AI Assistants That Cut Insurance Handling Time

Most insurers don’t have a “people problem” in service and distribution—they have a document and decision problem.

Policy wording is dense. Product versions multiply. Advisors and claims handlers are expected to be fast, compliant, and precise, even when they’re toggling between half a dozen systems and a PDF from 2019 that looks almost identical to the 2023 endorsement.

This is why AI in insurance is shifting from flashy demos to practical workflow tools. Zelros by Earnix’s Sweet Garden release is a good example of that shift: it focuses on two pain points that quietly drive cost, complaints, and compliance risk—understanding policy terms and asking the right KYC and needs-discovery questions at the right time.

The real bottleneck: policy wording + fragmented knowledge

Insurance operations slow down for one predictable reason: answers live in documents, not in people’s heads.

Zelros cites a McKinsey estimate that advisors can spend up to 1.8 hours per day searching for information. Even if your number is lower, the pattern is the same:

• Service teams waste time finding the right policy version.
• Advisors improvise when they’re under pressure.
• Claims handlers escalate files because they don’t trust what they’ve found.
• Customers get a generic response—or worse, an inaccurate one.

And December is when this pain shows up loudly. End-of-year policy changes, renewals, billing questions, and travel/auto claims spikes mean contact volumes jump at the exact moment teams are trying to close the year cleanly.

AI in claims automation and policy servicing only works when AI can reliably connect a customer’s question to the correct contractual truth. That’s the foundation Sweet Garden is aiming at.

Sweet Garden’s policy-terms agent: turning “general conditions” into usable answers

The first Sweet Garden feature tackles the “terms and conditions nightmare” head-on: an AI agent specialized in reading and understanding insurance policy terms (general conditions).

Why “search” fails in insurance operations

Keyword search and basic RAG approaches often disappoint in policy servicing because insurance language is:

• Versioned (endorsements, amendments, riders, options)
• Context-dependent (coverage depends on circumstances, limits, deductibles, exclusions)
• Ambiguous without definitions (“residence premises,” “reasonable care,” “professional use”)

If your AI pulls a paragraph that looks right but belongs to the wrong edition, you’ve created a compliance incident, not a productivity gain.

Sweet Garden emphasizes three capabilities that matter in production environments:

• Version disambiguation so users land on the right document for the customer
• Whole-document understanding, not just “chunk matching”
• Reasoning transparency, so staff can see assumptions and context behind the answer

Here’s the stance I’ll take: explainability isn’t optional in insurance AI assistants. If the model can’t show what it relied on (and what it didn’t), teams won’t trust it—and regulators won’t either.

Practical outcomes you can measure

Zelros reports +15% productivity for customer service teams from this capability. You can translate that into operational metrics that actually drive decisions:

• Lower average handle time (AHT) for coverage questions
• Fewer transfers to expert centers
• Fewer repeat contacts due to vague or incomplete answers
• Better first-contact resolution (FCR)

The hidden win is decision confidence. Faster is nice; faster plus correct changes how work flows.

Example scenario: claim coverage check

A customer calls about water damage. The handler needs to confirm:

• Is accidental discharge covered under this policy version?
• Is there an exclusion for long-term seepage?
• What’s the deductible for this peril?
• Are there reporting timelines or mitigation duties?

In a manual world, that’s 10–20 minutes of document hunting and uncertainty.

With a policy-terms agent, the handler can ask in natural language, get an answer tied to the correct version, and see the reasoning trail. That turns a “maybe we should escalate” moment into a clear next step.

This is also where AI in underwriting and risk pricing quietly benefits: better servicing data (what customers ask, what confuses them, what gets escalated) becomes input for product simplification and pricing refinement.

Magic Question upgrades: AI-guided needs discovery + KYC that doesn’t annoy customers

The second Sweet Garden feature is an enhancement to “Magic Question,” Zelros’s module for needs discovery and KYC collection.

The key idea is simple: the best compliance and sales outcomes come from better conversations, not longer forms.

Zelros points to a stat from Insurance Argus: 62% of dissatisfied customers cite lack of responsiveness. That tracks with what most teams see—customers don’t hate questions; they hate delays, repetition, and irrelevant steps.

What changes when AI prioritizes questions in real time

Instead of a static questionnaire, Sweet Garden’s approach suggests the most relevant questions based on:

• the client profile n- the call context
• data already available
• missing regulatory KYC elements

This matters because KYC and suitability checks often fail for mundane reasons:

• Advisors don’t know what’s missing until after the call.
• Teams skip “extra” questions to keep AHT down.
• Systems prompt the wrong questions at the wrong time.

