GenAI in Insurance: What Gartner’s Cool Vendor Callout Means

Twenty percent of insurance technology and business leaders said they were already using or experimenting with generative AI, and another 30% planned to do so within six months. That’s not a distant “someday” trend—it's operational pressure showing up in underwriting queues, contact center backlogs, and policyholder expectations.

Gartner’s decision to spotlight insurtechs “adding GenAI” (including Zelros, recognized for personalization) matters because it signals a shift: GenAI is moving from pilots to productized capabilities insurers can buy, test, and scale. And when GenAI is embedded in insurance software—not bolted on in a side project—leaders finally get a shot at measurable outcomes like faster cycle times, more consistent decisions, and better customer conversations.

This post is part of our AI in Insurance series, where we track what’s actually working across underwriting, claims automation, fraud detection, risk pricing, and customer engagement. Here’s the practical angle: what a Gartner “Cool Vendor” callout tells you, where GenAI is delivering real value, and how to evaluate vendors and use cases without getting stuck in demo theater.

Why Gartner is spotlighting GenAI insurtechs

Gartner isn’t handing out participation trophies. A “Cool Vendor” mention is a signal that a category is forming—and that insurers should pay attention now, not after competitors have already operationalized the playbook.

In the GenAI-insurance wave, the biggest change is this: software vendors are embedding GenAI into workflows people already use. That’s the only way GenAI becomes more than a curiosity. When GenAI sits directly inside underwriting workbenches, claims handling tools, and agent desktops, it can influence speed, quality, and consistency.

Gartner’s report highlighted three different use-case directions:

• Underwriting automation and ingestion (e.g., summarizing unstructured risk information)
• Commercial actuarial modeling (e.g., accelerating modeling workflows)
• Personalization and guidance (e.g., tailoring coverage recommendations and customer conversations)

These three lanes matter because they map to core insurance economics:

1. Loss ratio control (better risk selection, fewer blind spots)
2. Expense ratio control (fewer touches per policy/claim)
3. Growth (higher conversion, better retention, stronger cross-sell)

When GenAI helps with even one of these, insurers start treating it as a budget line, not an experiment.

Where GenAI is delivering value in insurance right now

If you want a realistic view of GenAI in insurance, skip the sci-fi and focus on workflow acceleration plus decision support. The highest ROI tends to come from making experts faster and more consistent, not replacing them.

Underwriting: from “document chaos” to risk-ready summaries

Underwriting is full of unstructured inputs—submissions, broker emails, PDFs, inspection reports, loss runs, and narrative notes. GenAI’s sweet spot is turning that mess into a structured briefing that an underwriter can act on.

Practical underwriting use cases that are working:

• Ingestion and summarization: Pulling key fields and red flags from long submission packets.
• Risk narrative generation: Drafting clear rationales for why a risk is accepted/declined.
• Guideline and appetite assistance: Helping underwriters quickly compare a risk against internal rules.

What to watch for: the best systems don’t just summarize—they cite the source passages inside your documents, so underwriters can verify quickly.

Claims: fewer touches, faster resolution, better notes

Claims teams spend a lot of time re-reading the same information and rewriting the same updates. GenAI can reduce cycle time by improving triage and documentation.

High-utility claims applications:

• First notice of loss (FNOL) summarization into a clean claim synopsis
• Adjuster assist for drafting claimant communications that reflect policy language and claim status
• Call and document summarization for consistent file notes
• Subrogation and recovery support by surfacing indicators and missing documentation

The biggest operational win I’ve seen across insurers is simple: better, faster notes. Better notes reduce rework, reduce handoff friction, and make audits less painful.

Customer engagement: personalization that actually reduces confusion

Insurance products are still confusing. That’s not a marketing problem; it’s a product complexity problem. Gartner’s callout of Zelros for personalization points to a category insurers care about: “copilot” experiences that help agents and service teams explain coverage and recommend options in plain language.

Personalization in insurance works when it does three things:

1. Explains coverage clearly, using the customer’s context (life stage, asset, business type)
2. Recommends next best actions (coverage options, endorsements, risk prevention)
3. Keeps humans accountable (the agent owns the recommendation; the AI supports it)

If GenAI only generates pleasant-sounding text, it won’t matter. If it reduces misunderstandings and improves decision confidence, it changes outcomes.

