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This study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs’ growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs’ ability to compete on the global stage.

Small and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.

This study was deliberately designed to empirically examine how Artificial Intelligence (AI) enhances supply chain performance within Tanzanian brewery companies, addressing a critical gap between global theoretical potential and local practical realities. Using a mixed-methods, embedded single-case study approach, data were collected from 86 participants at Tanzania Breweries Limited (TBL) through questionnaires, semi-structured interviews, and focus group discussions. The findings revealed a fragmented yet influential adoption landscape, where tools like predictive analytics and machine learning significantly boosted service quality and certain efficiency measures; however, these benefits were notably limited by organizational resistance, weak data governance, and major external infrastructural barriers. As an original contribution, this study offers fresh, context-specific insights into the socio-technical dynamics of AI adoption in a lesser-studied Tanzanian industrial setting. Practically, managers are urged to prioritize core investments in change management and data integrity, while socially, effective integration can promote market stability and skills growth, contributing to wider socioeconomic progress. In conclusion, the study emphasizes that AI’s transformative promise is not assured by technology alone but depends on strong internal capacity and a supportive external environment. A noted limitation is the single-case focus, which may restrict generalization across other industries. Therefore, it is recommended that firms adopt a phased implementation strategy grounded in solid data governance and pilot projects, while fostering public-private partnerships to address the broader external challenges slowing digital transformation.

How are artificial intelligence (AI) and automating things changing how much we get done, how many new ideas happen, what jobs look like, and what is morally right? Specifically, this study looks at all of that…but in manufacturing. It gives you ideas for achieving an industrial revolution that’s fair to everyone and addresses the main challenges companies face as they start using AI at scale. The research is based on a thorough review of information from industry reports, official plans, and publications by other experts. And what it shows is that AI makes things run more smoothly by helping with fact-based decisions, creating designs, and figuring out when equipment needs fixing. But using AI is expensive to get started, the data it needs isn't always high-quality, and there are not enough people with the skills to use it, all of which make many companies hold back. As for automation in general, it raises tricky ethical questions: algorithms showing prejudice, people losing their jobs, and the impact on the planet. The study says that for AI to really work well, you need good oversight, help employees learn new skills, and lots of different types of organisations working together.

How are artificial intelligence (AI) and automating things changing how much we get done, how many new ideas happen, what jobs look like, and what is morally right? Specifically, this study looks at all of that…but in manufacturing. It gives you ideas for achieving an industrial revolution that’s fair to everyone and addresses the main challenges companies face as they start using AI at scale. The research is based on a thorough review of information from industry reports, official plans, and publications by other experts. And what it shows is that AI makes things run more smoothly by helping with fact-based decisions, creating designs, and figuring out when equipment needs fixing. But using AI is expensive to get started, the data it needs isn't always high-quality, and there are not enough people with the skills to use it, all of which make many companies hold back. As for automation in general, it raises tricky ethical questions: algorithms showing prejudice, people losing their jobs, and the impact on the planet. The study says that for AI to really work well, you need good oversight, help employees learn new skills, and lots of different types of organisations working together.

This study was deliberately designed to empirically examine how Artificial Intelligence (AI) enhances supply chain performance within Tanzanian brewery companies, addressing a critical gap between global theoretical potential and local practical realities. Using a mixed-methods, embedded single-case study approach, data were collected from 86 participants at Tanzania Breweries Limited (TBL) through questionnaires, semi-structured interviews, and focus group discussions. The findings revealed a fragmented yet influential adoption landscape, where tools like predictive analytics and machine learning significantly boosted service quality and certain efficiency measures; however, these benefits were notably limited by organizational resistance, weak data governance, and major external infrastructural barriers. As an original contribution, this study offers fresh, context-specific insights into the socio-technical dynamics of AI adoption in a lesser-studied Tanzanian industrial setting. Practically, managers are urged to prioritize core investments in change management and data integrity, while socially, effective integration can promote market stability and skills growth, contributing to wider socioeconomic progress. In conclusion, the study emphasizes that AI’s transformative promise is not assured by technology alone but depends on strong internal capacity and a supportive external environment. A noted limitation is the single-case focus, which may restrict generalization across other industries. Therefore, it is recommended that firms adopt a phased implementation strategy grounded in solid data governance and pilot projects, while fostering public-private partnerships to address the broader external challenges slowing digital transformation.

Small and Medium Enterprises (SMEs) in South Africa previously faced challenges due to limited resources, restricted access to technology, and the need to constantly adapt to a dynamic business environment. The introduction of Generative Artificial Intelligence (AI) emerged as a potential solution to these issues, promising to enhance operational efficiency and strategic decision-making. As a representative of developing economies, South Africa experienced a growing interest in AI technologies. This study was conducted to explore the impact of generative AI on SME productivity in South Africa, an area which had been underexplored. Employing a qualitative methodology, the study evaluated the current state and implications of generative AI in South African SMEs. It involved in-depth interviews to gather perceptions, experiences, challenges, and benefits from SME owners and managers regarding the adoption of generative AI technologies. The findings analysed via R Statistical Software revealed significant insights into the specific areas where generative AI substantially impacted SME productivity. It also identified the challenges and opportunities associated with the adoption of generative AI by SMEs, as well as the potential long-term implications. Key findings included notable improvements in data-driven decision-making, operational efficiencies, and market expansion strategies. However, the study also highlighted barriers such as the lack of technical expertise, initial setup costs, and concerns over data security. Overall, the impact of generative AI on SMEs in South Africa was found to be predominantly positive, paving the way for further technological advancements and adoption in the sector.

This study advances research and practice related to adopting artificial intelligence (AI) in the context of South Africa (SA). The study evaluated AI adoption by South African manufacturing Small and Medium Enterprises (SMEs); established the challenges faced by manufacturing SMEs in adopting AI; and developed a framework for adopting AI for manufacturing SMEs’ growth and survival. The study adopted a systematic literature review approach. Articles from Scopus and Google scholar databases, ranging from the years 2018 to 2024, were used. Of the 206 articles found, 54 were shortlisted. The systematic review analysis was performed using the PRISMA framework. The results identified AI adoption by South African manufacturing SMEs is low, limiting their innovation and productivity. The results also show, despite the numerous benefits AI adoption can offer manufacturing SMEs in the country, a major constraint is the lack of a framework to enhance adoption and implementation. Hence, this study was conducted to develop a framework to improve AI adoption by South African manufacturing SMEs. The findings contribute to the body of knowledge and provide new insights to manufacturing SME owners/managers, policymakers and practitioners into AI adoption to enhance manufacturing SMEs’ ability to compete on the global stage.