Rapid advances in automation and artificial intelligence (AI) have transformed accounting operations, raising the need for new metrics to evaluate productivity improvements in managerial accounting. Traditional measures often emphasized output quantity while overlooking quality and human factors. This paper introduces the Units of Productive Intelligence (UPI) framework, a comprehensive productivity metric that integrates quantitative efficiency with qualitative performance indicators. Building on the Tasks-to-Time Ratio (TTR) as a core measure of efficiency, UPI also incorporates error rate reduction, output quality improvement, and employee satisfaction enhancement to yield a holistic productivity index. The framework’s utility was demonstrated through three case studies in accounting contexts (internal audit, financial reporting, and cost accounting), each involving an AI-driven or process innovation intervention. Results showed significant increases in TTR alongside improved quality scores and staff satisfaction, reflected in positive UPI values. The study contributes to managerial accounting literature by providing a practical tool for assessing productivity in the AI era, and it underscores the importance of including quality and human-centric outcomes in performance measurement.
Mfon Akpa (Fri,) studied this question.