An AI assistant that prioritizes questions can reduce friction while improving capture rates.

Where this fits in the AI in insurance stack

Think of Magic Question as the front end of a broader pipeline:

1. Conversation guidance (ask the right questions)
2. Structured capture (store KYC/risk attributes cleanly)
3. Decisioning (eligibility, underwriting rules, pricing)
4. Monitoring (compliance audits, sales quality)

If step 1 is weak, the rest becomes guesswork.

Example scenario: motor insurance change of use

A customer calls to update vehicle details. It sounds administrative—until AI prompts an advisor to ask:

• “Has your commute changed in the last 6 months?”
• “Do you use the vehicle for deliveries or business purposes?”
• “Is the vehicle kept overnight at the same address?”

Those questions are risk pricing inputs and claims defensibility inputs. Capturing them at the moment of change reduces downstream disputes and improves underwriting quality.

And because the AI is prioritizing, you don’t need 40 questions. You need the 5 that matter for this customer, right now.

Why this matters for claims automation, underwriting, and fraud detection

Sweet Garden is presented as a customer experience and efficiency release—but the implications go deeper across the AI in insurance roadmap.

Claims automation: fewer escalations, cleaner decisions

Policy interpretation is a major reason claims get routed to specialists.

When AI can:

• identify the right policy version,
• interpret conditions/exclusions,
• and show the supporting context,

…you can automate more of the “triage and explain” layer. That doesn’t eliminate human judgment; it eliminates unnecessary waiting.

Underwriting: better data quality beats more data

Underwriting models and rules engines don’t struggle because there isn’t enough data. They struggle because:

• data is missing,
• data is stale,
• or data is buried in unstructured notes.

AI-guided KYC and needs discovery improves completeness and consistency. That’s what moves underwriting from reactive clean-up to proactive decisioning.

Fraud detection: clarity reduces noise

Fraud teams drown in false positives when operational data is messy.

If service and claims interactions capture:

• clear answers,
• consistent risk attributes,
• and documented reasoning,

…fraud detection systems can focus on anomalies that matter, not on gaps created by process breakdown.

My opinion: a lot of “fraud tech” investment is wasted because upstream workflows are sloppy. Fix the workflow first, then the detection models get smarter automatically.

An implementation checklist for insurance leaders (what to ask before you buy)

If you’re evaluating AI assistants for policy servicing, contact centers, or advisor workflows, here’s what I’ve found separates pilots from production wins.

1) Can it prove it used the right policy version?

You want explicit handling of:

• product/version matching
• endorsements and amendments
• customer-specific policy attachments

If the tool can’t reliably disambiguate versions, it will create risk.

2) Does it handle “whole-document” logic?

Insurance answers depend on multiple sections working together:

• coverage grant
• definitions
• exclusions
• conditions
• limits and deductibles

Ask for demonstrations where the correct answer requires reasoning across these sections.

3) What’s the human override and audit trail?

For compliance, you need:

• visible reasoning or citations
• an audit log of interactions
• clear escalation paths

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

4) How does it improve customer experience, specifically?

Don’t accept “better CX” as a vague promise. Push for measurable outcomes:

• fewer repeat contacts
• faster resolution
• lower complaint rates
• higher CSAT for service journeys

5) How does it feed underwriting and claims learning loops?

The best AI in insurance tools don’t just answer questions—they produce structured signals you can use to:

• simplify products
• tune underwriting questions
• improve scripts and training
• reduce claim disputes

What Sweet Garden signals about the next phase of AI in insurance

The trend is clear: insurers are moving from “AI that generates text” to AI that makes operations simpler and safer.

Sweet Garden’s focus—policy understanding plus prioritized, context-aware questioning—targets the two things that determine whether AI actually reduces costs:

• time to the correct answer
• quality of data captured during the interaction

If you’re planning 2026 initiatives, this is the direction I’d bet on: AI assistants that sit inside real workflows, enforce good habits, and reduce the cognitive load that drives errors.

If you’re exploring AI in claims automation, underwriting, and customer engagement, map your next pilot to one practical question: Where do we lose the most time finding truth, and where do we lose the most money capturing it too late?

The insurers that win with AI won’t be the ones with the most models. They’ll be the ones that remove the most friction from everyday decisions.