Zurich’s Award Win: What AI-Led Insurance Looks Like

Most insurers say they’re “transforming.” Far fewer can stand in front of an industry audience, take live questions, and still win the vote.

Zurich Insurance Group did exactly that—earning the Insurer Transformation Award at the Global Finals 2024 after pitching its program to The Digital Insurer community and beating three other finalists in a competitive, Q&A-backed format. Awards aren’t the point, but they’re a useful signal: peers are seeing real operational change, not just modern branding.

This post is part of our AI in Insurance series, and I’m using Zurich’s recognition as a practical lens on a bigger question: what does “AI-led transformation” actually mean inside an insurer’s day-to-day underwriting, claims, fraud, and customer experience? If you’re trying to generate results in 2026 planning cycles (budget, roadmap, vendor selection, governance), this is the kind of blueprint you want.

Why “transformation awards” matter for AI in insurance

Awards matter when the judging mechanism rewards execution. In Zurich’s case, the format described by The Digital Insurer is telling: finalists pitched live, then faced a short Q&A before votes were cast. That makes it harder to hide behind vague promises.

For leaders evaluating AI initiatives, here’s the real value of an award-style benchmark:

• It forces a narrative tied to outcomes, not just “we implemented a tool.”
• It highlights organizational capability (operating model, adoption, governance), which is where many AI programs fail.
• It reflects what peers consider credible—a proxy for market maturity.

A stance I’ll take: AI in insurance doesn’t fail because models aren’t accurate enough. It fails because workflows, data rights, controls, and people don’t change. Transformation recognition tends to follow the insurers who push through those harder parts.

What award-winning insurer transformation usually includes

If you strip away the logos and slide decks, insurer transformation programs that hold up under scrutiny tend to share five traits. Zurich’s win is a good prompt to sanity-check your own roadmap against these.

1) A single operating model across underwriting, claims, and service

The fastest AI wins show up when insurers stop treating AI as a “data science project” and start treating it as workflow design.

Practically, that means:

• Underwriting, claims, and customer operations align on one intake process (structured capture + document ingestion).
• Decisions are broken into human + machine steps with clear responsibility.
• Exceptions are explicit: if a case falls outside appetite or confidence thresholds, it routes cleanly.

When insurers don’t do this, they end up with “AI islands”: a triage bot in claims, an OCR vendor in new business, and a separate fraud tool—each with different data, different audit trails, and different KPIs.

2) Data readiness that’s boring—and that’s the point

AI readiness in insurance is rarely glamorous. It’s:

• Policy and claims data normalized (so features mean the same thing across books)
• Document management that supports search, retrieval, and retention
• Clear lineage for rating, underwriting decisions, and claim outcomes

Here’s the blunt truth: Generative AI is only as useful as your ability to retrieve the right information at the moment of work. Many insurers rush to copilots before they’ve fixed retrieval.

3) Automation focused on cycle time, not “percent automated”

Teams often obsess over automation rates (“we automated 40% of tasks”). It’s the wrong headline.

In underwriting and claims, the KPI that changes customer and combined ratio outcomes is usually:

• Cycle time (quote-to-bind, FNOL-to-settlement)
• Touch time (minutes a person spends per case)
• Rework rate (how often files bounce back due to missing info)

AI is strongest at reducing waiting and rework by:

• Extracting and validating information earlier
• Routing work to the right handler sooner
• Drafting communications that reduce back-and-forth

4) Governance designed for regulators and reality

Insurance is a regulated business built on trust. So an AI-led transformation needs controls that are easy to explain.

A workable governance baseline typically includes:

• Model inventory (what’s in production, where it’s used, owners)
• Decision audit trail (what inputs drove outcomes)
• Bias and drift monitoring tied to business thresholds
• Human override rules with documentation expectations

And for GenAI specifically:

• Guardrails for hallucination risk (retrieval-first patterns, not free-form answering)
• Data loss prevention for sensitive documents
• Role-based access and prompt logging for auditability

5) Adoption measured weekly, not quarterly

I’ve seen more AI programs stall due to adoption than accuracy.

The fix is simple, but not easy: treat adoption like a revenue pipeline.

