TRADITIONAL COSTING VS. AI BASED COSTING: A COMPARATIVE STUDY

The rapid digital transformation of business operations has significantly altered the landscape of cost accounting and managerial decision-making. This research paper presents a comparative study of Traditional Costing and AI-based Costing using secondary data from academic literature. The study first examines the conceptual frameworks of traditional costing methods such as absorption costing, marginal costing, and activity-based costing and contrasts them with AI-driven costing systems that leverage data analytics, automation, and predictive modeling. The research further investigates the inherent weaknesses of traditional costing approaches in today’s data-intensive business environment, highlighting issues such as inaccurate overhead allocation, delayed reporting, limited scalability, and inability for managing massive amounts of both structured and unstructured data. In contrast, the study identifies specific AI technologies including Machine Learning for predictive cost estimation, Robotic Process Automation for automated data extraction and allocation, Natural Language Processing for interpreting unstructured financial documents, and optimization algorithms for dynamic resource planning that are currently transforming cost estimation and allocation practices. Additionally, the paper evaluates how the shift to AI-based costing enhances management’s capacity to optimize pricing strategies, allocate resources efficiently, and reduce operational waste. By enabling real-time analytics, improved forecasting accuracy, and data-driven insights, AI-based costing supports proactive and strategic decision-making. The findings suggest that while traditional costing systems remain foundational for compliance and reporting purposes, AI-based costing provides a more adaptive, accurate, and strategically valuable framework in modern organizations. The study concludes that integrating AI technologies into costing systems offers a significant competitive advantage in an increasingly complex and data-driven business environment.

Keywords: Traditional costing, Absorption Costing, Activity Based Costing, AI, Machine Learning, Robotic Process Automation.