Nationwide

In insurance, claims processing efficiency and operational throughput are core competitive differentiators. Nationwide, a Fortune 500 property and casualty insurer with 22,000 employees, spent over two years accumulating dozens of AI use cases across the organization — with none achieving enterprise scale. Individual productivity improved among roughly 1,000 early AI adopters, but leadership could not connect those gains to measurable business outcomes or explicit ROI targets. Ten C-suite leaders convened and found the landscape concerning: AI was proliferating without prioritization, and workers gaining efficiency had no structured direction for applying freed capacity. The status quo risked squandering years of experimentation investment without delivering the large-scale transformation the business required.

Nationwide restructured its AI strategy around 18 flagship use cases selected through C-suite consensus, each paired with explicit ROI targets. The portfolio spans two primary domains: claims automation and software development acceleration. In claims processing, a generative AI assistant synthesizes complex claim log histories into concise single-paragraph summaries, giving representatives immediate context before engaging customers on large-loss cases. A dedicated AI tool automates 80% of pet insurance claims end-to-end, with 25% of those claims resolved through instant settlement — freeing operations staff to pursue new business development. For farm and agricultural lines, AI-driven document processing reduces claim review time by 20%. On the technology side, an AI coding assistant — originally developed during an internal company hackathon — accelerates migration of legacy code to modern platforms, directly attacking Nationwide's software development backlog.

Six AI initiatives have already been scaled to production. The flagship technology outcome is a 50% reduction in code conversion time, delivered by the AI-powered legacy code migration tool. In claims, the pet insurance automation tool processes at scale, with instant settlement available for a quarter of all automated claims. Adoption is accelerating across the workforce: nearly half of Nationwide's 22,000 employees use Microsoft Copilot or other AI tools daily, with a target of 90% adoption by 2026. Financially, the company committed $100 million annually through 2028 in dedicated AI spending — a 20% increase over prior annual technology investment — as part of a broader $1.5 billion technology modernization commitment.

• Transitioning from broad experimentation to a focused set of 18 flagship use cases, each with explicit ROI targets, is the critical inflection point for moving AI from pilot to enterprise scale in large insurers.
• Setting targets ambitious enough that partial achievement still delivers meaningful value enables bold investment without organizational paralysis.
• Claims automation and developer productivity represent the highest-leverage AI applications in insurance — prioritizing these domains produces measurable impact quickly.
• Internal innovation programs (hackathons) can yield enterprise-grade tools; building structured pathways from prototype to production captures that value before it stalls.
• Broad workforce adoption requires deliberate enablement investment — tool access alone does not achieve the 90% adoption rates Nationwide is targeting.