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arXiv · 2210.01252

Estimating productivity gains in digital automation

Mauricio Jacobo-Romero·Danilo S. Carvalho·André Freitas
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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.

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BibTeXRIS

Mauricio Jacobo-Romero, Danilo S. Carvalho, André Freitas. 2022-10-03. Estimating productivity gains in digital automation. https://arxiv.org/abs/2210.01252

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