Jul 2026· Stardom Scientific Journals of Economy and Management Studies· Vol 4, pp. 1-38· 0 citations
TL;DR
A bounded constructs, observable mechanisms, and longitudinal research designs for examining why organizations sometimes lose an AI-supported capability while the underlying technology continues to operate are offered.
Abstract
Technical operation is a weak test of whether artificial intelligence (AI) has become an organizational capability. An AI system can remain accurate, available, and institutionally visible while its outputs lose influence over routines, decision rights, and managerial judgment. Yet existing research on adoption, implementation, AI capability, and risk governance offers limited insight into why technically sound AI systems may gradually lose organizational consequence after deployment. This conceptual paper locates that problem in organizational internalization. It distinguishes technical implementation from operational, structural, and cognitive embedding and develops Organizational AI Survivability as the continuity of an AI-supported capability under disruption. Enabling governance links models to routines, accountability, interpretation, and learning; cumulative governance realignment explains how those links are repaired across successive episodes of leadership change, restructuring, regulatory change, and model drift. Five propositions organize the argument as a temporal sequence from embedding to continuity or fragility. The resulting process theory shifts explanation away from deployment as an endpoint and toward the governance work through which organizations repeatedly reproduce accountable alignment. It offers bounded constructs, observable mechanisms, and longitudinal research designs for examining why organizations sometimes lose an AI-supported capability while the underlying technology continues to operate.
AI systems increasingly enter organizations through policies, procedures, playbooks, prompts, and other explicit representations of work. Yet formal descriptions often differ from situated practice, and captured know-what can omit the contextual know-how experts use when judgments are uncertain. We argue that a recurring class of organizational AI failures arises partly from a knowledge representation problem at the sociotechnical interface: the AI receives the procedure, while the organization operates on the procedure plus negative boundaries, runtime judgments, responsibility assignments, and learning history. We introduce O-I-B-A-R (OPEN, IS, BUT, ACTION, RESULT), a scaffold for externalizing these missing decision boundaries. IS records when a judgment holds. BUT records a concrete failure containing information beyond the logical negation of IS. Comparable success and failure cases are decomposed toward a minimally sufficient changing variable, which becomes a value-bearing decision dimension. A suspension represents the state in which the dimension is known but its current value is unresolved, specifying what must be measured, asked, retrieved, or escalated to a human. RESULT confirms a boundary, shifts a threshold, or exposes a new dimension. Incidents can generate new dimensions, unresolved values can define human-AI handoffs, and feedback can expand the decision space. We also identify a sociotechnical tension: durable and attributable failure histories can suppress the candor on which useful boundary knowledge depends. Externalization must therefore be designed as an organizational intervention with real costs and incentives.
Artificial intelligence (AI) adoption is accelerating, yet enterprise value remains uneven because technical capability often outpaces organizational redesign, workforce adaptation, and governance maturity. This paper develops an original theoretical model, the AI productivity-governance frontier (PGF), to explain why the same agentic AI capability can generate measurable value in one organization but produce negligible or negative returns in another. Using integrative theoretical modelling, the study synthesizes recent empirical evidence on generative AI productivity, enterprise adoption, AI risk management, labor-market exposure, and prior conceptual work by Kwan Hong TAN on AI-form organizations, AI stakeholder recognition, and temporal displacement-adaptation equilibrium. The resulting PGF model formalizes AI value as the interaction between automation-augmentation gains, learning spillovers, decision velocity, scalability, and institutional absorptive capacity, offset by governance drag, risk externalities, and identity-coordination costs. The paper proposes six testable propositions and a practical maturity pathway moving from experimental AI use to validated autonomy. The central argument is that sustainable AI value does not increase monotonically with either automation intensity or governance intensity. Instead, organizations approach maximum value when they design human-AI work systems that combine use-case fit, accountable autonomy, adaptive reskilling, and proportionate assurance. The contribution is threefold: a formal value equation for enterprise AI, a governance-sensitive interpretation of AI productivity heterogeneity, and an implementation framework for managers, policymakers, and researchers studying AI-enabled business transformation.
K. Tan· International Journal of Sci...· 0 citations
This paper reconceptualizes managerial rationality in artificial intelligence (AI)-augmented decision-making through the notion of algorithmic-bounded rationality (ABR). It argues that AI does not remove boundedness but relocates it into algorithmic constraints related to data volatility, model opacity and governance maturity.
