| Abstract [eng] |
The main purpose of this master thesis is to design a data-driven process measurement system (PMS) that integrates AI and expert knowledge to identify key business process indicators which support organizational performance. In addition, the study examines motives and benefits that encourage organizations to adopt process measurement systems, with focus on how AI-enabled approaches can improve adaptability, transparency, and decision-making. The thesis is structured in four parts: (1) a literature review that analyses PMS theories while locating the Multi-Perspective Model Approach as primary conceptual framework; (2) the research methodology, combining a qualitative research design in a Analytic Hierarchy Process (AHP) setting; (3) the development of a conceptual framework that synthesizes findings from literature and empirical research; and (4) the results, conclusions, and recommendations. Empirical data were collected through semi-structured, in-depth interviews with professionals in senior or leadership roles, including Data Analysts, Audit Managers, AI Specialists, Performance Managers and Project Leads. Their insights were analysed using AHP to structure and prioritize key performance indicators across multiple organizational perspectives. The results contribute to both theory and practice by presenting a multi-perspective, AI-enhanced framework for process measurement, demonstrating how organizations can align strategic objectives with dynamic, data-driven performance indicators to improve effectiveness in an ubiquitous digital environment. |
