The project began as a collaborative evaluation by PBL and an external service provider (the vendor) to assess four administrative processes for RPA suitability. While standard financial metrics like ROI were calculated for each use case, it was mutually recognized that these figures alone could not form the basis for a sound strategic decision. Crucial non-financial factors, namely strategic alignment and process suitability, emerged as the dominant considerations, highlighting the need for a formal framework to systematically capture the true drivers of a successful automation strategy.

The thesis employs a qualitative single-case study methodology at PBL. It systematically analyzes and refines the initial financial models for four administrative use cases, two implemented, one deprioritized, and one rejected. Through the analysis of internal documentation, process videos, and expert interviews, the true non-financial decision drivers behind the final outcomes were identified and contrasted with the theoretical success factors established in the academic literature.

This study concludes that while Return on Investment (ROI) is an important motivator for RPA initiatives, it is not the sole decisive factor. This was confirmed by the case of the Psychiatric Hospital Baselland (PBL), where the project with the highest projected ROI was strategically postponed due to its high complexity and risk. Instead, the true decision drivers were found to be non-financial factors: strategic alignment, process suitability, and organizational maturity. The primary result is therefore the development and validation of a strategic evaluation framework. This weighted scorecard evaluates projects against five key criteria, derived from both academic literature and this case study's findings: Strategic Alignment (30%), Process Suitability (30%), Economic Potential (20%), Implementation Complexity (10%), and Organizational Impact & Change Management (10%). The framework’s validity was confirmed by retroactively applying it to the four PBL cases, where its scores accurately reflected the final implementation decisions. The result is a robust, multi-criteria tool enabling healthcare institutions to make more sustainable and strategically sound automation decisions.