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Has generative AI become of age

Meshel Muzuva ORCID ↗ · Helper Zhou ORCID ↗ · Robert Zondo ORCID ↗

International Journal of Research in Business and Social Science · 2024 · Vol. 13 / Issue 7 · 527-537

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Overview

Abstract

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.

Keywords: {'name': 'Generative Artificial Intelligence'}, {'name': 'Operational Efficiency'}, {'name': 'SME Productivity'}, {'name': 'Technological Adoption'}, {'name': 'Qualitative Methodology'}

Authors & affiliations

Meshel Muzuva
Affiliation not supplied

Helper Zhou
Affiliation not supplied

Robert Zondo
Affiliation not supplied

Deposited references

Supplied references, not a list of publications citing this article.

DOI: 10.1007/s44244-023-00012-4

DOI: 10.5863/1551-6776-29.2.200

DOI: 10.1016/j.indmarman.2021.09.003

Brossard, M. , Gatto, G. , Gentile, A. , Merle, T., & Wlezien, C. (2020). How generative design could reshape the future of product development. https://www.mckinsey.com/capabilities/operations/our-insights/how-generative-design-could-reshape-the-future-of-product-development. [Accessed March 10 March 2024].

Chui, M., Hazan, E., Roberts, R., Singla, A., Smaje, K., Sukharevsky, A., Yee, L., & Zemmel, R. (2023). The economic potential of generative AI: The next productivity frontier. https://www.mckinsey.com/~/media/mckinsey/business%20functions/mckinsey%20digital/our%20insights/the%20economic%20potential%20of%20generative%20ai%20the%20next%20productivity%20frontier/the-economic-potential-of-generative-ai-the-next-productivity-frontier.pdf. [Accesses 12 January, 2024].

Creswell, J. W. (2007). Qualitative inquiry and research design: Choosing among five approaches (2nd ed.). Sage Publications, Inc.

Department of Communications and Digital Technologies, (DCDT) South Africa. (2023). South Africa's Artificial Intelligence (AI) Planning.

https://www.dcdt.gov.za/images/phocadownload/AI_Government_Summit/National_AI_Government_Summit_Discussion_Document.pdf . [Accesses 12 February, 2024].

DOI: 10.1016/j.promfg.2020.03.090

DOI: 10.11648/j.ajtas.20160501.11

DOI: 10.18637/jss.v083.b01

DOI: 10.3390/systems12030103

DOI: 10.1177/1525822X05279903

Grün, B. and Hornik, K. (2021). Topic Models in R. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=96441eb3ddc28257d46f730212d9d6fc11750ed1. Accessed 14 April, 2024].

DOI: 10.1016/j.ijin.2022.08.005

Kauppinen, R. T. (2024). Exploring the role of generative artificial intelligence in digital content marketing. Masters, University of Jyväskylä. https://jyx.jyu.fi/bitstream/handle/123456789/95714/1/URN%3ANBN%3Afi%3Ajyu-202406104485.pdf

DOI: 10.1177/09726225221124035

Lucas, P. (2023, November 23). Generative AI makes product management inroads.

DOI: 10.3390/technologies11020044

Moyo, D. (2024). How SMEs can transform their business with AI. IOL. https://www.iol.co.za/business-report/economy/how-smes-can-transform-their-business-with-ai-7028db3c-6ff1-47dc-8234-eb1e3d3ac761. [Accessed; 10 April, 2024]

Oclarino, R. (2021, November 22). Enabling an AI-ready culture. International Organization for Standardization. https://www.iso.org/news/ref2763.html. [Accessed, 10 February, 2024}

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). Sage Publications.

DOI: 10.1007/978-1-4842-9579-3_5

Pradhan, D. , Dash, B. , Sharma, P. , & Ullah, S. (2023). The Impact of Generative AI on Product Management in SMEs. 1–16.

https://www.researchgate.net/publication/378103355

Radhakrishnan, R. (2024). Generative AI for Customer support: Time to embrace. https://devrev.ai/blog/generative-ai-for-customer-support. [Accessed 10 May, 2024].

DOI: 10.1080/08839514.2019.1661576

R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ . [Accessed, 18 February, 2024].

DOI: 10.29007/c4rr

DOI: 10.1002/9781119709183

Shrivastava, M. (2023). Generative AI vs. Traditional Machine Learning: What's the Difference? https://www.linkedin.com/pulse/generative-ai-vs-traditional-machine-learning-whats-shrivastava/. [Accessed 8 January 2024].

Soni, V. (2023). Impact of Generative AI on Small and Medium Enterprises’ Revenue Growth: The Moderating Role of Human, Technological, and Market Factors. Reviews of Contemporary Business Analytics, 6(1), 133–1536. https://researchberg.com/index.php/rcba/article/view/169

DOI: 10.23962/10539/31371

Zhou, H. and Gumbo, V. (2021b). Comparative Analysis of a Traditional and Machine Learning Techniques in Predicting SMMES Growth Performance. Academy of Entrepreneurship Journal, 27(3). https://www.abacademies.org/articles/comparative-analysis-of-a-traditional-and-machine-learning-techniques-in-predicting-smmes-growth-performance-11451.html