Assel Kozhakhmetova

Project managers from different areas struggle to find universal set of factors to achieve high project efficiency. However, the project efficiency assessment varies across industries, making it difficult to apply general concepts and guidelines in the specific project environment. The study’s main purpose is to assess the impact of organizational support on project efficiency in different areas and identify critical processes that increase it. The paper presents quantitative research results by using data from 261 project managers in the Republic of Kazakhstan during 2018–2019. The research sample consists of commercialization projects, scientific projects, social projects, and projects from the service industry. The research findings show the different quality levels of top management support and its high correlation with project efficiency. Furthermore, the study reveals the specific processes for each investigated type of project that highly correlate with project efficiency dimensions. Commercialization projects that intensively use the organizational support processes were the most efficient projects by showing the best result in cost overrun (15%) and the second resulted in schedule overrun (17%). These findings have theoretical and practical implications, supporting project managers in increasing their project efficiency by choosing the critical, proper project management tools and techniques.

The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency.

Acknowledgments
This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).