From AI Pilot to Production: Scaling Agentic AI in Insurance
8 min read
Scaling agentic AI from a pilot to production in insurance is a recurring topic across industry events this year. In claims organizations, the practical challenge begins inside their own operations: different lines of business, jurisdictions, delegated authorities, and SOPs.
A successful pilot proves that an AI claims solution works within a particular setup. Broader deployment requires configurable workflows, approved authority limits, and evidence that performance holds as the operating conditions change across the book.
For claims executives, that means making the production operating model part of the pilot from the start. Your pilot should demonstrate an immediate improvement and establish whether the same solution can address the wider set of claims problems on your transformation roadmap.
What you will learn:
- How to define an AI claims pilot that can progress into production across the organization.
- What to look for in a configurable AI claims management solution.
- How to prepare ownership, workflows, and adjusters for expansion.
- Which operational results justify scaling to the next segment.
Why successful AI claims pilots don’t automatically scale
A pilot validates a use case within a limited scope and under specific conditions. Scaling pilot to production across claims operations demands separate configuration and validation wherever coverage, evidence, authority, regulatory requirements, or operating procedures differ.
Most claims organizations running agentic AI have a pilot that worked. Far fewer have one that is still expanding, claim type by claim type, across the operation. Capgemini’s World Property and Casualty Insurance Research 2026 reports that 10% of P&C insurers have successfully scaled AI, while 60% remain in exploration or proof of concept.
A pilot usually proves that AI can handle one task at one stage of the claim: reading and routing incoming email, for example, or capturing FNOL. Production asks the same solution to carry claims from FNOL to resolution, across every line of business, state or country, client program, and SOP the organization runs.
Personal auto, commercial property, and liability claims have different evidence needs and handling paths, and requirements differ across states and countries within a single line. TPAs and MGAs add another layer: the authority and reporting requirements agreed with each carrier or program. A workflow that’s appropriate for one account may need different referral rules for another.
Take a pilot that automates claims email handling. AI reads each incoming message, matches it to the right claim, extracts what matters, and routes it to the right adjuster or queue. That establishes performance for one task. A claim needs much more: FNOL intake and setup, coverage review, document requests and follow-up, reserving, vendor assignment, payments, recoveries, and closure. Each stage has its own inputs, decision points, authority limits, and handoffs to adjusters, and each has to pass work cleanly to the next. Automating one step removes effort from that step, while the claim still waits wherever the next step depends on manual work.
A pilot can already run on live claims. The next challenge is reliable execution from one stage to the next, across the intended scope, with an approved setup for each material variation.
How to choose an AI claims management solution that scales
Evaluate the AI claims management solution against your wider claims requirements before the pilot starts. It should offer reusable capabilities and controlled configuration for differences between lines, jurisdictions, carrier agreements, and SOPs across all stages of the claim lifecycle.
The common mistake is choosing the pilot by use case alone: “let’s try AI on FNOL.” The use case is the easy part. Production depends on whether the solution can stretch across the rest of your operation.
Before kickoff, map the recurring problems you want to solve, such as incomplete intake, document handling, manual follow-up, fragmented information, and delays around decisions. Define the intended rollout so you can assess the solution beyond its first use case.
Ask the provider to demonstrate:
- A second line of business: added through configuration, vendor services, or new development?
- A new jurisdiction: who configures its rules, deadlines, and review requirements?
- A different carrier or program: separate authorities, reporting, and escalation routes, without changing other programs’ settings?
- Your SOPs and authority limits: can your compliance team review, change, and audit them?
- Change over time: are rules versioned, tested, and approved before release?
- Lifecycle coverage: claims process automation across stages, or one task?
- The next claim stage: can the same solution continue the workflow, with the necessary integrations and permissions?
- Your claims system: does it run on top of the claims management system you’re keeping or requires full system transition?
Establish what your operations team can configure, what requires vendor services, and what requires development. Include the cost and ownership of maintaining those settings after launch.
During evaluation, ask the provider to configure a second variant using your requirements. Keep capabilities available today separate from roadmap commitments. Five Sigma’s AI-native platform criteria provide a further checklist for assessing execution and oversight.
Red flags showing an AI claims solution won’t survive production
Red flag | What to verify |
|---|---|
Every expansion requires development | Cost, lead time, and reusable components |
Rules can’t be reviewed or versioned | How approved requirements are maintained and audited |
Authority settings can’t vary by program | Whether permissions match delegated responsibilities |
Adjusters rekey completed AI work | Whether the solution can update the claim record directly |
Success means accuracy or interactions alone | Whether total human effort and claims quality improve |
These are evaluation questions. Two or three of these usually mean the tool was built for a single use case.
Planning beyond the pilot?
Request a demo around the workflows, lines of business, and rules your operation needs to support.
What AI-driven claims automation must execute reliably
AI-driven claims automation needs current claim information, permissions matched to business rules, confirmation that actions succeeded, and a controlled path for uncertainty, failure, and human intervention.
Your claims management platform is central to this test. The solution needs to act on the correct claim and apply the approved workflow for its line of business, jurisdiction, and carrier or program. Verify those capabilities whether you’re modernizing the platform or adding AI to an existing claims management system.
A claims management system you choose to operate with should become a system of action that lets agentic AI and human adjusters work directly on the same live claim file. AI executes the work a stage requires automatically within insurer-defined rules, advances the claim when the stage goal is met, and logs every AI and human change. If an AI reaches its operational boundary or flags conflicts, the decision escalates to an adjuster; automated workflows resume only after obtaining proper human authorization.
