Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/210218
Title: DRIVERS AND BARRIERS TO ARTIFICIAL INTELLIGENCE (AI) ADOPTION IN PROJECT MANAGEMENT (PM)
Authors: LIM XIN YING VALEN
Keywords: Project Management
Artificial Intelligence
AI in PM
Drivers and barriers to AI Adoption
AI Adoption
Issue Date: 14-Nov-2021
Citation: LIM XIN YING VALEN (2021-11-14). DRIVERS AND BARRIERS TO ARTIFICIAL INTELLIGENCE (AI) ADOPTION IN PROJECT MANAGEMENT (PM). ScholarBank@NUS Repository.
Abstract: The rise of AI and differing attitudes towards its adoption in the Building & Engineering (B&E) industry impacts whether companies can meet changing demands to remain relevant and competitive. This emergence of Industry 4.0 technologies, coupled with repercussions from COVID-19 increase the urgency and opportunities offered that companies must react to, as disruptive technologies impact how Project Management (PM) professionals work and necessitate the acquisition of new skills. This paper serves to identify the drivers and barriers, 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. To substantiate and validate the findings, in-depth interviews with several experienced industry professionals and existing overseas studies comparisons were also performed. Statistical tests found that top drivers include top management support and leadership, organisational readiness, and the need for higher 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 considerably new and 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 are paramount to kickstart AI implementation in PM. Little research has been done on the use of AI in PM locally. AI’s potential for increasing productivity and efficiency of PM processes in the B&E industry cannot be overlooked. An understanding of the drivers, barriers, 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.
URI: https://scholarbank.nus.edu.sg/handle/10635/210218
Appears in Collections:Bachelor's Theses

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