10 Insurance Claims Workflows to Automate First with Agentic AI
Begin with the routine workflows that consume adjuster time on every claim, then extend agentic AI to all work that does not require human expertise, authority, or judgment.
10 min read
Most claims organizations already run some form of automation. Rules assign new losses by jurisdiction, triggers issue acknowledgment letters, and diaries fire on fixed dates. This automation performs well when the input is a structured field. It reaches its limit as soon as the input is an email with three attachments, a police report, or a broker’s narrative, which describes most of what arrives on a claim.
Agentic AI extends automated insurance claims processing into that unstructured work. It reads the material as it arrives, determines what needs to happen, acts within the insurer’s SOPs, and hands the claim to a relevant person when it reaches a defined boundary of authority or threshold.
The practical entry point is a set of 10 routine workflows. Once their performance has been validated and the claims team has confidence in the output, automation gradually extends to every task that does not require human expertise, authority, or judgment, including the escalation of the claims that do.
What you will learn:
- How agentic AI automation differs from rules and triggers, and where each belongs
- Why routine claims work is the right place to start, and what automating it is worth
- The 10 workflows to automate first, mapped to the claim lifecycle
- How to decide, on evidence, when the AI can take on more of the claim
- How automated escalation brings the adjuster in at the point of decision
How agentic AI claims automation differs from rules-based automation
Rules-based automation executes a predefined instruction when a structured field meets a condition. Agentic AI interprets the claim as it arrives, determines which task applies, performs it within the insurer’s SOPs and thresholds, and escalates to a person when the claim falls outside them.
Dimension | Rules and triggers | Agentic AI |
|---|---|---|
Input it can act on | Structured fields | Emails, documents, photos, notes, and structured fields |
How the next action is determined | A condition written in advance | The context of the claim, checked against the insurer’s SOPs |
Response to an unexpected input | No action, or the item falls into a work queue | The item is classified and acted on, or flagged with a stated reason |
Where automation stops | At the edge of the rule | At the boundary of authority, threshold the insurer defines, or AI confidence |
What the adjuster receives | A task | A prepared file with a recommendation, or a live claim processed with a full audit log, escalated at a boundary or resolved end to end (depending on the tool and configuration) |
Rules remain the right instrument for fixed controls. A licensing check, a payment limit, or a regulatory deadline should stay deterministic. The change lies in who interprets the input. Under a rules-only model, an adjuster reads the email and keys the field so that the rule can execute. With agentic AI, the system reads the email, extracts what matters, and the rule executes on what it found.
The distinction matters because a claim file consists largely of documents and correspondence. As our analysis of why STP rates stall near 10% sets out, a fixed rule set escalates the whole file once a claim stops matching a template, while agentic AI continues to work the tasks it can.
Why should routine claims work be automated first?
Routine tasks absorb a large share of claims professionals’ time, account for most of the idle periods in a claim’s lifecycle, and produce output that can be verified quickly. Automating them returns capacity to adjusters and builds the evidence needed to extend automation further.
The administrative load is familiar to any claims leader. A meaningful share of an experienced adjuster’s week goes to opening and filing documents, matching correspondence to claims, chasing missing information, and keeping the file current. This work makes little use of their technical training, and it grows with every additional claim on the desk.
Handling time is only part of the cost. A claim progresses at the pace of its slowest waiting step: an unfiled document, an unanswered information request, a diary entry no one reviewed. Collectively these minor delays produce silent stalls, files that sit idle without anyone aware that progress has stopped. Automating the routine steps keeps each of them moving on every claim, regardless of caseload or staffing.
The benefits extend beyond the claims desk:
- Policyholder experience. A claim is a stressful event, and the routine steps (acknowledgment, status updates, a clear statement of what is still required) make up most of the policyholder’s contact with the insurer. Their prompt, consistent execution shapes how the entire claim is perceived.
- Consistency. When the same SOP is applied to every incoming document and every follow-up, file quality no longer depends on which adjuster holds the claim.
- Supervisory capacity. Supervisors spend less time confirming that routine tasks were completed, and more on exceptions, quality, and coaching.
- Operational capacity. Adjusters can carry more claims and give more attention to complex losses, and the organization can absorb volume growth without proportional hiring.
Routine output can also be checked quickly: an adjuster can confirm a filed document or a drafted request in seconds. That makes these workflows the right place to establish, on evidence, how far the AI can be relied on.
Which 10 claims workflows insurers should automate first with AI
Start with work that is high in frequency, governed by documented procedure, and quick to verify: intake set-up, document and mail handling, routing, information requests, routine communications, action monitoring, and exception escalation. The decisions within each workflow remain with people.
