Agentic Artificial Intelligence and the Future of Multinational Enterprises: Opportunities, Risks, and Reconfigurations

Introduction

An artificial intelligence agent (AI agent) refers to an intelligent machine that performs cognitive tasks and acts autonomously on behalf of humans once decision authority and task execution are delegated (Raisch & Krakowski, 2021). While the term AI agent is rapidly capturing public imagination we have been using AI agents for decades in global financial transactions, social media, or across industry sectors like gaming, manufacturing, logistics, or aviation. The rapid adoption of agentic artificial intelligence is fundamentally transforming how multinational enterprises (MNEs) compete, strategize, and organize in the global economy (e.g., Luo and Zahra, 2023; Meyer et al., 2023; Teece, 2025). Gartner predicts that by 2028, 33% of all enterprise software will be agentic AI (Coshow, 2024).

Unlike traditional AI systems that primarily support prediction or recommendation, agentic AI systems are characterized by autonomy, goal orientation, and the capacity to initiate and coordinate actions across tasks and organizational boundaries (Raisch and Krakowski, 2021; Krakowski, 2023; Nam et al., 2025). AI agents and have various levels of autonomy raising interesting questions across borders, as these systems can independently gather information, evaluate alternatives, and execute decisions, often interacting with other agents, humans, and digital infrastructures in real time (Berente et al. 2021; Nam et al., 2025).

For globally operating firms, agentic AI holds particular strategic significance (Menzies et al., 2025; Teece, 2025). By enabling continuous sensing, decision-making, and execution across dispersed markets, agentic AI can reshape how MNEs manage cross-border complexity, respond to environmental volatility, and coordinate international operations (Menzies et al., 2025). Agentic AI can support autonomous market entry decisions, dynamic pricing, supply-chain reconfiguration, alliance management, and real-time risk mitigation. Agentic AI is also available as products such as autonomous cars, robotic systems, like drones, toys, but also as more advanced concepts like virtual persons that can act on behalf of a person either by design, consent or ignorance. As a result, agentic AI is increasingly embedded in core strategic and organizational processes across functions like human resources, marketing, or governance, rather than confined to traditional peripheral analytical or decision-support functions (Meyer et al. 2023; Teece 2025). 

At the same time, the adoption of agentic AI introduces profound challenges. Granting AI systems greater autonomy raises concerns about control, accountability, transparency, and alignment with organizational goals and societal norms (Menzies et al., 2024). Agentic AI systems may amplify risks related to algorithmic bias, unintended strategic actions, regulatory noncompliance, and cascading errors across interconnected global operations (e.g., Kellogg et al., 2020; Berente et al., 2021). Moreover, as agentic AI diffuses rapidly across firms and industries, sustaining competitive advantage becomes more difficult, while the consequences of misaligned or poorly governed AI agents become more severe (Raisch and Krakowski, 2021). Several examples exist as per AI risk registers (e.g. MIT AI Risk Register, OECD AI incidents monitor) and the impacts are global.

Understanding how MNEs design, deploy, and govern agentic AI in international contexts is therefore critical for advancing international business and strategic management research. This Special Issue seeks to deepen insight into how MNEs worldwide leverage agentic AI in their international strategies—including market sensing, managing global teams, supply chains, exports, foreign direct investment (FDI), strategic alliances, joint ventures, and mergers and acquisitions (M&As)—while managing the organizational, strategic, and ethical challenges associated with autonomous, action-taking AI systems.  This call for papers builds on the work by Hughes et al (2025), Buckley et al (2026) and invites authors to respond to the following questions where AI is an agent with various levels of autonomy.  

Key themes and research questions for the special issue

The Special Issue focuses on three interrelated themes that capture how agentic AI reshapes global competition of MNEs: competing, strategizing, and organizing. 

1. Strategizing with Agentic AI

This theme focuses on how agentic AI transforms strategic planning and decision-making in MNEs. Unlike traditional analytics, agentic AI can autonomously generate strategic options, simulate outcomes, and implement decisions with minimal human intervention. This shifts strategizing from episodic, human-centered processes toward continuous, AI-enabled strategic adaptation.

Yet, greater AI autonomy raises important questions about strategic human control, judgment, and alignment. Agentic AI systems may prioritize short-term optimization over long-term strategic coherence or misinterpret local institutional and cultural contexts. Effective strategizing therefore requires carefully designed human–AI governance arrangements that integrate managerial oversight and responsibility with AI-driven autonomy. 

Indicative research questions include (but are not limited to):

• How do MNEs integrate agentic AI into strategic decision-making while retaining human oversight?
• What governance mechanisms ensure alignment between agentic AI procurement, actions and firm-level strategic intent and values at the local country level of operation?
• How do firms mitigate bias and path dependence in agentic AI–driven strategizing, especially given the origin of AI (data, software, models etc.)?
• Which strategic decisions are most effectively delegated to autonomous AI agents and does this vary across countries?
• How does agentic AI influence opportunity recognition, market entry timing, and international expansion paths in emerging markets versus more ‘so called” developed markets?
• How do human managers adapt their roles when strategizing becomes increasingly AI-driven and does this vary across countries?
• What international ethical challenges originate through the blackbox of agentic AI and what are organizational responses to these challenges, especially given the dissimilarities across countries in regulations and country preferences? 

2. Competing using Agentic AI 

This theme examines how MNEs use agentic AI to compete in global markets, create value, and sustain competitive advantage. By autonomously sensing market signals and executing strategic actions, agentic AI enables firms to respond more rapidly to competitive moves, dynamically adjust pricing and offerings, and coordinate complex international activities. These capabilities can fundamentally alter the speed, scope, and intensity of global competition.

