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

Abstract

PurposeThe 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.Design/methodology/approachThis 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.FindingsIt 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.Originality/valueLittle 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.

Analyst’s Perception on the Use of AI-based Tools in the Software Development Life Cycle

“…In addition, adequate training is also required for companies that intend to adopt such technology. This finding is in line with the result of other study (Shang et al 2023) which explains that lack of knowledge on AI technologies and applications and lack of skilled and trained employees are two of several other barriers to the adoption of AI in the scope of project management. This is due to the use of AI systems that are still relatively new in their industry where familiarity with the use of AI itself is still lacking.…”

“…This is due to the requirement of each of these parties, which does not require complex technology and is sufficient to be completed with existing technological resources. These two factors are in line with the findings of a study (Shang et al 2023) where the study showed that the lack of need for adoption and lack of support from project stakeholders are several things that become obstacles in the adoption of AI technology in the scope of project management. The last factor influencing the decision to use this technology is the limited resources available, specifically financial resources.…”

E-Government in Nigeria: Can Generative AI Serve as a Tool for Civic Engagement?

“…More so, the successfully prototyped use cases, such as Cantador et al (2021) open data chatbot and AI Singapore's Ask Jamie virtual assistant (Ojeda, 2021;Shang et al, 2023), exemplify how generative AI models can collect and convey multifaceted governance information in easily understandable formats. By abstracting away complexity through fluent dialogues, these systems democratise access to the policy sphere, fostering a more participatory dynamic.…”

Statistically Significant Differences in AI Support Levels for Project Management between SMEs and Large Enterprises

“…AI-powered tools can facilitate better communication among team members by providing real-time updates and predictive insights, leading to more coordinated efforts. Machine learning algorithms can tailor solutions based on specific project needs, ensuring a more targeted approach in project management [8,38]. Moreover, in projects with varying work conditions, AI aids in identifying early warning signs of potential accidents during execution.…”