Artificial Intelligence in Management Science

Introduction

Artificial Intelligence (AI) is fundamentally transforming management science, offering new opportunities to enhance decision-making, operations, marketing, supply chain management, finance, strategy, and human resource management. AI can be broadly defined as systems capable of performing tasks that normally require human intelligence, such as learning, reasoning, and problem solving (Jordan & Mitchell, 2015).

Organizations are rapidly adopting AI to improve performance, responsiveness, and innovation, yet this transformation raises complex management challenges. Integrating AI into organizational processes requires alignment with existing structures, cultures, and strategies (Ayinaddis, 2025, Jarrahi et al., 2021; Raisch & Krakowski, 2021,). Employees must adapt to new technologies, redefining roles and skills, while managers must address change management, coordination, and resistance (Korzynski et al., 2025, Tarafdar et al., 2019).

AI is emerging as a powerful driver of management innovation by enabling real-time decision support, creative problem-solving, and dynamic strategy formulation. AI transforms managerial tasks by automating cognitive processes and fostering novel configurations of organizational knowledge (Zhang and Zhang, 2025)

AI offers a support for decision-makers within the organization to encourage knowledge-sharing activities that will be advantageous for both the workforce and the organization itself (Argote, 2013; 2015). As a result, AI is capable of spotting redundancies in business operations and recommending the best use of resources to enhance performance.  
  
In addition to its transformative potential, AI brings new challenges such as model drift, hallucinations in generative models, and decision opacity in deep learning systems. The automation of managerial and operational processes raises questions about explainability, trust, and systemic bias (Tomczyk, Brueggemann & Vrontis, 2024)Understanding how organizations manage the balance between efficiency gains and algorithmic risks is crucial for sustainable AI integration. (Rai 2020,Ahadian & Guan, 2025).

Moreover, ethical considerations are central to AI adoption: issues of transparency, accountability, fairness, and bias demand new governance models (Martin, 2019; Mittelstadt, 2019, Radanliev, 2025). Despite growing interest, there remain critical research gaps in understanding how AI is implemented and used across management science domains, and which factors shape successful outcomes.

We call for research that advances our understanding of AI in management science, encompassing strategic management, operations, marketing, finance, human resources, organizational change, and business ethics. Authors should spell out actionable implications for managers, policymakers and regional development bodies operating across the full spectrum of industrial and service sectors.

List of topic areas

• Artificial Intelligence
• Management Science
• Strategic Management
• Operations Management
• Supply Chain
• Knowledge Management
• Marketing
• Finance
• Human Resource Management
• Organizational Change
• Ethics and Trust
• Public and Private Sector Differences

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available at: https://mc.manuscriptcentral.com/emjb
Author guidelines must be strictly followed. Please see: https://www.emeraldgrouppublishing.com/journal/emjb#jlp_author_guidelines
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to "Please select the issue you are submitting to".

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key deadlines

Opening date for manuscripts submissions: 01/09/2026
Closing date for manuscripts submission: 31/12/2026

References

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2. Argote, L. (2013). Organizational learning: Creating, retaining and transferring Knowledge (2nd ed.). Springer, New York
3. Argote. L. (2015). An Opportunity for Mutual Learning between Organizational Learning and Global Strategy Researchers: Transactive Memory Systems. Global Strategy Journal, 5 (2), pp. 198-203
4. Ayinaddis, S. G. (2025). Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: A systematic review and bibliometric analysis. Journal of Innovation & Knowledge, 10(3), 100682.
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6. European Union (2024) Regulation (EU) 2024/1689 of the European Parliament and of the Council of 12 June 2024 laying down harmonised rules on artificial intelligence (AI Act). Official Journal of the European Union, L 221, 1–247. https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-…
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9. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
10. Korzynski, P., Edwards, A., Gupta, M. C., Mazurek, G., & Wirtz, J. (2025). Humanoid robotics and agentic AI: reframing management theories and future research directions. European Management Journal.
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12. Mittelstadt, B. D. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507.
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