On-demand AI-driven Predictive Analysis: Bridging the Gap for Small and Medium Enterprises (SMEs)

Abstract

Real-time predictive analytics driven by Artificial Intelligence provides benefits to all major sectors while small and medium enterprises (SMEs) need additional study regarding its effects. Active AI predictive analytics technology serves as the focus of this research since it optimizes real estate energy consumption and improves market predictive models. Small business operations apply big data analytics with machine learning to enhance efficiency in energy usage and waste management and create precise market projection data. The research article utilizes Random Forest (RF), XGBoost together with Long Short-Term Memory (LSTM) which are evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and R² scores. The forecasting of energy efficiency achieves its highest level with XGBoost whereas the housing market prediction shows maximum accuracy with LSTM implementation. Predictive systems can be executed with effective budget control methods by SMEs thanks to the systematic research approach despite numerous obstacles with AI implementation. The study uses AI for demonstrating how business data structured within industry knowledge enhances operational and strategic decision-making capabilities. Advanced AI predictive analytics powered by contemporary technology systems allows SMEs to boost their operations and strengthen their market position according to findings. Scientists need to develop deep learning systems together with combined forecasting techniques to enhance the accuracy and deployment approaches in their studies.