Abstract

The rapid evolution of artificial intelligence (AI) and machine learning (ML) presents a transformative opportunity for IT Project Management (ITPM). Yet despite increasing interest, the field still lacks a coherent, research-based foundation for understanding how AI capabilities can align with project management processes and decision structures. Drawing directly from our work on intelligent ITPM, this keynote attempts to lay out conceptual groundwork, proposing a structured way to think about how project tasks—classified into routine, decision, problem, and “fuzzy” categories—may map onto different machine learning paradigms across the PMBOK lifecycle. Rather than specifying solutions, the talk highlights promising areas where supervised, unsupervised, and reinforcement learning approaches could reshape estimation, risk identification, adaptive planning, and execution support, thereby offering a roadmap for future inquiry.

Complementing this conceptual foundation, our fairness research shows that the successful integration of AI in ITPM depends not only on technical performance but also on practitioner trust and expectations surrounding responsible use. Our findings reveal that fairness perceptions vary with project characteristics such as purpose, ML type, and data modality, underscoring that fairness is a contextual design requirement, not a universal constraint. Grounded in our TAR (Transparency, Accountability, Representativeness) framework, this talk proposes some initial guidance for how fairness considerations can be embedded into the design and governance of AI-enabled project environments.

Together, these research streams present a unified vision of AI-enabled ITPM as a socio-technical system. The keynote concludes by identifying opportunities and challenges for researchers, including the need for richer empirical studies, improved access to project data, fairness-aware methodological approaches, and new theoretical lenses for understanding the interplay between AI, human judgment, and organizational context in ITPM.

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