The Growing Importance of Generative AI in the Insurance Industry

Against this background, two significant challenges are set to make this revolutionary technological transition even more critical, while demanding new processes and ways of working:
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1. Coping with strict regulatory frameworks, the industry has historically been slower at adapting to change
2. The inherent complexity of insurance processes further hampers the speed at which a transformative innovation can be implemented

Overcoming these barriers with GenAI will be key to enhancing operational efficiency, customer experience, and overall competitiveness.

‍Our aim here is to provide digital transformation leaders in the insurance industry with a practical roadmap for getting started with GenAI, and then going on to unlock its full potential for lasting impact. We look at the issues through the lens of BCG’s new processes and strategies for helping insurance leaders take full advantage of these opportunities.

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Getting Started with GenAI in Insurance: a Fresh Process for a new landscape

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Simply because GenAI has such vast applications for nearly every aspect of the insurance business, it can be challenging to know where to begin. A common pitfall for insurers is to launch GenAI initiatives across multiple departments simultaneously, with each operating independently and with no central oversight. This siloed approach often leads to fragmented efforts, inadequate business use cases, and can be a significant waste of time and resources.

To avoid these challenges, a well-structured framework is essential. BCG’s Enterprise Foundation Framework provides a proven pathway, guiding GenAI implementation with a clear, organized approach that ensures alignment and impactful results.

The framework categorizes business use cases into three levels to streamline GenAI adoption — Deploy, Reshape, and Invent, each of which we’ll examine with a case study here.

Deploy: Embedding GenAI in everyday tasks, enhancing core operations such as underwriting, claims processing, and customer support through automation and improved decision-making.

Reshape: Transforming critical business functions by redefining processes, upskilling talent, and modernizing technology infrastructures. This stage targets high-impact areas like risk assessment, site inspection, and compliance management.
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‍Invent: Creating entirely new business models, products, and internal functions with GenAI. This dimension explores innovative opportunities, including personalized insurance products, predictive analytics for emerging risks, and GenAI-driven operating models.
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By following the framework, we have found that insurance clients can better prioritize their GenAI efforts, ensuring that they not only improve existing operations but also reshape and invent new avenues for growth and efficiency.

The case studies below have been selected to demonstrate the transformative potential of GenAI for the industry, showing how it can significantly enhance business efficiencies across the three key dimensions of BCG’s Enterprise Foundation Framework. To summarize: Deploy to improve daily operations, Reshape to transform critical functions, and Invent to develop entirely new business models.
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‍Case Study 1: Deploy
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Deploying a smart contracts tool to enhance daily operations for underwriting

As part of a Deploy phase, BCG partnered with a top global reinsurance provider to implement a GenAI-powered smart contracts tool, automating critical tasks such as contract reviews, exposure management, and financial reporting. This solution streamlined daily operations, improved accuracy, and enhanced efficiency, addressing a key operational need to manage high volumes of data with reduced manual effort. This case demonstrates that with a structured, scalable approach, GenAI can effectively transform daily business functions in insurance, delivering both immediate operational gains and a framework for future innovation.

‍This deployment highlighted the ways in which GenAI can deliver immediate business value in core processes, especially in areas requiring data-intensive, repetitive tasks. Automating traditionally manual processes enabled a significantly reduced operational burden, enabling underwriters to make quicker, data-driven decisions and spend more time on higher-value tasks. The project also underscored the importance of planning for scalability from the outset, with a roadmap created for further GenAI integration across the underwriting value chain.

A primary risk in this deployment was the potential for GenAI models to misinterpret complex contractual language, which could compromise accuracy. This was mitigated by incorporating a robust validation process that cross-checked GenAI outputs with key data points, ensuring high accuracy before results were used indecision-making. Further risk was presented by the time and resource investment required for the initial proof of concept (PoC); to mitigate this, the PoC was designed to be reusable across similar contexts.

Case Study 2: Reshape
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Revolutionizing risk assessment with site inspector copilot
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As part of a Reshape dimension within the BCG Enterprise Foundation Framework, a property insurance giant transformed the critical function of risk assessment through the development of a site inspector copilot. The innovation allowed inspectors to summarize site photos and reports, turning them into risk and underwriting conclusions with far less manual effort, thereby dramatically improving efficiency and accuracy.

In fact, this tool significantly reshaped the risk assessment process by leveraging an extensive internal knowledge base to provide site inspectors with real-time answers to their queries across multiple content types, including complex tables, engineering diagrams, formulas, and guided workflows.

A significant risk was the potential for GenAI to misinterpret complex information types. However, the case showed that combining GenAI with Human in the Loop (HITL) methodology can effectively balance automation and quality assurance, particularly in high-stakes areas like risk assessment. In fact, by automating the initial report drafting and ensuring this kind of oversight, the client achieved operational improvements without compromising on accuracy. The tool freed up 20-30%additional capacity for inspectors, enabling them to spend more time on client-facing interactions while driving a 10-15% increase in the adoption of risk recommendations.

An additional risk was whether scalability and integration with the client’s existing IT landscape could be achieved. To address this, the technical architecture was designed to be robust and compatible with future GenAI innovations, so the solution could evolve as a strategic asset rather than a one-time improvement. Overall, the case demonstrated the enormous potential within GenAI to reshape core functions in insurance, delivering immediate value while establishing a scalable foundation for further AI-driven transformation across the organization.
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Case Study 3: Invent

Inventing new operating model for migration of legacy IT systems‍

BCG’s Invent methodology helped a leading insurance company to tackle the complex challenge of migrating outdated legacy systems to a modern, cloud-based architecture.

Legacy systems are a widespread issue across the insurance industry, and the need to optimize and migrate these systems is critical for insurers aiming to stay competitive in an evolving digital landscape. For this client, the monolithic applications were unsustainable, and a shift to a microservice and serverless architecture was essential for growth. However, the manual migration and code rewrite presented significant challenges, especially given the existing disconnect between IT and business departments.

This case underscored the importance of an integrated approach — one in which IT and business functions are aligned from the outset. The experiment highlighted the way a hybrid operating model fosters collaboration and reduces the risk of ‘shadowIT’, by merging IT and business expertise into cross-functional squads. Establishing a new model, the client was able to effectively bridge gaps and create a sustainable pathway for continuous transformation.

Looking at risks, a key concern was the potential for GenAI models to struggle with complex legacy code, which could lead to inefficiencies in the migration process. To mitigate this, BCG’s team conducted a three-month pilot to test and refine the GenAI models across multiple PoCs of varying complexity, allowing for adjustments before full-scale implementation.

Further risk was posed by the level of organizational buy-in we could secure for the new hybrid model. This we addressed by developing a comprehensive change management roadmap, guiding both IT and business departments through the transition and reinforcing collaboration.

It’s our overall conclusion that this exercise not only streamlined the migration process, but also established a reusable process for other insurers who face similar challenges. The combination of a scalable migration strategy coupled with a hybrid operating model offers a replicable solution for insurers who need to modernize legacy systems, while simultaneously driving digital transformation across the organization.