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

Authors

DOI:

https://doi.org/10.5585/2025.28348

Keywords:

artificial intelligence, project management, patent analysis, task-technology fit, technological innovation, PM methodologies

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 task-technology 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.

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Author Biographies

Fernando Antonio Ribeiro Serra, Universidade Nove de Julho (UNINOVE), São Paulo, SP, Brazil / City University of Macau, China

Holding a Ph.D. and a degree in Engineering from PUC-RJ, he completed post-doctoral research at FEA/USP and IPL (Portugal). A professor at UNINOVE’s graduate programs since 2012 and a CNPq Research Fellow, his work focuses on behavioral strategy, organizational decline, and digital transformation. He also serves as a Visiting Professor at the City University of Macau and has a history of leadership within ANPAD and academic journal editorial boards. In the corporate sector, he served as Academic Director at HSM and Unisul Business School; currently, he is a partner at ASKKLOG and sits on corporate boards. He is the co-author of several books on management and strategy.

Marcelo Martins Sá, Northumbria University (NU), Newcastle upon Tyne, Tyne and Wear, Reino Unido

Marcelo Martins Sá is an assistant professor in operations and programme leader for the BA (Hons) Business and Supply Chain Management at Northumbria University, UK. He holds a degree in Business Administration, and his research focuses on risk management and resilience in supply chain management, emphasising decision-making by organisations and networks. His work aims to understand how companies can strengthen their operations amid uncertainty and disruptions, thereby contributing to more sustainable strategies.

Renato Penha, Universidade Nove de Julho (UNINOVE), São Paulo, SP, Brazil

He holds a Postdoctoral and Doctoral degree in Administration, specializing in Project Management. He is a professor in the Professional Master's and Doctorate Program in Project Management at Universidade Nove de Julho - UNINOVE.

Cesar Augusto Rodrigues Ferrari, Universidade Nove de Julho (UNINOVE), São Paulo, SP, Brazil

Holds a degree in Aeronautical Engineering from ITA in Brazil (Aeronautics Institute of Technology), a master's degree in Management for Competitiveness from the Getulio Vargas Foundation, and an MBA from Erasmus University in Rotterdam, Netherlands. Currently works as a Value Engineer at Celonis, a company focused on Process Intelligence. Has experience in the field of Administration, with an emphasis on Information Technology. Professional experience in business management consulting and strategic use of information systems, more recently focused on innovations with practical application in Digital Transformation projects such as Cloud Computing, Business Process Management, and Generative Artificial Intelligence.

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Published

15.12.2025

How to Cite

Serra, F. A. R., Sá, M. M., Penha, R., & Ferrari, C. A. R. (2025). Artificial intelligence in project management: an analysis of patent trends and task-technology fit. International Journal of Innovation, 13(3), e28348. https://doi.org/10.5585/2025.28348

Issue

Vol. 13 No. 3 (2025): Sept./Dec.

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Articles

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Copyright (c) 2025 Fernando Antonio Ribeiro Serra, Marcelo Martins Sá, Renato Penha, Cesar Augusto Rodrigues Ferrari

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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