Special issue: Mapping Artificial Intelligence Implementation: State of Art, Case Studies and Industry Insights

2025-10-28

Mapping Artificial Intelligence Implementation: State of Art, Case Studies and Industry Insights

Organizations are moving beyond pilots to implement AI at scale, yet robust guidance on how to do so effectively remains scarce. Management Research Quarterly invites submissions to the special issue “Artificial Intelligence Implementation: Case Studies and Industry Insights.” We welcome rigorous empirical studies and curated practitioner contributions that unpack strategy, governance, workforce, data, operations, value realization, and responsible AI across sectors and geographies. Eligible article types include research articles, comparative case studies, practice insights, teaching cases, and data or methods notes. Full papers are due 31 May 2026. Submissions should follow MRQ guidelines and include AI tool disclosures and ethics statements.

Guest Editors

  • Guest Editor: Ekaterina Glebova, (1) Faculty of Business, Higher Colleges of Technology (Dubai, UAE) & (2) CIAMS, University Paris Saclay (Orsay, France)
  • Guest Editor: Samar Shilbayeh, Faculty of Business, Higher Colleges of Technology (Dubai, UAE)
  • Guest Editor: Paulina Mihail’ova, Faculty of Management, Comenius University in Bratislava (Bratislava, Slovakia)

Background

This special issue spotlights rigorous conceptual and practice-grounded research on implementing AI at scale. We welcome actionable case studies, cross-industry comparisons, and executive-level perspectives that clarify success factors, common failure modes, governance and ethics practices (Sahut et al., 2023a), workforce implications, and measurable performance outcomes. By combining scholarly rigor with managerial relevance, the issue offers evidence-backed guidance for decision-makers across sectors and regions (Glebova et al., 2024). We invite systematic reviews, integrative frameworks, and theory-led contributions that synthesize the state of knowledge and specify boundary conditions. Recurring managerial challenges include model robustness in production, change management at scale, data governance, vendor lock-in, responsible AI, and ROI attribution (Abonamah et al., 2021). Submissions should advance theory on technology implementation (Monod et al., 2023), dynamic capabilities, socio-technical change, and governance (Sahut et al., 2023b), while remaining anchored in real organizational contexts. The special issue advances management theory through empirically rich, transparent inquiry, and elevates practice relevance through engaged scholarship and curated industry contributions held to scholarly standards. It aligns with MRQ’s mission to publish impactful, managerially oriented research with methodological rigor and clear implications for decision-makers.

Scope and Rationale

AI has moved decisively beyond pilots into enterprise-wide deployment, yet lessons on effective implementation remain dispersed and context-specific. This issue brings the evidence together, clarifying what works, for whom, and under which conditions, while translating insights into practical playbooks that leaders can adopt. Our aim is to consolidate fragmented findings and present a coherent agenda that advances both practice and theory.

Thematic Areas and Sample Topics

Strategy and governance topics include AI roadmaps, portfolio governance, build–buy–partner choices, platform and operating model decisions, and the mechanics of responsible AI, risk management, model oversight, and compliance. Organization and workforce topics cover reskilling, human–AI teaming (Monod, 2025), job redesign (Chapuis and Guégan, 2024), change management (Lissillour and Monod, 2024), capability building and centers of excellence, and the shift from project to product structures, alongside evolving labor relations, professional identities, and union engagement. Data, technology, and operations contributions may address prompt engineering at scale, model evaluation and drift management, data architecture and lineage, privacy-preserving analytics, security-by-design, legacy integration, and vendor ecosystem orchestration (Wei, 2024, Zhang and Gao, 2025). Performance and value realization research should illuminate business cases, ROI attribution, productivity and quality outcomes, customer impact, time-to-value, scaling patterns, risks of value erosion, and ESG-linked effects with credible measurement approaches. Ecosystems and regulation work can examine vendor ecosystems, standards, open-source versus proprietary stacks, regulatory responses, auditability and documentation, assurance practices, interoperability, switching costs, and multi-cloud strategies.

Methodological Expectations

We emphasize empirical rigor, encouraging multi-site case studies, longitudinal fieldwork, mixed methods, process tracing, action research, and, where feasible, practice-based approach (Bahadori & Ramjawan, 2025). We expect transparency in the chain of evidence, data access protocols, preregistration when appropriate, and formal ethics approvals. To support replicability, authors should provide methodological detail, instruments, coding schemes, and analytic code when possible. We welcome research articles, comparative case studies, practice insights, teaching cases, and data or methods notes. Article types and indicative lengths should follow MRQ’s general guidelines. Submissions should reflect diverse organizational contexts across Middle East, Europe, North America, and Asia-Pacific. We encourage studies of small and family businesses, and public agencies, alongside large enterprises and multinational ecosystems, to capture variation in resources, constraints, and adoption paths.

 

References

Abonamah, A. A., Tariq, M. U., & Shilbayeh, S. (2021). On the commoditization of artificial intelligence. Frontiers in psychology, 12, 696346.

Bahadori, M., & Ramjawan, S. (2025). Putting Bourdieu's theory of practice in practice in management studies. Management Research Quarterly, 2(2).

Chapuis, V., & Guégan, D. (2024). Empowering the exercise of responsibility in the design, implementation and use of AI‐based systems. Management Research Quarterly, 1(3), 23-32. https://doi.org/10.63029/jrgvn456

Glebova, E., Madsen, D. Ø., Mihaľová, P., Géczi, G., Mittelman, A., & Jorgič, B. (2024). Artificial intelligence development and dissemination impact on the sports industry labor market. Frontiers in Sports and Active Living, 6, 1363892.

Lissillour, R. & Monod, E. (2024). The Illusion of Transparency: Examining Power Dynamics in AI Implementation Strategies. Revue internationale de psychosociologie et de gestion des comportements organisationnels, 30(80), 79-114. https://doi.org/10.3917/rips1.080.0079

Monod, E. (2025). AI-Human Interaction: Insights from Organizational Behavior and AI Ethics in AI Supervision. Management Research Quarterly, 2(1), 41-49. https://doi.org/10.63029/rqg5z883

Monod, E., Lissilour, R., & Joyce, E. (2023). AI IMPLEMENTATION ISSUES. Digital Transformation: Organizational Challenges and Management Transformation Methods, pp. 49-69

Sahut, J. M., Braune, É., & Lissillour, R. (2023). AI development and ethical issues: Shifting from a static to a dynamic perspective. Management & Avenir, 137(5), 137-158. https://doi.org/10.3917/mav.137.0137

Sahut, J. M., Braune, É., & Lissillour, R. (2023a). A problematization of Artificial Intelligence Ethics: deliberative democracy versus agonistic pluralism. Management & Prospective, 40(6), 186-199.

Wei, Y. (2024). Exploring the Emerging Intellectual Structure in AI Marketing Publications: A Text Analysis Study. Management Research Quarterly, 1(2), 25–33. https://doi.org/10.63029/sf9va853

Zhang J., & Gao, X. (2025). Theory, Transformation, Practice and Exploration — A Review of An Introduction to Computational Advertising. Management Research Quarterly. 2(2). https://doi.org/10.63029/s4q7w409