Industry Insights

From AI Pilot to Production: What P&C Insurers Need Before AI Touches a Policy or Claim

An artificial intelligence pilot can be impressive. In a controlled setting, an AI tool may summarize a claim file, identify missing information, detect a pattern, or recommend an underwriting action in seconds. Moving that tool into production is different. Once its output begins influencing real policies, claims, or customer interactions, the consequences—and the insurer’s responsibilities—become much greater.

Moving AI from a controlled experiment into insurance operations requires more than an accurate model. It requires reliable data, defined accountability, clear human authority, documented decisions, third-party oversight, and an operational environment capable of enforcing those controls.

For insurers, the important question is no longer simply whether AI can produce a useful answer. It’s whether the organization can understand, govern, document, and act responsibly on that answer. As insurance regulators examine AI governance, third-party models, data inputs, and consumer impact more closely, that distinction is becoming increasingly important.

The Regulatory Conversation Is Moving Toward Accountability

The National Association of Insurance Commissioners (NAIC) adopted its Model Bulletin on the Use of Artificial Intelligence Systems by Insurers in December 2023. The bulletin reminds insurers that decisions and actions made or supported by AI remain subject to existing insurance laws and regulations. It also establishes regulatory expectations surrounding how insurers govern AI systems used by insurers or on their behalf.

One of the bulletin’s central expectations is that an insurer develop a written AI Systems Program appropriate to the risks created by its use of AI. That program may address governance, risk management, internal controls, testing, data practices, documentation, monitoring, and oversight of third parties.

The significance is clear: using an outside model or AI application does not remove the insurer’s responsibility for the resulting insurance decision.

The bulletin has been adopted or addressed by a growing number of state insurance departments, although requirements and implementation may vary by jurisdiction. Insurers must therefore consider both the broader principles and the expectations of the individual states in which they conduct business.

The NAIC’s work has continued beyond the bulletin. During 2025 and 2026, its Big Data and Artificial Intelligence Working Group has been developing and piloting an AI evaluation tool intended to help regulators examine how insurers use AI, how those systems are governed, which applications may present greater risk, and what data is used as input.

The NAIC has also been developing a regulatory framework addressing third-party data and predictive-model vendors used in P&C pricing and underwriting. This initiative reflects an important reality: an insurer may rely on outside technology, but regulators may still need sufficient information to evaluate how that technology affects regulated insurance activity.

Together, these efforts point toward a more mature stage of AI adoption. Experimentation will continue, but insurers should be prepared to explain how an AI system works within their organization, how its risks are managed, and how decisions affecting consumers remain compliant and accountable.

Reliable AI Begins with Reliable Data

An AI system can analyze information quickly, but speed does not correct incomplete, outdated, or inconsistent data. If an underwriting tool receives inaccurate property information, mismatched policy records, or an incomplete loss history, its recommendation may be flawed even if the model itself performs exactly as designed.

Before an insurer moves an AI application into production, it should understand:

Where the data originates
Whether the data is current and complete
How information is validated
Which systems can update the data
Whether sensitive information is appropriately protected
How errors can be identified and corrected
Whether the insurer can trace data back to its source

These questions become more complicated when information comes from a third-party provider. Insurers may use external property data, telematics, imagery, fraud indicators, consumer information, weather data, or predictive scores. Each additional source can improve the analysis, but it can also introduce new questions about accuracy, relevance, permission, security, and accountability.

AI readiness is therefore inseparable from data readiness. An insurer cannot confidently govern an AI-assisted decision if it cannot identify and evaluate the information behind it.

A Recommendation Is Not the Same as a Decision

AI systems can help professionals identify patterns and prioritize work. They may flag a submission for review, detect missing information, summarize a file, recommend an assignment, or identify activity that falls outside an expected range.

However, insurers must define where an AI recommendation ends and an insurance decision begins. The insurer should establish which activities may be automated, which require professional review, who has authority to approve an action, and when a matter must be escalated. The level of oversight should reflect the significance and potential consumer impact of the decision.

A well-designed workflow should help the reviewer understand what the system recommended, identify the information supporting that recommendation, recognize exceptions, and document why the final action was taken.

Accountability Does Not End with the Vendor

Many small and midsized insurers will not build their own AI models. They will obtain AI capabilities from technology companies, data providers, analytics firms, and other specialized vendors.

