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This review paper investigates the potential of Artificial Intelligence (AI) solutions to drive innovation within Small and Medium-sized Enterprises (SMEs), addressing adoption barriers and exploring future growth opportunities. The primary objective is to synthesize existing literature on AI applications in SMEs, identifying the benefits, challenges, and strategies for successful implementation. The paper highlights that AI technologies can significantly enhance operational efficiency, product development, customer engagement, and competitive advantage for SMEs. Despite these benefits, several barriers hinder widespread AI adoption, including limited financial resources, lack of technical expertise, resistance to change, and concerns about data security and privacy. By reviewing various case studies and research findings, the paper identifies key strategies to overcome these challenges. These strategies include government incentives, public-private partnerships, affordable AI-as-a-Service models, and targeted training programs to build AI competencies within SMEs. The importance of fostering a supportive ecosystem with robust infrastructure, favorable regulatory frameworks, and access to funding is emphasized. The paper concludes that AI has the potential to revolutionize SMEs by enabling rapid and efficient innovation. However, realizing this potential requires concerted efforts from multiple stakeholders to address adoption barriers and create an enabling environment for AI-driven growth. Future research should focus on developing frameworks for scalable AI implementation tailored to the unique needs of SMEs and tracking the long-term impact of AI adoption. This review provides a comprehensive understanding of the current state of AI in SMEs, offering insights into overcoming challenges and capitalizing on future opportunities for growth and innovation.
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Toluwalase Vanessa Iyelolu
Edith Ebele Agu
Courage Idemudia
International Journal of Science and Technology Research Archive
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Iyelolu et al. (Sat,) studied this question.
synapsesocial.com/papers/68e5cb6fb6db643587562552 — DOI: https://doi.org/10.53771/ijstra.2024.7.1.0055
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