AI Copilots in Insurance: What Zelros Gets Right

A lot of insurers say they’re “doing AI.” Fewer can point to a production system that actually changes what happens during a customer call, a claims email, or an underwriting decision—and does it in a way compliance teams can live with.

Zelros is an interesting case study because it’s not pitching AI as a shiny layer on top of insurance operations. It’s building AI copilots that sit inside the day-to-day tools used by advisors, agents, and back-office teams—trained on product, procedure, and regulatory knowledge, and designed to speed up high-stakes work where errors are expensive.

This matters for our AI in Insurance series because the next wave of adoption isn’t about experiments. It’s about operational AI: underwriting and claims automation, fraud detection, and customer engagement improvements that hold up under audit. Zelros’ trajectory—insurance first, then banking—also hints at where this is going across financial services.

Why insurers are adopting AI copilots faster than most industries

Insurance is unusually well-suited for generative AI because so much of the work is knowledge-heavy and document-heavy. If your teams spend their day reading policies, interpreting exclusions, summarizing claims files, and explaining decisions to customers, you’re already doing “language work.” Generative AI is built for language work.

The other reason is pressure. Customer expectations have moved faster than many insurer operating models. People want:

• Immediate answers (not “we’ll get back to you in 48 hours”)
• Clear explanations (not policy jargon)
• Personalization (not generic scripts)

An AI copilot can’t fix a broken process on its own, but it can reduce the friction that customers actually feel: long holds, inconsistent answers, and back-and-forth for missing documents.

The “unsexy” advantage: insurance already runs on data and rules

Here’s what I’ve found in real implementations: AI succeeds where there’s a strong spine of structured policy data, documented workflows, and clear accountability.

Insurers already have:

• Product catalogs and contract versions
• Underwriting rules and rating logic
• Claims procedures and service-level targets
• Compliance frameworks

That structure makes it easier to ground generative AI outputs in approved knowledge and to build guardrails that prevent the system from guessing.

What Zelros is building (and why “copilot” is the right word)

Zelros positions its platform as a copilot for insurance agents and bank advisors—software integrated into the advisor’s environment that helps answer questions, explain products, and support the duty of advice.

The key design choice is this: a copilot is not an autopilot.

A strong insurance copilot does three things consistently:

1. Retrieves the right knowledge (policy wording, procedures, internal memos, regulatory guidance)
2. Applies it to the customer context (profile, products held, life events, claim status)
3. Produces usable output (an explanation, recommended next step, summary, or checklist)

Zelros’ public story emphasizes customer engagement and advisor effectiveness—exactly where many carriers get quick ROI because it reduces handling time and rework.

Why this changes customer engagement (not just agent productivity)

Most carriers measure productivity like average handle time (AHT) or cases closed per day. Customers experience something else:

• “Did they understand my situation?”
• “Did they explain it in plain language?”
• “Did I get a confident answer the first time?”

A well-trained copilot helps advisors respond faster, but the bigger win is consistency. When the AI is grounded in approved sources, two different advisors are less likely to give two different answers to the same coverage question.

That consistency reduces complaints and escalations—especially important going into year-end and early Q1 when service volumes often spike due to renewals, billing changes, and policy adjustments.

Where AI copilots deliver the fastest impact: underwriting, claims, and service

The Zelros interview focuses on advising and relationship management, but the same architecture maps cleanly to the three core areas most insurers care about in 2026 planning cycles: underwriting automation, claims automation, and fraud detection support.

Underwriting: less chasing documents, more decision clarity

Underwriting teams drown in semi-structured inputs:

• Broker emails
• PDFs and scanned documents
• Loss runs and statements of values
• Free-text notes

A copilot approach can:

• Extract key fields into underwriting workbenches
• Summarize submissions into a standard format
• Flag missing information before it hits an underwriter’s queue
• Draft broker follow-ups that are specific and compliant

The practical outcome isn’t “AI replaces underwriters.” It’s underwriters spend more time on risk judgment and less on document triage.

Claims: faster cycle times with better explanations

Claims is where AI gets tested because it touches money, emotions, and regulation.

A claims copilot can help by:

• Summarizing the claim file history for handoffs
• Identifying the next best action (based on procedure and claim type)
• Drafting customer updates in plain language
• Highlighting coverage-relevant clauses and exclusions

The biggest benefit I see is reduced “ping-pong.” When the AI helps the handler request the right evidence the first time, you cut days out of the cycle time.

Fraud detection: AI as an investigator’s assistant, not a judge

Fraud teams already use models and rules engines, but generative AI adds value in how it handles narratives and messy signals:

• Contradictory statements across calls and forms
• Repeated patterns across claims notes
• Similar wording across different claimants

Used responsibly, AI can surface why a claim is suspicious (the explanation trail), not just label it. That makes SIU work faster and easier to defend.