Ask your implementation team to demonstrate the difficult transitions:
- Incomplete evidence: missing information is requested before the file is treated as decision-ready.
- Authority boundary: the action pauses and reaches the right person with the evidence.
- Failed system update: the failure is recorded and recovery assigned, without duplicate action.
- Concurrent human work: intervening changes are detected before the AI acts.
An existing claims management system can support this where the necessary access and controls exist. Verify them for each workflow.
How to measure AI claims automation before scaling to production
Agree on measures and thresholds before the pilot begins against a comparable baseline. Scale up deployment once live operational testing demonstrates sustained efficiency gains, dependable quality standards, and reliable exception handling.
Capgemini found that 42% of P&C insurers track no AI metrics. Build a baseline that counts review, correction, and manual fallback, and compare similar claim segments.
Measure | What it establishes |
|---|---|
Human minutes per eligible task | Total effort, including review, correction, and fallback |
Time from readiness to action | How long work waits after prerequisites are met |
Workflow completion | Whether approved sequences finish without an unplanned restart |
Quality and escalation | Whether actions are correct and required referrals happen |
Cost per completed workflow | Benefit after technology, supervision, and support costs |
Compare similar claim segments and account for staffing, claim mix, and catastrophe activity. Audit automated work that never reaches an adjuster as well as referred exceptions. Aggregate results can conceal weak performance in a newly added segment.
INSHUR UK: evidence from a defined workflow
Five Sigma’s INSHUR UK case study reports:
60%
reduction of claim-related email handling
97%
precision in email-to-claim matching
33%
reduction in overall claim handling time
“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
The results cover specific work scopes. The lesson: pair handling-time gains with a quality measure and feedback from the team doing the work.
How do you scale agentic AI across lines of business and jurisdictions?
Scale by configuration in a fixed order: the same stage to more adjusters, then more jurisdictions and programs, then the next stage, then the next line of business, each with its own SOPs, thresholds, and baseline.
A pilot is ready to grow when adjusters’ corrections drop and stay low, exceptions arrive prepared, and handling effort beats the baseline. Leakage and severity on the automated path should track the manual one, as WWT recommends checking.
Then widen deliberately, reviewing results by segment. Give the AI more autonomy task by task and line by line: a task that runs without approval on auto physical damage in one state may still need review on bodily injury in another. Keep every configuration change versioned and traceable, so you can show an auditor which rule applied to a claim on a given day. Our article on how agentic AI changes the claims operating model covers the people side.
How Five Sigma claims management platform supports broader claims transformation
Five Sigma is configured across lines of business, jurisdictions, programs, and SOPs, so the pilot and the rollout run on the same AI claims solution.
Five Sigma starts with the operational problems carriers, TPAs, and MGAs have spent years trying to solve: repetitive handling, disconnected information, follow-up delays, and control across complex claims operations.
Our AI claims solution combines claims expertise with workflow execution. Clive™ works natively within Five Sigma’s AI-native claims management platform or on top of an existing claims system, supporting work across the lifecycle within insurer-defined rules and SOPs.
Five Sigma builds what each agent does, and the insurer sets the rules, guardrails, limits, and exceptions for each line, jurisdiction, and program. Clive completes a stage when it has the information it needs. Beyond the insurer’s thresholds, it stops, flags the issue, and brings the adjuster in. Every accept, edit, and reject is captured, giving accuracy per agent and line of business for each expansion decision.
Begin with the problems across your intended operating scope. Establish the required configuration, integrations, and human authority, then validate each deployment. That gives the first pilot a clear role in a broader transformation plan.
Key takeaways
- Pilot results apply to the conditions tested; expansion needs validation of material differences.
- Evaluate configuration across lines, jurisdictions, programs, and SOPs before selecting a solution.
- Test execution, exceptions, and recovery alongside AI output quality.
- Scale when comparable evidence shows less work and acceptable quality within approved boundaries.
Bring your claims transformation priorities.
Let’s discuss the problems you want to solve and the workflows, rules, and rollout plan they require.
Frequently asked questions
What is agentic AI in insurance claims?
Agentic AI plans and executes connected claims tasks toward a defined goal within insurer permissions. It updates claim information, performs authorized work, and refers decisions requiring human judgment.
Can an AI claims management solution work with an existing CMS?
Yes. Clive AI can be deployed on top of your existing claims management system, executing claims tasks within your rules and authority limits. Implementation connects the data, permissions, and controls needed for your workflows, with adjusters stepping in where judgment is required.
Which workflow should insurers automate first?
Choose recurring work with accessible inputs, clear completion criteria, and manageable correction requirements. Email triage or document intake can be suitable. Evaluate how the same solution supports the next planned use cases.
How should insurers measure claims automation ROI?
Measure human effort removed, including review and corrections, then deduct implementation and operating costs. Track waiting time and quality separately. Distinguish capacity released from cash savings actually realized.
Why is scaling AI in insurance claims difficult?
Claims operations vary by line of business, jurisdiction, policy wording, delegated authority, and SOPs. Success in one setup doesn’t establish performance in another, so each variation needs its own configuration, testing, and owner.
How should leaders evaluate an AI claims management solution?
Test the initial use case, then have the vendor show how workflows, permissions, and rules change for another line, state, or program, and confirm what needs custom development.
Which claims workflow should be automated first?
Choose recurring work with accessible inputs, clear completion criteria, and low consequences if it needs correction. Email triage and document intake are common starting points for claim automation.