This approach can make sophisticated technology more accessible, but it does not eliminate the need for governance.

Before placing a third-party AI system into production, an insurer should consider questions such as:

What insurance activity will the system support?
What data does the vendor collect, create, or retain?
Can the insurer understand the factors influencing an output?
How is the system tested before and after implementation?
How does the vendor identify errors or unexpected results?
What happens when the vendor changes its model?
Can the insurer monitor performance using its own results?
What documentation will be available during an examination?
How can the insurer suspend or override the system if a problem arises?

Vendor due diligence should not be treated as a one-time purchasing exercise. AI systems, data sources, and model behavior can change. Oversight must continue throughout the relationship.

The NAIC’s attention to third-party data and models reinforces this point. Even when a vendor considers its technology proprietary, insurers still need enough visibility and control to meet their own obligations.

Insurers Must Be Able to Reconstruct a Decision

Traditional insurance systems already preserve significant information about a transaction: application data, policy versions, underwriting notes, forms, endorsements, payments, claim records, correspondence, and approvals.

AI adds another potential participant to that history.

If an AI-assisted action is questioned months or years later, the insurer may need to determine:

What information was available at the time
Which AI application or model was involved
What the system recommended
Which business rules were applied
Whether the matter was referred to a person
Who reviewed or approved the action
Whether the recommendation was accepted or overridden
What communication was sent to the agent or policyholder

A final result without its supporting history may not be enough. Documentation should allow the insurer to reconstruct how the outcome was reached.

This does not mean every low-risk use of AI requires the same level of recordkeeping. It does mean that documentation should be proportionate to the importance and potential consumer impact of the activity.

Governance Continues After Deployment

Successful testing does not guarantee that an AI system will continue to perform as expected.

Data changes. Business conditions change. Products and underwriting appetites change. Vendors revise their models. Employees may begin using a tool in ways that were not anticipated during implementation.

Insurers should establish ways to monitor results after deployment. Depending on the application, that may include reviewing:

Error and exception rates
Employee overrides
Consumer complaints
Referral patterns
Processing outcomes
Unexpected differences among groups or territories
Changes in input data
Vendor updates
Performance against the original business purpose

Monitoring should lead to action. If results fall outside approved tolerances, the insurer should be able to increase human review, modify the workflow, correct the data, revise applicable rules, suspend the integration, or discontinue the use of the tool.

An AI implementation is not complete merely because the application has been connected and activated. Production is the beginning of the governance cycle, not the end.

The Core Insurance Platform Remains Important

AI applications will continue to evolve, and insurers may use different specialized tools for underwriting, claims, fraud detection, customer service, document processing, or analytics.

Those applications still need to operate within the insurer’s established business environment.

A modern core platform can provide the operational structure surrounding an AI tool: current policy and claim information, defined business rules, user authority, workflow controls, documentation, transaction history, and connections to other approved services.

This is where the architecture of the BindExpress Suite can support an insurer’s broader technology strategy. BindExpress provides connected policy, billing, and claims capabilities, along with configurable business rules, workflows, and open APIs for integration with specialized third-party technologies.

BindExpress is not intended to replace every AI or predictive application an insurer may choose. Its role is to provide a configurable core environment in which data, transactions, established rules, professional authority, and external services can work together.

That distinction will become increasingly important as AI tools change. Insurers should be able to evaluate and replace individual technologies without losing control of the core processes and records on which their business depends.

Moving Forward with Discipline

The goal of AI governance is not to prevent innovation. It is to help insurers move promising applications into production without losing accountability, consistency, or control.

Before an AI tool influences a policy, claim, or customer interaction, an insurer should be able to answer seven basic questions:

What business purpose does the AI system serve?
What data does it use, and can that data be verified?
What recommendation or action can the system produce?
Which decisions require qualified human review?
Who is accountable for the final outcome?
Can the insurer reconstruct and explain what occurred?
How will performance be monitored after deployment?

An impressive pilot demonstrates what technology can do. A responsible production environment demonstrates that the insurer remains in control of how it is used.

For P&C insurers, that is the real measure of AI readiness.

The Future of Insurance Talent – Part Three

The Future of Insurance Talent – Part Two

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