EVALUATION OF THE EFFICIENCY OF IMPLEMENTING INTELLIGENT AUTOMATION IN BUSINESS PROCESSES: A METHODOLOGY FOR MEASURING ECONOMIC EFFECT AND PRODUCTIVITY

This article examines methodological approaches to measuring the economic impact and productivity of implementing intelligent automation (IA) in enterprise business processes. Based on data from McKinsey, Deloitte, and Forrester, as well as the experience of large companies, a comprehensive evaluation model is proposed that combines financial metrics (ROI, NPV, and TCO) and non-financial indicators (cycle time, operational accuracy, and customer satisfaction). It is shown that organizations combining robotic process automation (RPA) with artificial intelligence technologies achieve a 22–27% reduction in operating expenses and an 8.5% increase in revenue–three times more than those using RPA without AI components. The article also substantiates the use of the Kaplan-Norton Balanced Scorecard (BSC) for strategically assessing the impact of automation across four dimensions. Practical recommendations for developing a KPI system for monitoring automation projects are provided.