Maarten Renkema, University of Twente

Laura Lamers, University of Twente

Christoph Lutz, BI Norwegian Business School

Anna B. Holm, Aarhus University

Artificial intelligence (AI) is being rapidly adopted across organisations, driven by promises of effectiveness and efficiency, yet accompanied by well-documented risks such as algorithmic bias and opacity. As competitive pressures compel AI adoption regardless, AI literacy—defined as the knowledge, skills, and attitudes that allow individuals (i.e., employees and HR-professionals) to engage effectively and critically with AI technologies—becomes crucial. This urgency is further underscored by the EU's AI Act, which includes the requirement for a sufficient level of AI literacy in organisations.

AI literacy research stands at a critical point. While widely acknowledged as necessary, it remains insufficiently understood in organisational and HRM contexts. Extant research on AI literacy has produced conceptual frameworks and measurement tools, but this work is predominantly embedded in educational research and is focused on individual learners, leaving the organisational perspective largely under-researched and under-theorised.

This special issue addresses that gap directly. It places AI literacy at the core of organisational development, learning and consequences for HRM, and invites contributors across varied disciplines and methodologies to advance understanding of what AI literacy means as an organisational phenomenon, how it is facilitated by HRM, and the role HR managers play in AI-literate organisations. The aim is to contribute to theory and practice at a moment when the need for actionable insights into AI literacy could not be greater.

This special issue aims to advance theoretical understanding of AI literacy by moving beyond two common perspectives in the current literature: uncritically optimistic framings that treat AI literacy as a solution to unlock AI's strategic potential and neutralise AI's risks and challenges, and dismissive pessimistic framings that treat AI literacy as little more than a legitimising instrument for AI adoption. Rather, critically engaged, practically relevant, and empirically grounded perspectives are advocated that position AI literacy between these two extremes, as a significant challenge for organisations and HRM, generating knowledge that genuinely benefits organisations and HR professionals.

Theoretical perspectives are welcomed that conceptualise AI literacy as a broader organisational capability than a set of individual competencies, for example, as emergent, relational, or organisationally constituted phenomenon. The scope is organisational, focusing on AI literacy within organisations and its implications for HRM, rather than on AI literacy at the national, legal policy or individual employee levels. To make a substantial theoretical contribution, manuscripts are invited that view AI literacy across organisational levels, adopting a multilevel organisational perspective.

Given the current state of the literature on AI literacy, empirical work is strongly encouraged with varied methodological approaches—qualitative, quantitative, and mixed-methods. Innovative and non-traditional research designs and approaches are welcomed as long as they respect the standards of rigor and relevance, such as interventions, action research, ethnographies, and longitudinal studies. Studies that conceptualise AI literacy as an organisational, dynamic, context-dependent phenomenon rather than a static individual attribute are particularly valued. All authors are encouraged to aim for societal and practical impact and ethical conduct of science. High-quality conceptual contributions that advance extant theoretical understanding are also welcome. The issue will not include systematic literature review papers. Submissions should have an explicit link to HRM theory and practice.

Submissions are welcomed from diverse sectors, organisation types (from small to large; public and private) and geographical contexts, including studies from the Majority World and from contexts that remain underrepresented in AI and HRM research.

Potential topics

• Implications of AI literacy for core HRM functions, systems, policies and practices, including recruitment and selection, learning and development, performance management, and employee well-being
• Organizational AI literacy: definition, design, implementation, and governance of HRM activities
• Emergence mechanisms of individual AI literacy into collective organizational capability and HRM contributions
• Relationship between organizational AI literacy and adjacent constructs such as organizational AI readiness, antecedents and outcomes
• Competition for AI talent in the labour market and inequality implications inter- and intra-organizationally
• Team reshaping and creation to enable, develop and foster AI literacy
• HRM and HRD practices supporting development of AI-literate teams through workforce planning, staffing, job design, training and allocation of responsibilities
• AI literacy levels and combinations needed across different teams and roles, and HRM practices supporting capability building
• Manifestation of AI literacy in everyday work practices and its transformation of work
• Technical, ethical and practical dimensions of AI literacy in AI-mediated recruitment, fairness perceptions, and AI overreliance
• AI literacy of HR professionals and influence on adoption of AI tools

