Jul 2026· Scholedge International Journal of Management & Development ISSN 2394-3378· Vol 13, pp. 34· 0 citations
TL;DR
This paper examines how AI reshapes managerial decision-making by distinguishing decision augmentation from decision automation, and considers the governance tensions between centralized and decentralized approaches to AI deployment, as well as identifying the leadership competencies that gain value once routine managerial tasks are delegated to algorithmic systems.
Abstract
Hybrid work models developed largely in response to workplace disruptions caused by the COVID-19 pandemic, treating location flexibility as the central organizing question. This paper argues that artificial intelligence has moved the debate past questions of where work happens toward questions of how work is structured, decided, and governed. Drawing on Dynamic Capabilities Theory, the Resource-Based View, Knowledge-Based View, Sociotechnical Systems Theory, Organizational Learning Theory, Contingency Theory, and Human Capital Theory, the paper develops a conceptual framework linking AI capability, organizational agility, human-AI collaboration, leadership capability, employee empowerment, organizational learning, innovation performance, and organizational performance. The paper examines how AI reshapes managerial decision-making by distinguishing decision augmentation from decision automation, and considers the governance tensions between centralized and decentralized approaches to AI deployment. It also considers how algorithmic management affects trust, autonomy, and engagement among employees, and identifies the leadership competencies that gain value once routine managerial tasks are delegated to algorithmic systems. Because the paper is conceptual, it proposes a research design using PLS-SEM to test the relationships identified, along with measurement constructs, sampling considerations, and reliability and validity procedures for a future empirical study. The paper closes with implications for managers, policy makers, and organizational designers, and identifies open questions that current literature has not resolved, including the long-term effects of algorithmic management on organizational trust and the conditions under which decentralized AI governance outperforms centralized models. The paper does not report new empirical data. Its contribution lies in integrating separate streams of research on AI adoption, organizational design, and leadership into a single framework intended to guide subsequent empirical testing.
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
M. R· International Journal of Phi...· 0 citations
As artificial intelligence (AI) tools become embedded in everyday work, employees increasingly engage in adaptive, experiential collaboration with AI systems. While such collaboration often generates valuable employee-level tacit knowledge, organizations struggle to translate this learning into strategic capabilities. This study examines how organizations can harness the knowledge emerging from employee–AI collaboration to build knowledge-based dynamic capabilities (KBDCs).
We conducted an inductive, qualitative study based on 29 semi-structured interviews with frontline employees, middle managers and senior leaders across multiple industries.
We develop a process model showing how tacit knowledge generated through micro-level human–AI collaboration is externalized as articulated experiential insights, validated and simplified into shared heuristics, and ultimately codified and embedded in organizational routines. This bottom-up transformation may help firms to renew internal knowledge resources and enhance KBDCs. The process is contingent on enabling conditions such as managerial support, organizational culture and employees' perceived agency in interacting with AI.
Organizations seeking to build dynamic capabilities from AI use should create structures that facilitate articulation, validation and dissemination of employee-level AI insights. Leaders play a critical role in translating individual experimentation into collective learning. Firms can assess their position in the transformation process and invest in systems that support phase-to-phase transitions.
While prior research has emphasized acquiring codified or external knowledge for dynamic capabilities, this study shifts attention to the internal, tacit and evolving knowledge that arises from employee–AI collaboration. We advance theory by unpacking a bottom-up pathway of KBDC formation through which such knowledge is articulated, validated and embedded over time. In doing so, the study also identifies a recursive organizational learning mechanism suited for high-velocity, AI-enabled environments.
Erica Wen Chen, Hongkun Tang, Ben Nanfeng Luo et al.· Management Decision· 0 citations
Agentic artificial intelligence is reshaping how organizations plan, coordinate activities, allocate resources, monitor risks, and make strategic decisions. Unlike conventional AI systems that primarily provide predictions or recommendations, agentic AI can interpret objectives, plan actions, use organizational tools, coordinate workflows, and execute tasks with varying levels of autonomy. This shift creates important implications for strategic leadership, particularly regarding executive authority, decision rights, accountability, and organizational control. This study examines how strategic leaders can redefine executive decision-making and governance structures in organizations adopting agentic AI systems. Drawing on dynamic capability’s theory, agency theory, organizational control theory, and socio-technical systems theory, the study develops a framework that links strategic leadership capability, AI governance maturity, executive oversight, agentic AI autonomy, accountability clarity, and organizational performance. A mixed-methods approach is proposed, combining survey evidence from executives and AI governance professionals with qualitative interviews involving senior leaders, digital transformation managers, and risk professionals. The study investigates how leadership capabilities, governance arrangements, and control mechanisms influence decision quality, trust in AI-enabled processes, and organizational resilience. The proposed framework emphasizes the importance of clearly defined decision boundaries, human approval thresholds, explainability, auditability, escalation procedures, and continuous governance review. The study contributes to strategic leadership and AI governance research by positioning executives not simply as final decision-makers, but as architects of human-AI decision systems. It offers practical guidance for organizations seeking to balance agentic AI autonomy with responsible executive control, ethical accountability, and long-term strategic value.
Satyasri Akula· International Journal of Eme...· 0 citations
This paper introduces two novel analytical frameworks—the Human-AI Collaboration Spectrum and the Work Transition Matrix—and proposes the ADAPT Framework, a five-pillar governance architecture for responsible AI-driven workforce transformation. Moving beyond the simplistic displacement-versus-augmentation debate, the paper argues that AI's employment impact is conditional rather than deterministic—shaped by organizational governance choices rather than the technology itself. Drawing on labor economics, organizational theory, supply chain management research, and illustrative case applications in legal services, advanced manufacturing, and financial services, the paper demonstrates the framework's cross-sector applicability and develops specific implications for supply chain organizations managing AI-enabled planning, sourcing, manufacturing, logistics, and operational decision-making. The paper offers actionable implementation guidance for leaders, policymakers, and researchers.
Piu Ghosh· International journal of sup...· 0 citations
Artificial intelligence now performs many of the analytical and operational tasks that once defined managerial work: forecasting, scheduling, performance evaluation, and even elements of strategic scenario planning. This paper asks a question that follows directly from that shift: once machines handle information processing at a scale and speed no human can match, what remains for human leaders to do? Rather than treating this as a question of job loss or job survival, the paper reframes it as a question about the composition of leadership itself. Drawing on and critically comparing agency theory, stewardship theory, upper echelons theory, dynamic capabilities, institutional theory, stakeholder theory, and several strands of leadership scholarship including transformational, authentic, adaptive, complexity, responsible, and servant leadership, the paper argues that these theories were built for an era in which cognition and judgment were bundled together in a single human actor. AI unbundles them. What remains once information processing is delegated to machines is a residue of ethical judgment, contextual interpretation, meaning-making, institutional stewardship, and the capacity to hold ambiguity long enough for a considered choice to emerge. The paper develops an original conceptual model, the Human Leadership Advantage Framework, which organizes sixteen interdependent leadership dimensions into four clusters: moral and ethical grounding, relational and cultural capacity, institutional and strategic stewardship, and adaptive and generative capability. The framework explains why these dimensions do not simply survive AI adoption but become more consequential as routine cognition is automated. The paper closes with implications for organizational design, executive development, board oversight, and leadership education, together with a research agenda for scholars studying leadership in AI-saturated organizations.
Ana Fernanda· Scholedge International Jour...· 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
