Within the context of integral ecology, modern enterprises face unprecedented pressure to adopt holistic sustainability paradigms that go beyond mere formal compliance. In this context, Quality Management Systems must evolve from control mechanisms into dynamic and adaptive ecosystems.  In the face of these challenges, artificial intelligence should not be viewed merely as an automation technology, but as an active tool supporting decision-making and organisational processes. This Special Track explores the intersection between AI-driven Quality Management and Integral Ecology, analysing how advanced AI technologies can be deployed to govern quality system models whilst respecting human agency and dignity, as well as organisational ethics. It directly addresses how artificial and human intelligence can co-exist and co-create value without eroding human judgment, professional expertise, or social and environmental balance. This Track aims to map and examine the key dimensions of ecosystemic quality management. Specifically, it encourages contributions that investigate how algorithms, predictive analytics, and digital twins reshape traditional TQM principles and continuous improvement processes in quality systems integration. Crucially, the track also welcomes research examining the direct effects of this technology integration on human resources within organisations.

From an academic perspective, this track advances quality management, intellectual capital, and organisational literature by bridging the gap between AI governance and organisational quality management. Rather than treating quality control as a reactive or isolated function, the track explores how AI integration reshapes organisational learning systems and collective intelligence. It fosters a theoretical dialogue on how human-AI symbiosis influences quality culture and decision-making integrity.

For practitioners, executives, and quality managers, the track offers actionable pathways to transition from rigid “checklist compliance” to dynamic, ecosystem-oriented management models. Operationally, it shows how AI-driven data infrastructures can transform quality management by improving auditability and traceability of environmental, social, and economic impacts across complex supply chains. Crucially, the track provides managers with practical insights on designing human-centric re-skilling strategies, ensuring that AI augmentation reinforces rather than diminishes professional judgment, intuition, and domain expertise. Ultimately, it guides decision-makers in using AI-enhanced quality standards to design resilient, innovative, and sustainable business models that create long-term value.