The hard part: security, compliance, and “audit-ready AI”

If you’re building generative AI in insurance and you’re not talking about controls, you’re not serious.

Zelros emphasizes security posture (including ISO 27001). Certifications aren’t magic, but they’re a signal that the vendor has implemented an information security management system and can support the kind of reviews insurers require.

What “audit-ready” should mean for an insurance copilot

When you evaluate any AI copilot for underwriting or claims, push for specifics in these areas:

• Grounding and citations: Can the system show which internal sources it used?
• Access control: Does it respect role-based permissions (claims vs. underwriting vs. sales)?
• Data boundaries: Is customer data isolated by tenant, region, and legal entity where needed?
• Human-in-the-loop: Where are approvals required (e.g., adverse decisions, coverage denials)?
• Monitoring: Can you detect drift, hallucinations, or rising error rates?
• Retention and logging: Are prompts and outputs logged in a compliant way, with retention controls?

If a vendor can’t answer these cleanly, you’ll pay for it later—either in remediation work or in a stopped rollout.

Insurance-first AI is becoming bank-ready (and that’s not accidental)

Zelros’ planned expansion into banking is a predictable pattern: insurance is a proving ground for regulated generative AI.

Insurance forces solutions to handle:

• Complex product language
• High volumes of unstructured documents
• Strict privacy requirements
• Frequent audits

If a copilot can survive that environment, it’s well-positioned for adjacent financial products like consumer credit, savings, and investments.

The more interesting strategic point is what happens inside bancassurers: once you have one AI knowledge layer that can support advice and servicing, you can spread it across product lines. That creates a flywheel:

• Shared governance model
• Shared knowledge management
• Shared integration pattern into CRMs and workbenches

For buyers, this is a practical question: Do you want one copilot per line of business, or one governed platform that can be extended? In my view, platform wins—if governance is strong.

A realistic rollout plan for insurers (90 days, not “someday”)

Most companies get this wrong by starting with the fanciest use case.

Start where you can control scope and prove value fast. Here’s a rollout pattern that works for many carriers and MGAs.

Step 1: Pick one workflow with high volume and clear rules

Good candidates:

• Policy servicing Q&A (billing, endorsements, renewals)
• Claims status updates and document requests
• Agent support desk (internal help for advisors)

Step 2: Build the knowledge backbone before tuning prompts

Do the boring work:

• Identify source-of-truth documents
• Remove outdated versions
• Define ownership (who updates what, and how often)
• Add metadata (product, jurisdiction, effective date)

This is where copilots either become trusted—or get banned.

Step 3: Instrument results with metrics people agree on

Track metrics that matter to operations leaders and compliance:

• First contact resolution (FCR)
• Reopen rate / rework rate
• Average handle time (AHT)
• Escalations and complaints
• QA scores and compliance exceptions

If you can’t measure improvement, you can’t defend expansion.

Step 4: Expand into underwriting and claims decision support

Once the copilot is stable in service, move into higher-stakes tasks:

• Submission summaries for underwriting
• Coverage clause retrieval for claims
• SIU case summarization and narrative analysis

Keep a clear rule: AI suggests, humans decide—especially for adverse actions.

What to ask a vendor like Zelros before you book a demo

If your goal is leads and real progress (not theater), ask questions that expose whether the product is truly production-ready:

1. Where does the model get its answers? (approved knowledge base vs. free generation)
2. Can you restrict answers by jurisdiction and product version?
3. How do you handle updates to policies and procedures?
4. What integrations are standard? (CRM, telephony notes, claims systems, document management)
5. How do you evaluate accuracy and compliance at scale?
6. What does implementation look like in weeks 1–4?

A good vendor won’t dodge. They’ll show the controls.

Where this goes next for AI in insurance

AI copilots are becoming the default interface for insurance knowledge work—especially in customer service, underwriting triage, and claims handling. The winners won’t be the teams with the flashiest model. They’ll be the teams that ship governed, measurable, audit-friendly AI.

If you’re planning your 2026 roadmap, the Zelros case study is a clear signal: insurers are no longer asking “should we use generative AI?” They’re asking where to deploy it first, how to control it, and how to scale it across lines of business.

If you’re evaluating an AI copilot for underwriting, claims automation, or customer engagement, start with a narrow workflow, demand audit-ready controls, and measure outcomes from day one. What would it change in your operation if every advisor and handler had instant, compliant access to product and process knowledge—without waiting on a supervisor or searching five systems?