Estimating productivity gains in digital automation
It addresses a key economic puzzle (Solow's Paradox) for businesses and policymakers by offering a data-driven approach to assess AI's impact on productivity.
This paper tackles the problem of measuring productivity gains from AI adoption in production chains, proposing a novel estimation model that provides theoretical and empirical evidence to explain Solow's Paradox as a result of metric mismeasurement.
This paper proposes a novel productivity estimation model to evaluate the effects of adopting Artificial Intelligence (AI) components in a production chain. Our model provides evidence to address the "AI's" Solow's Paradox. We provide (i) theoretical and empirical evidence to explain Solow's dichotomy; (ii) a data-driven model to estimate and asses productivity variations; (iii) a methodology underpinned on process mining datasets to determine the business process, BP, and productivity; (iv) a set of computer simulation parameters; (v) and empirical analysis on labour-distribution. These provide data on why we consider AI Solow's paradox a consequence of metric mismeasurement.