Estimating productivity gains in digital automation

TL;DR

This paper introduces a new data-driven model for estimating productivity gains from AI adoption in production, addressing Solow's Paradox by highlighting measurement issues and providing empirical evidence.

Contribution

It presents a novel methodology using process mining data to accurately assess productivity changes due to AI, combining theoretical, empirical, and simulation approaches.

Findings

AI productivity gains are often underestimated due to metric mismeasurement.

The model effectively estimates productivity variations in AI-enabled processes.

Empirical analysis shows AI's impact on labor distribution and productivity.

Abstract

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.