Building on bounded rationality and socio-technical systems theory, this conceptual study develops an ABR framework linking three decision modes (AI-led, human-first and collaborative) to mechanisms of algorithmic boundedness. The framework is further extended through propositions on mode–task fit and governance conditions for sustaining hybrid decision architectures.
The analysis shows that human–AI collaboration represents a distinct rationality configuration rather than a midpoint between automation and human judgment. Under ABR, each decision mode becomes effective under different combinations of data intensity, contextual ambiguity and accountability demands.
Managers should treat AI integration as a redesign of decision governance rather than a technological upgrade, emphasizing appropriate authority allocation and oversight mechanisms.
The study reframes rationality in the AI era by showing how boundedness shifts from human cognition to socio-technical decision infrastructures. It contributes a mechanism-based framework linking decision modes, task conditions and governance arrangements in AI-augmented decision systems.
The widespread adoption of Artificial Intelligence (AI) has led organizations to establish formal governance frameworks aimed at mitigating ethical, legal, and operational risks. Despite these efforts, AI governance frequently fails in practice, as evidenced by the growing prevalence of Shadow AI the unsanctioned use of AI tools by employees. Existing scholarly and practitioner discourses predominantly frame this phenomenon as a compliance failure or security vulnerability, thereby emphasizing stricter controls and enhanced employee training as primary remedies. This conceptual study challenges that prevailing view by arguing that Shadow AI represents a structural manifestation of policy–practice misalignment rather than a problem of individual deviance. The study develops a diagnostic framework that identifies three constitutive dimensions of misalignment: temporal gaps (mismatches between governance processes and operational speed), utility gaps (misalignment between sanctioned tools and task-specific needs), and autonomy–control gaps (tensions between professional discretion and standardization). Drawing on a theory-driven conceptual methodology integrating sociotechnical systems theory with policy–practice analysis, and illustrated through structured synthetic organizational scenarios, the study demonstrates how governance designs that overlook the realities of situated work systematically generate Shadow AI practices. The analysis further suggests that adaptive governance models incorporating structured flexibility such as curated AI tool marketplaces and expedited approval pathways are theoretically more effective than highly rigid governance regimes. The primary contribution lies in advancing a practice-aware AI governance model that reframes Shadow AI as a diagnostic signal of systemic design flaws and provides a foundation for more legitimate and responsive AI governance.
Mia Wilson, Ethan Moore· Journal of Management and In...· 0 citations
A framework in which the microfoundations of dynamic capabilities operate through organizational readiness to shape AI-driven industrial management capability and, in turn, operational and managerial performance outcomes is developed.
Hoogendijk Ha· Journal of Economic, Finance...· 0 citations
As organizations integrate artificial intelligence into knowledge‐intensive work, a phenomenon is emerging that knowledge management theory has yet to adequately explain: institutional knowledge can become progressively encoded into AI‐enabled organizational infrastructure through sustained patterns of human use. This paper develops that phenomenon into a formal construct, organizational AI acculturation, defined as the process through which an organization's collective knowledge, values, and decision heuristics become embedded in AI‐enabled systems through repeated human interaction, such that the systems' outputs increasingly reflect the organization's distinctive ways of framing problems and evaluating alternatives. The paper makes three contributions. First, it defines the construct and distinguishes it from adjacent concepts, including organizational memory, absorptive capacity, and transactive memory systems. Second, drawing on organizational learning theory, the resource‐based view, and dynamic capabilities, it explains how institutional knowledge encoded into AI‐enabled systems through sustained human use can become a source of competitive advantage when organizations govern the quality and renewal of what their systems encode. Third, it advances four testable propositions linking the quality of human‐AI interaction to competitive advantage, knowledge persistence under employee turnover, and adaptive capacity. Published organizational cases illustrate the construct in practice, showing that deliberate governance of human‐AI interaction can build defensible knowledge assets, whereas poor governance can stabilize error, bias, and outdated assumptions. The paper concludes with a research agenda focused on measurement, mechanism, governance, and scope conditions. The central claim is that knowledge management theory must expand to account for a new kind of knowledge repository: AI‐enabled systems whose behavior is shaped by the cumulative judgments of the organizations that use them.
Adrienne N. Moore Terrell, Antwon D. Woods· Knowledge and Process Manage...· 0 citations
