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.

本文提出了一种新型生产力评估模型，用于评估在生产链中采用人工智能 (AI) 组件的影响。我们的模型提供了支持解决 “AI” 的 Solow 悖论的证据，其基础包括（i）理论和实证证据以解释 Solow 的二分法；（ii）数据驱动模型以评估和评估生产率变化；（iii）基于过程挖掘数据集的方法论，以确定业务流程（BP）和生产率；（iv）一组计算机模拟参数；（v）以及关于劳动力分配的实证分析。这些为我们认为 AI Solow 悖论的后果是指标误测提供了数据。