The “Insurance Copilot” idea: why it resonates (and where it fails)

A copilot is valuable when it sits in the flow of work and supports real decisions. Zelros describes its approach as an Insurance Copilot for agents, designed to help throughout tasks and customer interactions—an understandable response to a real pain: agents are expected to be faster, more compliant, more consultative, and more empathetic… all at once.

Here’s what a strong insurance copilot should do in practice:

What good looks like

• Policy and product Q&A grounded in approved sources (policy wording, product docs, underwriting guides)
• Conversation assistance: suggested explanations, objections handling, and comparison tables that make coverage trade-offs easier
• After-call work automation: summaries, CRM updates, follow-up tasks, and reminders
• Compliance nudges: reminders when disclosures or key steps are missing

Where copilots break

Most copilots fail for one of three reasons:

1. They don’t have trustworthy grounding (answers aren’t traceable to insurer-approved content)
2. They don’t integrate (agents have to copy/paste between systems)
3. They don’t fit governance (security, privacy, model risk management, auditability)

If your copilot can’t answer “where did that come from?” with a clean reference trail, you’ll spend your rollout period arguing with risk and compliance.

A practical vendor evaluation checklist for GenAI in insurance

Buying GenAI in December 2025 looks different than buying it in 2023. Insurers are less impressed by demos and more focused on control, auditability, and measurable throughput.

Here’s a checklist I’d use to evaluate GenAI insurance vendors—especially for underwriting, claims automation, and customer engagement.

1) Data grounding and accuracy controls

• Can the system restrict answers to approved sources?
• Does it provide citations to the exact document section?
• Can you tune confidence thresholds and define “I don’t know” behavior?

2) Security and privacy

• How is customer data handled (retention, encryption, tenant isolation)?
• Can you run in your preferred cloud or region?
• What’s the approach to PII masking and access controls?

3) Workflow fit (this matters more than model choice)

• Does it integrate with policy admin, CRM, claims, and knowledge bases?
• Can it write back updates (notes, tasks) with approvals?
• Does it support your service and distribution channels (phone, email, chat)?

4) Governance and auditability

• Are prompts, responses, sources, and user actions logged?
• Can you produce audit trails for regulators and internal risk teams?
• How do you manage model updates and change control?

5) ROI instrumentation

A vendor should help you track outcomes such as:

• Average handle time (AHT)
• After-call work time
• Underwriting cycle time and touch count
• Reopen rates in claims
• Compliance adherence rates
• Conversion and retention lifts (where measurable)

If you can’t instrument it, you can’t scale it.

How to pick your first GenAI use case (and avoid pilot purgatory)

Most companies get this wrong by starting with the flashiest demo use case. The better approach is to choose a workflow with (1) lots of repeatable work, (2) clear quality rules, and (3) clean measurement.

A simple scoring method

Score each candidate use case from 1–5 on:

1. Volume (how often it happens)
2. Time saved (minutes saved per transaction)
3. Risk level (lower is better for first rollouts)
4. Data readiness (are the sources accessible and reliable?)
5. Ease of measurement (can you prove improvement?)

Start with the highest total score.

Good “first” use cases in AI in Insurance programs

• Call summarization + CRM note drafting for service teams
• Underwriting submission summarization for a narrow product line
• Claims file note drafting with mandatory templates
• Knowledge assistant for agents grounded in approved product documents

These are boring on purpose. Boring scales.

What Gartner’s callout should prompt you to do next

A Gartner Cool Vendor mention isn’t an instruction to buy a specific tool. It’s a prompt to run a disciplined evaluation of GenAI use cases and vendors in areas where your expense ratio and customer experience are under pressure.

If you’re leading underwriting, claims, operations, data, or distribution, the best move is to commit to a 60–90 day cycle:

1. Pick one workflow with clear measurement.
2. Run a controlled pilot with strict grounding and logging.
3. Publish results internally—time saved, quality impact, and risk controls.
4. Scale to the next adjacent workflow.

That’s how GenAI becomes an operating advantage rather than a collection of experiments.

The open question heading into 2026 is straightforward: Will your GenAI program be a set of tools people try, or a set of workflows people rely on? If you want help pressure-testing a use case, building a vendor scorecard, or defining success metrics for an AI in insurance rollout, that’s the conversation worth having now.