Digital transformation and the AI imperative in public and private sector projects: methods and skills for project management

Abstract

The acceleration of digital transformation initiatives across public and private sector projects has reached an inflection point with the rise of Artificial Intelligence (AI) tools. This technology has advanced at a remarkable speed, driven by radical innovations and the proliferation of generative AI platforms such as ChatGPT, Gemini, Claude and Copilot. These platforms have contributed to the increasing integration of AI into both public and private sector projects. As such, this research examines the impact of digital transformation and AI on the project management methodologies, skills and strategic approaches required for successful implementation. A mixed-methods design was employed, including a systematic literature review, a cross-sectional survey and in-depth interviews, to capture practitioner perspectives across organisational size, sector and experience. Key findings reveal that 61% of professionals report enhanced decision-making through real-time analytics and predictive modelling. Data governance emerged as a critical concern, with strong emphasis placed on addressing the risks of “garbage in, garbage out”. Agile methodologies (63%) and digital tools (68%) are now essential for project success. Strategic leadership competencies have improved for 49% of respondents, and 82% noted strengthened digital capabilities within their teams. Performance gains include 68% improvement in on-time delivery, 38% reduction in costs, and 61% improvement in quality. In the public sector, 71%of respondents reported a growing focus on responsible AI, although a gap remains between policy and practice. The study concludes with a digital skills taxonomy and a set of policy recommendations to support project professionals and organisations in navigating the evolving demands of AI-driven transformation.

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Identifiers

ORCID for Nicholas Dacre: orcid.org/0000-0002-9667-9331

ORCID for David Baxter: orcid.org/0000-0003-1983-7786

ORCID for Hao Dong: orcid.org/0000-0002-3458-4986

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