Aug 2026· Journal of Organizational Behavior· 2 citations· 49 references
TL;DR
The eight contributions examine how design choices such as human‐likeness and gendered cues shape perceptions of AI, how AI alters team processes including decision‐making, trust, and stress, and how training and organizational integration condition sustainable human–AI collaboration.
Abstract
Artificial intelligence (AI) is increasingly part of collaborative work that was once carried out exclusively by humans. In this special issue, we ask whether our future colleagues will even be human and what this means for today's organizations. The eight contributions examine how design choices such as human‐likeness and gendered cues shape perceptions of AI, how AI alters team processes including decision‐making, trust, and stress, and how training and organizational integration condition sustainable human–AI collaboration. Together, the contributions suggest that AI reshapes collective work processes, not only individual judgments. Building on this insight and on sociotechnical systems thinking, we develop a framework that distinguishes where AI is positioned in the social system of work and how much agency it exercises. The framework also raises the normative question of how AI's position in organizations should be governed. Together, the contributions and the framework point toward a research agenda that differentiates between human–AI configurations and treats teams rather than dyads as the unit of analysis.
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
Human-AI collaboration (HAIC) has moved from research curiosity to strategic priority for organizations pursuing faster, more inventive, and more reliable innovation outcomes. Many implementations underperform, however, because leaders treat artificial intelligence as a generic productivity tool rather than as a designed teammate whose role, capabilities, and trust requirements must match the task. This article synthesizes recent scholarship and practitioner experience into a structured playbook for executives, design leaders, and human resources partners. It argues that the value of HAIC depends on three deliberate design choices: who initiates the collaboration, how broad the AI's knowledge scope must be, and whether the cognitive mode is analytical or synthetic. Drawing on engineering design, aerospace, industrial product development, hospitality, and mental health contexts, the discussion translates research findings into operating practices, governance structures, and capability investments. The contribution is practical: a clearer way to decide what kind of AI teammate to build, deploy, and trust for any given problem.
Jonathan H. Westover· Human Capital Leadership Rev...· 0 citations
This study examines the transformative impact of artificial intelligence (AI) on the future of work from a human-centered, interdisciplinary perspective, highlighting the necessity of interdisciplinary collaboration to ensure that AI not only enhances efficiency but also promotes justice, well-being, and sustainability.
Cumali Kılıç· Çukurova Üniversitesi Sosyal...· 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
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
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.
R. Narenderajan· Scholedge International Jour...· 0 citations
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