A Systematic Literature Review of AI-Driven
Analytics and Digital Governance Frameworks
in Multi-Site Infrastructure Delivery
PEER REVIEWED PAPER
By Zeeshan Ahmed
Dublin, Ireland
Abstract
Infrastructure project delivery continues to suffer from persistent cost overruns, schedule slippage, and governance failures globally, despite decades of research into their root causes. The emergence of artificial intelligence (AI), real-time data analytics, and digital programme controls presents a potentially transformative opportunity for Project Management Offices (PMOs) responsible for governing complex multi-site infrastructure programmes. This paper presents a systematic literature review of 62 studies published between 2015 and 2025, supplemented by emerging 2026 professional literature, examining the intersection of AI-driven analytics, digital PMO governance, and infrastructure project performance. Three primary themes are identified: the persistent and multi-causal nature of infrastructure underperformance; the promise and limitations of AI tools in project management contexts, including the important distinction between AI-enabled task automation and AI-augmented human judgment; and the critical absence of an integrated digital PMO governance framework applicable to multi-site environments. The paper proposes a conceptual Digital PMO Governance Framework (DPGF) as a vehicle for future empirical inquiry and makes an original contribution by systematically mapping the research gap at the convergence of these themes.
Keywords: Project Management Office; PMO governance; artificial intelligence; automation; augmentation; infrastructure delivery; digital analytics; schedule performance; cost overrun; multi-site projects
- Introduction
Infrastructure project delivery remains one of the most challenging and consequential domains in professional project management. Across sectors including transportation, utilities, data centres, energy, and public works, large-scale infrastructure programmes are characterised by extended timescales, high capital investment, multiple concurrent workstreams, and complex stakeholder environments. Despite significant advances in project management methodology over the past three decades, performance data consistently reveals that infrastructure projects routinely fail to meet their original cost and schedule targets.
Research conducted by Flyvbjerg et al. (2003) established that infrastructure megaprojects suffer cost overruns at a rate that has remained largely unchanged over a 70-year period, a finding replicated in multiple global contexts. McKinsey Global Institute (2020) reported that large infrastructure projects typically exceed their budgets by an average of 80 percent, while PMI (2021) found that organisations waste an average of USD 97 million for every USD 1 billion invested due to poor project performance. In the context of the post-pandemic infrastructure investment surge, the consequences of these persistent failures are more significant than ever.
The emergence of artificial intelligence (AI), machine learning, and real-time digital analytics presents a potentially transformative opportunity for the project management profession. AI-assisted scheduling, predictive risk analytics, intelligent resource optimisation, and digital twin technologies are entering mainstream project delivery environments, offering the prospect of earlier variance detection, more accurate forecasting, and more responsive governance. The pace of this shift has accelerated markedly through 2026, with a growing professional literature arguing that AI is reshaping the operating model of the PMO itself rather than simply speeding up existing tasks (Alieva and Pirozzi, 2026; Nieto-Rodriguez, 2026). Yet the academic literature on how these tools integrate into PMO governance structures, particularly across complex multi-site environments, remains underdeveloped relative to the pace of technological adoption in practice.
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How to cite this paper: Ahmed, Z. (2026). Towards Intelligent Project Management Offices: A Systematic Literature Review of AI-Driven Analytics and Digital Governance Frameworks in Multi-Site Infrastructure Delivery; PM World Journal, Vol. XV, Issue IX, September. Available online at https://pmworldjournal.com/wp-content/uploads/2026/09/pmwj168-Sep2026-Ahmed-Towards-Intelligent-PMOs.pdf
About the Author
Zeeshan Ahmed
Dublin, Ireland
Zeeshan Ahmed is a project and programme coordination professional based in Dublin, Ireland. He holds an MSc in Project Planning and Management with Distinction from the University of Bradford, specialising in project scheduling, programme management, stakeholder coordination, and PMO functions. He is currently working as an Assistant Project Manager at Handy Lads Solution Ltd, coordinating work programmes across multiple concurrent infrastructure projects on different sites and liaising with major principal contractors on data centre demolition and EU infrastructure programmes.
Prior to this role, Zeeshan served as an R&D Trials Officer at STRI Group in Bradford, delivering multiple concurrent research projects end to end with full responsibility for PMO reporting, governance, and SharePoint-based data management. He is an active Member of the Association for Project Management (APM) and a Student Member of the Royal Institution of Chartered Surveyors (RICS), currently working toward APM Chartered Project Professional (ChPP) status. His research interests lie at the intersection of digital PMO governance, AI-driven project analytics, and infrastructure project performance. He can be contacted at Zeeshan.Technical@outlook.com.
