# Estimating productivity gains in digital automation

arXiv: https://arxiv.org/abs/2210.01252 · HTML: https://mlsanity.com/paper/2210.01252/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 36 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Microeconomic Foundations of Decentralised Organisations](https://mlsanity.com/paper/2201.07666/) (2022)

## Abstract (arXiv)

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.