Aug 2026· ICCK Transactions on Systems Safety and Reliability· 0 citations· 41 references
TL;DR
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.
Abstract
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.
A normative analysis of thirteen recent studies on the challenges of technology implementation, ethical trust, and legal regulation suggests that the current governance dilemma stems not only from technological limitations but also from institutional neglect, which enables accountability avoidance.
The paper argues that algorithmic governance should not be assessed only by whether systems are accurate, explainable or compliant, but also by whether affected persons retain interpretive agency, contestatory power, relational recognition and meaningful participation in institutional life.
K. Tan· International Journal of Law...· 0 citations
Autonomous artificial intelligence (AI) agents are no longer advisory. They execute transactions, orchestrate multi-agent pipelines and modify enterprise data with minimal human intervention. Yet the governance frameworks organisations rely on were built for a different artefact: one that recommends rather than acts. This paper argues that delegation, not architecture, is the primary variable that governance frameworks for agentic AI must address and that existing frameworks, including NIST AI RMF and the EU AI Act, do not adequately operationalise governance at the delegation level. A multi-corpus bibliometric analysis of 795 peer-reviewed publications (2020-2026) provides evidence of three structurally isolated scholarly communities (AI ethics governance, MLOps operationalisation and agentic enterprise integration) developing in parallel without convergence, leaving the high-autonomy, high-accountability quadrant theoretically underserved. Grounded in three complementary theoretical pillars (IT Governance theory, Socio-Technical Systems theory and IS Artefact Delegation theory), we derive the Delegated Agentic Governance Model (DAGM): a conditional governance matrix that assigns governance requirements to each level of autonomy delegated to AI agents across three tiers (Advisory, Operational, Autonomous). We introduce Generative AI governance debt as a prerequisite construct, articulate seven design principles and derive three falsifiable propositions linking delegation-governance alignment to enterprise failure rates. The DAGM provides AI managers with an immediately actionable governance readiness instrument and establishes the theoretical foundation for a research agenda on delegation-calibrated AI governance across finance, healthcare and manufacturing.
Leila Gluszak, Filip Gluszak· Journal of Information &...· 0 citations
Artificial intelligence (AI) increasingly mediates leadership-relevant judgment through models, dashboards, metrics, decision-support systems, and autonomous agents. This conceptual article develops a socio-technical systems theory of systemically mediated leadership, defined as a nested system-level condition and recurrent process configuration through which human actors, AI systems, organizational routines, governance institutions, and affected stakeholders jointly produce and revise direction, meaning, consequential judgment, legitimacy, and accountability through recursive feedback. A problem-driven conceptual synthesis was updated through 3 August 2026. A structured discovery pass yielded 97 candidate records; 85 sources were retained after relevance screening, citation chaining, concept mapping, and comparison of eight candidate mechanism families. Four proposed qualification conditions jointly define the construct within the present framework: AI mediation, leadership relevance, distributed judgment, and recurrent institutional embedding. Five mechanism families explain transformations in responsibility, legitimacy, control, attention, and feedback timing: moral delegation, interpretive laundering, ceremonial oversight, metric-driven sensemaking, and ethical latency. A causal-loop model specifies justificatory reinforcement, capability atrophy, power insulation, and accountable correction. Their relative dominance produces three ideal-type dynamic regimes: accountable adaptation, stabilized trade-offs, and destructive drift. The theory predicts that organizations using equally accurate models may produce divergent leadership and accountability outcomes because their feedback, power, and oversight architectures differ. Responsible AI leadership thus depends on system architecture and contestable institutional practice, not leader intention, formal human approval, or model accuracy alone.
Purpose – This systematic review examines how artificial intelligence (AI) can support strategic decision-making in private universities, the organizational conditions shaping its value, and the governance and implementation risks that constrain responsible use.Methodology – Searches were conducted in the Web of Science Core Collection, Scopus, and Google Scholar between March and May 2026, with the final update on May 31, 2026. After duplicate removal, screening, full-text assessment, and evidence appraisal, 46 substantive sources published between 1955 and 2025 were included in this review. Four additional methodological references supported the review reporting and synthesis. Because the evidence base was heterogeneous, narrative thematic synthesis was applied while distinguishing direct private university evidence from evidence transferred from general higher education, organizational decision research, and AI governance.Findings – The synthesis identifies four interconnected roles of AI: environmental intelligence, decision augmentation, strategic execution, and governance infrastructure. AI can strengthen institutional sensing, the comparison of strategic alternatives, implementation coordination, and decision traceability. However, direct empirical evidence specific to private universities is limited. Strategic value depends on data quality, organizational learning, analytical capability, decision ownership, auditability, governance capacity, strategic fit, and alignment with the institutional mission. Therefore, AI is best understood as a human-led decision-support capability rather than a substitute for institutional judgment.Research limitations – The heterogeneous corpus prevents statistical estimation of a common institutional effect, while the review is restricted to English-language sources from three search platforms. Therefore, the four-part architecture should be treated as an evidence-organizing framework rather than a validated causal model.Originality – This review integrates higher education, organizational decision-making, strategic management, and AI governance evidence into an institution-level capability architecture for responsible AI-supported strategic decision-making.
Yun-Dong Wu, Wei-Jian Kong· Artificial Intelligence in E...· 0 citations
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.
M. R· International Journal of Phi...· 0 citations
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