Enterprise agentic artificial intelligence (AI) increasingly converts model outputs into consequential actions involving payments, records, customer communications, infrastructure, and regulated decisions. Existing safeguards commonly emphasize refusal, confidence thresholds, expected loss, or human approval, but they insufficiently distinguish a recoverable task failure from an irreversible or externally propagated harm. This paper develops Reversibility-Aware Staged Delegation (RASD), a multidisciplinary decision framework integrating AI governance, resilience engineering, transaction processing, real-options reasoning, and human-centered automation. RASD introduces an Action Recoverability Index, Non-Recoverable Exposure, and an option-value decision rule that allocates each proposed action among direct execution, staged commit, human review, and block/defer modes. The staged mode separates preparation, validation, commitment, and compensation so that an agent can make progress while preserving the organization’s ability to inspect, reverse, or contain side effects. A formal dominance condition shows when staging creates greater expected value than direct execution. The framework is evaluated in a Monte Carlo design comprising 120,000 synthetic enterprise tasks across 240 episodes, including a controlled distribution shift. RASD achieved a mean net value of 7.408 normalized units per task, compared with 5.735 for a confidence-threshold policy and 5.282 for an expected-loss gate. Its severe-incident rate was 0.390%, versus 5.937% and 4.166%, respectively, while preserving positive value after distribution shift. RASD had a higher raw task-failure rate than the expected-loss gate, demonstrating that failure frequency alone is an inadequate safety metric when recovery and consequence containment differ. The findings support a shift from binary autonomy decisions toward recoverability-preserving execution architectures and provide operational guidance for auditability, human escalation, and risk-adjusted enterprise value creation.
K. Tan· Open Access Journal of Multi...· 0 citations
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
Agentic artificial intelligence (AI) alters the governance problem because model outputs can become multi-step actions with financial, legal, informational, and social consequences. Existing governance instruments widely endorse human oversight, transparency, accountability, and redress, yet they do not consistently specify what people must remain able to do when agency is delegated to an AI system. This qualitative study conducts a comparative document analysis of ten influential governance instruments issued by UNESCO, the OECD, the European Union, the Council of Europe, the United States National Institute of Standards and Technology, the United Kingdom, the Group of Seven, and Singapore. Provision-level coding, abductive pattern analysis, negative-case examination, and a cross-framework coverage matrix identify six themes: human-centric convergence with operational divergence; oversight without empowerment; late-stage contestability; a reversibility deficit; fragmented accountability; and temporal-capability asymmetry. The paper develops cognitive sovereignty as the practically exercisable capacity to understand, authorize, interrupt, contest, restore, and assign responsibility for consequential processes delegated to AI. It then proposes the CLEAR² framework, comprising Comprehension, Legitimate authorization, Effective intervention, Appeal and contestation, Restoration and reversibility, and Responsibility and remedy. CLEAR² integrates ex ante, runtime, and ex post controls and treats the weakest capability as a constraint on meaningful human control. The study advances AI governance theory by shifting the unit of analysis from human presence to preserved agency, while offering organizations a maturity model, lifecycle control architecture, and audit questions for responsible agentic deployment.
K. Tan· Open Access Journal of Multi...· 0 citations
Artificial intelligence is increasingly becoming a governing medium through which institutions classify persons, allocate opportunities, structure work, produce knowledge and mediate public trust. Current AI governance frameworks emphasise risk classification, technical assurance, transparency, accountability and human oversight. These instruments are necessary, but they remain incomplete when algorithmic decisions reshape the meaning of agency, dignity, responsibility and social recognition. This paper develops a humanities-based framework for algorithmic governance suitable for law, management and public life. Using an interdisciplinary conceptual methodology, it synthesises legal-policy frameworks, AI ethics scholarship, management studies and contemporary philosophical work on ontological instability, AI stakeholder recognition and moral responsibility. 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. It proposes the Human Agency Impact Matrix, a six-dimensional framework that evaluates algorithmic systems through interpretability, contestability, relational accountability, dignity preservation, participatory design and institutional reversibility. The analysis shows that risk-based regulation is strongest when complemented by humanistic assessment of how AI changes roles, identities, vulnerabilities and obligations. The paper concludes that responsible AI governance must be understood as a cultural and institutional practice: a way of preserving human agency within socio-technical systems that increasingly act before, beside and sometimes instead of human judgment.
K. Tan· International Journal of Law...· 0 citations
Asian firms increasingly face a dual strategic requirement: they must accelerate digital transformation while meeting rising environmental, social and governance (ESG) expectations. This paper develops the ESG-digital nexus as a strategic management framework explaining how digital capabilities can strengthen ESG performance and how ESG objectives can discipline digital investment toward long-term value creation. Using an integrative conceptual synthesis informed by the resource-based view, dynamic capabilities theory and selected Asian sustainability contexts, the paper reframes digital transformation and ESG integration as mutually reinforcing organisational capabilities rather than separate compliance or technology agendas. The framework identifies three mechanisms: digitally enabled ESG measurement, behavioural embedding through digital institutional behavioural design and stakeholder-facing transparency. The illustrative evidence and maturity matrix suggest that firms with high digital and ESG maturity are better positioned to convert sustainability commitments into operational routines, financial resilience and reputational advantage. The paper contributes a practical quadrant model for diagnosing organisational maturity and for guiding staged managerial action in Asian firms operating under tightening sustainability reporting regimes.
K. Tan· International Journal of Sci...· 0 citations