Artificial Intelligence Democratization in Developing Economies through the Lenses of Small Business Operations
Year: 2026
Type: article
Abstract: The concept of artificial intelligence (AI) democratisation from the perspective of small business operations in developing economies was looked at in this study. While AI has become a transformative force across various sectors, its adoption among small and medium-sized enterprises (SMEs) remains relatively low due to multiple structural and operational barriers. The paper explored the key hindrances to AI democratisation, including economic and infrastructural constraints, high implementation costs, limited access to quality data, and inadequate digital and technical skills among business owners and employees. Additionally, organisational resistance, ethical concerns, and weak regulatory frameworks further limit the widespread adoption of AI in these contexts. Drawing on extant literature, the study identified strategic pathways for enhancing AI accessibility and usability among SMEs. These include policy-driven interventions such as subsidised access to AI technologies, development of shared computing infrastructure, and promotion of data-sharing initiatives. The paper also highlighted the importance of leveraging cloud-based solutions, low-code platforms, and Automated Machine Learning (AutoML) tools to simplify AI deployment. Furthermore, it emphasised the need for human capital development through digital literacy programs, upskilling initiatives, and participatory leadership approaches. The study concluded that AI democratisation is not merely a technological advancement, but a strategic imperative for improving the competitiveness and sustainability of small businesses in developing economies. It recommended, among others, that governments in developing countries need to provide financial support such as grants, tax incentives, and subsidised access to AI tools and cloud infrastructure to reduce the cost barrier for small businesses.
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7160207648