Reinventing Standard Costing In The Digital Era: An AI-Driven Empirical Framework For Predictive Cost Management

Standard costing has traditionally served as a key tool for cost control and performance evaluation; however, its relevance has been challenged by the growing complexity of the digital business environment. The emergence of advanced technologies such as artificial intelligence (AI), machine learning (ML), big data analytics, and enterprise resource planning (ERP) systems has significantly transformed conventional costing practices. This study examines the evolving role of standard costing in the digital era, with particular emphasis on current trends in India and global industries. The research adopts an empirical and analytical approach, integrating statistical techniques such as multiple regression and Analysis of Variance (ANOVA) with machine learning models, including Random Forest and Long Short-Term Memory (LSTM). A hybrid AI-driven standard costing framework is proposed and evaluated using cost-related data. Model performance is assessed using error metrics and further validated through an ablation study. The findings indicate that the integration of AI and predictive analytics improves cost estimation accuracy by approximately 25–30 percent compared to traditional methods. The study concludes that standard costing is evolving into a dynamic, real-time, and predictive system, enhancing both operational efficiency and strategic decision-making in the digital economy.