[Guide] Adjuster + Agentic AI: Put Claims Expertise Where It Pays Off
4 min read
A practical guide for claims leaders on redesigning the operating model so AI handles routine work on every claim and adjusters handle judgment, authority, and resolution.
TL;DR: the top-performing claims team is the one that consistently puts claims expertise where it creates value and strips the routine work out from around it. Our new guide, Adjuster + Agentic AI: Building a Top-Performing Claims Team, walks through the 6 step framework that gets you there.
Most claims functions still run on a 30-year-old division of labor
Adjusters still read and classify incoming mail. They still hunt for one fact buried on page 11 of a police report, chase missing photos, send the same status update for the fifth time, set diaries, and check whether the thing they asked for last Tuesday actually happened.
All of it is necessary. None of it requires the expertise needed to decide coverage, weigh liability, assess damages, or guide a policyholder through a difficult moment.
Agentic AI makes it possible to divide that work differently. AI can handle the repetitive administrative tasks, while adjusters step in where judgment, empathy, negotiation, or authority are required.
But adding AI doesn’t create a high-performing operation on its own. If adjusters repeat the work, review every automated action, or get handoffs that force them to reconstruct the story, they become the new bottleneck.
That’s an operating model problem, and it’s what the guide is about.
What is an adjuster + agentic AI operating model?
An adjuster + agentic AI operating model is a claims operating model where agentic AI completes defined sequences of routine tasks, applies business rules, monitors progress, and escalates when human judgment or authority is required. The adjuster enters at the decision, then hands the claim back.
The operating model follows a simple pattern throughout the claim lifecycle.
- Agentic AI completes routine work such as processing documents, requesting missing information, monitoring progress, and applying business rules.
- When a defined decision boundary is reached, the adjuster steps in to make decisions about coverage, liability, damages, settlement, or other situations that require human judgment or authority.
- Once the decision is made, agentic AI continues the claim by carrying out the approved next steps, including documentation, communications, status updates, follow-up, and workflow execution.
All of it is necessary. None of it requires the expertise needed to decide coverage, weigh liability, assess damages, or guide a policyholder through a difficult moment.
Agentic AI makes it possible to divide that work differently. AI can handle the repetitive administrative tasks, while adjusters step in where judgment, empathy, negotiation, or authority are required.
But adding AI doesn’t create a high-performing operation on its own. If adjusters repeat the work, review every automated action, or get handoffs that force them to reconstruct the story, they become the new bottleneck.
That’s an operating model problem, and it’s what the guide is about.
This approach applies to every claim—not just the simplest ones. Even complex claims that require several human decisions can be supported by agentic AI handling the routine work surrounding those decisions. The result is an operating model that removes administrative work across the entire claims portfolio while ensuring adjusters apply their expertise where it creates the most value.
What you'll get from the guide
A 6-step framework you can run against your own operation this quarter, with the tools to do each step.
| What you’ll be able to do | What the guide gives you |
|---|---|
| Find the work worth automating | The 5 questions to ask of every claims task, and how to inventory routine work across mail, documents, email, diaries, communications, and follow-up |
| Draw the ownership lines | A three-category work-split map (AI-owned, human-owned, human-in-the-loop) with the control that governs each, plus the 5 steps to build the map from your own recent claims |
| Write a handoff adjusters can act on | The common decision boundaries, the 6 elements every handoff needs, and a worked example following an estimate submission from inbox to closed action |
| Rebuild the adjuster day around decisions | What adjusters stop doing and what they own instead, how to build one prioritized intervention queue, and where supervisors gain leverage |
| Get adoption without shadow work | The task-ownership matrix to publish, the 3 failure modes to coach (blanket rejection, blind trust, duplicate work), and a controlled 4-stage rollout from baseline to scale |
| Prove it with numbers you can defend | The task-level and claim-level measures that hold up under scrutiny, why straight-through processing rate hides most of the gain, and how to build a credible scorecard |
Key takeaways
- Automate the repetitive work on every claim, not only the entirely simple claims.
- Keep judgment, empathy, negotiation, and authority with your people.
- Make every handoff specific, sourced, and easy to act on.
- Redesign queues, roles, and supervisor routines around exceptions.
- Measure operating gains at the task level and improve the workflow continuously.
Frequently asked questions
What is agentic AI in claims?
Agentic AI completes a defined sequence of routine claims tasks, applies business rules, monitors progress, and escalates when human judgment or authority is required. It works across the claim lifecycle rather than on a single isolated task.
Does agentic AI replace claims adjusters?
No. It removes the administrative work that keeps adjusters from using their expertise. Coverage, liability, damages, negotiation, empathy, and settlement authority stay with people.
Why do AI rollouts in claims stall?
Usually at the handoff. When automation stops at an exception and leaves the adjuster to reconstruct what happened, the adjuster becomes the bottleneck and the time savings disappear into rework.
How should we measure an AI-enabled claims team?
Baseline first, then compare the same work. Track time per routine task, manual touches, response time, backlog, claims per adjuster, and supervisor load, paired with correction, override, and exception rates.
Which agentic AI solution should you choose for handling complex claims?
Choose one that automates at the task level, so routine work comes off complex files too. Ask vendors to show the decision boundaries, what a handoff contains, permission and confidence controls, and the audit trail. Five Sigma’s Clive™ is built for that split.
What should you look for in an agentic AI platform for insurance claims processing?
Four things: coverage across the full claim lifecycle, execution inside your existing workflow, controls you can set per action, and handoffs that carry sourced context. Five Sigma owns both the workflow layer and the AI layer, so recommendations carry through to execution.
Can AI claims tools sit on top of an existing claims system?
Yes. Clive™ runs on top of any existing claims system. Your CMS stays the system of record while Clive analyzes, coordinates, and advances the work, so you add agentic AI without a migration.
How much manual claims work can agentic AI remove?
Manual intake and claim setup can take 30 minutes per claim. Document ingestion, field population, exposure creation, and assignment can run in seconds. Baseline your own task times first, then compare the same tasks after rollout.
See how agentic AI can optimize your claims operation?
Book 30 minutes with our claims team.
Tirtza Bensoussan
Related resources
- Guide: Adjuster + Agentic AI: Building a Top-Performing Claims Team
- Blog: From AI-assisted to agentic AI claims: where the adjuster’s role moves
- Blog: Better Together: How AI and Human Expertise Create Better Claims Management
- Blog: Agentic AI in claims: autonomy levels and guardrails
- Article: What is an AI-native claims platform?
