GenAI Copilots in Insurance: What Gartner Signals

A lot of insurers are treating generative AI like a shiny add-on: a chatbot on the website, a summary tool for a handful of PDFs, maybe a pilot hidden inside a single team. Most companies get this wrong.

The real opportunity in AI in insurance isn’t “AI everywhere.” It’s GenAI in the few workflows where complexity, compliance, and customer expectations collide—especially in underwriting, claims, and agent-assisted customer service. That’s why Gartner calling out “Insurtechs Adding GenAI” (and recognizing Zelros as a Cool Vendor for personalization) matters. Not because awards sell policies, but because it’s a signal that the market has shifted from experiments to operational bets.

Gartner’s own data point from that research is blunt: 20% of insurance technology and business leaders said they’re already utilizing or experimenting with GenAI, and another 30% planned to do so within six months (reported in 2023). If you’re leading operations, distribution, customer experience, or data/IT, that’s not a trend to observe—it’s a competitive baseline forming under your feet.

What Gartner’s “Cool Vendor” recognition actually tells insurers

Answer first: Gartner recognition is less about “who’s best” and more about “which use cases are becoming productized.” That’s the part insurers should care about.

In the Gartner Cool Vendors research referenced by Zelros, three vendors were highlighted, each mapped to a practical insurance use case:

• Underwriting (Sixfold)
• Commercial and actuarial modeling (Planck Resolution)
• Personalization (Zelros)

That spread is revealing. It mirrors where insurers feel pain right now:

1. Unstructured information overload (emails, loss runs, medical notes, broker submissions, adjuster notes)
2. Decision latency (too many handoffs, too much waiting for interpretation)
3. Experience gaps (customers don’t understand coverage; agents struggle to explain it clearly and consistently)

My stance: GenAI wins in insurance when it reduces interpretation work—the messy human task of reading, translating, comparing, and explaining. It loses when it’s asked to “decide” without guardrails.

The shift from “chat” to “copilot”

A chatbot answers questions. A copilot sits inside the workflow and helps complete real work. In insurance, that distinction matters because most value sits in the process, not the FAQ.

A well-designed insurance copilot supports tasks like:

• Summarizing a customer’s situation from notes, CRM history, and policy docs
• Suggesting coverage options and explaining trade-offs in plain language
• Drafting compliant, brand-safe messages for agents to review
• Surfacing missing information for underwriting or claims triage

This is exactly why Zelros positions itself as “The Insurance Copilot,” designed for agents dealing with complex products and customer confusion.

Where GenAI delivers value in underwriting, claims, and service

Answer first: The highest ROI GenAI use cases in insurance are “read + write” workflows: intake, summarization, explanation, and documentation—done with strong controls.

Gartner’s discussion of GenAI applications in insurance includes several areas that are already becoming mainstream:

• Ingestion and summarization of unstructured data for underwriting and claims
• Fraud detection (often paired with non-GenAI ML models)
• Conversational experiences (for customers and internal users)

Let’s get specific about what that looks like in the real world.

Underwriting: faster intake, cleaner submissions, better handoffs

Underwriting teams don’t just price risk—they manage uncertainty. GenAI helps when it reduces the time spent wrangling inputs.

Practical GenAI underwriting workflows include:

1. Submission triage: Extract key fields from broker emails and attachments; flag what’s missing.
2. Risk narrative creation: Draft an underwriter-ready summary that explains the “why” behind the risk profile.
3. Guideline navigation: Help underwriters find relevant appetite rules and exclusions quickly.

What to watch: GenAI can hallucinate details. So the design requirement is simple: every extracted fact should be traceable to a source (document snippet, field, system record). If it can’t cite it, it should label it as “needs confirmation.”

Claims: better FNOL, clearer documentation, fewer callbacks

Claims operations are a perfect storm of time pressure, emotion, and compliance. GenAI can make claims teams faster—but more importantly, it can make them clearer.

Strong claims use cases:

• First Notice of Loss (FNOL) assistance: Convert a chaotic call into structured claim data.
• Next-best-action prompts: Suggest required steps based on claim type (without auto-deciding).
• Customer explanation drafts: Produce plain-language updates that reduce inbound “what’s happening?” calls.

A winter-season note (it’s December 2025): weather-driven spikes in auto and property claims tend to amplify operational stress. If your team is already stretched, GenAI-based summarization and drafting can remove minutes per claim interaction—which is often the difference between meeting SLAs and missing them.

Customer service and agents: personalization that doesn’t break compliance

Personalization is where GenAI gets controversial in insurance—fast. Done poorly, it’s a compliance headache. Done well, it’s a retention engine.

