The growing integration of Artificial Intelligence (AI) in organizations is transforming knowledge and process management practices, reshaping and creating new opportunities for sustainable development (Rezaei, 2025). Far from being only a technological challenge, the adoption of AI has profound strategic and organizational implications: it influences leadership styles (Bevilacqua et al., 2025), reshapes decision-making processes (Albashrawi, 2025), and opens the way for new forms of hybrid human–AI collaboration (Simón et al., 2024). Also, by facilitating knowledge creation, codification and sharing (Olan et al., 2022; Pai et al., 2022) and supporting process innovation, AI contributes to the development and implementation of sustainable organizational practices (Kulkov et al., 2024). In the context of sustainability, AI helps organizations align their strategies with UN Sustainable Development Goals (SDGs) and Environmental, Social and Governance (ESG) standards. Applications range from supporting circular economy practices and energy efficiency, to embedding sustainability metrics into knowledge management systems, to enabling more transparent and responsible reporting (Raut et al., 2025; Zhou, 2025). By augmenting human capabilities with intelligent knowledge, organizations can develop new value creation processes that combine competitiveness with environmental and social responsibility (Zhang & Yang, 2024).

The widespread diffusion of AI also raises important managerial questions on ethics, trust, and security (e.g. Kappel et al., 2026; Rosemann et al., 2024; Simonis and Overes, 2025).

For example, as to security the proliferation of AI technologies, with particular reference to machine learning (ML), expands the organizational cyber-attack surface, exposing models and data pipelines to malicious exploits, data poisoning, and model theft. To address such threats, AI-dedicated security control and governance systems throughout the lifecycle of the technology used are needed (Jedrzejewski et al., 2024). Concerns related to data protection, organizational resilience to new cyber-attacks, and stakeholder confidence highlight the need for frameworks that ensure the responsible use of AI-generated knowledge (Floridi et al., 2018; Papagiannidis et al., 2025). In this light, the main challenge for managers and policymakers is not the adoption of technology, but the creation of organizational cultures, governance patterns, and business practices that balance innovation with responsibility. For instance, the integration of sustainable AI-based cyber risk assessment practices promises the evolution of digital infrastructures without compromising their security, maintaining a balance between technological innovation and resource protection (Ramachandran et al., 2023).

The special track invites researchers, practitioners, and decision-makers to explore key dimensions of responsible and sustainable AI integration into knowledge and process management practices. The ambition is to stimulate a dialogue on how organizations can use intelligent knowledge to foster sustainable growth, resilience, and stakeholder trust.

The topics of interest for the call for papers include, but are not limited to:

• AI and sustainable business process management: conceptual frameworks, main challenges and outcome;
• Integrating AI into business processes: conceptual frameworks, main challenges and outcome;
• Integrating AI into knowledge management: conceptual frameworks, main challenges and outcome;
• AI-driven knowledge and process management from an ESG and SDGs perspective;
• Human–AI collaboration for organizational resilience and sustainable value creation;
• AI and Business Processes: ethical implications on workplace;
• Governance and ethical patterns for trustworthy AI adoption;
• AI-based cybersecurity, privacy-by-design, and AI risk management across data pipelines and the ML lifecycle (e.g., adversarial robustness, model/data governance);
• Organizational cultures, governance patterns, and business practices for intelligent and sustainable knowledge and process management;
• Case studies and lesson learnt on the use of AI for sustainability-oriented practices and processes;
• Case studies and lesson learnt on the use of AI for knowledge management:
• Measuring the impact of AI-enabled knowledge and process management on long-term organizational sustainability.