Artificial intelligence in the insurance industry: Key success factors and strategy

Not every AI breakthrough warrants adoption. Insurers should focus on what delivers measurable value.

The insurance industry is undergoing a profound transformation driven by digitalisation and technological innovation. Artificial intelligence (AI) is playing an increasingly central role, enabling insurers to optimise rule-based risk assessment, increase operational efficiency, and refine decision-making processes. It is essential to consider not only how established processes can be reimagined and integrated with existing technologies but also how technological advancements can drive business process innovation. The goal is not merely to digitise processes as they were but to embrace technological change as an opportunity to address existing challenges effectively.

Key success factors for AI-driven systems in the insurance industry

When developing AI-driven systems for the insurance industry, several specific factors set them apart from traditional digitalisation solutions. Unlike standard automation, AI systems require a more nuanced approach to ensure efficiency, accuracy, and reliability. Three key aspects must be considered to successfully implement AI in insurance, which are outlined below.

Problem-driven AI development

AI initiatives must be based on clearly defined business challenges and not developed as technology-led solutions. The most effective AI models are those developed to address core business processes, where the greatest value lies in achieving efficiency gains through targeted support. One example in underwriting is targeting support for manual work so that AI can take care of automated information management to derive targeted decisions more quickly.

Structured data communication as a prerequisite

The benefits of AI are inextricably linked to the quality and availability of structured and unstructured data. Insurers need to manage the complexity of integrating data from different sources while ensuring consistency and accuracy. Effective AI implementation requires robust data governance frameworks and advanced aggregation mechanisms to enable seamless data utilisation and create the required communication channels between disparate information systems. Without these fundamental elements, there is a risk that AI-driven insights are unreliable or do not align with business requirements.

Integration with core business processes

AI solutions must be closely adapted to the complex workflows in the insurance industry. A deep understanding of the business processes is essential for the development of AI systems. It is not just about developing or integrating a model; the functionality and the associated risk and quality of the decision in the process should also be constantly monitored. Cross-functional collaboration between subject matter experts and data scientists is therefore essential to effectively tailor AI models to real-world applications and integrate them in such a way that the end user is supported in the best possible way.

AI as a strategic enabler in the insurance sector

The success of AI systems depends heavily on a clear strategy and a good data infrastructure. For AI to add value, its use should be well thought out, beginning with clear business objectives, ensuring high-quality data, and integrating the technology into existing processes.

While the understanding of AI continues to evolve, one fundamental truth remains: certain types of AI are better suited to certain problems than others. For example, an insurance company can use its own unique, exclusive data to make better decisions with specially trained AI models. A generative AI that produces text would not be suitable for this, as it is not trained on such data and therefore has not seen the full spectrum of information on, for example, the history of an insured. The situation is different when it comes to more generic problems like writing or summarising text. Here, generative AI models are ideal because they have been trained on large volumes of text and can mimic human writing styles well. The difference is that this task does not require a great deal of prior knowledge.

Before using AI in the insurance sector, therefore, one should carefully consider what problem is being solved and what type of AI is best suited to solve it. This also raises the question of whether to develop proprietary AI models or use existing solutions on the market. However, it is critical to remember that the model alone is not the solution—it will only add value when it is effectively integrated into a system. A key aspect is the integration of the solution into existing IT and business processes, so the decision may also depend on factors such as scalability, quality management and long-term strategic focus.

Focusing on what matters in AI adoption

There are two major developments for the AI technology in insurance: large technology companies are releasing better and better large language models, and insurers are looking for solutions that will bring them concrete benefits. It is important not to be blinded by AI technology news. For example, it does not matter if a new AI model performs better in generic tests if it does not add value to the insurer’s existing processes. What matters is whether a model performs its specific task, such as accurately extracting contract details, better and more reliable than previous solutions. Only then is a change worthwhile. Current observations from research and practice clearly demonstrate that smaller models are justified if they are significantly cheaper to run than larger models and the difference in the quality of the solution is not decisive.

Using AI wisely requires a strategic, problem-based approach. New trends are constantly shaping the market, but it is important to examine which developments, particularly in the field of generative AI, really have an impact on specific challenges. AI is a game changer because of its ability to process large amounts of data with high performance, to enable individualisation on a whole new level. It is able not only to process traditional structured data, but also to retrieve information from unstructured data sources. A clear data strategy is essential to ensure that the right information is processed at the right time and place.