  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.