• Weekly dashboards: users active, tasks completed, time saved
• Targeted enablement: who’s stuck and why
• A feedback loop that changes workflows fast (not “next quarter”)

Awards tend to follow organizations that build this muscle.

Where AI delivers the most value in insurer transformation

Zurich’s award announcement doesn’t list specific AI use cases, but it points to a transformation program credible enough to win a global final. So let’s translate “insurer transformation” into the AI value chain most insurers should be prioritizing.

AI in underwriting: better decisions with fewer cycles

The best underwriting AI programs don’t replace underwriters; they reduce administrative drag and help underwriters spend time where judgment matters.

High-value patterns include:

• Submission triage: classify and route by complexity, appetite, and completeness
• Document intelligence: extract exposures, endorsements, loss runs, and key terms
• Risk enrichment: blend internal history with third-party signals to fill gaps
• Underwriting workbench copilots: draft notes, highlight missing info, suggest next-best actions

A practical benchmark: if a mid-market submission takes 2–5 days end-to-end, AI should be able to cut the “waiting for info / rework” portion sharply—often more than the “underwriter thinking” portion.

Claims automation: speed with controls

Claims is where AI in insurance becomes visible to customers. Faster settlement is the promise; poor decisions are the risk.

Winning approaches usually start with segmentation:

• Fast-track low severity, low complexity claims
• Assist (not automate) complex claims with summarization and guidance
• Escalate suspicious patterns early

Common building blocks:

• FNOL capture and validation
• Automated document review (medical bills, repair invoices)
• Claim file summarization for adjusters
• Communication drafting that matches policy language

If you’re selling internally, frame it like this: every day removed from claim cycle time reduces cost and increases retention.

Fraud detection: fewer false positives, earlier interventions

Fraud is a balancing act: aggressive rules catch more, but they also create friction for legitimate customers.

Modern fraud programs combine:

• Network analytics (connections across parties, providers, vehicles)
• Behavioral signals (inconsistencies, timing anomalies)
• Generative AI support (summarizing indicators for investigators)

The performance metric that matters is not “flags raised.” It’s:

• Hit rate (confirmed fraud / referred cases)
• Investigator capacity (cases per investigator per week)
• Customer friction (additional steps imposed on non-fraud claims)

Customer engagement: trust is the differentiator

Insurance customers don’t want “AI experiences.” They want clarity and momentum.

The strongest GenAI customer engagement patterns are:

• Policy and coverage explanations grounded in the customer’s actual documents
• Claim status updates with “what happens next” guidance
• Agent and broker copilots that answer product questions consistently

One line I use with exec teams: If your AI can’t cite where it found the answer in your policy or process docs, it’s not ready for customers.

A practical checklist: how to benchmark your own transformation

If Zurich’s win triggered a “we should be doing more” reaction, use this checklist to assess whether you’re building transformation or collecting pilots.

The 90-day test

Within 90 days, you should be able to show:

1. One production workflow improved end-to-end (not a demo)
2. A documented control set (audit trail, access, monitoring)
3. Adoption metrics by role (underwriter/adjuster/service)
4. A measurable operational outcome (cycle time, touch time, rework)

The “AI value vs. AI theater” questions

Ask your team:

• Where does the AI output get used—in a live decision or a slide deck?
• Who is accountable when the AI is wrong?
• What’s the fallback path when confidence is low?
• Can we explain the decision to a regulator and a customer?

If those answers are fuzzy, you’re not behind—you’re just early. But you do need to tighten the operating model before scaling.

What to do next if you want leads, not just learning

If you’re responsible for underwriting, claims, fraud, or digital operations, Zurich’s award is a reminder that credible transformation is visible. It shows up in cycle times, customer satisfaction, leakage reduction, and employee capacity.

The next step I’d take is straightforward: pick one value stream (like claims FNOL-to-settlement or mid-market quote-to-bind) and run a disciplined AI program around it—data, workflow, controls, adoption—then scale what works.

If 2026 is the year your organization expects “AI results,” what part of your insurance value chain will you be willing to redesign first: underwriting intake, claims triage, fraud investigation, or customer service?