Artificial Intelligence, Firm Heterogeneity, and Labor Market Adjustment: Evidence from Service and Industrial Tech Sectors in Italy

Ebrima K. Ceesay (University of Insubria)
Mamadou Salieu Jallow (West African Science Service Center on Climate Change and Adapted Land Use, UCAD, Senegal)
Cosimo Magazzino (Department of Management, Finance and Technology, LUM University “Giuseppe Degennaro”, Casamassima, Italy Economic Research Center, Western Caspian University, Baku, Azerbaijan ARUCAD Research Centre, Arkin University of Creative Arts and Design, Kyr)
Alasana Gitteh (School of Business and Public Administration, Department of Economics and Finance, University of The Gambia)
Baseedy Bojang (School of Business and Public Administration, Department of Economics and Finance, University of The Gambia)

Muslim Business and Economic Review
Vol. 5 No. 1 (2026)

This study assesses how changes in AI patent stock affect employment, wages, productivity, and labor cost shares. It compares pooled estimates with firm-specific effects for UniCredit and Zerynth in Italy, representing the banking services and industrial tech sectors, respectively. Using a firm-level panel dataset from 2005 to 2024, the study applies task-based and skill-biased technological change frameworks to analyze how exposure to Artificial Intelligence (AI), proxied by AI patent stock, affects labor-market dynamics in the Italian economy. Our empirical strategy used fixed-effects regressions. The results show a dual pattern of technological adjustment. In pooled models, AI-related innovation has a positive and statistically significant effect on labor-market outcomes. However, when firms are analyzed separately, the effects diverge. For UniCredit, AI patent stock reduces employment and labor cost shares while raising productivity growth, with wage effects remaining small or insignificant. On the other hand, for Zerynth, AI patent stock produces consistently negative and significant effects on employment, wages, and labor cost shares. Overall, the findings highlight strong task-level substitution but heterogeneous firm-level outcomes, underscoring the need for targeted reskilling and labor-augmenting innovation policies in Italy’s digital transition.