Generative AI in the insurance industry

Savvy insurers will seize the gen AI opportunities that intrigue their customers —

By Christian Bieck, IBM Institute for Business Value, et al —

For the insurance industry, is generative AI more of a risk or an opportunity? Insurance CEOs are equally divided: 49% say it’s more of a risk, 51% say it’s more of an opportunity. Industry executives anticipate using gen AI to fuel competitive advantage with improved sales, customer experiences, and organizational capabilities—but they are wary of the risks to cybersecurity and operations and the issues that can arise from inaccuracy and bias, according to the results of the IBM Institute for Business Value’s 2023 generative AI impact on hybrid cloud pulse survey of 414 executives.

Regardless of the insurer tendency for prudence and risk mitigation, the pressure is on to seize the opportunities. A recent IBM survey of 5,000 executives across 24 countries and 25 industries revealed 77% of industry executives say they need to adopt gen AI quickly to keep up with rivals. 

As insurance organizations walk a tightrope between rapidly building new gen AI capabilities and managing gen AI risk and compliance, they are pushing forward with adoption, based on new research from the IBM Institute for Business Value (IBM IBV). Investments in gen AI are expected to surge by over 300% from 2023 to 2025 as organizations move from pilots in one or two areas to implementations in more functions across the business. 

Organizations are also getting a taste of success. Early adopters using gen AI significantly in their customer-facing systems are seeing a marked improvement in customer satisfaction over insurers not using it at all, including a 14% higher retention rate and a 48% higher Net Promoter Score. And insurers that use gen AI across their direct, agent, and bank channels are improving sales, customer experiences, and customer acquisition costs.

However, our comprehensive survey of 1,000 global insurance and bancassurance executives and 4,700 insured customers also reveals significant areas of discord between insurers and their customers regarding generative AI expectations and concerns. To continue realizing the benefits, insurers would be wise to take the time to evaluate both what they are doing with the technology and how they are doing it.

In this report, we explore three critical factors that financial service and insurance providers should consider in assessing their gen AI strategy. 

1. Bridging the AI technology gap with customers. 

Customer engagement is already a top priority among insurers’ AI initiatives. Most executives report progress on AI assistants (chatbots or virtual assistants), augmented customer service, direct customer help, and developer productivity. But here’s the disconnect: gen AI tools in customer service are not a top priority for insurers’ customers. They prioritize the use of generative AI for personalized pricing or promotions and tailored products to meet their needs. Additional gaps are exposed when looking at customers’ gen AI concerns such as data privacy and potential risks for AI-generated inaccurate information. While these divides challenge current insurer efforts, they also provide opportunities for savvy insurers to jump ahead of competitors. 

2. Conquering complexity. 

Generative AI is the tip of a well-known insurance iceberg: a complex technology estate that is aging and not always receptive to new gen AI models and code. Technical debt in insurance core systems makes it difficult to adapt these systems to new AI capabilities amid quickly changing market conditions, customer preferences, and regulatory requirements. Complex systems also hamper generative AI adoption by limiting the underlying training data for the large language models. 52% of executives cite data constraints—inadequate, inaccessible, incomplete, or otherwise unusable data—as slowing speed to market of products. For gen AI to work across the enterprise, insurers must consider AI approaches that operate within their complex reality while working to improve the situation over time. A hybrid-by-design architecture can help the enterprise start paying off technical debt and bring down run/build ratios.

3. Betting on a flexible operating model. 

As insurance sector organizations invest in AI and customer data analytics, they must ensure that their operating model for gen AI supports the rapid development and deployment of new products and services. But will that be better achieved through centralized or decentralized AI development and services, or some combination of the two? While executives report different approaches, the few that have chosen a decentralized operating model are more successful across multiple metrics, including run/build ratio, speed to market, and customer retention. Democratizing AI decision-making across the enterprise while retaining central governance and implementation is essential to generating real gen AI value.

Download the report to explore in more detail the factors impacting the insurance industry’s adoption of generative AI. An action guide for each factor offers specific steps insurers can take to convert the potential of generative AI to reality.

About IBM

IBM is a leading provider of global hybrid cloud and AI, and consulting expertise. We help clients in more than 175 countries capitalize on insights from their data, streamline business processes, reduce costs and gain the competitive edge in their industries. More than 4,000 government and corporate entities in critical infrastructure areas such as financial services, telecommunications and healthcare rely on IBM’s hybrid cloud platform and Red Hat OpenShift to affect their digital transformations quickly, efficiently and securely. IBM’s breakthrough innovations in AI, quantum computing, industry-specific cloud solutions and consulting deliver open and flexible options to our clients. All of this is backed by IBM’s legendary commitment to trust, transparency, responsibility, inclusivity and service. Visit www.ibm.com for more information.

Source: IBM

Tags: Artificial Intelligence (AI), IBM, outlook / predictions, survey