THE ADOPTION RATE OF ARTIFICAL INTELLIGENCE ON DECISION MAKING OF MICRO, SMALL AND MEDIUM ENTERPRISES (MSMES.) IN ENUGU STATE
Abstract
ABSTRACT
This project on the adoption rate of Artificial Intelligence (AI) on Decision Making in Micro, Small and Medium Sized Enterprises (MSMEs), examines how AI technologies influence business decision processes, efficiency, and competitive advantage. The objectives were to: access the level of awareness and adoption rate of AI tools among MSMEs in Enugu state; explore the factors influencing the adoption rate of AI on management decision-making in MSMEs in Enugu state; evaluate the effect of AI adoption on the quality, speed and accuracy of management decisions in MSMEs in Enugu state, and explore the benefits and challenges of AI in decision-making in MSMEs in Enugu state. This study employs a quantitative approach through the use of a structured questionnaire to examine the adoption rate and effect of AI in decision-making processes within MSMEs organizations in Enugu state with a sample size of 53 MSMEs. The findings reveal that AI adoption significantly improves decision accuracy, speed, and operational efficiency. However, barriers such as high implementation costs, lack of skilled personnel, and ethical concerns limit broader adoption. Though, many MSMEs reported that AI supports predictive analytics, customer relationship management, and inventory optimization, but still requires human oversight to ensure contextual judgment. The study concludes that AI is a valuable tool for enhancing decision-making in MSMEs, but its effectiveness depends on strategic integration, staff training, and ethical governance. To optimize benefits, MSMEs should start with scalable AI applications, invest in capacity-building, and adopt clear policies addressing data privacy and algorithmic transparency. This research contributes to understanding the practical role of AI in MSMEs’ decision-making, offering actionable strategies for business leaders and policymakers aiming to balance technological potential with organizational realities
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