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 emphasize 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 is 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 show significant increases in TTR alongside improved quality scores and staff satisfaction, reflected in positive UPI values. These findings suggest that UPI offers a consistent and interpretable way to capture multifaceted productivity gains in accounting processes. 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.

Keywords: Managerial Accounting; Productivity Measurement; Artificial Intelligence; Tasks-to-Time Ratio; Quality Improvement; Employee Satisfaction
JEL Codes: M41; M42; O33; D24