Assessing the impact of artificial intelligence on project efficiency enhancement

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).

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Publication Details

Journal
Knowledge and Performance Management
Published
2024-12-18
DOI
https://doi.org/10.21511/kpm.08(2).2024.09
Citations
8
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
4.23
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article

Assessing the impact of artificial intelligence on project efficiency enhancement

Svitlana Bilan, Aknur Zhidebekkyzy, Assel Kozhakhmetova, Almas Mamyrbayev
8 citations
Knowledge and Performance Management
Big Data and Business Intelligence
4.23
article

Assessing the impact of artificial intelligence on project efficiency enhancement

Svitlana Bilan, Aknur Zhidebekkyzy, Assel Kozhakhmetova, Almas Mamyrbayev
article en
8 citations

Abstract

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).

Knowledge and Performance ManagementVol. 8(2)
Almaty Management University (KZ), Rzeszów University of Technology (PL), Kazakh-British Technical University (KZ), Sumy State University (UA), Institute for Sustainable Development (ET)
Openalex Percentile: Top 5%
Big Data and Business Intelligence
4.23
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