This thesis investigates the impact of Generative Artificial Intelligence (GenAI) on the role of the Italian Chief Financial Officer (CFO). The research stems from the need to fill a gap in the existing literature, which lacks empirical studies based on direct evidence gathered from professionals in the sector. Methodologically, a qualitative and quantitative analysis was conducted, focusing on the collection of empirical evidence through the administration of a survey. This method made it possible to interpret the respondents' points of view and concrete experiences, going beyond a purely descriptive dimension. The results show that GenAI is widely recognised as a crucial driver for the evolution of the finance function, capable of automating low value-added operational activities and allowing the CFO to focus on strategic activities. At the same time, some barriers to adoption have emerged, including difficulty in assessing the concrete impact in terms of efficiency and profitability, a lack of specialist internal skills, and the absence of significant use cases. The research also reveals a gap between the high expectations placed on advanced financial activities and the reality of their use, which instead focuses mainly on less complex activities. The perception of the benefits is generally positive, but their actual realisation depends on the presence of enabling factors such as adequate training programmes, clear company policies, and solid technological integration with existing systems. Overall, the research highlights how GenAI is already having a tangible impact on improving operational efficiency, but its strategic potential remains only partially realised at present. The results offer an original empirical contribution to the academic debate on the subject and provide companies with concrete guidance for the responsible and effective implementation of this technology.
Questa tesi indaga l’impatto dell’Intelligenza Artificiale Generativa (GenAI) sul ruolo del direttore finanziario italiano. La ricerca muove dalla necessità di colmare un divario nella letteratura esistente che presenta una carenza di studi empirici basati sull'evidenza diretta raccolta dai professionisti del settore. Sul piano metodologico, è stata condotta un’analisi qualitativa e quantitativa focalizzata sulla raccolta di evidenze empiriche attraverso la somministrazione di un questionario. Questo metodo ha permesso di interpretare il punto di vista e l'esperienza concreta dei rispondenti andando oltre la dimensione puramente descrittiva. I risultati evidenziano che la GenAI è ampiamente riconosciuta come un driver cruciale per l'evoluzione della funzione finanziaria, in grado di automatizzare le attività operative a basso valore aggiunto e permettere al direttore finanziario di concentrarsi su attività di carattere strategico. Allo stesso tempo, sono emerse alcune barriere di adozione, tra cui la difficoltà nel valutare l’impatto concreto in termini di efficienza e redditività, la mancanza di competenze interne specialistiche e l’assenza di casi d'uso significativi. Dalla ricerca emerge inoltre un divario tra le elevate aspettative riposte nelle attività finanziarie avanzate e la realtà dell'utilizzo, che si concentra invece prevalentemente su attività di minore complessità. La percezione dei benefici risulta generalmente positiva, ma la loro effettiva concretizzazione dipende dalla presenza di fattori abilitanti quali percorsi di formazione adeguati, policy aziendali chiare e una solida integrazione tecnologica con i sistemi già esistenti. Nel complesso, la ricerca evidenzia come la GenAI abbia già un impatto concreto sul miglioramento dell’efficienza operativa, ma il suo potenziale strategico resti al momento solo parzialmente realizzato. I risultati offrono un contributo empirico originale al dibattito accademico sul tema e forniscono alle imprese indicazioni concrete per orientare un’implementazione responsabile ed efficace di questa tecnologia.
The impact of GenAI on the CFO role: an empirical analysis in the italian context
Rao, Filippo;Piccolboni, Giovanni
2024/2025
Abstract
This thesis investigates the impact of Generative Artificial Intelligence (GenAI) on the role of the Italian Chief Financial Officer (CFO). The research stems from the need to fill a gap in the existing literature, which lacks empirical studies based on direct evidence gathered from professionals in the sector. Methodologically, a qualitative and quantitative analysis was conducted, focusing on the collection of empirical evidence through the administration of a survey. This method made it possible to interpret the respondents' points of view and concrete experiences, going beyond a purely descriptive dimension. The results show that GenAI is widely recognised as a crucial driver for the evolution of the finance function, capable of automating low value-added operational activities and allowing the CFO to focus on strategic activities. At the same time, some barriers to adoption have emerged, including difficulty in assessing the concrete impact in terms of efficiency and profitability, a lack of specialist internal skills, and the absence of significant use cases. The research also reveals a gap between the high expectations placed on advanced financial activities and the reality of their use, which instead focuses mainly on less complex activities. The perception of the benefits is generally positive, but their actual realisation depends on the presence of enabling factors such as adequate training programmes, clear company policies, and solid technological integration with existing systems. Overall, the research highlights how GenAI is already having a tangible impact on improving operational efficiency, but its strategic potential remains only partially realised at present. The results offer an original empirical contribution to the academic debate on the subject and provide companies with concrete guidance for the responsible and effective implementation of this technology.| File | Dimensione | Formato | |
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2025_10_Rao_Piccolboni_Tesi_01.pdf
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Descrizione: Testo della tesi
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2025_10_Rao_Piccolboni_Executive Summary_02.pdf
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Descrizione: Executive Summary della tesi
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https://hdl.handle.net/10589/243739