Artificial intelligence in project management: an analysis of patent trends and task-technology fit

Abstract

Objective of the study: This study identifies emerging technological trends and key advancements in AI patents related to project management (2018–2024). Using Task-Technology Fit (TTF) theory, it examines how AI innovations address challenges across the five major phases of project management: Initiating, Planning, Executing, Monitoring and Controlling, and Closing. Methodology/approach: Patent data from Lenz.org was filtered for project management-related terms, yielding 1,044 patents. The study applied temporal trend analysis, jurisdiction-based analysis, technology classification, and phase-specific problem-solution mapping using TF-IDF vectorization. Patents were categorized based on PMI's five project management phases using keyword-based classification, enabling systematic assessment of innovation patterns and tasktechnology alignment. Originality/Relevance: While previous research explored isolated AI applications, this study provides a systematic analysis of AI’s alignment with project management tasks. Applying TTF theory, it assesses AI's role in enhancing efficiency and decision-making across project phases, pioneering the application of TTF theory to patent analysis. Main Results: AI-driven innovations strongly align with Planning and Monitoring, enhancing scheduling, resource allocation, and risk management. The Executing phase shows evolving AI adoption, while Initiating and Closing exhibit weaker alignment. The study highlights jurisdictional trends, with the U.S. leading AI patent filings. Theoretical/methodological contributions: This study applies TTF theory to AI patent analysis in project management, offering a replicable framework for examining technological advancements and assessing innovation-task alignment across project phases. Social/management contributions: Findings provide insights for project managers, organizations, and policymakers on AI adoption. The study highlights AI’s potential to improve efficiency while identifying gaps requiring further technological development, particularly in human-centric project phases.