Timeline

• Invalid DateTime: Special issue completion
• September 1, 2026: Special Issue announced
• February 1, 2027: Submission window opens
• April 30, 2027: Submission deadline
• July 1, 2027: Decision on first submissions

This Journal of Enterprise Information Management Special Issue seeks to understand Agentic Artificial Intelligence and Generative AI (GenAI) in healthcare, and how these technologies impact the governance, strategy, and value creation of healthcare organizations. In technological innovation, digital technologies are reconfiguring value creation processes and prompting organizations to develop new adaptive strategies. In healthcare, this transformation is driving the adoption of innovative solutions to enhance value for stakeholders while supporting more personalised, predictive, and preventive models of care. AI and GenAI are emerging as strategic levers for optimising resource allocation, supporting new care delivery paradigms, and accelerating research and development. The rapid emergence of Agentic AI systems introduces a further step in this transformation, with AI technologies moving from reactive tools towards semi-autonomous systems able to plan, coordinate and monitor actions across complex organizations.

Healthcare is a relevant setting for examining how innovation management shapes competitiveness, sustainability, and value-creation capabilities. Recent debate has shifted from a focus on the technical performance of AI systems to broader concerns related to implementation, accountability, trustworthiness, evaluation, and organisational sustainability. This shift is crucial in healthcare, where Agentic AI and GenAI are not merely digital transformation tools, but sociotechnical systems that potentially affect clinical practices, decision-making processes, care coordination, patient-doctor relationships, resource allocation and costs optimization.

AI and GenAI are sociotechnical systems with growing autonomy and interactive capabilities, thereby raising new questions around trust, responsibility, human oversight, and governance. As such, they pose a significant challenge to enterprise information management, affecting processes, data, professional roles, compliance, procurement, and monitoring systems.

The deployment of Agentic AI and GenAI occurs in high-risk, highly regulated, data and human-intensive settings. Healthcare organizations must balance innovation with patient safety, care quality, ethical and regulatory issues, data protection and human oversight preservation. Errors, biases and unclear accountability may affect patients, professionals and healthcare ecosystems.

This Special Issue seeks theoretical and empirical contributions examining how health systems, healthcare organizations, and providers develop capabilities, governance structures, and evaluation practices to move from experimentation to technology adoption and integration. Particular attention will be given to agentic workflow integration, responsible value creation, data governance, clinical and managerial accountability, human oversight, professional role reconfiguration, patient-doctor relationship, organisational capabilities and compliance with existing regulatory frameworks.

By focusing on healthcare as the empirical and theoretical context, this Special Issue aims to generate new insights into how Agentic and GenAI systems can be responsibly embedded in healthcare organizations while balancing innovation, safety, equity, trust, regulatory compliance and measurable clinical, organisational, and societal value.

Potential topics

• How are GenAI and Agentic AI reshaping clinical, administrative, and managerial workflows in healthcare organizations?
• How do healthcare organizations govern Agentic AI systems across care pathways?
• What organizational capabilities are needed to move from experimental GenAI applications to integrated and scalable Agentic healthcare systems?
• How can healthcare organizations ensure meaningful human oversight when AI systems become more autonomous, proactive, and embedded in clinical or administrative processes?
• How do GenAI and agentic AI create, capture, or potentially destroy value for different healthcare stakeholders?
• How do Agentic and GenAI systems transform healthcare knowledge management?
• How can healthcare organizations evaluate and measure the clinical, organizational, economic, ethical, and societal value generated by GenAI and Agentic AI adoption?
• What governance mechanisms are needed to ensure accountability, transparency and regulatory compliance in AI-enabled healthcare organizations?
• How do GenAI and Agentic AI affect decision-making processes within healthcare organizations?
• How can healthcare organizations manage risks related to automation bias, inequitable outcomes and over-reliance on AI?
• How do agentic AI and GenAI support healthcare system sustainability?

Timeline

• January 1, 2027: Opening date for manuscript submissions
• June 30, 2027: Closing date for manuscript submissions