Growth mindset at the helm of insurance's AI era

Why a growth mindset is key to leading insurance into the AI era

AI is changing how insurance works, from claims to underwriting to client service. The wins will go to carriers and brokers that develop leaders who treat change as a skill, not a threat. That's the core message from Chris Reese, vice president of product - professional liability and cyber at Zurich North America.

"Whenever there's a big change, it also can cause some uncertainty. And so change adaptation is important," Reese said. "Overall, this is an exciting time, especially for women who have a growth mindset."

AI is an enabler-humans still set the standard

AI can take on repetitive work so teams can focus on judgment, client outcomes, and risk decisions. Claims handlers can feed documents into models and pull insights in minutes. In cyber claims, models can flag whether a breach came from an email attack or a compromised password-speed that helps contain losses.

On the front end, models can detect anomalies in claims photos and help triage underwriting submissions to reduce manual queues. But the point isn't to remove people. "We have humans in the process who help train the model and then who are evaluating the model output," Reese said. AI is a partner-useful, but not a cure-all.

Start small, stay within budget, let the business lead

"AI is not a magic bullet," Reese cautioned. If models are weak or misapplied, they can push a program off course. The remedy: begin with contained, high-signal use cases and iterate.

• Pick one business problem with clear ROI (claims summarization, FNOL routing, fraud flags).
• Stand up a pilot with a small cross-functional team (underwriting, claims, tech, legal).
• Define success metrics upfront (cycle time, severity impact, loss ratio, customer satisfaction).
• Review outputs with subject-matter experts every sprint; adjust or stop if value isn't there.

The growth mindset advantage

A growth mindset is the belief that skills can be developed through learning and effort. Leaders who bring this mindset build resilient teams, spot practical entry points for AI, and communicate clearly about scope and value. "Be strategic and say, 'This is where we can have an impact,'" Reese said. "If you try to start too big, it's likely to crash."

If this concept is new, leverage modern learning paths-internal enablement, LinkedIn Learning, and free programs such as Google's AI education resources. Set a cadence: learn a skill, ship a pilot, measure impact, repeat.

Women in insurance: this is a leadership moment

AI opens doors for women to lead priority initiatives. The opportunity is to frame a focused investment, earn quick wins, and scale the approach. That means clear communication, smart scoping, and a bias for action-hallmarks of growth mindset leadership.

Reese's advice: claim a use case, build a business case, and align with the people who will use the tool daily. Then prove it with results.

Make adoption safe and useful

• Introduce tools in low-risk settings first (drafting presentations, summaries, email outlines) so teams can experiment without fear.
• Embed underwriters and claims pros with developers to keep features grounded in real workflows.
• Stand up change management: clear training, office hours, and support channels.
• Share wins and lessons in short loops to build trust and momentum.

Risk, liability, and data discipline

AI upside depends on strong data governance and cybersecurity. Treat data as an asset with legal and reputational exposure. Vet all third-party datasets, anonymize personal information, and verify lawful sources. Poor diligence can trigger consumer complaints, regulatory scrutiny, and litigation.

• Map data flows and access; enforce least privilege.
• Log prompts and outputs for auditability; monitor for bias and drift.
• Run threat modeling on new integrations; validate vendors for security and compliance.

A simple playbook for insurance leaders

• Define one line-of-business use case tied to loss cost, expense ratio, or customer outcomes.
• Set governance: data policy, human review checkpoints, and model performance standards.
• Pilot in weeks, not months. Cap scope and budget. Kill or scale based on data.
• Upskill continuously. Make AI literacy part of underwriting and claims training.

What's next

Chris Reese will share further insights as a panelist at the Women in Insurance Summit Los Angeles, where AI, digital transformation, and leadership will be front and center. Expect practical guidance on leading teams through change with confidence and clarity.

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