We asked Christopher Freese, the managing director and senior partner of BCG’s Insurance practice, to reflect on the potential of generative artificial intelligence to transform the insurance industry.

Christopher Freese

Managing Director & Senior Partner

Berlin

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BCG: Generative AI has become a polarizing subject. Some call it a fad, others see it as a revolution. For the insurance industry, is the emergence of GenAI any different from traditional AI?
Christopher Freese: The insurance industry, along with many other sectors, is currently experiencing a sea change. GenAI is not just an improvement on traditional machine-learning models used for natural language processing or computer vision, for example. It democratizes access to AI, simplifying the interface so profoundly that anyone, regardless of programming expertise, can leverage AI’s power. In that sense, it’s reminiscent of the introduction of the iPhone and its app store, which paved the way for a whole new ecosystem of applications and services.

This marks a fundamental difference from the advent of traditional AI. While virtually all insurance companies are using AI today, its impact has fallen short of the transformative change that many had hoped for. Traditional AI has been restricted largely to an approach based on use cases, optimizing niches of existing operating models, rather than fundamentally transforming them.

But leading insurers recognize the potential of GenAI as a catalyst for transformation. They look beyond individual use cases, focus on the big wins, and deploy GenAI to redesign their operating model end to end. By embracing this transformative approach, these leaders are rapidly pulling ahead of their competitors.

What approaches are leading insurers taking to transform their businesses through GenAI?
We see leaders adopting two very different approaches. The first is to focus on a few game-changing applications and scale them across the value chain. For large parts of the company, these GenAI applications have a substantial impact on day-to-day operations. Two early examples are knowledge assistants and coding assistants. Knowledge assistants dramatically cut the time required to research documented knowledge. Using a chatbot interface, they provide agents with information from policy documents, wiki sites, and process manuals. They can present answers in layman’s terms, ready to be shared directly with customers. This application is highly scalable, proving useful across claims and customer service teams as well as across lines of business. Similarly, a coding assistant accelerates software development by offering autocompletion, code translation, and debugging capabilities. It has the potential to remove bottlenecks in IT capacities throughout the value chain and help address the difficulties of dealing with legacy code and mainframe programming languages.

These applications are early-stage examples, providing simple yet significant capabilities. As the technology evolves, more sophisticated applications will further leverage conventional machine learning for enhanced functionality.

The second approach is to focus on transforming individual verticals end to end. Insurers adopting this approach rethink the entire customer journey and internal processes within a vertical, making the most of the new possibilities afforded by GenAI. One prime example is the end-to-end automation of the claims process in auto insurance. Every step in the journey is revised, from first notice of loss to settlement. Using an uploaded image, GenAI can automatically generate an instant settlement offer, relying on an archive of millions of vehicle damages photos and incident reports. At least initially, a human remains in the loop to check the AI’s work, but the process is radically simplified. This significantly enhances many customers’ experiences, eliminating the need to endure lengthy processes with assessors. For insurance companies, it’s a substantial opportunity to reduce cost.

By embracing the transformation, what’s the bottom-line impact an insurer stands to gain?
GenAI applications will create a significant impact across the value chain, yielding substantial efficiency gains and cost savings. The biggest savings, ranging from 40% to 60%, are expected to come from productivity gains in customer service. Think back to the example of the GenAI-powered knowledge assistant: we estimate that up to 35% of customer service agents’ time is spent retrieving information in policies, terms, and other documents. In such tasks, GenAI can more than double agents’ productivity. Agents can query these documents directly—for instance, to check a specific policy’s coverage conditions—and get answers in seconds.

In addition to productivity gains, there is significant potential to save cost on claims management during the claims settlement process. While considerable efficiency gains of 20% to 30% can be achieved through streamlined documentation, these effects will be dwarfed by the savings generated from reducing assessor-related spending using end-to-end automated claims appraisals.

Is GenAI’s impact on insurers driven primarily by cost reductions?
Savings are a big driver, but its impact extends far beyond, offering opportunities for top-line growth as well. The democratization of AI enables employees to shift their focus to the most value-adding activities. One significant area of impact is sales and distribution, where administrative tasks can be reduced to free up more sales time.

Apart from assisting employees, GenAI applications provide fresh opportunities to boost sales and cross-selling. GenAI lends new strength to “next best action” engines based on traditional machine learning, for instance, and could enable hyper-personalized policies, even for retail clients.

In addition, GenAI offers new ways of improving the experience for customers, distribution partners, and employees. Tools such as GenAI-powered sentiment analyzers have the potential to greatly enhance customer service by empowering agents to consistently apply best practices of empathetic customer care. This, in turn, boosts customer satisfaction, which eventually leads to increased retention and cross-selling opportunities.

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How do insurers build powerful applications that unlock the transformational journey you describe?
Fundamentally, it helps to think of GenAI as a set of building blocks that can be connected and assembled in different ways to deliver new applications. The set consists of four key functions: search, summary, content, and code. While the search function looks up specific terms and conditions of insurance policies across sources, the summary function condenses this information into more concise or simpler language. The content function ranges from the generation of responses to customer inquiries, to claims reports and policy explanations. The fourth building block, the code function, involves activities such as the translation of natural language to SQL queries, or the documentation of code in natural language.

As a simple example of how these building blocks complement one another, you can think of the knowledge assistant as a combination of the search and summary functions. First, it retrieves relevant information from various sources, before synthesizing the information into simple terms ready to share with customers. The knowledge assistant provides a user-friendly experience, delivering valuable insights and clarifying complex information.

What’s your advice to insurance leaders embarking on this transformation journey?
Leading insurers are already seizing the moment by deploying big-win GenAI applications that scale well. To go beyond isolated use cases, they’ve launched a transformation based on a multilayered operating model. The new model articulates a clear strategy, enabling them to focus resources. It future-proofs the organization and culture, planning proactively for the shift in skills and talent required to run a GenAI-empowered organization. It builds the partnerships and tech architecture to develop and scale GenAI applications that generate true impact, as well as an underlying policy that shapes GenAI to strengthen corporate values.

Setting up a cohesive approach covering all these dimensions will be key for insurance leaders to unlock the full power of GenAI. It will enable them to steer clear of disjointed and loosely connected applications, guide them on where to focus key resources, and set their organizations on a trajectory that will leave them fundamentally transformed.