Exploring the Conceptual Realm of Machine Learning in Small and Medium-sized Industries: A Qualitative Study - Raza, Syed Shahid (Ushus Journal of Business Management, 2023)
The potential of machine learning in small and medium businesses remains largely untapped. Through a qualitative study, it is explored how the SMEs can practically apply ML to optimize processes and spark innovation. We aimed to demystify key concepts like supervised learning, data preparation, and model evaluation. Real-world examples across industries demonstrate ML's versatility, from enhancing decision-making to improving efficiency. Our research highlights the transformative power of ML, especially for resource-constrained SMEs. With some guidance, even small teams can implement ML solutions that unlock new opportunities. Though adoption has barriers, from data to skills, ML’s value for SMEs is clear. With a strategic approach, companies of all sizes can tap into its possibilities. Current research on implementing machine learning in small and medium enterprises has gaps. More investigation is required to understand adoption challenges fully, highlight successes, and meet unique needs. A qualitative approach that explores experiences and perspectives can provide those rich insights. Some small and medium companies use machine learning, but many face hurdles adopting it. Research case studies showcase machine learning success stories, though each company's path differs. By understanding individual difficulties, researchers can help more small and medium enterprises use machine learning appropriately. This literature review examines machine learning models, adoption trends, triumphs, and examples in small and medium enterprises. Moreover, it also examines the advantages and obstacles SMEs encounter when adopting ML tactics.