A RESPONSIBLE A.I ADOPTION FRAMEWORK FOR SMALL AND MEDIUM-SIZED ENTERPRISES IN DEVELOPING ECONOMIES: EVIDENCE AND IMPLICATIONS FOR THE UGANDAN CONTEXT

Artificial intelligence (AI) has evolved from a specialist computing capability into a general-purpose technology used in customer service, marketing, finance, operations, information management, software development, and decision support. For small and medium-sized enterprises (SMEs) in developing economies, these tools can create meaningful opportunities to improve productivity and service delivery. However, the conditions under which SMEs adopt AI are often very different from those of large firms and highly digitized economies. This paper develops a responsible AI adoption framework for SMEs in developing economies, with particular attention to Uganda, and grounds it in secondary data drawn from international and national reports and statistics. Global evidence shows that AI adoption has accelerated sharply, with 72% of surveyed organizations reporting AI use and 65% regularly using generative AI in at least one business function in 2024 (McKinsey & Company, 2024). In Uganda, however, internet penetration stood at 28.0% of the population and access to electricity at roughly 30% in recent measurements (DataReportal, 2025; International Energy Agency [IEA], 2023), while micro, small, and medium enterprises make up about 90% of private-sector production yet report only 35% technology adoption, with 53.4% of owners possessing neither a smartphone nor a computer (Ministry of Trade, Industry and Cooperatives [MTIC], 2024). This contrast is the empirical core of the paper: access alone does not explain or guarantee responsible AI adoption. The framework brings together the Technology-Organization- Environment (TOE) perspective, the Technology Acceptance Model (TAM), responsible AI governance principles, cybersecurity requirements, and the development context reflected in recent Ugandan policy materials. Six dimensions are proposed: technological readiness, organizational readiness, human capability, data readiness, environmental conditions, and responsible AI governance.