Timeline

• January 31, 2027: Full paper submission deadline

Asha Thomas, Wrocław University of Science and Technology

Moreno Frau, Corvinus University of Budapest

Dominyka Venciūtė, ISM University of Management and Economics

Across contemporary organizations, advances in artificial intelligence (AI) are transforming AI from a discrete technological resource into a systemic organizational capability that actively shapes decision-making, business model innovation, and competitive advantage. Traditionally, AI interfaces have largely been reactive, responding to human prompts and predefined inputs. The emergence of Agentic Artificial Intelligence represents a fundamental shift, as agentic systems are designed to operate with increasing autonomy, enabling goal-driven planning, workflow orchestration, coordination across systems, and machine-initiated action with limited human intervention.

For organizations, this growing autonomy presents both significant opportunities and substantial risks. Agentic AI promises new forms of value creation by enhancing efficiency, scalability, personalization, and decision quality across organizational functions such as human resource management, marketing, customer engagement, knowledge management, and operations. At the same time, the delegation of agency to autonomous systems heightens concerns related to governance, transparency, accountability, and oversight, particularly when organizations have limited visibility into how agentic systems reason, learn, and act. Moreover, misaligned interactions and problematic resource integration may produce unintended negative outcomes, underscoring the coexistence of value creation and value co-destruction in AI-enabled organizational processes.

Despite these unresolved challenges, agentic AI is no longer a speculative phenomenon. Organizations have already begun embedding agentic systems into core practices, including recruitment, onboarding, performance management, customer service, marketing operations, and knowledge-intensive work. This diffusion reflects a broader shift in which AI is increasingly understood as a normalized and enduring component of contemporary organizational and marketing systems rather than a temporary technological trend. In parallel, early implementations in knowledge management demonstrate how agentic systems can unify fragmented knowledge bases, dynamically adapt insights, and support continuous organizational learning.

From an academic standpoint, these developments challenge existing organizational and information management theories. While socio-technical systems theory, agency theory, and organizational learning have traditionally conceptualized AI as a support tool within human-centric systems, they offer limited explanatory power for autonomous, multi-agent systems capable of independent coordination and action. In marketing and customer engagement contexts, interactive value formation is increasingly shaped by emotional and relational dynamics emerging from human–AI interactions, further complicating assumptions about control and responsibility. As such, new theoretical perspectives are needed to capture agency, accountability, and knowledge dynamics in AI-enabled organizations.

Potential topics

• How does Agentic AI enable new forms of value creation, capture, and measurement in organizations? Under what conditions can agentic systems also lead to value co-destruction or unintended negative outcomes due to misaligned autonomy, resource integration, or decision logic?
• How do organizations design governance, accountability, trust, and regulatory compliance mechanisms for Agentic AI systems operating with increasing autonomy? What challenges arise when human oversight is limited or distributed across functions?
• How do ethical considerations, responsibility, and moral agency evolve when AI systems act as semi-autonomous organizational actors rather than decision-support tools?
• How does Agentic AI reshape business process redesign, orchestration, and automation across organizational functions such as marketing, human resource management, operations, finance, and customer engagement?
• In what ways is artificial intelligence becoming normalized within organizational and marketing practice, shifting from experimental adoption to routinized, AI-embedded decision-making and workflows?
• How does Agentic AI influence workforce transformation, the future of work, and human resource management practices, including recruitment, performance evaluation, learning, and employee autonomy?
• How does the adoption of Agentic AI differ across organizational contexts, such as small and medium-sized enterprises versus large corporations, and what factors shape successful implementation and impact?
• How do emotional, relational, and interactional dynamics shape human–AI engagement in Agentic AI–driven sales, marketing, and customer experience contexts?
• How can human–AI collaboration and human-in-the-loop design be sustained when AI systems increasingly initiate actions, coordinate tasks, and learn autonomously?
• How do multi-agent systems, coordination mechanisms, and organizational architectures evolve as multiple human and artificial agents interact within complex socio-technical environments?
• How can existing theories of agency, organizational learning, and socio-technical systems be extended or reconfigured to explain machine agency and autonomous action in Agentic AI–enabled organizations?
• How does Agentic AI transform knowledge management, organizational learning, and decision support by enabling systems that not only retrieve and integrate knowledge but also reason, adapt, and act upon it?

Timeline

• March 1, 2026: Opening date for manuscript submissions
• September 30, 2026: Closing date for manuscript 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

Mark Camilleri, University of Malta

Generative Artificial Intelligence (GenAI) and Agentic Artificial Intelligence (Agentic AI) are transforming how services are designed, delivered, experienced and led. While GenAI refers to systems, such as large language models (LLMs), that produce content in response to human prompts; Agentic AI technologies may be considered as active agents that can implement tasks (rather than merely functioning as passive generators). The latter can monitor situations, allocate resources, initiate and manage processes as well as co-ordinate multiple activities. Hence, Agentic AI algorithms and their governance affect service outcomes.

Generative AI capabilities often constitute the communicative and cognitive foundations of Agentic AI. In other words, many Agentic AI systems rely on GenAI models to reason, communicate and interact. Together, these AI technologies challenge conventional assumptions about agency, control, responsibility and value creation in service environments. Unlike earlier forms of automation and analytics, these AI systems can engage in social interactions, reason in a contextual manner and may dynamically adapt to changing situations. As such, they raise profound theoretical questions about anthropomorphism, social presence, trust, autonomy, creativity, emotion, accountability, responsibility and moral agency.

These capabilities indicate that Generative and Agentic AI represent more than incremental advances in automated technologies. They introduce different forms of interaction and agency that cannot be fully explained by utility-driven adoption frameworks. Consequently, there is a growing need for theory-driven and conceptually rigorous research that explains how, why and under what conditions Generative and Agentic AI are deployed, adapted, governed, or even resisted in service environments.

This special issue seeks to advance services marketing research by encouraging scholars to utilize, extend, integrate or critically evaluate existing theories to investigate user engagement with Generative and Agentic AI across diverse service settings. The guest editorial team particularly welcomes submissions that move beyond descriptive accounts. Prospective contributions are expected to offer strong theoretical explanations of AI acceptance and usage in services.

Submissions that integrate multiple perspectives, compare existing conceptual frameworks and develop new theoretical models specific to GenAI and Agentic AI in services are especially encouraged for this special issue. The special issue welcomes conceptual, qualitative, quantitative, experimental or mixed-methods approaches, provided that the contributing authors demonstrate strong theoretical grounding and relevance to the underlying objectives of this journal.

Potential topics

• Theoretical perspectives on Generative and Agentic AI adoption in service environments
• Comparative or multi-theoretical frameworks for studying human-AI interaction in services
• Anthropomorphism, social presence and human-AI relationships
• Perceived affordances, interface design and service experiences
• Emotions, expectations and psychological responses to AI
• Adoption, acceptance and continued use of AI in services
• Trust, ethics, accountability and relational governance
• AI as a service actor within socio-technical systems
• Contextual and contingency-based perspectives
• Value co-creation, value co-destruction and service outcomes
• Organizational, strategic and policy implications of Generative and Agentic AI in services

Timeline

• June 23, 2026: Opening date for manuscripts submissions
• February 26, 2027: Closing date for manuscripts submission