AI Worker Management technologies in traditional industries

Claudia Collodoro (), Lucrezia Fanti (), Jacopo Staccioli () and Maria Enrica Virgillito ()
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Claudia Collodoro: Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy
Lucrezia Fanti: Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy
Jacopo Staccioli: Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy
Maria Enrica Virgillito: Dipartimento di Politica Economica, DISCE, Università Cattolica del Sacro Cuore, Milano, Italy - Instituto di Economia, Scuola Superiore Sant’Anna, Pisa, Italy

No dipe0056, DISCE - Working Papers del Dipartimento di Politica Economica from Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE)

Abstract: This work provides a comprehensive large-scale analysis of artificial intelligence-based worker management (AIWM) systems from an industry-wide exposure perspective focusing on traditional industries. We begin by examining the knowledge production underlying these workforce management tools and leverage technology patent-classification to identify their dynamics and specific features. For this purpose, we use patent data retrieved from Orbis Intellectual Property covering the years 1975 to 2022, considering patents filed with both the EPO and the USPTO. Furthermore, to identify patents related to AIWM heuristics, we retrieve their full text from Google Patents and conduct a textual analysis using a dependency parsing algorithm. Finally, using the dictionary of human tasks provided by O*NET, we construct a measure of exposure to AIWM systems for individual human tasks and occupations. Linking the technological and labour market domains, we find that the professions most exposed to AIWM systems are those at the top of organisational hierarchies.

Keywords: Artificial Intelligence Worker Management; Sector-level Analysis; Patenting Activity; Techno-organisational Change (search for similar items in EconPapers)
JEL-codes: O14 O33 (search for similar items in EconPapers)
Pages: 42
Date: 2026-01
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