Artificial Intelligence Adoption and Financial Performance in Large Italian Firms

Artificial Intelligence is increasingly adopted by large organisations seeking efficiency gains and competitive advantages. However, empirical evidence on whether these investments translate into improved financial performance remains limited, particularly at the firm level in the Italian context. This thesis investigates the relationship between AI adoption and financial performance in large Italian firms. The study adopts a multiple case study approach examining five major companies — Poste Italiane, Telecom Italia, Intesa Sanpaolo, Generali Group, and Prysmian Group — over the period 2018–2025. Financial data were collected primarily from the AIDA and ORBIS Europe databases and complemented by company annual reports and public disclosures. The analysis focuses on key performance indicators including revenues, EBITDA margins, ROA, and ROE, and compares outcomes before and after major AI implementation phases. Findings provide partial support for a positive association between AI adoption and financial performance. Improvements were more consistent in highly data-intensive sectors such as banking and insurance, while results were more modest in telecommunications. Implementation costs, regulatory constraints, and organisational challenges emerged as important moderating factors. Overall, the study shows that AI can contribute to better financial outcomes, but the magnitude of these benefits depends heavily on sectoral characteristics and organisational capabilities. The thesis contributes firm-level evidence from Italy and offers practical implications for managers and policymakers interested in the financial value of digital transformation.