Real-World Evaluation of an AI-Empowered Predictive Decision System to Optimise Enterprise Performance in SMEs

Small and medium-sized enterprises increasingly rely on data-driven intelligence to navigate competitive markets, yet most decision-support technologies remain insufficiently validated under real-world operational conditions. This study develops and evaluates an AI-empowered predictive decision system designed to optimise enterprise performance across key operational domains, including sales forecasting, customer retention, financial risk assessment, and inventory management. Drawing on real-world datasets obtained from publicly accessible SME performance repositories and internationally recognised business analytics databases, the system integrates machine learning models, interpretability layers, and operational decision rules to enhance managerial insight and responsiveness. The research applies a multi-stage evaluation framework consisting of model accuracy assessment, business impact estimation, error distribution analysis, and comparative benchmarking against traditional decision-making approaches. The empirical results indicate that predictive intelligence significantly improves performance predictability and operational stability in SMEs, particularly under fluctuating market conditions. The system demonstrates measurable improvements in forecasting precision, early detection of performance deterioration, and optimisation of resource allocation. The embedded interpretability mechanisms provide transparent decision rationales that facilitate managerial trust and alignment with strategic objectives. The study concludes that real-world validation of AI-driven decision systems is essential for designing scalable and evidence-based business optimisation strategies. This research contributes a practical framework for integrating AI-driven predictive analytics into SME management processes and offers actionable insights for policymakers, researchers, and enterprise leaders seeking to advance data-driven decision-making capabilities.Small and medium-sized enterprises increasingly rely on data-driven intelligence to navigate competitive markets, yet most decision-support technologies remain insufficiently validated under real-world operational conditions. This study develops and evaluates an AI-empowered predictive decision system designed to optimise enterprise performance across key operational domains, including sales forecasting, customer retention, financial risk assessment, and inventory management. Drawing on real-world datasets obtained from publicly accessible SME performance repositories and internationally recognised business analytics databases, the system integrates machine learning models, interpretability layers, and operational decision rules to enhance managerial insight and responsiveness. The research applies a multi-stage evaluation framework consisting of model accuracy assessment, business impact estimation, error distribution analysis, and comparative benchmarking against traditional decision-making approaches. The empirical results indicate that predictive intelligence significantly improves performance predictability and operational stability in SMEs, particularly under fluctuating market conditions. The system demonstrates measurable improvements in forecasting precision, early detection of performance deterioration, and optimisation of resource allocation. The embedded interpretability mechanisms provide transparent decision rationales that facilitate managerial trust and alignment with strategic objectives. The study concludes that real-world validation of AI-driven decision systems is essential for designing scalable and evidence-based business optimisation strategies. This research contributes a practical framework for integrating AI-driven predictive analytics into SME management processes and offers actionable insights for policymakers, researchers, and enterprise leaders seeking to advance data-driven decision-making capabilities.

کلیدواژه ها:

predictive analytics ، SME performance ، AI-based decision systems ، machine learning optimisation ، real-world evaluation