Harnessing Artificial Intelligence for Project Management Efficiency

Sammanfattning

This thesis investigates and analyzes how to implement Artificial Intelligence (AI) into project management, addressing the process through a two-step approach. Initially, a framework is developed to identify and prioritize project management areas where AI can enhance operations. This framework was constructed based on a comprehensive literature review, adapting existing frameworks to the specific requirements of project management. It assesses tasks currently performed by project managers and ranks these tasks according to their potential for AI implementation. Subsequently, the thesis investigates the practical implementation of AI within these identified high-potential areas. Two AI solutions were developed as demonstrations; the first, a risk register utilizing a retrieval augmented generation (RAG) architecture, was evaluated to offer limited value. In contrast, the second demonstration, a budget tool designed to automate information extraction from PDF files, demonstrated significant potential. This tool successfully automated the extraction of information from various PDF structures provided by different suppliers, achieving a 98% accuracy rate for readable PDFs through techniques such as prompt engineering and fine-tuning. Furthermore, a business case analysis for the budget tool suggested a potential payback period of 0.36 years for deploying a fully functional application. The findings suggest that effective AI implementation in project management should begin with identification of tasks suitable for AI implementation. These tasks should then be prioritized based on financial, time, and risk implications, alongside the effort required for AI integration. The implementation process should foster collaboration between technical experts and domain specialists, embrace rapid iterative feedback, and initiate pilot demos for stakeholder evaluation prior to full-scale production. The thesis also concludes that successful AI deployment in organizations demands robust data management, data protection measures, comprehensive AI education for the workforce, and a culture that trusts but also critically evaluates AI solutions.