The evolution of AI is changing the landscape of project management. The integration of AI into project management brings many advantages, yet it is also accompanied by prominent weaknesses and serious challenges. In addition, rapidly evolving technologies continue to transform the field’s dynamics. These evolving dynamics result in ambiguity about the current state of the field, and consequently, create an uncertainty regarding a roadmap for future advancements. The purpose of this paper is to address this challenge by developing a well-grounded conceptual insight that identifies the risks associated with AI adoption in project management, guiding both academia and industry towards a structured approach to its future advancements. This paper conducts a detailed structured literature review, adhering the PRISMA protocol, to evaluate the impact of AI on key facets of project management, its potential benefits and implementation challenges. Then it analyzes the literature and synthesizes the key findings. Finally, it conducts comprehensive analysis to identify both positive and negative risks i.e. opportunities and threats. This in-depth analysis and its findings enable us to understand the nature of the risks, and how those can be harnessed or mitigated to advance the field. Furthermore, it provides both academia and industry the foundation to plan improved risk mitigation strategies and to develop a structured adoption framework. This study is expected to make a significant contribution to the advancement of the field.

The construction supervision consultant is an appointed party responsible for overseeing the implementation of construction projects from start to finish. The satisfaction level of project owners with the consultant’s performance is a key indicator of project success. This study aims to determine the level of satisfaction and key factors influencing it in public building projects. A quantitative method was employed by distributing questionnaires to respondents from the Public Works Office, particularly within the Cipta Karya Division in Tapin Regency, which serves as the case study location. Data were analyzed using the Customer Satisfaction Index (CSI) and Importance Performance Analysis (IPA) methods. The results showed that overall, the consultant’s performance was rated as very satisfactory, with the highest CSI score on the communication indicator (86.64%) and the ability to ensure and improve work quality (85.33%). The lowest CSI score was found in the documentation/administration indicator (80.11%). Although the CSI score indicates a high level of satisfaction, there is still room for improvement, particularly in administrative aspects and understanding of technical regulations. The IPA analysis also shows that several indicators fall under top priority, such as documentation/administration, supervision, internal human resources, communication, and the ability to ensure and enhance work quality. The improvement strategies proposed include training, high discipline, and effective communication.

The rapid advancement of digital technologies has raised uncertainty about the adequacy of traditional maintenance models to meet Industry 4.0 requirements. This study develops and validates an asset management framework to support the South African petrochemical industry’s transition to Maintenance 4.0. The framework was validated through a quantitative survey conducted within a leading petrochemical company in South Africa, ensuring its practical applicability. Descriptive statistical analysis confirmed 15 of 17 framework characteristics and supported five of seven theoretical propositions. Key enablers of Maintenance 4.0 adoption include the integration of human intelligence, machine learning, and real-time data, as well as the role of organizational culture and asset resilience in shaping outcomes. The study offers both theoretical contributions and practical guidance for maintenance professionals seeking to align maintenance practices with Industry 4.0 principles, with relevance extending beyond the immediate case context.