Governing the Complexity of Artificial Intelligence: Models and Strategies for Project Management in Multi-Practice Contexts. The Case Study of Impresoft Engage.

In the current landscape of rapid technological evolution, Artificial Intelligence (AI) has emerged as a central pillar for the digital transformation of companies, offering innovative tools that can drastically improve the management of complex projects and optimize operational processes. This thesis aims to conduct an in-depth analysis of AI governance within a multi-practice organization. The analysis is based on a concrete case study and seeks to examine how a structured governance framework can address the challenges of systemic fragmentation, such as duplicated efforts and knowledge silos, thereby enhancing operational efficiency and fostering a collaborative innovation culture. The analysis of the existing literature provides a comprehensive overview of the theoretical models adopted by various organizations, particularly the "hub-and-spoke" model, and the common challenges that emerge during the adoption of AI in complex corporate structures. Building on this evidence, an empirical investigation was conducted at Impresoft Engage S.r.l., a multi-practice company operating in the CRM consultancy. The study documents the company's transition from a state of spontaneous, uncoordinated AI experimentation to the implementation of a deliberate and centralized governance strategy, formalized in the "AI Workforce" initiative. The implementation of this new framework led to significant improvements in terms of cross-functional collaboration, knowledge capitalization, and the strategic alignment of innovation projects. Through a detailed analysis of the benefits obtained, the challenges encountered, and the final results, this thesis offers valuable insights into the process of governing AI in a multi-practice context and on the impact that such an innovation can have on corporate performance and competitive advantage.