However, the diffusion of agentic AI also challenges firms’ ability to sustain advantage, as autonomous agents may rely on similar data sources, architectures, or pretrained models. Further, the role of country-of-origin of Agentic AI may matter where firms compete in a geopolitical environment or where there is regulatory fragmentation. Moreover, competitive use of agentic AI raises risks related to unintended strategic escalation, algorithmic collusion, data leakage, and regulatory scrutiny—particularly when autonomous agents operate across multiple jurisdictions.

Indicative research questions include (but are not limited to): 

• How do MNEs build, select or deploy agentic AI to compete in global markets characterized by volatility and rapid change?
• What types of competitive advantages or value emerge from agentic AI, and how durable are they across markets and industries?
• How does agentic AI reshape competitive dynamics, including rivalry intensity and strategic imitation?
• How do firms manage risks associated with autonomous action, such as unintended coordination or regulatory violations across borders?
• What complementary organizational and technological assets are required to capture value from agentic AI and how do you transfer these from HQ to subsidiaries?
• How does agentic AI enable firms to maintain competitive positioning during global crises or disruptions? 

3. Organizing around Agentic AI

This theme examines how agentic AI reshapes organizational structures, coordination mechanisms, and governance systems in MNEs. Agentic AI can automate coordination across borders, reallocate tasks between humans and machines, and enable more fluid and decentralized organizational forms. At the same time, it may disrupt established hierarchies, alter power distributions, and challenge traditional accountability structures.

Organizing around agentic AI requires firms to design systems that foster effective human–AI collaboration, manage workforce transitions, and ensure ethical and responsible AI use. The delegation of decision authority to autonomous agents raises fundamental questions about responsibility, control, and legitimacy in global organizations.

Indicative research questions include (but are not limited to):

• How do MNEs redesign organizational structures to accommodate autonomous AI agents?
• What governance frameworks support accountability and transparency in agentic AI–driven MNEs?
• How does agentic AI reshape power dynamics and managerial roles within global firms and across the global value chain?
• How do firms manage workforce adaptation and resistance in response to autonomous AI systems across countries?
• What hybrid human–agentic AI models optimize coordination and performance between global stakeholders?
• How do MNEs maintain organizational coherence when agentic AI operates across international subsidiaries? 

We welcome quantitative, qualitative, and mixed-methods research, including deductive, inductive, and abductive approaches, as well as purely conceptual and theory-building contributions. This special issue also welcomes a range of methodologies to address the above questions. Authors are encouraged to explore boundary definitions of agentic AI and test existing theories looking at various contexts.

References 

Berente, N., Gu, B., Recker, J. and Santhanam, R. (2021), “Managing artificial intelligence”, MIS Quarterly, Vol. 45 No. 3, pp. 1433–1450.

Buckley, P. J., Elia, S., Gaur, A., Krakowski, S., Kuivalainen, O., & Pedota, M. (2026). Artificial intelligence and international business: Theoretical challenges, strategic implications, and research agenda. Journal of World Business, 101710.

Coshow, T. (2024), “Intelligent Agents in AI Really Can Work Alone. Here’s How”, Gartner, available at: https://www.gartner.com/en/articles/intelligent-agent-in-ai (accessed 20 December, 2025).

Csaszar, F.A., Steinberger, N.M. and Ostler, J. (2024), “Artificial intelligence and strategic decision making”, Strategy Science, Vol. 9 No. 1, pp. 1–20. 

Hughes, L., Dwivedi, Y. K., Li, K., Appanderanda, M., Al-Bashrawi, M. A., & Chae, I. (2025). AI agents and agentic systems: Redefining global it management. Journal of Global Information Technology Management, 28(3), 175-185.

Kellogg, K.C., Valentine, M.A. and Christin, A. (2020), “Algorithms at work: The new contested terrain of control”, Academy of Management Annals, Vol. 14 No. 2, pp. 366–410. 

Krakowski, S. (2023), “Artificial intelligence and the changing sources of competitive advantage”, Strategic Management Journal, Vol. 44 No. 2, pp. 456–480.

Luo, Y. and Zahra, S.A. (2023), “Industry 4.0 in international business research”, Journal of International Business Studies, Vol. 54 No. 3, pp. 403–421.

Menzies, J., Sabert, B., Hassan, R., and Mensah, P.K. (2024), “Artificial intelligence for international business: Its use, challenges, and suggestions for future research and practice”, Thunderbird International Business Review, Vol. 66 No. 2, pp. 185-200. 

Meyer, K.E., Li, J., Brouthers, K.D. and Jean, R.J.B. (2023), “International business in the digital age: Global strategies in a world of national institutions”, Journal of International Business Studies, Vol. 54 No. 4, pp. 577–598.

Nam, H., Li, Y., Kannan, P.K. and Choi, J. (2025), “Liability of foreignness in immersive technologies: evidence from extended reality innovations”, Journal of International Business Studies, Vol. 56 No. 3, pp. 422–439.

Raisch, S. and Krakowski, S. (2021), “Artificial intelligence and management: The automation–augmentation paradox”, Academy of Management Review, Vol. 46 No. 1, pp. 192–210.

Teece, D. J. (2025). “The multinational enterprise, capabilities, and digitalization: governance and growth with world disorder”, Journal of International Business Studies, Vol. 56 No. 1, pp. 1–23.

Submissions Information 

Submissions are made using ScholarOne Manuscripts. Registration and access are available at:  ​https://mc.manuscriptcentral.com/roibs

Author guidelines must be strictly followed. Please see: ​https://www.emeraldgrouppublishing.com/journal/ribs​

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: ​01/08/2026​ 

Closing date for manuscripts submission: ​31/01/2027​ 

Final acceptance date for manuscripts:  ​31/11/2027

For abstracts, please email: [email protected]