Many insurance CFOs already know, sense, or expect that their finance organizations will look very different five years from now. As a powerful and highly capable technology, artificial intelligence is reshaping ways of working, organizational structures, and collaboration between people and technology, as well as among employees themselves. These changes are already becoming visible and cannot be ignored. 

At the same time, there is considerable uncertainty about what this transformation journey might look like. With this article, we aim to provide a perspective on how it can be approached. 

It is reasonable to assume that AI will transform the finance function on multiple levels: strategically, organizationally, and technologically. 

To highlight just a few aspects: 

• AI will fundamentally change the skills and capabilities required from finance professionals. Employees will need to work alongside artificial intelligence and develop a strong understanding of how to use and control the technology effectively. 

• AI will significantly alter the way processes are executed today. Many transactional activities currently performed in spreadsheets, planning applications, or reporting systems will be carried out primarily through AI or in close collaboration with AI, resulting in entirely new process designs. 

• Employees will increasingly focus on creating prompts and validating AI-generated outputs. 

This raises important questions about the future organizational structure of the finance function and the governance mechanisms required to ensure that AI is deployed in a controlled and secure manner. As we know, AI systems can generate inaccurate results or "hallucinations." It is our responsibility to design and operate AI solutions in a way that minimizes risk and ensures reliability. 

How can organizations ensure that outputs generated by artificial intelligence are at least as reliable, or perhaps even more reliable, than those created by human intelligence? These are key questions that finance leaders must address, alongside concerns about how employees will react to these changes and how teams will evolve. 

Will employees be able to embrace this journey? Will they want to? 

To address these challenges, we have developed a proposal for how an insurance company's finance organization could transition toward an AI-native future. This journey will span several years and will require a wide range of topics to be addressed in a structured sequence rather than all at once. 

We suggest approaching the transformation in three phases. 

The first phase focuses on taking structured initial steps. In many organizations, AI technology has already been made available by IT, such as Microsoft Copilot or Anthropic Claude. The key is to identify motivated and digitally minded employees who are willing to champion the topic. Establishing an AI task force can help drive the transformation and eventually form the nucleus of dedicated AI-related roles within the finance organization. 

Experience shows that it is beneficial to begin with strategic considerations: 

• What do we want to achieve? 

• What could the finance organization look like in five years? 

• Which outcomes will be critical for success? 

A clear vision is essential for successful change management. Organizations need a mission statement, a compelling vision, and open communication with employees. 

The next step is to identify and implement individual AI use cases. The specific technology used is less important than gaining experience, learning quickly, and communicating successes. During this phase, organizations typically assemble a portfolio of AI opportunities that provides a meaningful backlog for the task force to prioritize and execute. This program will generally span about one year and serves as an effective introduction to the world of generative AI and large language models (LLMs). 

The second phase, ideally beginning in the second year, focuses on identifying business processes that can be redesigned and optimized through AI. 

Organizations may take a first step from generative AI toward agentic AI by leveraging tools such as Claude Code, Power Automate, Make, or n8n. 

Two examples illustrate the potential: 

Claude Code or Microsoft Fabric Apps can be used to develop internal tools that support, structure, and improve financial close processes, such as IFRS individual or group reporting. 

Workflow automation platforms such as n8n can automate accounting activities, for example the booking of insurance reserves, as well as reporting processes such as the end-to-end creation of Quantitative Reporting Templates (QRTs). 

At this stage, organizations are often ready to make strategic software and platform decisions. Swiss Re, for example, has established a strategic partnership with Palantir, while Allianz collaborates strategically with Anthropic. At the same time, widely used enterprise platforms from Oracle, IBM, SAP, and others are continuously expanding their AI capabilities. 

For organizations aiming to translate AI benefits into measurable profit-and-loss impact, a deliberate technology and platform strategy is essential. 

By this stage, initial successes are already contributing measurable business value. However, large-scale adoption typically gains momentum during the second and third years of the transformation. 

A key prerequisite is close collaboration with IT to establish the required foundations. 

One of the greatest challenges is moving away from isolated connections to individual general and sub-ledgers and toward a consistent, integrated data foundation. Analytics platforms such as Databricks, Microsoft Azure and Fabric, or Snowflake have proven effective in this context and are often already in use from an IT perspective. These platforms make it possible to embed additional intelligence into processes across risk management, FP&A, and financial accounting and reporting functions. 

At the same time, organizations need a robust governance framework with clearly defined roles across business, governance, and technology domains. This is important not only for meeting regulatory requirements such as the EU AI Act and DORA, but also for ensuring effective collaboration among all stakeholders. 

At this point, the role of AI within the finance function becomes much clearer. Organizations gain visibility into whether they have achieved their original mission and whether their vision has proven realistic or has already been partially realized. 

The finance organization itself should then be transformed accordingly. Most likely, this will involve organizing around new capabilities and roles rather than traditional functional silos such as actuarial services, accounting, and controlling. However, that is a broader discussion in its own right. 

AI presents significant opportunities for finance organizations. It is up to us to shape this transformation proactively and responsibly. 

Wiegard, M., Porsch, J.