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In response to escalating global environmental challenges, mainly the urgent issue of climate change delineated by the United Nations Sustainable Development Goals (UN SDGs), large corporations are increasingly pressured to mitigate their carbon emissions and ecological footprint. The integration of artificial intelligence (AI) technologies has emerged as a pivotal strategy in this endeavour, promising to revolutionise how businesses approach sustainability while maintaining economic competitiveness. AI offers capabilities such as optimising operational efficiencies, enhancing energy management systems, and facilitating data-driven decision-making processes—all of which are instrumental in achieving sustainability objectives. However, the effective application of AI in environmental sustainability initiatives faces several challenges. These include addressing data scarcity, ensuring ethical deployment of AI technologies, and complying with evolving regulatory frameworks. This study investigates how large corporations are leveraging AI to reduce their environmental impact through AI-driven strategies for carbon footprint reduction. The study follows a qualitative approach, six medium-large corporates in Gauteng were interviewed to gain insights on the subject matter. The findings highlight that AI adoption enhances operational efficiency and environmental stewardship while highlighting ongoing challenges and the need for robust ethical and regulatory frameworks. Moreover, emerging trends such as AI-driven autonomous vehicles for logistics and advanced climate modelling illustrate AI's transformative potential in reshaping corporate ethos and addressing environmental concerns. The study contributes insights grounded in the Resource-Based View (RBV) theory, offering practical recommendations for corporations to harness AI effectively for sustainable growth within the framework of the UN SDGs and global environmental norms.

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.