Gartner’s mapping of Zelros to personalization hints at an important point: personalization isn’t just marketing. In insurance, personalization means:

• Matching coverage explanations to the customer’s context (life stage, property type, business profile)
• Helping agents recommend options consistently
• Reducing misunderstanding-driven churn and complaints

Here’s what works: constrained personalization.

• Constrained personalization = the model can tailor language and explanation style while recommendations stay within approved product rules.
• Unconstrained personalization = the model improvises coverage advice. That’s where trouble starts.

The 5 requirements for a GenAI insurance copilot that’s safe in production

Answer first: If your GenAI system can’t prove where it got its answers, control what it’s allowed to say, and measure outcomes, it’s not production-ready.

Insurance leaders don’t need more demos. They need a checklist that prevents expensive mistakes.

1) Grounding in approved sources (and showing the evidence)

A production copilot should operate with retrieval-augmented generation (RAG) or equivalent grounding methods so outputs are based on:

• Policy wordings
• Product guides and underwriting manuals
• Claims procedures
• Knowledge articles
• Customer interaction history (where permitted)

And it should provide evidence snippets internally so agents and reviewers can trust the output.

2) Role-based behavior

An agent copilot, an underwriter copilot, and a claims adjuster copilot should not behave the same.

• Agents need explanation, empathy, and compliant phrasing.
• Underwriters need extraction, comparison, and appetite alignment.
• Claims teams need structured intake and clear documentation.

If the tool can’t separate roles cleanly, you’ll get messy outcomes and governance nightmares.

3) Guardrails: “draft, don’t decide”

I’m firmly in the “draft, don’t decide” camp for most insurance GenAI deployments.

Good guardrails include:

• Mandatory human review for customer-facing outputs
• Disallowed topics or phrasing (jurisdiction-specific)
• Confidence flags and “needs verification” markers
• A clear refusal behavior when data is missing

4) Logging, auditability, and governance

Insurers live in audits—internal, external, regulatory, and legal. GenAI must be treated the same way you treat core decision systems.

Minimum governance capabilities:

• Prompt and response logging
• Versioning of prompts, policies, and model changes
• Escalation workflows for problematic responses
• Reporting on drift and recurring failure modes

5) Measurement tied to insurance KPIs (not vanity metrics)

If your success metric is “% of users who tried it,” you’re still in pilot land.

Better metrics:

• Average handling time (AHT) reduction in contact center
• First contact resolution (FCR) improvement
• Quote-to-bind cycle time reduction
• Rework rate on underwriting files
• Claims documentation completeness scores
• Complaint rate and policyholder understanding proxies

A practical rollout plan for insurers in 30–90 days

Answer first: Start with one workflow, one team, one set of documents, and one measurable KPI. Expand only after the tool proves it can behave.

Here’s a rollout approach that works in the real world.

Days 1–15: pick the narrow use case with obvious friction

Good starting points:

• Agent assist for coverage explanations
• Submission summarization for a specific line of business
• Claims call summarization + follow-up drafting

Selection rule: choose a use case where GenAI output can be reviewed quickly (seconds, not minutes).

Days 16–45: build the knowledge base and guardrails

You’ll spend more time here than you expect. That’s normal.

Key tasks:

• Curate authoritative documents and label “single source of truth”
• Define approved phrasing and disallowed claims
• Create test sets (real interactions, anonymized)
• Establish escalation and feedback loops

Days 46–90: pilot, measure, and harden

Run the copilot with a small group and measure before/after:

• Time saved per interaction
• Accuracy and compliance checks
• User adoption by role
• Customer outcome indicators (callbacks, complaints)

If you can’t show improvement on at least one KPI, don’t scale. Fix the workflow or the data first.

What this means for the AI in Insurance series (and your 2026 roadmap)

Answer first: GenAI is turning insurance operations into a speed-and-clarity competition, and copilots are the format most likely to win.

Gartner’s recognition of Zelros in the “Insurtechs Adding GenAI” category fits a broader pattern we’re tracking in this AI in Insurance series: insurers are moving away from isolated AI experiments and toward embedded AI copilots for underwriting, claims, and customer engagement.

If you’re building a 2026 roadmap, I’d make one bet: use GenAI to reduce complexity for humans—customers, agents, and internal teams. That’s where the durable gains come from.

If you’re evaluating a GenAI insurance copilot right now, ask yourself one forward-looking question: Which customer or employee moment would improve immediately if “interpretation work” dropped by 30%—and what would that do to your growth and retention next year?