What generative AI means for Insurance

Generative AI in insurance refers to a class of artificial intelligence technologies capable of creating new content—such as text, summaries, images, code, or conversational responses—based on patterns learned from large datasets. These systems are typically powered by large language models and multimodal models that work with both structured and unstructured data.

In the insurance industry, generative AI has gained attention as an extension of earlier AI and advanced analytics capabilities. Its ability to interpret and generate natural language and images makes it particularly relevant for insurance processes that are complex, document heavy, and knowledge intensive across life, property and casualty (P&C), and health insurance.

How generative AI works in Insurance

Generative AI systems are trained on large volumes of data to learn statistical relationships between words, concepts, and visual elements. Once trained, they generate outputs in response to prompts or queries.

In insurance settings, generative AI often operates alongside traditional AI models. Inputs may include policy documents, claims files, underwriting notes, call transcripts, emails, images, and data from connected devices. Generative models synthesize or summarize this information, while traditional analytics handle prediction, scoring, or rule-based decisions. These systems are commonly embedded within existing workflows, providing assistance to employees, agents, and customers rather than making autonomous decisions.

Types of generative AI used in Insurance

Several generative AI approaches appear across insurance use cases:

• Large language models (LLMs) are used for drafting text, summarizing documents, answering questions, and supporting conversational interfaces.
• Multimodal models combine text and image understanding, supporting scenarios such as image-based claims analysis paired with written explanations.
• Retrieval-augmented generation integrates generative models with internal knowledge bases and enterprise data to produce context-aware outputs.
• Generative tools for software and analytics assist with code generation, translation, and technical documentation in IT and data functions.

These approaches complement, rather than replace, existing AI and analytics capabilities.

Where generative AI Is used across Insurance

Generative AI use cases span the insurance value chain and apply to both customer-facing and internal activities:

• Marketing and distribution: content generation, customer sentiment analysis, personalized offers, and agent or broker enablement
• Underwriting: data synthesis, coverage review support, conversational guidance, and document summarization
• Claims management: claims intake assistance, file summarization, fraud investigation support, and automated communications
• Customer service: digital assistants providing 24/7 multilingual support and access to policy information
• Product development: product and trend identification, description drafting, and internal knowledge search
• Operations and support functions: knowledge management, reporting, compliance documentation, procurement support, and IT productivity tools

Applications vary by insurance segment, with different emphases across life, P&C, and health insurance.

Potential benefits of generative AI in Insurance

Across the industry, generative AI is associated with several potential benefits:

• Higher productivity in knowledge-driven roles such as underwriting, claims handling, and customer support
• Improved accessibility of complex insurance information for both employees and customers
• More consistent outputs in documentation and communications
• Faster interaction cycles across sales, service, and claims processes

Industry analyses frequently link these benefits to potential cost efficiency, revenue support, and experience improvements, although actual outcomes vary widely by use case and maturity.

Challenges and considerations for insurers

Despite its potential, generative AI introduces several challenges for insurers:

• Output accuracy and hallucinations, particularly in regulated or customer-facing contexts
• Data and systems integration complexity, given legacy insurance technology environments
• Regulatory and compliance requirements, including explainability and audit trails
• Data privacy and cybersecurity risks, especially when handling sensitive personal information
• Adoption and scaling barriers, as many organizations have yet to move beyond the pilot stage or fragmented deployment

Our research indicates that fewer than one in five companies have meaningfully scaled AI initiatives, reflecting these challenges.

Trends shaping the future of generative AI in Insurance

Several trends are shaping the evolution of generative AI in insurance:

• Rapid growth in the broader generative AI market, with sustained investment across industries
• Increasing use of domain-specific and hybrid AI models that combine generative and traditional analytics
• Greater emphasis on human-in-the-loop governance and responsible AI frameworks
• A shift from isolated productivity tools toward end-to-end process redesign
  

Over time, generative AI is expected to play a larger role in core insurance processes as technology maturity, regulatory clarity, and organizational capabilities continue to evolve.

How insurers are getting started with generative AI

At a high level, insurers typically begin by exploring generative AI in information-heavy internal workflows, where learning potential is high and risk is more manageable. Early efforts often focus on experimentation, data readiness, and governance foundations rather than broad transformation.

Looking ahead

Generative AI represents a significant development in how insurers process information, interact with customers, and support employee decision making. Its relevance stems from the industry’s dependence on unstructured data, expert knowledge, and complex documentation. While adoption remains uneven, generative AI is increasingly viewed as a foundational capability that may reshape multiple parts of the insurance value chain over time.

Seeking additional context? Explore our AI services along with published insights and case studies on generative AI in financial services, including trends and data drawn from real-world deployments.