Most people think about their insurer twice: When they buy a policy and when they need it.
The second moment is the one that counts, and it usually comes down to a simple question: When will I know?
Insurers have spent years digitising customer journeys. Portals, apps, automated notifications and online claims processes have made it easier to submit information. But digitising the front door does not necessarily make the decision behind it faster.
A claim can still move through several people, systems and checks before anyone can give the customer an answer. The same is true in underwriting, where an application may require documents, medical information, declarations and further evidence before a decision is possible.
This is where the next opportunity for artificial intelligence (AI) will lie: Not simply in completing individual tasks faster, but in changing the work that happens before a decision can be made.
The Work Before the Decision
AI is a broad term for technology that can perform tasks that normally involve aspects of human intelligence, such as interpreting information, recognising patterns or generating responses.
Most of the AI tools people encounter today respond to a request. Ask a question, upload a document or give the system an instruction, and it produces an output.
Agentic AI goes further. An AI agent can work towards an objective through a sequence of actions. It might review information, determine what is missing, retrieve relevant material, decide what needs to happen next and involve a person when judgement is required.
That distinction matters in insurance because many delays do not come from the final decision itself. They come from everything that has to happen before someone is in a position to make it.
Consider a straightforward motor claim. A customer has a minor collision on Saturday morning, submits photographs and uploads a repair estimate.
On Monday, someone reviews the claim. A detail is missing. The customer is contacted again. Another document has to be located. Coverage needs to be checked. The estimate needs to be
considered against the circumstances of the accident. And the information needed rarely sits in one place: The policy in one system, the customer’s documents in another, earlier conversations in a third.
None of those steps is particularly complex. Together, however, they create waiting.
An AI agent could coordinate much of that preparation. It could review what the customer has already provided, identify an unanswered question early, retrieve relevant information, trigger follow-up actions where appropriate and organise the case for an assessor.
The assessor still makes the decision. But instead of starting with a folder of information, they start with a case that is much closer to being ready.
That is a different kind of productivity gain. It is not simply completing one task faster. It could reduce the end-to-end time from claim submission by reducing the time spent gathering, checking and preparing information.
Where Complexity Makes It More Valuable
The opportunity becomes even clearer when the case is less straightforward.
Take a home insurance claim involving water damage. The cause is uncertain, the policy includes exclusions and the relevant evidence is spread across photographs, emails, an inspection report and previous customer correspondence. Speed matters, but getting the answer right matters more.
An agent could build a timeline, identify the relevant policy provisions, organise the available evidence and highlight inconsistencies or unanswered questions. Instead of asking a claims professional to spend the first part of their time assembling the case, the system could prepare the material around the questions that require human judgement.
The same principle applies beyond claims. In life insurance underwriting, straightforward applications may already receive an immediate result based on established rules. Referred cases are different. An underwriter may need to review medical information, declarations, earlier correspondence and a list of outstanding requirements before determining whether more evidence is needed. In more complex cases, underwriters may need to work through substantial medical information and understand how different conditions, treatments and test results affect the risk being assessed.
An agent could check submitted material against those requirements, identify what remains outstanding and organise the information around the decision that has to be made.
This is where agentic AI becomes more interesting than simply asking AI to summarise a document, extract information or draft a reply. The value comes from connecting several actions around one objective.
For customers, the result may feel surprisingly ordinary. Fewer requests for information they have already provided. Less time waiting while a case moves between stages. A clearer understanding of what is still required. An earlier answer when the case is ready.
Those outcomes may matter far more than whether the customer ever knows an AI agent was involved.
Autonomy Needs Boundaries
The moment AI begins taking actions rather than simply providing information, responsibility becomes harder to ignore.
If an agent gathers evidence, interprets policy information and recommends what should happen next, insurers need to be clear about where assistance ends and authority begins. A recommendation presented to a trained assessor is very different from an automated decision about whether a customer is covered or whether a claim should be paid.
That distinction will matter for governance, accountability and trust.
An insurer needs to understand what information contributed to an outcome, particularly when that outcome is challenged. Sensitive customer information needs appropriate controls. Unusual cases need clear escalation paths. Some decisions will continue to require human review because the consequence of getting them wrong is too significant.
The strongest applications of agentic AI may therefore be the ones where autonomy is deliberately bounded. AI can do more of the searching, checking, coordinating and preparing, while accountability for consequential decisions remains clear.
That brings the conversation back to the customer. Claims, underwriting and administration are processes inside an insurer. Customers experience them as answers.
Am I covered? Will my claim be paid? What else do you need from me? When will I know?
Agentic AI could change insurance not because every decision becomes automatic, but because less time may be needed to get a case ready for a good decision.
For insurers, that may be the more meaningful shift: Less time assembling the case, more time deciding it well.
