In an economic landscape defined by volatility and the growing urgency for sustainable practices, Artificial Intelligence (AI) is often hailed as a definitive solution. However, its potential is at risk of remaining untapped if organizations fail to address a fundamental, preliminary challenge: the strategic management of their most valuable asset, knowledge. Many firms still lack effective Knowledge Management (KM) processes, incurring significant “hidden costs” such as lost productivity from searching for information, duplication of effort, and the erosion of critical know-how. 

This track aims to shift the debate from the acritical adoption of AI to the construction of a structured “Knowledge Journey,” laying the groundwork for a truly intelligent and sustainable organization.

The goal is not merely automation, but the creation of hybrid ecosystems where human and artificial intelligence collaborate to generate lasting value, in alignment with ESG and sustainable development goals.

The track seeks to solicit theoretical and empirical contributions that explore the following thematic areas, conceived as stages of a virtuous KM pathway: 

1.     Culture, Awareness, and Knowledge Architecture (The Planning Phase): What are the best practices that companies should adopt to ensure proper governance of the knowledge journ ey, and who are the people responsible for it? How can a company implement a Knowledge Base or an intelligent intranet with consistent taxonomies, metadata, and tags?  How can organizations foster a culture oriented toward knowledge sharing? What are the strategies to overcome internal resistance (e.g., information silos, fear of losing power)?  This section will explore the definition of taxonomies, metadata, and information architectures (Knowledge Bases) as a prerequisite for any technological implementation, and the role of leadership in promoting awareness of knowledge’s strategic importance.

2.     Knowledge Acquisition and Integration in the Human-AI Ecosystem (The Acquisition Phase): How can the systematic documentation of processes and projects be promoted, and how can an operational model for the organization’s knowledge journey be created? How can we capture the tacit knowledge that resides “in people’s heads” and integrate it with structured data from systems like ERP and PLM? This area of inquiry will focus on the role of new technologies (e.g., NLP, virtual assistants) in the acquisition process, as well as the dynamics of human-machine interaction. We encourage studies that analyze how AI can support knowledge extraction without devaluing human experience and intuition. 

3.     Knowledge Distribution, Application, and Impact on Organizational Sustainability (The Impact Phase): Once collected and structured, how can knowledge be effectively distributed to fuel all corporate touchpoints and support strategic decisions? This section invites research on how effective KM, enhanced by AI, translates into measurable benefits: improved efficiency, error reduction, accelerated time-to-market, and, most importantly, more informed decision-making in the realm of sustainability (e.g., ESG reporting, sustainable product innovation).

How can a company measure and continuously improve the knowledge journey?  – by defining indicators of engagement in knowledge sharing and knowledge reuse? –
How can it create a continuous feedback process to enhance platforms and knowledge-sharing workflows?

In summary, this track aims to create a dialogue between academics and practitioners to define operational models that enable companies not to be passive recipients of the AI revolution, but to lead it through conscious and strategic knowledge management, turning a hidden cost into a sustainable competitive advantage.