Agentic AI in Policy Servicing, Part 2: Four Places to Start

"A complex system that works is invariably found to have evolved from a simple system that worked." John Gall

The strongest early candidates for agentic AI share three traits. They occur in high volume, require coordination across several systems, and operate within established business rules. The four use cases below run from low-risk servicing tasks to workflows that need tighter controls and more involvement from underwriting and other specialists.

Use case 1: Service request intake and data collection

Before a request can be acted on, someone often has to make sense of it. Customer and broker requests arrive unstructured, incomplete, or without the context needed to begin processing.

Agents can classify plain-language requests, extract information from emails and documents, and retrieve relevant policy details. They can also run customer follow-up and collect missing data. Once the case is complete, they can trigger the right servicing workflow or assemble a ready case for employee review.

This is among the most approachable starting points. It avoids coverage decisions while removing many hours of repetitive administrative work. It is also the most reusable capability, since updates, endorsements, renewals, and cancellations can all build on it later.

Use case 2: Nonmaterial policy data updates

A customer submits a new phone number or mailing address, and teams then coordinate repetitive updates across multiple systems. Mortgagees, lienholders, and distribution partners may also need notice so they can update their own records.

For nonmaterial changes, an agent can interpret and validate the request, identify downstream dependencies, decide which internal and partner systems need updates, and coordinate the whole sequence. It can update customer information across systems, regenerate policy documents where required, validate the changes, and notify the customer and third parties.

The operative word is nonmaterial: a change to administrative information that does not alter the insured risk, coverage, eligibility, premium, or other contract terms. If an address change affects rating territory, catastrophe exposure, eligibility, or premium, the agent should move the request into the endorsement or underwriting workflow.

Use case 3: A narrowly defined policy endorsement

Endorsements follow a similar coordination pattern, but they modify the contract. Agents must therefore initiate underwriting and premium recalculation when required.

Take a policyholder who writes, "I bought a new car and need to update my insurance." An agent can identify the endorsement type, retrieve the policy, collect the vehicle identification number, garaging address, and lienholder details, and validate them against policy records and external data where appropriate.

Once the case is complete, existing rules and authoritative services, meaning the policy administration, underwriting, and rating systems, evaluate and process the change. The agent coordinates the workflow. It does not make underwriting decisions or calculate premium adjustments. If the rules approve straight-through processing, the agent coordinates the resulting policy, billing, and document updates. Otherwise it hands an assembled case to a specialist.

Resist automating endorsements across every product and channel at once. A manageable first deployment is one high-volume endorsement type, in one line of business, through one intake channel, such as vehicle replacements on personal auto submitted through the customer portal. That gives enough process variation to benefit from agentic coordination without inheriting the exceptions of the whole endorsement portfolio.

Use case 4: Renewal preparation and exception triage

Renewals tempt automation, but AI-driven rewriting, repricing, and coverage decisions carry considerably higher financial and regulatory risk. In an early deployment, agents should prepare renewal cases and hold no authority over those decisions.

Agents can gather claim history, policy changes, asset information, and valuation data across systems. They can reconcile the data, identify missing documents, flag material exposure changes, and assemble cases that underwriters can act on immediately.

Exception triage follows naturally. Agents can check each case against approved renewal rules and separate routine renewals from those needing attention because of unusual claims activity, material exposure changes, or another predefined exception. Routine cases move into existing renewal workflows. Exceptions go to an underwriter.

What waits until later

Cancellation and nonrenewal handling, retention offers, coverage recommendations, and broader renewal decisioning belong in a second phase. A customer who cancels because of a premium increase is a good example. An agent can capture the reason, query systems for approved discounts and alternative products, present options, and coordinate the customer's choice. Even then, dedicated algorithms and business rules must identify and approve the options. The agent coordinates how they are presented and acted upon.

The final installment covers how to keep agents inside their limits.

Contributed by Vital Soupel, Senior Insurance IT and AI Consultant, ScienceSoft. This is part two of a three-part series.

Previous: Part 1, Where It Fits and Where It Does Not. Next: Part 3, Keeping Agents in Control