocacy
- KPI examples: SLA compliance; time to resolution; repeat complaint rate
Move from AI aspirations to real outcomes in claims
Download the agentic prioritization worksheet to identify the right insurance use cases to address first before scaling to other domains, regions, or lines of business.
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