In light of its potential to contribute between $2.6 trillion and $4.4 trillion to the global economy annually, which is approximately equivalent to the United Kingdom’s entire GDP in 2023 (approximately $3.3 trillion), commercial lines insurers are becoming increasingly enthusiastic about deploying generative AI (gen AI)[1]. For insurers, gen AI could enhance areas like underwriting, predictive risk assessment, and personalization. Due to the prevalence of manual tasks at various stages of underwriting and claims processing and the industry’s abundance of unstructured data, such as PDFs, images, Word documents, and web pages, insurance stands to gain significantly. Insurance companies are acutely aware of these opportunities. In a recent McKinsey survey of more than 50 leaders from the largest European insurer groups, more than half of the respondents say gen AI could lead to productivity gains of 10 to 20 percent, premium growth of 1.5 to 3.0 percent, and improvement in technical results by 1.5 to 3.0 percentage points. Meanwhile, a third of insurers indicate they have initial gen AI use cases in production, 20 percent describe their AI maturity is advanced, and 60 percent say their traditional data is “evolving.” Relatedly, we find that the unique mentions of AI and gen AI in the major insurer groups’ annual reports have more than doubled from 2022 to 2023.
The question is no longer whether to adopt gen AI but how to do so effectively. Drawing upon our work in supporting gen AI transformations, we have identified the following six traits that organizations leading in gen AI adoption are performing well.
1. Choose a core business area where generation
AI can improve key operations. Gen AI is used strategically by leading businesses to transform their core business processes. The inquiry that needs to be made is, “What top-priority function can gen AI improve to fundamentally enhance our work?” For instance, McKinsey’s knowledge platform, Lilli, reimagined content management after identifying knowledge management as central to its operations. Lilli, which has 45,000 users, has reduced cost per query by 96% and has responded to over 4.5 million queries using insights from over 200,000 documents. The journey has offered several important lessons:
Define the next horizon for cloud strategy and functional teams. Gen AI use cases’ successful launch and scalability depend on a strong cloud strategy and a cross-functional team. Understand the trade-offs between cost and performance. Understanding costs versus performance is essential. To avoid vendor lock-in, concentrate on selecting individual models and components that aid in answering prompts while ensuring scalability and flexibility. Recognize the problem with the data. Before building the tool, it is essential to answer data-related questions. For the development of Lilli, this meant tackling the data retrieval challenge, as content is stored primarily in PPT format, and ensuring efficient extraction from the existing data structure.
Put twice as much money into change management and adoption than you do on building the solution. Investing in change management and adoption is necessary. Users effectively leveraging gen AI can boost productivity by over 20 percent, and scaling this across 45,000 users requires codifying and refining training.
2. Deliver E2E domain-level transformation to drive value capture
Leading businesses prioritize delivering end-to-end domain and subdomain transformation over individual use case builds to unlock significant investments and increase value capture. This involves identifying end-to-end functionalities within a domain that link multiple AI and gen AI use cases. The question to ask is “Which domain would most benefit from E2E domain-level transformation?”
A full-scale E2E transformation of the claims-processing domain could, according to our estimation, yield up to 14 times the impact, whereas applications like claims eligibility checks, enhanced fraud detection, settlement optimization, and improved customer experience through gen-AI-enabled chatbots are valuable in and of themselves. This is achieved through synergies where different use cases interact and reinforce each other, creating a more cohesive and efficient system.
3. Focus on gen AI and AI with integration of other technologies
While tools like ChatGPT have generated significant hype around gen AI, our estimates indicate that the overall value from AI in insurance is divided into approximately 60 to 80 percent from traditional AI and 20 to 40 percent from gen AI. Leading organizations understand that gen AI should complement, not replace, traditional AI. “How do we build a systematic’mapping’ of our business that identifies improvement opportunities regardless of whether they are powered by gen AI, traditional AI, or other technologies?” is the question that needs to be asked. For example, call center analytics rules and interactive voice response systems can be enhanced with gen-AI-powered intent identification and action. In a similar vein, weather data and customized outreach can be integrated into an AI-enabled claims severity engine and proactive gen AI claims prevention. As a result of this all-encompassing strategy, businesses are guaranteed to make full use of AI and gen AI, in addition to other technologies, in order to bring about significant operational enhancements.
4. Develop a strategic view on build, buy, or wait
Leading organizations develop a strategic perspective on when and where to build in-house gen AI solutions versus purchasing off-the-shelf vendor solutions. This balance reduces the risk of vendor lock-in while simultaneously maximizing the benefits of external technologies. The inquiry that needs to be made is, “In what areas of our business do we want to build competitive advantage and, consequently, develop in-house gen AI applications?” It may be advantageous to develop a bespoke solution when dealing with large language models that incorporate proprietary data. On the other hand, off-the-shelf solutions can be more practical for generation AI applications that integrate into enterprise-grade platforms like customer relationship management, particularly if they require little to no customization. A strategic approach ensures that organizations invest in building internal capabilities where it counts while leveraging external solutions to enhance efficiency and effectiveness.
5. Create code that can be reused for scalability and maintenance
It is essential to develop truly reusable code components and workflows, similar to automated automotive assembly lines, for businesses that choose to build AI solutions rather than purchase them. These reusable parts can be put together into a variety of modules and application archetypes for particular generation AI applications. The question that needs to be asked is, “What modular components do we require to begin building, and how can we develop a recipe for this at a large scale?”
6. Facilitate operations by accelerating risk management
It is essential to include the internal risk team from the beginning of the product development process in order to effectively mitigate risks. The question to ask is “How do we use the development of gen AI applications to rethink and enable risk management?”
For example, a global bank that recently implemented a gen AI customer-facing chatbot conducted more than 50 meetings with its risk stakeholders from the outset. The team defined risk frameworks across key categories with fail-safes, including the ability to monitor performance in real time and pull the plug if issues arose. Risk considerations are incorporated into the development process right from the start with this collaborative approach. Gen AI’s enormous potential is set to influence businesses’ performance, but there is no magic solution for scaling it. Leading organizations are distinguishing themselves by implementing bold, end-to-end, domain-level, and enterprise-scale transformations. They create reusable code, create strong cross-functional teams, and strategically balance decisions about whether to build or buy. As the possibilities of gen AI continue to evolve, organizations at the forefront of adoption exemplify these practices.
Carlo Giovine, Phil Hudelson, and Sid Kamath are partners in McKinsey’s London office, where Khaled Rifai is a partner in the Berlin office.