Explore the evidence.
Understand the reach.

State-owned enterprises (SOEs) have significant opportunities to enhance operational efficiency, decision-making, and service delivery through artificial intelligence (AI). However, several barriers hinder widespread AI adoption in these organizations. This article measures the impact of AI adoption on performance indicators like production output and customer satisfaction in State-Owned Enterprises. It analyses financial sustainability, resource management, and efficiency metrics while identifying key barriers such as high implementation costs, lack of technical expertise, and resistance to change. The barriers to AI adoption in the SOEs are studied quantitatively. Data was gathered from 240 carefully chosen SOE personnel in South Africa (SA) using a survey approach, which had a 93.28% response rate. The population of this study comprised employees of South African SOEs. The focus of the article is on examining the perspectives of individuals in SOEs in SA regarding AI implementation. Further complicating AI integration are data security and privacy concerns, as well as legal and compliance issues. A lack of strategic vision and financial limitations are also major obstacles to advancement. Developing successful plans to encourage AI adoption in SOEs, which will eventually spur innovation and enhance public sector performance, requires an understanding of these obstacles.  The article concludes that despite significant barriers to AI adoption in South African SOEs—such as lack of senior management support, inadequate infrastructure, resistance to change, data quality issues, and a shortage of skilled staff—there are clear opportunities for improvement.  This is one of the articles that focuses on identifying barriers of adopting AI in South African SOEs and it lays a foundation for future research on AI in SOEs.

State-owned enterprises (SOEs) have significant opportunities to enhance operational efficiency, decision-making, and service delivery through artificial intelligence (AI). However, several barriers hinder widespread AI adoption in these organizations. This article measures the impact of AI adoption on performance indicators like production output and customer satisfaction in State-Owned Enterprises. It analyses financial sustainability, resource management, and efficiency metrics while identifying key barriers such as high implementation costs, lack of technical expertise, and resistance to change. The barriers to AI adoption in the SOEs are studied quantitatively. Data was gathered from 240 carefully chosen SOE personnel in South Africa (SA) using a survey approach, which had a 93.28% response rate. The population of this study comprised employees of South African SOEs. The focus of the article is on examining the perspectives of individuals in SOEs in SA regarding AI implementation. Further complicating AI integration are data security and privacy concerns, as well as legal and compliance issues. A lack of strategic vision and financial limitations are also major obstacles to advancement. Developing successful plans to encourage AI adoption in SOEs, which will eventually spur innovation and enhance public sector performance, requires an understanding of these obstacles.  The article concludes that despite significant barriers to AI adoption in South African SOEs—such as lack of senior management support, inadequate infrastructure, resistance to change, data quality issues, and a shortage of skilled staff—there are clear opportunities for improvement.  This is one of the articles that focuses on identifying barriers of adopting AI in South African SOEs and it lays a foundation for future research on AI in SOEs.