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AI can support tasks such as risk scoring, image analysis and fraud detection, but it cannot by itself make an insurer’s data complete, connect legacy systems, clarify who owns an exception or ensure a complaint is handled correctly. Those conditions shape whether an AI-assisted process works from end to end. The gap is operational, not simply a problem of choosing a better model.
What AI can do—and what adoption figures do not prove
Insurers already use AI for defined parts of pricing, underwriting and claims. In its 2024 report on findings from the 2023 Digitalisation Market Monitoring Survey, the European Insurance and Occupational Pensions Authority (EIOPA) reported AI use among 50% of non-life insurer respondents and 24% of life insurer respondents. These are survey results from European respondents, not estimates of global adoption.
EIOPA also found that many surveyed AI solutions were developed in-house for simpler tasks, with more explainable algorithms and human oversight. That shows adoption of specific applications; it does not establish that an insurer has integrated its full workflow, fixed its source data or made every downstream decision reliable. EIOPA described digitalisation among European insurers as varied and, in most cases, still at an early stage.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThe distinction is between a model performing a bounded task and an organization consistently completing the whole process around it. A model may flag a claim for review, for example, but people and systems still need to receive the flag, understand it, route it to an accountable handler, record the decision and communicate the result. A successful model output is not proof that those steps happened correctly.
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Why data and workflow remain the hard part
Incomplete maps and uncertain lineage
A model can only work with the inputs it receives. If records are incomplete, definitions differ between departments or the origin and transformation of a field cannot be traced, a technically sound model may still support a poor decision. Insurers need to know which data matters, where it comes from, how it changes and which process depends on it.
France’s Autorité de contrôle prudentiel et de résolution (ACPR), summarizing a 2024 survey of 224 insurance organizations on data used for prudential calculations in a report published in 2026, described limits in data inventories and flow maps. Inventories could remain focused on actuarial data, omit relevant departments or lack clear definitions of criticality. Data-flow mapping had expanded, but completeness, manual processes and traceability—or lineage—remained concerns. The summary also raised issues with data from external providers.
That evidence concerns French insurers and prudential-calculation data; it is not a finding that every insurer has the same weaknesses. It illustrates a broader operational dependency: adding AI does not automatically repair the records or the route by which information moves through a business.
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Manual handoffs and control ownership
Many insurance processes cross teams, software systems, service providers and customer channels. When a handoff relies on a spreadsheet, an inbox or an undocumented workaround, automation can make one step faster while leaving the next step manual or opaque. Exceptions can also disappear between systems unless someone is responsible for seeing and resolving them.
ACPR’s summary described persistent first-line control weaknesses, even where second-line control and internal-audit coverage had progressed. It also said senior leaders did not routinely receive even brief information about periodic data-quality indicators. This points to a control and accountability issue, not just a technical shortage: organizations need owners who can detect a failure, escalate it and act on it.
Skills are part of the operating model
AI requires people who can maintain data, assess model outputs, handle exceptions and connect technical work to insurance decisions. EIOPA identified talent and skills as a major barrier to digital transformation and said insurers view adequate talent and skills as central to success. A model that no one can monitor or challenge is not a durable operational improvement.
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Scaling AI adds governance and resilience work
In its 2025 discussion of scaling AI, EIOPA identified legacy IT, fragmented data sources and limited AI-specific skills as structural constraints. It also highlighted explainability, discrimination and bias, cybersecurity vulnerabilities and concentration in the AI supply chain. These are supervisory concerns, not proof that every insurer or application has experienced each failure.
Third-party dependency deserves attention alongside model design. EIOPA reported that nearly 80% of its respondents used BigTech companies for cloud storage. That figure describes cloud-storage use among survey respondents; it does not measure dependence on AI vendors. Still, it underscores why resilience reviews should examine providers and dependencies, not only the model an insurer directly operates.
Governance should match the application and its risks. EIOPA advocates proportionate human oversight, data-quality controls and documentation. The more consequential a decision is for access to coverage or a claim outcome, the more important it is to establish how a result can be understood, challenged, corrected and audited.
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Legal obligations also remain in force when AI supports a decision. The US National Association of Insurance Commissioners (NAIC) says its Model Bulletin on the Use of AI by Insurance Companies was adopted in December 2023. The bulletin reminds insurers that decisions or actions made or supported by AI must comply with applicable insurance laws and regulations. This is a US regulatory example, not a single rule for every jurisdiction; requirements vary by location, product and use case.
As of March 2026, NAIC described its AI Systems Evaluation Tool as being piloted by 12 states, with adoption anticipated at the 2026 Fall National Meeting. That was a planned future step in the March status description, not confirmation that adoption later occurred.
Customer outcomes reveal whether operations work
Complaint handling in Australia
ASIC’s December 2024 review covered 11 Australian general insurers, representing about 86% of that market by premium. In a sampled exercise, one in six customer complaints were not identified. This was not a census of every complaint across all insurers. ASIC also found that every insurer reviewed failed to meet one or more mandatory internal dispute resolution obligations.
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One in five delay notifications did not meet mandatory content requirements, with non-compliance ranging from 7% to 75% between insurers. All reviewed insurers missed required timeframes for delay notifications. These findings concern the insurers and review scope ASIC examined; they are not a universal benchmark. They show why complaint capture, clear communications and escalation need to be tested as operational controls. A capable AI system cannot establish that the complaint entered the right process or that a required notification was sent on time.
Interpreting UK value measures
The UK Financial Conduct Authority’s (FCA) publication of 2025 general-insurance value-measures data in 2026 offers another example of why outcomes need context. Reported claims acceptance rates were 62–71% for home insurance, 83–86% for travel and 99% for motor. The FCA cautions that home claims reporting is inconsistent, so the home figures require particular care. They should not be treated as a simple ranking of insurers or evidence that AI caused an outcome.
The FCA also reported that average home-insurance claims payouts rose 17% and that combined home buildings and contents claims costs were 48% of premiums in 2025. These are market measures, not direct measures of operational efficiency. The FCA advises considering value measures alongside factors such as business mix, product age, target market, claim volumes, climate and inflation; the historical dataset is not intended to help consumers choose policies directly.
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Before treating deployment as an operational success, assess the full path from input to customer outcome. A model’s accuracy or speed is only one part of that path.
- Check the inputs. Confirm that required data is complete, consistently defined and traceable to its source. Identify external-provider data and who is responsible for its quality.
- Trace the workflow. Follow a case through legacy systems, teams and customer channels. Record manual handoffs and identify where information can be delayed, altered or lost.
- Make exceptions accountable. Establish who receives a flag or uncertain result, how it is routed, how quickly it must be resolved and where the resolution is recorded.
- Test controls and oversight. Define first-line checks, second-line review, audit evidence and escalation ownership. Ensure people can question or override outputs where appropriate.
- Review risk and resilience. Evaluate explainability, potential bias, cybersecurity, provider dependencies and concentration exposure for the specific application.
- Measure customer-facing results. Track whether complaints are captured, communications are complete and timely, and claims outcomes are understood in context. Do not interpret a single metric without considering data quality and business mix.
- Plan for ongoing capability. Assign people to maintain data and controls, monitor performance and manage changes after deployment—not just to launch the model.
Compare an AI-assisted process with the process it replaces across these same dimensions: input quality and lineage, handoff reliability, exception resolution, control ownership, customer outcomes, risk management and the skills needed to sustain it. Otherwise, an apparent improvement in one automated step can conceal an unchanged or newly exposed failure elsewhere.
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