# | Claim stage | Workflow | What the AI does | Where people decide |
|---|---|---|---|---|
1 | Intake | FNOL set-up | Reads the notice and attachments, creates the claim, opens exposures by rule, assigns by license and workload | Adjuster reviews coverage questions flagged at set-up |
2 | Intake | Document classification | Classifies, summarizes, and files each document to the correct claim and exposure | Adjuster reviews items flagged as inconsistent |
3 | Intake | Mail and email indexing | Matches each email to its claim, identifies the original sender, files attachments | Unmatched items go to a person with a suggested match |
4 | Triage | Routing and assignment | Routes by loss type, complexity, jurisdiction, and license | Supervisors set the routing rules and handle overrides |
5 | Investigation | Missing-information requests | Checks the file against the SOP, drafts and sends requests, follows up | Adjuster approves requests that bear on coverage or liability |
6 | Investigation | Business-rule checks on documents | Compares dates, parties, and amounts across documents and flags inconsistencies | Adjuster or SIU determines what an inconsistency means |
7 | Communication | Routine status updates | Sends SLA-driven updates to policyholders, claimants, brokers, and vendors | Adjuster handles sensitive or disputed conversations |
8 | Communication | Claim summaries and file notes | Drafts the first report and keeps it current as information arrives | Adjuster reviews and signs off |
9 | Monitoring | Action and SLA monitoring | Tracks diaries and SLAs and surfaces files that have gone idle | Supervisor decides on intervention |
10 | Monitoring | Exception escalation | Routes threshold breaches, reserve triggers, and complexity changes to the right person with the file prepared | Adjuster or supervisor makes the call |
These workflows share four characteristics:
- they occur on nearly every claim,
- follow a documented procedure,
- take minutes each,
- produce output that is easy to verify.
Their operational effect is larger than their size suggests. A misrouted claim waits with the wrong handler while the cycle-time clock runs, and a misfiled document can hold up a decision for days.
INSHUR, a UK motor insurer, began with email triage. Incoming claim emails are now read, matched to the correct claim, and filed with their attachments without manual sorting. Claim-related email handling time fell from about 5 minutes to about 2, a 60% reduction, with claim-matching precision of about 97%.
“Removing the need to manually sort and route general emails has taken a real burden off their plates.”
Charmaine Pattenden, Head of Claims, INSHUR UK
When agentic AI should take on more of the claim
When a workflow demonstrates consistent accuracy on sampled work, a stable exception rate, and a low rate of adjuster correction across meaningful volume. Autonomy should be extended one workflow at a time on that evidence, and withdrawn if performance declines.
The first 10 workflows should run with adjusters in the loop: the AI performs the work, and an adjuster approves it before it takes effect. Each acceptance, edit, and rejection becomes a measured signal of performance by workflow, line of business, and claim type.
Once a workflow has demonstrated reliable performance, it moves from approval to oversight: the adjuster reviews a sample, along with anything the AI flags. If accuracy deteriorates, the workflow returns to approval. Autonomy is granted, and withdrawn, workflow by workflow.
One caution applies throughout. A low correction rate is an ambiguous signal on its own: it can indicate that the AI is performing well, or that reviewers have stopped scrutinizing its output. Random review of work that adjusters accepted without change is the only reliable way to tell the two apart, and it is the question an auditor or regulator is likely to ask.
Five Sigma describes this progression, as part of its Automation First approach, in three stages. In Assist, the AI answers questions and summarizes. In Collaborate, it drafts actions for the adjuster to approve. In Lead, it moves the claim forward within the rules people set, and people oversee. Our guide to agentic AI autonomy levels and guardrails sets out the controls for each stage, and how to measure agentic AI in claims covers the measures that indicate when a workflow is ready to advance.
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What agentic AI should automate after the first 10 workflows
Every task that does not require human expertise, authority, or judgment. This includes escalation itself: the AI recognizes when a claim reaches a defined boundary, stops, assembles the file, and routes it to the person with the appropriate authority.
Once the initial workflows run reliably, the scope widens: reserve set-up under the insurer’s reserving rules, vendor assignment, payment preparation within limits, subrogation identification, diary management, and closing checks. The test for each task remains the same. If it does not require a claims professional’s expertise, authority, or judgment, it is a candidate for automation.
