What Zurich’s 2024 Win Reveals About AI in Insurance

Zurich Insurance Group didn’t win a global transformation award because it bought a shiny new platform. It won because transformation is now judged where it hurts: execution under pressure—clear outcomes, a believable roadmap, and proof that the business (not just IT) changed.

That’s why Zurich’s recognition as the Global Finals Winner (2024) for the Insurer Transformation Award, as voted by The Digital Insurer community, matters for anyone working on AI in insurance. Awards aren’t strategy. But they’re a useful signal of what the market is rewarding: insurers who are turning AI and digital capabilities into faster decisions, cleaner operations, and better customer experiences.

If you’re leading underwriting, claims, operations, distribution, or data, here’s the practical question behind Zurich’s win: what does “good” transformation look like in 2025, when AI is everywhere and patience for multi-year programs is thin?

Why this award matters to AI leaders (and why it isn’t fluff)

A transformation award is meaningful when it reflects peer scrutiny and a focus on real outcomes. In the Global Finals format described by The Digital Insurer, finalists pitch their programs live, then face Q&A before votes are cast. That setup filters out a lot of vague storytelling.

For AI in insurance teams, that’s the point: you don’t get credit for potential—only for operational change.

Here’s what I’ve seen consistently across successful carriers: winning transformations tend to combine three elements.

1) AI tied to specific decisions, not “innovation”

The fastest way to kill an AI program is to sell it internally as “innovation” without tying it to a decision that has an owner and a metric.

In insurance, the highest-leverage decisions are predictable:

• Underwriting triage: Which risks are straight-through, which need manual review, and which should be declined?
• Claims routing: Which claims can be paid fast, which need investigation, and which are likely subrogation?
• Fraud detection: Which cases justify additional friction and which will only degrade customer satisfaction?
• Customer engagement: What action should we take next—proactively, compliantly, and profitably?

When transformation programs win recognition, it’s usually because they can point to faster cycle times, reduced leakage, higher accuracy, or better customer outcomes—not because they trained a model.

2) Digital foundations that make AI usable at scale

AI in insurance doesn’t fail because the model is “bad.” It fails because the surrounding system can’t operationalize it:

• fragmented policy and claims data
• inconsistent document intake
• unclear process ownership
• weak monitoring and controls

A credible transformation program fixes the plumbing while it modernizes the house. That’s less exciting than a GenAI demo, but it’s what makes AI durable.

3) Change management treated as a product, not a memo

If AI changes how work gets done, you have to manage it like product adoption:

• build workflows that match how adjusters and underwriters actually work
• redesign handoffs and escalation paths
• train teams with realistic scenarios
• measure usage, not just deployment

The organizations that get recognized are the ones that make transformation feel inevitable inside the business.

The Zurich lesson: transformation wins when it’s “auditable”

Zurich’s win, as described in the source, came after intense competition and audience Q&A. That environment rewards programs that are auditable—easy to explain, easy to validate, hard to hand-wave.

So what makes an AI-enabled insurance transformation auditable?

Clear metrics that map to insurance economics

Insurance transformation metrics should connect to how carriers make (or lose) money:

• Loss ratio impact: leakage reduction, better selection, improved fraud capture
• Expense ratio impact: fewer touches per claim, fewer underwriting reworks
• Growth impact: conversion lift, faster quote-bind, broker/agent satisfaction
• Risk impact: compliance outcomes, model governance, auditability

If your AI program can’t explain which of these it improves—and by how much—you’ll struggle to get sustained funding.

A narrative that matches how insurance really operates

Strong transformation stories don’t pretend everything is centralized. They acknowledge reality:

• products differ by region
• regulatory expectations vary
• lines of business have distinct workflows
• legacy systems won’t disappear quickly

The winning approach typically standardizes where it matters (data definitions, controls, reusable components) while allowing controlled variation.

Proof that the organization learned, not just built

The best insurers treat AI programs as learning loops:

• models are monitored for drift and bias
• humans give feedback that improves routing and confidence thresholds
• exceptions are studied and turned into rules, training data, or process updates

That’s how you get compounding returns instead of one-off pilots.

What it takes to “win” AI transformation in 2025 (the practical blueprint)

You don’t need to be Zurich to build a transformation program that would stand up in a Global Finals-style Q&A. You need discipline.

