Insurers are facing tighter margins, rising costs, and pressure to modernize, which are all challenges that traditional levers alone can no longer solve. Amidst the struggle, Generative AI offers a breakthrough. With the potential to add $2.6 to $4.4 trillion annually to the global economy, which is higher than the UK’s GDP, it can redefine how insurers create value.
Roughly 75% of this value lies in customer operations, marketing, software engineering, and R&D. For insurance, that translates into faster claims, smarter underwriting, and hyper-personalized customer service. Up to 70% of employee time involves tasks generative AI can augment or automate. Early adopters who embed AI across the enterprise to improve their processes can start to unlock meaningful ROI.
1. Internal transformation
Insurers are embedding gen AI across underwriting, claims, servicing, and compliance to drive speed, consistency, and cost-efficiency. Key internal use cases include:
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Underwriting: Automated risk profiling, LLM copilots for documentation
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Claims: Root cause analysis, fraud detection, intake automation
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Customer Service: Virtual agents for multilingual, 24/7 support
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Compliance: AI-generated audit trails, solvency monitoring
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Testing & QA: AI-authored test scripts, regression coverage, TestOps integration
Execution requires cross-functional teams, high-quality proprietary data, and tight integration with IPA and analytics platforms.
2. Customer-facing transformation
Externally, gen AI enables real-time, personalized engagement across the full policyholder journey. Strategic levers include:
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Chatbots: Natural language advisors for quoting, onboarding, and claims
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Personalization: Tailored offers based on behavior, policy data, and intent
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Smart Search: Contextual product discovery to reduce friction
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Quote-to-Bind: Interactive assistants to compress sales cycles
Leading insurers adopt a “shaper” approach, where they customize foundation models to their data and workflows to deliver differentiated experiences while managing risk and cost.
How to scale AI in insurance?
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Target high-impact domains first: Focus on core insurance functions like claims processing, underwriting, and customer servicing where gen AI can drive measurable gains. Combine it with automation testing and RPA to streamline workflows, reduce errors, and free up human capital for higher-value tasks.
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Upskill cross-functional teams: Equip underwriters, claims specialists, and QA testers with the skills to interact with gen AI tools, use prompt engineering, and co-create automated test scenarios. This ensures adoption is business-led, not just IT-driven, and builds confidence in AI-generated outputs.
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Establish a centralized AI & QA task force: Form a team that includes business, IT, and QA leaders to manage AI governance, validate LLM outputs using test automation, and ensure use cases meet business objectives. This task force accelerates scaling by avoiding fragmentation and enforcing consistency.
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Build modular, testable infrastructure: Use cloud-native, loosely coupled architectures that support easy integration of LLMs, RPA bots, and test automation frameworks. This allows insurers to switch AI providers, test new use cases safely, and scale without system lock-in.
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Ensure enterprise-grade data readiness: Clean, tag, and structure customer, policy, and claims data for gen AI consumption. Feed production usage data into intelligent test generation tools, enabling more accurate AI testing and coverage for real-world insurance scenarios.
How enterprise-scale automation testing allows AI to bring ROI?
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