Knowledge management in the artificial intelligence era is increasingly framed as the cultivation of an ecosystem in which human and machine intelligence jointly generate organizational value. This track examines a prior condition of that project: what an organization is equipped to perceive and tell about the value it creates and destroys across the parties it affects.

Today real value has become invisible in knowledge-based organizations: We are witnessing an increasing disconnect between short-term stock price performance and long-term economic success. Even more concerning, value does not reflect a company’s internal health, strategic competitiveness, long-term viability, customer satisfaction and trust, employee attractiveness and happiness, the sustainability of business activities, or its broader contribution to society. It represents neither stakeholder value, customer value, human value, nor total value created. Therefore, we are convinced that organizational long-term value must be redefined and made visible again. Key components include human-centric values, a clear vision and strategy, agile execution, effective resource allocation, and the creation of shared value.

Disclosure has expanded without reliably translating into performance improvement (Christensen et al., 2021; Tashman et al., 2019), and decoupling between disclosed metrics and behavioral outcomes appears to widen as transparency requirements grow (Bromley and Powell, 2012). Frameworks that fix measurement boundaries a priori render some value flows imperceptible even where the affected parties are formally acknowledged. Managerial cognition compounds this: dominant logics simplify complex environments while creating systematic blind spots (Bettis and Prahalad, 1995; Hambrick and Mason, 1984), and value flows that never register as decision-relevant produce moral exclusion through perceptual limitation (Opotow, 1990).

The track organizes around value invisibility and storytelling: the divergence between the value an organization’s choices produce across affected parties and the value its decision-makers perceive when they decide. The construct decomposes into coverage gaps, where parties are excluded from measurement entirely, and fidelity gaps, where included parties are measured with distortion. Both are endogenous to the organization’s measurement architecture. That makes invisibility a knowledge-infrastructure problem open to intervention.

This is where technology is required to enhance the visibility of impacts and value across stakeholder networks. Artificial intelligence supplies the processing capacity that a comprehensive stakeholder account requires, since the relevant data are heterogeneous, continuous and for practical purposes unbounded. Distributed ledger technology supplies a record of impacts and outcomes that affected parties can audit, in place of a record the assessed party discloses selectively (Nakamoto, 2008; Saberi et al., 2019). Sensing infrastructure extends observation past the boundary of conventional organizational measurement. Each capability is demonstrated in other domains and has yet to be applied to stakeholder value, a technology paradox the track takes as its central challenge. The track invites conceptual, formal, empirical, computational and critical contributions, and is equally interested in what these technologies threaten: algorithmic bias reproducing existing exclusions (Obermeyer et al., 2019), infrastructure costs that favor well-resourced organizations, and the prospect that AI-enabled storytelling enables more sophisticated forms of misrepresentation.