Intelligent Document Filing System for Enhanced Efficiency

This thesis addresses the inefficiencies in traditional document management systems (DMS), such as SharePoint, which often hinder project teams due to slow document retrieval, inconsistent tagging, and limited workflow automation. The proposed solution is a Minimum Viable Product (MVP) that integrates Artificial Intelligence (AI), Power Automate, and Power BI to enhance document classification, retrieval, and workflow efficiency. The MVP aims to increase productivity, reduce project delays, and optimize team collaboration by improving these processes.

Ledermann, Stefan, 2025

Type of Thesis Master Thesis
Client
Supervisor Renold, Manuel
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The study adopts a qualitative design within the Design Science Research (DSR) framework. The MVP was iteratively developed and evaluated using critical realism to understand organizational challenges. Data collection involved semi-structured interviews, user observations, and document analysis to assess the MVP's usability, integration, and efficiency. A cross-sectional time horizon ensured a focused examination of the MVP during its initial implementation phase.
The study shows that the developed MVP of an intelligent document management system can measurably increase the efficiency of project-related work processes. The combination of AI-supported classification, automated filing via Power Automate, and data-based transparency with Power BI enabled all five research questions to be answered positively. The experts confirmed improvements in search speed, error reduction, user satisfaction, and strategic overview. However, consistent governance and monitoring using KPIs remain prerequisites for sustainable success.
Studyprogram: Business Information Systems (Master)
Keywords
Confidentiality: öffentlich
Type of Thesis
Master Thesis
Authors
Ledermann, Stefan
Supervisor
Renold, Manuel
Publication Year
2025
Thesis Language
English
Confidentiality
Public
Studyprogram
Business Information Systems (Master)
Location
Olten