TY - GEN
T1 - Towards AI/ML-Powered Hybrid Project Management Strategy for the Healthcare Sector
AU - Khadgi, Manisha
AU - Ud Din, Fareed
PY - 2025/12/31
Y1 - 2025/12/31
N2 - In the rapidly evolving healthcare sector, project management faces challenges due to the complexity and dynamic nature of its environment. Traditional methodologies including PMI PMBoK, and PRINCE2, combined with Agile, provide structured and adaptable approaches; however, the potential of the integration of artificial intelligence (AI) techniques is still underexplored. This study investigates how AI-driven tools, such as machine learning (ML) and predictive analytics, embedded with project management methodologies can improve project efficiency, decision-making, and resource management in the healthcare sector. Through a comprehensive literature review, we identify key AI technologies that augment task automation, real-time insights, and predictive capabilities within healthcare project management. This study presents statistical evidence from the literature on the percentage distribution of project management methodologies with key aspects including adaptability to AI, compliance, flexibility, stakeholder engagement and risk management. We discuss how a hybrid approach that leverages the strengths of PRINCE2/PMI, Agile, and AI can accelerate timelines, improve adaptability, and enhance stakeholder satisfaction. Despite challenges such as data privacy and compliance, this study presents a mapping of AI technologies and project management methodologies along with a conceptual design towards building a strategic framework that aligns AI advancements with organizational goals, optimizing healthcare project outcomes.
AB - In the rapidly evolving healthcare sector, project management faces challenges due to the complexity and dynamic nature of its environment. Traditional methodologies including PMI PMBoK, and PRINCE2, combined with Agile, provide structured and adaptable approaches; however, the potential of the integration of artificial intelligence (AI) techniques is still underexplored. This study investigates how AI-driven tools, such as machine learning (ML) and predictive analytics, embedded with project management methodologies can improve project efficiency, decision-making, and resource management in the healthcare sector. Through a comprehensive literature review, we identify key AI technologies that augment task automation, real-time insights, and predictive capabilities within healthcare project management. This study presents statistical evidence from the literature on the percentage distribution of project management methodologies with key aspects including adaptability to AI, compliance, flexibility, stakeholder engagement and risk management. We discuss how a hybrid approach that leverages the strengths of PRINCE2/PMI, Agile, and AI can accelerate timelines, improve adaptability, and enhance stakeholder satisfaction. Despite challenges such as data privacy and compliance, this study presents a mapping of AI technologies and project management methodologies along with a conceptual design towards building a strategic framework that aligns AI advancements with organizational goals, optimizing healthcare project outcomes.
U2 - 10.1007/978-981-96-6400-9_15
DO - 10.1007/978-981-96-6400-9_15
M3 - Conference contribution
SN - 9789819663996
SN - 9789819664009
T3 - Communications in Computer and Information Science
SP - 202
EP - 215
BT - ICMLSC 2025: 9th International Conference Machine Learning and Soft Computing24th –26th January, 2025
A2 - Huang, Letian
PB - Springer Nature Singapore
CY - Singapore
T2 - ICMLSC 2025: 9th International Conference Machine Learning and Soft Computing
Y2 - 24 May 2025 through 26 May 2025
ER -