In recent times, there has been a remarkable exponential interest in green energy projects, driven by their potential to significantly decrease greenhouse gas (GHG) emissions and reduce reliance on fossil fuels. However, such high-tech projects' complexity, expensiveness, and high uncertainty level necessitate new ways of increasing efficiency. Therefore, this study, with its clear aim to evaluate the efficiency of green energy projects and identify the critical success factors that can enhance efficiency, remains a compelling and relevant research endeavour. The study employed a rigorous methodology, using multilinear regression analysis to survey 123 project managers from Kazakhstan. This allowed for a comprehensive comparison of the efficiency level of green energy projects and low-tech projects. Research results show that green energy projects exhibit the highest percentage of schedule overrun at 23.9% and cost overrun at 32.7%, indicating substantial delays in project completion and extra expenses in budget compared with low-tech projects. Moreover, green energy projects show the least favourable results (7.3) regarding technological performance. The study reveals the following critical success processes enhancing project efficiency: project planning, scope and cost management, communication, and team management. Based on the processes of the Project Management Body of Knowledge (PMBoK) Guide, an algorithm for managing green energy projects was developed. This tool equips project managers with a process-based map, enabling them to run their projects effectively and enhance their efficiency.

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.