Proving AI ROI in Insurance: A 10x Case Study

A lot of insurers say they’re “doing AI.” Far fewer can point to a clean ROI story that survives a budget review—especially when 2026 planning is happening right now and every line item is under pressure.

Here’s the stance I’ll take: if your AI business case is built on one benefit (usually “efficiency”), it’s too fragile to fund. The ROI that gets approved in insurance stacks multiple value drivers—distribution lift, retention, underwriting profitability, and IT simplification—then measures them with the same discipline you’d apply to loss ratio or expense ratio.

This post is part of our AI in Insurance series, and it uses a real-world style case (based on a large P&C carrier/MGA scenario) to show how AI-enabled recommendation and personalization tools can credibly produce 10x+ ROI—and how to replicate the measurement approach in underwriting, claims, and customer engagement.

The ROI problem in insurance: why pilots don’t scale

Answer first: AI projects fail to scale when insurers measure activity instead of financial outcomes.

Most internal updates sound like: “We deployed a copilot,” “We launched an AI layer,” “We trained the team.” Those aren’t outcomes. Insurance leadership ultimately cares about a short list:

• Premium growth (new business and cross-sell)
• Retention (persistency and churn reduction)
• Expense savings (time, headcount avoidance, vendor consolidation)
• Loss ratio improvement (risk selection, prevention, fraud leakage)
• Speed and quality (quote-bind time, claims cycle time, NPS)

Here’s what I’ve found works: build ROI as a portfolio of benefits with different risk profiles.

• Some benefits are high confidence (time saved per agent per day).
• Some are high upside but harder to attribute (conversion lift, retention).
• Some are strategic but real (IT simplification and avoided spend).

When you combine them, you get a business case that doesn’t collapse if one lever underperforms.

A 10x ROI scenario: what the numbers actually look like

Answer first: In the case study scenario, the largest financial impact comes from retention and conversion lift—not just efficiency.

Consider a large P&C carrier/MGA with:

• 10 million contracts (5M motor, 5M home)
• Average premium: $2,000/year per contract (≈ $20B revenue)
• Average net profit: $60/year per contract (≈ $600M annual profit)

Distribution mix:

• Agent channel: 1,000 agents generating 200,000 net new contracts/year at 30% conversion
• Digital channel: 50,000 new contracts/year at 1% visitor-to-contract conversion

The case study logic (from Zelros’ scenario) estimates value across six benefit areas. I’ll keep the math visible and add what insurers should measure to make it defensible.

Benefit 1: Faster agent ramp (training time reduction)

Answer first: Reducing time-to-proficiency is premium growth, not “training savings.”

With 15% turnover, the insurer trains 150 new agents/year. If AI-enabled guidance reduces training time from 6 months to 2 months, the organization gets 4 extra months of productive selling from those new hires.

In the scenario, that productivity gain yields a 5% efficiency boost, equivalent to 10,000 additional contracts/year.

• 10,000 contracts × $2,000 premium = $20M additional premium/year

How to measure it in your business:

• Time-to-first-quote, time-to-first-bind, and “steady-state” conversion by cohort
• New agent quote volume per week (weeks 1–12)
• Persistency of business written by new cohorts (to avoid low-quality growth)

Benefit 2: Agent productivity (time saved)

Answer first: The easiest ROI win is time saved—if you convert it into capacity or headcount avoidance.

If 1,000 agents save 30 minutes/day, that’s roughly 115,000 hours/year saved (as the scenario states), producing about $4.5M in annual cost savings.

What I’d add: time savings only becomes ROI if you decide what happens next:

• More quotes per agent (growth)
• Same volume with fewer overtime/temporary staff (expense)
• More time spent on complex cases (quality)

How to measure it:

• Handle time per quote/bind workflow step
• Quotes per agent-day (before/after)
• Percentage of time spent on “selling” vs “searching/typing”

Benefit 3: Customer retention (churn reduction)

Answer first: In mature books, small churn reductions create outsized ROI.

The scenario reduces churn from 10% to 9.5% (a 0.5 point improvement). On a 10M book, that’s 50,000 contracts saved/year.

• 50,000 × $2,000 premium = $100M premium retained/year

Retention improvements usually come from personalized, timely interventions (coverage gaps, life events, prevention nudges) and better service experiences.

How to make retention attribution credible:

• Holdout tests (no intervention vs intervention)
• Renewal cohorts tracked for 90–180 days post-renewal
• Segment-level churn (by product, tenure, claims history)

And if you want to extend this into claims automation: faster, clearer claims communication is one of the most reliable drivers of renewal intent.

Benefit 4: Conversion uplift (agent and digital)

Answer first: Conversion uplift is where “AI in insurance” pays—when it improves advice quality and personalization.

Agent channel: A 10% uplift in conversion rate drives 20,000 additional contracts/year in the scenario.

• 20,000 × $2,000 = $40M additional premium/year

Digital channel: The scenario estimates up to $60M additional premium from better engagement/personalization.

What to measure so finance believes it:

• Quote-to-bind conversion by journey step (drop-off analysis)
• Offer acceptance rate by recommendation type
• Incremental lift via A/B tests (and keep the test running long enough to avoid seasonality bias)

Where underwriting and pricing come in: conversion lift that comes from “discounting” is not a win. Conversion lift that comes from better fit and better coverage selection tends to improve both customer outcomes and loss performance.

