AI assistant for financial and sustainability reporting

Important results from the project

Yes, the project met its goals by demonstrating the potential of AI-assisted XBRL tagging and building a strong technical and knowledge foundation for future digital reporting solutions. Beyond the planned results, the project generated valuable insights into data-driven reporting, advanced AI methods and user-centred design, and strengthened collaboration between research, industry and reporting experts, accelerating innovation in financial and sustainability reporting.

Expected long term effects

In the long term, the project is expected to accelerate the digital transformation of financial and sustainability reporting. By enabling AI-assisted XBRL tagging and strengthening collaboration between research and industry, the project contributes to the development of scalable, cost-efficient reporting solutions that reduce manual work, improve data quality and transparency, and make regulatory compliance more accessible for SMEs across Europe.

Approach and implementation

The project was carried out according to the planned structure, covering project management, UX design, AI development and evaluation. Relevant activities supported the objectives, and the project developed largely as intended. Some technical and data-related challenges required additional iteration and refinement, which were handled within the project framework. Collaboration between research, industry and domain experts functioned well and was a key factor in achieving the project’s results.