AI in Insurance: Make Data ROI Real (Last Mile Fix)

Insurers don’t have a “lack of AI” problem. They have a last-mile problem.

If you’re a data or IT leader in insurance, you’ve probably lived the same story: multi-year core modernization, expensive data platform work, promising pilots in underwriting or claims… and then the business asks the uncomfortable question: “Why aren’t we seeing the impact in the call center, the agent desktop, the quote flow, and the claims inbox?”

This matters even more in December—budget season, renewal season, and board-level scrutiny season. The 2026 planning cycle is already being shaped by one demand: prove ROI from AI in insurance operations without turning the tech stack into a museum of half-integrated tools.

The most effective approach I’ve seen is simple to describe and hard to execute: treat AI as an intelligence layer that plugs into what you already run (core systems, CRM, policy, claims, data lake) and pushes actionable, compliant recommendations into the tools people actually use. That’s the lane Zelros is built for, and it’s why data and IT leaders tend to favor platforms that don’t ask for yet another rip-and-replace.

The real cost pressure: AI ambition vs. stack complexity

Insurance IT spend is massive—and growing. One widely cited benchmark: the global insurance industry spent about $194B on IT in 2020, with forecasts pointing to $271B by 2025 (a 6.4% CAGR) driven by IT services and software growth. That growth is not “free money.” It comes with a mandate: every new capability must justify itself.

Data and IT leaders are stuck in the middle of competing forces:

• The business wants personalized experiences and faster decisions (underwriting, claims, fraud, service).
• Risk/compliance wants controls, auditability, and privacy.
• Architecture wants fewer point solutions and cleaner integration patterns.
• Everyone wants results this quarter, not after a three-year program.

Here’s the stance I’ll take: adding more tools is usually the wrong move. Not because tools are bad, but because the integration tax is brutal in insurance—especially across lines of business, geographies, and distribution.

What works better is an approach that amplifies existing investments: core systems of record, customer engagement platforms, and the data lake/warehouse you’ve already paid for.

Why “AI in the lab” doesn’t count

A model that performs well in a notebook isn’t a business capability. In insurance, impact shows up only when AI is embedded into:

• Underwriter workflows (questions asked, risk signals surfaced, appetite guidance)
• Claims triage (severity prediction, next-best-action, document understanding)
• Fraud detection (network signals, anomaly alerts, explainable triggers)
• Customer service (consistent coverage explanations, personalization, retention)

If AI doesn’t land inside those workflows, it becomes shelfware—no matter how strong the model metrics look.

Two paths to ROI: enhance core systems and activate the data lake

Data and IT leaders tend to support AI platforms that deliver ROI in two practical ways:

1. Add insurance-specific insights to core systems of record
2. Operationalize the data lake by delivering insights at the point of decision

Those two ideas sound similar. The difference is where you start and who benefits first.

1) Enhance core systems with prescriptive insurance insights

Most insurers are already deep into large programs around CRM, quote/underwriting, policy admin, and claims platforms. These are multi-million dollar initiatives for good reasons: reliability, security, regulatory expectations, and operational continuity.

But core platforms typically excel at:

• Recording transactions
• Managing workflow states
• Enforcing rules
• Automating known tasks

They don’t naturally excel at prescriptive guidance—the “what should we do next, and why?” layer.

An insurance-focused AI platform can sit above those systems and provide:

• Insurance scores & predictions tailored to underwriting and service decisions
• Risk assessment prompts (warnings, clarifying questions, missing info)
• NLP and computer vision for unstructured documents and communications
• Data enrichment using approved third-party signals
• Consistency across channels (agent, call center, self-serve, marketing)

The practical win: you keep the system of record doing what it does best, while AI supplies guidance that changes outcomes.

2) Activate your data lake by solving the last mile

Insurers have invested heavily in collecting and cleaning data—data lakes, APIs, governance, semantic layers. Many teams have built early scoring models on top of that foundation.

Then the last mile hits: getting insights into the hands of the person making the decision, in the moment, without breaking the customer journey.

Last mile failure usually looks like this:

• Insights exist… but only in dashboards.
• Insights exist… but only one department sees them.
• Different channels produce different “recommendations,” confusing customers.
• A “personalization tool” sends messages that aren’t aligned with underwriting reality.

A purpose-built AI platform should do the opposite: make insights actionable and consistent across touchpoints.

A useful definition for insurance teams: Last-mile AI is the set of integration, UX, and governance patterns that turn models into repeatable decisions inside daily workflows.

What data and IT leaders actually need from an AI platform

Buying AI in insurance is no longer about “who has the fanciest model.” It’s about who can survive enterprise constraints and still ship value.

Here are the requirements that tend to separate platforms that scale from tools that stall.