Benefit 5: Loss ratio improvement (risk selection + prevention)

Answer first: AI ROI accelerates when it touches underwriting decisions and prevention behaviors.

The scenario assumes a 5% profitability improvement on 250,000 new contracts, producing $1M incremental profit.

That number is intentionally conservative compared to what many underwriting leaders aim for. The key is measurement discipline.

How to measure loss ratio impact without waiting years:

• Leading indicators: risk mix shift, coverage selections, deductible choices
• Early claims frequency/severity proxies (first 90–180 days)
• Fraud flags and leakage rates in claims handling

Practical example: Prevention messaging (weather alerts, water leak detection prompts, safe driving nudges) can reduce frequency—but only if it’s personalized and timed right. Generic blasts rarely move the needle.

Benefit 6: IT cost optimization (platform consolidation)

Answer first: IT ROI is real when AI reduces overlapping tools and duplicated analytics programs.

The scenario estimates that consolidating redundant “next best offer” and analytics projects yields a 5% optimization of transformation budget, contributing $7.2M to profits.

This is the part many teams underwrite poorly. Do it explicitly:

• Inventory overlapping tools (recommendations, content decisioning, rules engines, experimentation)
• Quantify license costs, vendor services, and internal run costs
• Add avoidance (projects you can cancel) separately from savings (costs you can remove immediately)

Turning those benefits into a CFO-proof ROI model

Answer first: A CFO-proof AI ROI model has four features: baseline clarity, attribution method, timing, and risk adjustment.

If you want AI ROI to be approved and renewed (not just piloted), build your model with:

1) A baseline that everyone agrees on

Define “before” with the same rigor as actuarial assumptions.

• What’s the baseline conversion? Over what period?
• What’s the baseline churn? Are you using written premium or in-force?
• What’s the baseline cost per quote, cost per call, cost per claim?

2) A measurement plan per benefit

Different benefits need different proof:

• Time savings: workflow instrumentation + utilization plan
• Conversion lift: A/B tests and controlled rollouts
• Retention: holdouts + cohort tracking
• Loss ratio: leading indicators + early claims emergence
• IT savings: decommission plan with dates and owners

3) A time-phased view (month 0–36)

AI value doesn’t arrive all at once.

A realistic timeline I like:

1. 0–3 months: pilot, instrumentation, baseline confirmation
2. 3–9 months: conversion and productivity lift (fast wins)
3. 9–18 months: retention lift becomes measurable
4. 12–36 months: loss ratio and full IT consolidation mature

4) Risk-adjusted ROI (not just “best case”)

Create three scenarios:

• Conservative (50% of expected lift)
• Expected
• Aggressive

Then pre-commit to decision rules: “If conversion uplift is below X by month 6, we shift focus to Y.” That’s how you keep the program from becoming political.

Where underwriting and claims fit into this ROI story

Answer first: The Zelros-style ROI framework generalizes cleanly: apply the same six benefit categories to underwriting and claims, and you’ll find your highest-impact levers.

Even if your initial AI investment is a recommendation engine for agents and digital journeys, the measurement approach transfers directly:

Underwriting ROI levers

• Risk appetite guidance that reduces misclassification
• Submission triage (straight-through vs refer)
• Better coverage matching (reduces disputes and friction)
• Pricing integrity checks and exception governance

Metrics: referral rate, quote turnaround time, hit ratio by segment, early loss emergence.

Claims ROI levers

• Intake automation and better FNOL data capture
• Next-best-action for adjusters (coverage checks, repair networks, comms)
• Fraud detection to reduce leakage
• Proactive customer updates to reduce inbound calls

Metrics: cycle time, severity leakage, reopen rates, litigation rate, customer satisfaction, call deflection.

A blunt opinion: claims is often the fastest path to measurable retention lift, because customers remember claims experiences more than advertising.

A practical ROI checklist you can use next week

Answer first: If you can’t answer these 10 questions, your AI ROI isn’t ready.

1. What business metric will move first in 90 days?
2. What’s the baseline, and who signed off on it?
3. What’s the unit of measurement (per quote, per policy, per claim)?
4. What’s the attribution method (A/B, holdout, difference-in-differences)?
5. What operational change converts model output into action?
6. Who owns that operational change?
7. What’s the expected lift, and what’s the conservative lift?
8. What data is required, and what’s already instrumented?
9. What’s the decommission plan for redundant tools?
10. What’s the governance plan for model drift and compliance review?

If you get clean answers, you’ll have something rare in insurance: an AI initiative that can be funded like a business, not like an experiment.

What a “10x ROI in three years” actually implies

Answer first: The case study’s 10x ROI comes from stacking gains—premium growth, retained premium, profit lift, and IT savings—against a modest annual investment.

In the scenario, the combined impact over three years includes:

• $660M additional premiums (cumulative)
• $78M additional net profit (cumulative)
• Example investment: $2.5M/year

That ratio is where “AI in insurance” stops being a nice-to-have and becomes a board-level decision.

If you’re building a 2026 plan, my advice is simple: don’t argue for AI because it’s innovative. Argue for it because your numbers are measurable.

If you want to pressure-test your own ROI assumptions, start by picking two quick-win levers (productivity + conversion) and one compounding lever (retention or loss ratio). Then measure them like you mean it.

What would change in your organization if you could prove—within 120 days—which AI use case produces premium lift, and which one is just noise?