In line with the theme of IFKAD 2027, “The Ecology of Knowledge in the Age of AI,” this special section examines how organizations can design resilient and adaptive knowledge ecosystems under conditions of technological and informational turbulence. This issue is becoming increasingly relevant as Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), cyber-physical systems (CPS), process mining applications, and other data-intensive technologies are progressively becoming interconnected within organizational processes. Such interconnections — which in some cases take the form of full system integration — are no longer limited to advanced manufacturing but increasingly characterize the healthcare, services, public administration, logistics, and inter-organizational networks. As these technologies become increasingly integrated into organizational activities, they generate growing volumes of heterogeneous data that are continuously updated and increasingly interdependent.

However, greater data availability does not necessarily translate into greater knowledge or organizational value (Günther et al., 2017). Instead, data-intensive environments can introduce a wide range of technological and organizational challenges associated with data collection, management, integration, analysis, and interpretation (Sivarajah et al., 2017). Among these challenges are fragmentation across systems and professional domains, interpretive ambiguity, mismatches between information-processing requirements and available cognitive or organizational capacities, and tensions concerning decision-making autonomy when algorithmic outputs are incorporated into organizational decisions. Notably, the growing influence of algorithms in organizational decision-making raises issues around transparency, control, specialized expertise, and the distribution of decision-making authority between human and technological actors (Newell and Marabelli, 2015; Faraj et al., 2018). Thus, a central issue concerns how signals generated by various technological artefacts are selected, interpreted, combined with existing expertise, shared across organizational boundaries, and ultimately translated into decisions and actions. From this perspective, a resilient knowledge ecosystem can be conceptualized as an organizational configuration designed to transform heterogeneous technological signals into shared, interpretable, and actionable knowledge, thereby preserving coordination, human judgment, and the ability to learn and adapt over time. In this conceptualization, the focus shifts from the mere availability or integration of data to the organizational conditions that enable data to acquire meaning and become actionable knowledge.

When conceived in these terms, the conceptualization, implementation, and maintenance of a knowledge ecosystem require coordinated initiatives at the strategic, operational, organizational and behavioural levels. Strategic decisions define how data and digital technologies contribute to organizational objectives and knowledge governance. Operational decisions translate these priorities into processes, data flows, technological architectures, responsibilities, and decision-making routines. The organizational and behavioural dimensions refer to how individuals and groups interact with technologies, share and integrate knowledge, adapt their roles and competencies, and respond to factors such as trust, autonomy, motivation, acceptance, and decision agency. These dimensions are strongly interdependent and collectively shape a broader sociotechnical transformation of the organization. The complexity of this transformation can be addressed through complementary theoretical perspectives. Each of these perspectives highlights a different mechanism through which data are transformed into organizational knowledge. The knowledge-based perspective explains how specialized knowledge – distributed among individuals, functions, and professional communities – can be integrated and mobilized at the organizational level (Grant, 1996). Organizational information processing theory focuses on how structures and coordination mechanisms can be configured to match growing information processing demands with available cognitive and organizational capacities (Galbraith, 1974). At the individual and group levels, sensemaking theory helps explain how ambiguous, incomplete, or conflicting signals are interpreted and converted into shared meanings that support coordinated action (Weick et al., 2005). A sociomaterial perspective further emphasizes that the creation and use of knowledge cannot be separated from the technologies, routines, and work practices through which information is produced and enabled (Orlikowski, 2007). Finally, Lean and continuous improvement provide a process-oriented perspective on how emerging knowledge can be incorporated into recurrent cycles of problem identification, experimentation, feedback, and adaptation (Rosa et al., 2023). Building on these perspectives, this Special Track explores how organizations can manage increasingly complex data environments to conceptualize, design, implement, govern, and sustain resilient, adaptive, and human-centric knowledge ecosystems. Particular attention is given to the mechanisms through which data are transformed into usable knowledge, integrated into organizational processes, shared across professional and functional boundaries, and continuously refined through learning and feedback.

We invite scholars, engineers, organizational theorists, and management practitioners to contribute conceptual, empirical, and methodological research that spans traditional disciplinary boundaries. Submissions should offer innovative perspectives on the design, governance, implementation, and continuous development of sustainable, resilient, and human-centric knowledge ecosystems in the AI era.