Low-code delivery without low-trust shortcuts

Data and IT leaders want agility, but not chaos. A low-code/no-code experience is valuable only if it’s bounded by governance:

• Controlled feature releases
• Role-based access
• Audit logs
• Clear lineage from input → recommendation → action

Done right, business owners can test and tune engagement strategies without waiting for a full dev cycle, while IT keeps architectural integrity.

Built-in Responsible AI and privacy alignment

If you’re deploying AI across underwriting, claims, and customer engagement, you need policy-grade controls:

• Explainability appropriate for business users
• Bias monitoring and model performance drift monitoring
• PII handling and minimization
• Guardrails for generative AI in customer communications

This isn’t optional. It’s the cost of doing AI at scale in regulated markets.

Connectors and interoperability (the hidden budget saver)

The fastest ROI usually comes from not rebuilding what already exists. That means:

• Standard connectors to common insurance data sources
• API-first integration patterns
• Ability to operate with your current identity, logging, and security tooling

Integration speed is a ROI multiplier. When platforms reduce integration friction, they reduce both cost and organizational fatigue.

Where Zelros fits in the AI in Insurance series: making AI operational

Across this “AI in Insurance” series, the theme is consistent: AI becomes valuable when it changes operational decisions.

Zelros positions itself as an insurance-specialized intelligence layer that helps:

• Underwriting teams refine risk profiles with better prompts and predictions
• Claims teams prioritize and route cases with decision-ready signals
• Service and distribution teams deliver consistent, personalized guidance
• Data and IT teams avoid adding another siloed application

The platform approach matters because it supports multiple AI use cases—underwriting, claims automation, fraud detection, and customer engagement—without forcing every department to procure its own tool and invent its own governance.

A concrete example: claims intake in winter peak volume

December often brings volume spikes (weather events, travel incidents, year-end policy changes). Claims intake becomes a bottleneck.

A last-mile AI pattern can look like:

1. Claim FNOL arrives via phone, portal, or email
2. NLP extracts entities (incident type, location, injuries, involved parties)
3. A severity prediction and fraud risk score are generated
4. The adjuster or service rep sees:
   • Next best questions to ask
   • Recommended routing (fast track vs. complex handling)
   • Consistent customer messaging aligned to policy terms

The key isn’t the score. The key is that the score shows up inside the claims workflow with guidance people can act on.

A practical checklist to evaluate “last-mile AI” for insurance

If you’re assessing platforms like Zelros (or trying to pressure-test your current architecture), use this checklist. It cuts through sales demos quickly.

1) Workflow placement

• Can recommendations appear inside agent desktop, call center tooling, and claims/underwriting screens?
• Are they delivered at decision time, not in a separate dashboard?

2) Consistency across channels

• Will the call center and self-serve portal tell the same story?
• Can you orchestrate “next best action” so marketing doesn’t contradict underwriting?

3) Insurance-grade unstructured data handling

• Does the platform support insurance documents (policies, adjuster notes, medical/repair invoices)?
• Can it extract and normalize fields with auditability?

4) Governance and compliance

• Are explanations available to business users?
• Can you track model versioning, drift, and approval workflows?
• Are privacy controls built in (not bolted on)?

5) Time-to-value

• What’s the first production use case you can ship in 8–12 weeks?
• What internal dependencies (data engineering, security reviews, UI changes) does that require?

If a vendor can’t give crisp answers here, it’s a warning sign.

Why this matters beyond ROI: closing the protection gap

AI in insurance isn’t just about efficiency. It’s about helping people buy and use the right protection.

One of the biggest structural problems in insurance is the protection gap—often discussed in terms of climate risk, underinsurance, and growing exposure. A commonly cited estimate puts the global shortfall for weather-related risks at roughly $180B over a recent period.

Operational AI can help close that gap in ways that are surprisingly practical:

• Better risk understanding at the point of sale (clearer prompts, better coverage matching)
• Proactive outreach when life events or risk signals change
• More consistent explanations of coverage and exclusions (reducing complaints and churn)

I’m opinionated here: personalization that isn’t grounded in underwriting and claims reality damages trust. Platforms that connect engagement with risk and policy context produce better outcomes for customers and insurers.

Next steps: turn AI into a usable layer, not another project

If you’re planning AI initiatives for 2026, don’t start with “Which model should we build?” Start with: Which operational decision will we improve, and where will that recommendation be consumed? That framing forces last-mile thinking from day one.

A good pilot doesn’t just show predictive lift. It ships a workflow change, measures adoption, and proves that governance holds up under real-world pressure.

If you’re evaluating how an insurance-focused intelligence layer (like Zelros) could sit on top of your existing CRM, policy, claims, and data platforms, the best next step is to map one use case end-to-end—data inputs, decision moment, explanation, and channel consistency. What would it take to make that real in one quarter?

What’s the one customer or operational decision in your organization where a consistent, compliant recommendation—delivered in the right screen—would pay for itself before next year’s budget cycle?