An AI-Driven Machine Learning Approach to Evaluating the Impact of Innovation Dynamics on SME Performance in the UAE

Abstract In today’s rapidly evolving business landscape, innovation capabilities (IC) are critical for the success of Small and Medium Enterprises (SMEs). While traditional statistical models have provided insights into the role of innovation dynamics in SME Performance (SP), they often fail to capture non-linear relationships and hidden patterns in complex datasets. This study introduces an AI-driven machine learning (ML) approach to evaluating the impact of Innovation Capabilities (IC), Innovation Culture (InC), Organizational Innovation (OI), and Employee Engagement (EE) on SP in the UAE. Building upon existing research, this study leverages advanced ML algorithms, including Random Forest (RF), XGBoost, and Artificial Neural Networks (ANN), to analyze data collected from 300 SME industry experts. The findings reveal that OI is the strongest predictor of SP, followed by IC and EE. The results emphasize the need for AI-driven decision making in SME management, providing executives and policymakers with actionable insights to foster innovation-led growth. Additionally, the study highlights the advantages of Explainable Artificial Intelligence (XAI) in making ML models transparent and interpretable for business leaders. This research bridges the gap between traditional statistical analysis and AI-driven insights, offering a scalable, data-driven framework for SP evaluation. The findings provide a new strategic direction for SMEs, enabling them to leverage AI in stimulating innovation and competitiveness in emerging economies.