The work that remains with people is the work they are trained, licensed, and accountable for:
- Coverage determinations and denials
- Liability, damages, and settlement
- Payments and reserve changes above delegated authority
- Represented claimants, litigation, and complex negotiation
- Vulnerable policyholders and sensitive communication
Escalation sits at the boundary between these two categories and is one of the most consequential tasks to automate. In most operations today it depends on someone noticing: a coverage issue on page 11 of a report, or a file that crossed a reserve threshold the previous week.
With agentic claims AI, escalation criteria are defined in advance from the insurer’s SOPs: authority limits, coverage questions, unresolved inconsistencies, complexity thresholds, and reserve movements. When a claim meets one of them, the AI stops, assembles the context the adjuster needs (the reason for escalation, the supporting evidence, and a recommendation), and routes the claim to the person with the appropriate authority. Once that decision is made, the AI resumes the remaining work.
The result is an operation in which adjusters spend their time on decisions, with the preparation already complete. Designing that handoff well is the subject of how to design an AI-to-human handoff that keeps every claim moving.
How Five Sigma applies Automation First across the claim lifecycle
Five Sigma’s Automation First approach makes AI automation the driving force of claims processing: the AI moves the claim through its whole lifecycle autonomously wherever it can, and the automation brings people in where expertise, authority, or judgment is required.
At Five Sigma our Automation First approach sets the direction, but insurers can reach it in steps as fast as they like. They can begin with one workflow, one line of business, one claim type, or a single Clive agent performing a single task, then scale toward end-to-end automation as each step proves itself.
Clive™ Claim Conductor runs the claim through its entire lifecycle and stops only at the boundaries and escalation points the insurer defines. There, the claim goes to the right person with the file prepared, and Conductor resumes once the decision is made.
Adjuster’s Cockpit shows, at a glance, where every claim stands in its automation process: progressing, waiting for human input, or unable to move forward. Our introduction to Clive Claim Conductor and Adjuster’s Cockpit explains how both work, including how insurers configure no-touch and low-touch handling by action, sub-organization, and line of business.
Key takeaways
- Rules and triggers act on structured fields. Agentic AI interprets unstructured claim material, acts within the insurer’s SOPs, and escalates at the boundaries the insurer defines.
- Routine work is the right starting point: it absorbs a large share of adjuster time, causes most silent stalls, and produces output that can be verified in seconds.
- The first 10 workflows are FNOL set-up, document classification, mail indexing, routing, information requests, business-rule checks, status updates, claim summaries, action monitoring, and exception escalation.
- Autonomy should be extended workflow by workflow, moving from adjuster approval to oversight as accuracy holds, with random review of accepted work.
- The end state is AI handling every task that does not require human expertise, authority, or judgment, including escalation to the right person.
Your next claims operation starts here.
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Frequently asked questions
What is automated claims processing in P&C insurance?
Software performing the repeatable work around claim decisions: reading documents, indexing mail, requesting information, sending updates, and escalating exceptions. With agentic AI, this extends to unstructured material, while adjusters retain coverage, liability, and settlement decisions.
How is agentic AI different from rules-based claims automation?
Rules execute on structured fields when a predefined condition is met. Agentic AI reads emails and documents, determines which task applies, acts within the insurer’s SOPs, and escalates to a person when a claim reaches a defined boundary.
Which claims tasks should insurers automate first?
High-frequency, procedure-driven tasks that are quick to verify: FNOL set-up, document classification, mail indexing, routing, information requests, status updates, and action monitoring. INSHUR began with email triage and reduced claim email handling time by 60%.
Can AI escalate a claim to an adjuster?
Yes. Escalation criteria such as authority limits, coverage questions, and complexity thresholds are defined from the insurer’s SOPs. When a claim meets one, the AI stops, prepares the file with a reason and recommendation, and routes it to the right person.
Can I automate claims without replacing the claims system?
Yes. Five Sigma’s CliveTM AI can be deployed on top of your claims management system through APIs and webhooks. It automates routine claims work within your rules and workflows, writing results back to the claim record. Your existing platform remains the system of record, and adjusters retain control.
What is the best AI claims automation software for P&C?
Five Sigma’s Clive™ is agentic AI built for P&C claims. It operates within each insurer’s SOPs and guardrails, escalates at defined boundaries, and logs every action as Clive Claim Conductor carries the claim through its lifecycle.
Tirtza Bensoussan
Related resources
- Blog: Introducing Adjuster’s Cockpit and Clive Claim Conductor for no-touch and low-touch claims
- Blog: Agentic AI vs STP: why your STP rate stalls near 10%
- Blog: How agentic AI changes the claims operating model
- Blog: Agentic AI in claims: autonomy levels and guardrails
- Blog: How to measure agentic AI in claims
