Degree of digitization in the insurance industry

Rankings on the degree of digitalization of different industries are only of limited value, as they can hardly take into account the different process complexities, market conditions and numerous industry-specific influencing factors comprehensively enough to enable a valid comparison. However, the fact that the digitalization of the insurance industry is making massive progress is impressively demonstrated by the following figure: 99% of all decision-makers surveyed in a recent Ernst & Young studyare already investing in artificial intelligence in a wide variety of formats. Nevertheless, many issues surrounding AI are making smooth adoption for progressive process automation difficult.

AI obstacles, solutions and key tasks at a glance

We have compiled an overview for you of the challenges, but also of existing solutions and key areas of responsibility for redesigning insurance processes using artificial intelligence.

• Regulatory uncertainty

Of course, in highly regulated industries, the organizational debate and detailed consideration of future AI regulations is particularly pronounced. As much of the EU AI Act is still undefined and depends on technological developments, among other things, there is a certain amount of uncertainty among many insurance companies. However, the core issue is the controllability of artificial intelligence, which can already be ensured in the long term with the right platforms. A key recommendation is therefore not to become dependent on certain LLMs as a basic technology in order to design the company-specific AI infrastructure sustainably and be able to expand it flexibly.

With the Kauz aiStudio it is currently possible to choose between 16 different large language models depending on the use case and to track the AI conversations transparently using numerous settings and control functions and to control the content in a targeted manner.

• Open questions about AI budgets

The economic situation will remain tense in 2025 and cost-benefit analyses must be convincing. This is not always easy with innovation projects. What is certain is that the time of large pilot projects is over; new applications must have an immediate impact. Insurers who already have experience with AI face a further challenge: scaling artificial intelligence in the company sometimes requires considerable project budgets, which top management is not so keen to make available in economically difficult times.

It is not necessary to develop the entire AI infrastructure yourself. An AI platform such as the Kauz aiStudio supports a variety of different, industry-specific use cases out-of-the-box and can be expanded in terms of scope of use according to the company’s pace of development. This means that initial implementations can be carried out with manageable investments, the ROI of which is measurable, so that the next expansion stage of company-specific AI can then be tackled on the basis of the same platform.