Environmental, social, and technological transformations are increasingly challenging traditional growth models. The transition towards sustainable societies therefore requires reconsidering the purposes of development and the criteria through which value is generated.

The Great Transition perspective interprets this change as a systemic transformation. Ecological sustainability, social justice, and human well-being become interdependent dimensions (Raskin, 2021). Progress can therefore no longer be assessed solely in terms of economic expansion. It must also consider well-being and the responsible use of resources (Soytas & Sari, 2025; Abdallah et al., 2026).

The debate on post-growth develops within this framework. Post-growth does not imply an indiscriminate reduction in economic activity, but rather moving beyond the assumption that quantitative growth and progress necessarily coincide. Some activities may need to be reduced because they generate waste, consume excessive resources, or produce limited social value. Others may instead be strengthened because they generate knowledge, well-being, and quality of work. This perspective recalls selective and managed degrowth (Pallante & Pertosa, 2017) and the concept of a-growth (Van den Bergh, 2010).

The regenerative perspective goes one step further. It does not merely aim to reduce negative impacts, but to renew social, environmental, and organizational resources over time. Organizations can therefore move from harm reduction towards generating positive effects within the systems of which they are part (Hahn & Tampe, 2020; Wahl, 2016).

The track connects with IFKAD 2027 by adopting the ecology of knowledge as a lens through which to interpret organizational transition. It focuses on how knowledge and Artificial Intelligence can guide more selective choices in resource use and value creation.

In this context, the shift from efficiency to sufficiency becomes particularly relevant. Efficiency concerns how an activity is carried out, while sufficiency also concerns whether it is necessary and what value it generates. AI can therefore support not only process improvement, but also decisions about what should be done, reduced, or transformed.

Organizations consequently assume a central role. Through their decisions, they may continue to support quantitative growth or move towards post-growth and regenerative models. The track considers three organizational domains:

• for-profit enterprises: AI and knowledge for developing business models with lower waste and more responsible resource use.
• Non-profit organizations: AI and knowledge for improving resource management and strengthening social value creation.
• Public Administrations: AI and knowledge for improving resource allocation, generating public value, and supporting territorial transitions.
</ul The guiding research question is: How can for-profit enterprises, non-profit organizations, and Public Administrations use Artificial Intelligence and knowledge to orient their decisions towards post-growth and regenerative models? The track welcomes theoretical and empirical contributions on AI, knowledge, and organizational transformation. Particular attention is devoted to organizational models, decision-making processes, knowledge management, sufficiency, and value measurement. The aim is to understand whether and how AI can help organizations perform better, reduce waste, and generate lasting positive effects.