Jul 2026· Management Decision· pp. 1-18· 1 citation· 35 references
Abstract
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.
Organizations increasingly adopt artificial intelligence systems under the assumption that computational efficiency, data-driven consistency, and predictive accuracy translate into superior decision-making. This article challenges that assumption by examining how algorithmic decision systems systematically erode organizational rationality even as they enhance certain computational capabilities. Drawing on bounded rationality theory and extensive empirical research across healthcare, criminal justice, human resources, and public administration, the analysis identifies four interconnected mechanisms through which AI diminishes decision quality: metric displacement (optimizing measurable proxies rather than authentic objectives), cognitive compression (narrowing human judgment around algorithmic defaults), contextual erasure (eliminating situational particularity essential to sound judgment), and reflexive capacity atrophy (suppressing organizations' ability to question their own premises). These mechanisms produce cascading institutional consequences including accountability diffusion, contestability reduction, and adaptive learning deterioration. The findings suggest that AI functions optimally as a bounded computational tool rather than as a rationality substitute, and that organizations treating algorithmic outputs as inherently superior judgment systematically compromise their institutional intelligence.
Jonathan H. Westover· Human Capital Leadership Rev...· 0 citations
Artificial intelligence (AI) is increasingly transforming organizational work by enabling humans and intelligent systems to collaborate in decision-making, problem-solving, and operational activities. Despite rapid adoption, existing research remains fragmented across organizational behavior, human factors, information systems, and AI governance, providing limited guidance on how organizations can effectively design and manage human–AI collaboration. This conceptual paper develops the Collaborative Intelligence Framework (CIF) through an integrative review of peer-reviewed literature published between 2020 and 2026. The framework identifies four essential conditions for successful human–AI teaming: task interdependence, calibrated trust, role clarity, and organizational enablement. It explains how these conditions promote effective collaboration while reducing the risks of algorithmic aversion and excessive reliance on AI recommendations. The study argues that collaborative intelligence should be viewed as an organizational capability rather than merely a technological outcome, requiring deliberate management of human judgment, ethical responsibility, and organizational design. The paper contributes to the growing literature on responsible AI by providing a structured framework that can guide researchers and practitioners in designing sustainable human–AI collaboration. It concludes by discussing managerial implications, study limitations, and opportunities for future empirical validation across diverse organizational and occupational contexts.
Keywords: organizational behavior; human-AI collaboration; human-AI teaming; collaborative intelligence; future of work; comple-mentarity
Manikanta R· International Journal of Phi...· 0 citations
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.
Artificial intelligence is moving from analytical support toward active participation in enterprise decisions, creating an organization-design problem: firms must decide which rights may be delegated to AI and how responsibility should follow the actors who can prevent, challenge, or remedy failure. Existing work explains automation, augmentation, delegation, human oversight, and responsible AI governance, but does not reveal how specific transfers of decision authority create responsibility gaps inside a focal enterprise decision. This conceptual paper develops a contingency governance framework through a transparent theory-synthesis procedure. A purposive corpus of 44 peer-reviewed studies, standards, and regulatory sources was assembled through anchor studies, targeted keyword searches, and citation chaining. First-order authority and responsibility terms were coded, compared, and abstracted until two successive search iterations produced no new categories. The resulting framework distinguishes seven decision rights—information access, recommendation, selection, approval, veto, execution, and escalation—and five responsibility domains—system design, decision process, outcome stewardship, oversight, and remediation. Its central mechanism is rights-control-responsibility alignment: delegating a right shifts effective control and evidence access, while governance fails when the responsible actor lacks the competence, authority, or information to intervene. Decision exposure and AI autonomy determine four governance archetypes, while AI reliability conditions the permissible scope of selection and execution rights. Eight empirically testable propositions specify mechanisms, moderators, competing explanations, and falsification conditions. Two worked applications show how the architecture produces more precise governance than a generic human-in-the-loop requirement. The paper contributes a decision-level theory of enterprise AI governance and provides managers with an auditable method for allocating rights, responsibilities, evidence, and lifecycle controls.
Fang Sun· ICCK Transactions on Systems...· 0 citations
BackgroundThe rapid integration of artificial intelligence (AI) into labour markets, migration governance, and social protection systems is increasingly reshaping how institutional decisions are produced, delegated, and enforced. While human-centric and ethics-based AI frameworks have established important normative principles, concerns regarding inequality, opacity, and accountability in AI-mediated decision-making continue to persist across labour-related environments.ObjectiveThis article examines why ethical approaches alone are insufficient to address the structural effects of AI once algorithmic systems become embedded within institutional governance architectures operating at scale.MethodsThe article draws on institutional and policy analysis, labour and migration governance literature, and illustrative examples of algorithmic decision-making in public-sector and labour-related systems.ResultsThe analysis conceptualises AI not merely as a technical tool, but as part of the operational infrastructure through which institutional visibility, discretion, prioritisation, and authority are organised. The article demonstrates how labour markets and migration governance function as "stress-test" domains in which continuous classification, automated risk assessment, worker scoring, and fragmented data environments can amplify existing structural inequalities. It further argues that human oversight frequently becomes procedural rather than substantive once algorithmic systems operate under conditions of scale, speed, and administrative complexity.ConclusionsThe article concludes that the governance challenges associated with AI in the world of work are not primarily ethical in nature, but institutional and architectural. Advancing fairness and accountability in AI-mediated environments therefore requires institutionally grounded governance architectures, meaningful contestability mechanisms, and enforceable operational oversight beyond ethics-first compliance frameworks.
A. Sinchev, Svetlana Bekmambetova· Work· 0 citations
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Karzan Ismael, Ali Mohammed Salih, Zryan Najat Rashid· Knowledge and Decision Syste...· 0 citations