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Prospects, drivers of and barriers to artificial intelligence adoption in project management

Gao Shang (Faculty of Architecture, Building and Planning, The University of Melbourne, Parkville, Australia)
Sui Pheng Low (Department of the Built Environment, National University of Singapore, Singapore, Singapore)
Xin Ying Valen Lim (Department of the Built Environment, National University of Singapore, Singapore, Singapore)

Built Environment Project and Asset Management

ISSN: 2044-124X

Article publication date: 20 June 2023

Issue publication date: 9 August 2023




The rise of artificial intelligence (AI) and differing attitudes towards its adoption in the building and environment (B&E) industry has an impact upon whether companies can meet changing demand and remain relevant and competitive. The emergence of Industry 4.0 technologies, coupled with the repercussions of COVID-19, increases the urgency and opportunities offered that companies must react to, as disruptive technologies impact how project management (PM) professionals work and necessitate acquisition of new skills. This paper attempts to identify the drivers of and barriers to, as well as the general perception and receptiveness of local PM professionals towards, AI adoption in PM and thereby propose potential strategies and recommendations to drive AI adoption in PM.


This study employs both quantitative and qualitative approaches to examine the findings gathered. A survey questionnaire was used as the primary method of gathering quantitative data from 60 local PM professionals. Statistical tests were performed to analyse the data. To substantiate and validate the findings, in-depth interviews with several experienced industry professionals were performed.


It is found that top drivers include support from top management and leadership, organisational readiness and the need for greater work productivity and efficiency. Top barriers were found to be the high cost of AI implementation and maintenance and the lack of top-down support and skilled employees trained in AI. These findings could be attributed to the present state of AI technologies being new and considerably underutilised in the industry. Hence, substantial top-down support with the right availability of resources and readiness, both in terms of cost and skilled employees, is paramount to kick-start AI implementation in PM.


Little research has been done on the use of AI in PM locally. AI's potential to improve the productivity and efficiency of PM processes in the B&E industry cannot be overlooked. An understanding of the drivers of, barriers to and attitudes towards AI adoption can facilitate more intentional and directed oversight of AI's strategic roll-out at both the governmental and corporate levels and thus mitigate potential challenges that may hinder the implementation process in the future.



Shang, G., Low, S.P. and Lim, X.Y.V. (2023), "Prospects, drivers of and barriers to artificial intelligence adoption in project management", Built Environment Project and Asset Management, Vol. 13 No. 5, pp. 629-645.



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