Here’s a blueprint I’d use to pressure-test any AI in insurance roadmap.

1) Start with one workflow, end-to-end

Pick a workflow that is:

• high volume
• measurable
• currently painful
• rich in data

Good candidates:

• FNOL intake to first adjuster action
• SME underwriting submission to decision
• claims document processing and coverage verification

Then redesign it end-to-end. Not “add AI.” Redesign.

A simple rule: If you can’t draw the new workflow on one page, you’re not ready to automate it.

2) Use GenAI where language is the bottleneck (and keep it on a leash)

GenAI is most useful in insurance where unstructured text slows everything down:

• summarizing claim notes and correspondence
• extracting entities from documents (invoices, medical bills, adjuster reports)
• drafting customer updates with approved language
• assisting agents and service teams with policy Q&A

But GenAI needs guardrails:

• retrieval from approved sources (policy forms, endorsements, procedures)
• restricted actions (draft vs send)
• audit logs
• clear human accountability

If your GenAI can’t be explained in an audit, it shouldn’t be in production.

3) Treat underwriting and claims AI as “decision support,” not “decision replacement”

Most carriers get adoption faster when they position AI as:

• triage
• prioritization
• recommendation
• anomaly detection

Not final authority.

This isn’t fear. It’s smart sequencing. The goal is to remove low-value work first, then scale trust.

4) Build governance that speeds you up

Governance is often framed as friction. In practice, good governance makes teams faster because fewer decisions are relitigated.

Minimum viable AI governance for insurers:

• model inventory (what’s deployed, where, for what decision)
• performance monitoring (accuracy plus operational KPIs)
• fairness and bias checks where relevant
• documentation for auditors and regulators
• clear escalation paths for incidents

The best teams also define “kill switches” and fallback processes before launch.

5) Show value in quarters, not years

Insurance transformation programs are long by nature, but credibility is earned in 90–180 day increments.

A practical cadence:

1. 0–90 days: workflow redesign + data readiness + baseline metrics
2. 90–180 days: pilot in one business unit + adoption metrics
3. 180–365 days: scale + automation of repeatable steps + governance maturity

If the first 180 days don’t show movement in cycle time, touch count, or leakage, the program is at risk.

Snippet worth stealing: AI transformation in insurance is successful when it improves a decision, shortens a cycle, and survives an audit.

“People also ask” questions (answered plainly)

What does insurer transformation actually mean in an AI context?

It means changing core insurance workflows—underwriting, claims, servicing, distribution—so decisions are faster, more consistent, and measurable, with AI embedded in daily work.

Where does AI create the most immediate ROI in insurance?

The fastest ROI usually comes from claims automation, document processing, and triage/routing—because volume is high and manual handling is expensive.

What’s the biggest risk in AI adoption for insurers?

The biggest risk isn’t the model. It’s deploying AI into a workflow that hasn’t been simplified, governed, or measured. That’s how you get cost overruns, low adoption, and compliance anxiety.

How do you know your AI program is ready to scale?

Scale readiness is visible when:

• business users rely on it without being forced
• you can measure impact on operational KPIs
• exceptions are handled predictably
• monitoring and controls are in place

Turning Zurich’s win into your 30-day action plan

Awards are nice, but leads come from execution. If you want your AI in insurance program to feel “award-ready” (and board-ready), do this over the next month:

1. Pick one workflow and define the decision points (who decides what, when).
2. Instrument the baseline: cycle time, touch count, rework rate, leakage indicators.
3. Map unstructured inputs (documents, emails, notes) and identify where GenAI can reduce reading/writing work.
4. Define guardrails: approved knowledge sources, human-in-the-loop checkpoints, audit logs.
5. Draft a one-page scorecard that ties AI outcomes to loss ratio, expense ratio, and customer metrics.

If you can’t put your program on one page with metrics and controls, you don’t have a transformation program yet—you have activity.

Zurich’s 2024 recognition is a reminder that the market is rewarding insurers who can prove transformation under scrutiny. The next wave of AI in insurance winners won’t be the ones with the flashiest demos. They’ll be the ones who can answer hard questions about outcomes, governance, and adoption—without flinching.

What would your underwriting or claims leaders say if you asked them, today, to explain exactly where AI improved a decision and what it did to cycle time?