AI FOR ORGANIZATIONS

I The impact of AI on Organizations
1 Introduction
2 Organizations and Artificial Intelligence
3 Enabling Organizational Readiness
II Technical Foundations for Managers
4 Machine Learning for Managers
5 Deep Learning for Managers
6 Generative AI for Managers
III Applications in the Financial domain
7 AI applications in the Financial domain
8 Building and Operationalizing AI Capabilities in Regulated Financial Services
IV The Multi-Layered Challenges of AI Adoption in Organizations
9 From Explainable to Responsible AI
10 Security, Risk, and Trust Challenges
11 Data Governance and Control Challenges
V Conclusions
12 Conclusion: The Organizational Constraint

Available evidence indicates that, despite substantial investment in Artificial Intelligence (AI), only a small share of organizations report significant financial benefits. This book offers an interpretation of this AI productivity paradox, grounded in an extensive review of the literature in economics, organization studies, and information systems. The main constraint lies less in the technical capabilities of the models than in the capacity of organizations to absorb and govern their socio-technical complexities. The analysis focuses on financial services and insurance, treated as a litmus test for the application of AI in mission-critical contexts. In regulated industries, model accuracy is not enough: decisions must be explainable to supervisors, defensible before customers, and reconstructible over time. It is here that the difficulties emerge most clearly.

Simone Gabbriellini is a Researcher at the Università Cattolica del Sacro Cuore in Milan, where he teaches Organizzazione dei Sistemi Informativi Aziendali (Information Systems and Organization). Since 2024, he has taught university seminars on the use of large language models (LLMs) in economics and business for undergraduate and graduate students in the Faculty of Economics.