Strategic Leadership in the Age of Agentic AI: Redefining Executive Decision-Making and Organizational Control
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V6I2P117Keywords:
Agentic AI, Strategic Leadership, Executive Decision-Making, Organizational Control, AI Governance, Human-AI Collaboration, Digital TransformationAbstract
Agentic artificial intelligence is reshaping how organizations plan, coordinate activities, allocate resources, monitor risks, and make strategic decisions. Unlike conventional AI systems that primarily provide predictions or recommendations, agentic AI can interpret objectives, plan actions, use organizational tools, coordinate workflows, and execute tasks with varying levels of autonomy. This shift creates important implications for strategic leadership, particularly regarding executive authority, decision rights, accountability, and organizational control. This study examines how strategic leaders can redefine executive decision-making and governance structures in organizations adopting agentic AI systems. Drawing on dynamic capability’s theory, agency theory, organizational control theory, and socio-technical systems theory, the study develops a framework that links strategic leadership capability, AI governance maturity, executive oversight, agentic AI autonomy, accountability clarity, and organizational performance. A mixed-methods approach is proposed, combining survey evidence from executives and AI governance professionals with qualitative interviews involving senior leaders, digital transformation managers, and risk professionals. The study investigates how leadership capabilities, governance arrangements, and control mechanisms influence decision quality, trust in AI-enabled processes, and organizational resilience. The proposed framework emphasizes the importance of clearly defined decision boundaries, human approval thresholds, explainability, auditability, escalation procedures, and continuous governance review. The study contributes to strategic leadership and AI governance research by positioning executives not simply as final decision-makers, but as architects of human-AI decision systems. It offers practical guidance for organizations seeking to balance agentic AI autonomy with responsible executive control, ethical accountability, and long-term strategic value.
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[1] Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics, 5, 3265–3279. DOI: 10.1007/s43681-024-00653-w
[2] Bello y Villarino, J.-M., & Bronitt, S. (2024). AI-driven corporate governance: A regulatory perspective. Griffith Law Review, 33(4), 355–374. DOI: 10.1080/10383441.2024.2405752
[3] Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: Themes, knowledge gaps and future agendas. Internet Research, 33(7), 133–167. DOI: 10.1108/INTR-01-2022-0042
[4] Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(7), e13406. DOI: 10.1111/exsy.13406
[5] Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., Heim, L., & Anderljung, M. (2024). Visibility into AI agents. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 958–973). Association for Computing Machinery. DOI: 10.1145/3630106.3658948
[6] Csaszar, F. A., Ketkar, H., & Kim, H. (2024). Artificial intelligence and strategic decision-making: Evidence from entrepreneurs and investors. Strategy Science, 9(4), 322–345. DOI: 10.1287/stsc.2024.0190
[7] Doshi, A. R., Bell, J. J., Mirzayev, E., & Vanneste, B. S. (2025). Generative artificial intelligence and evaluating strategic decisions. Strategic Management Journal, 46(3), 583–610. DOI: 10.1002/smj.3677
[8] Felin, T., & Holweg, M. (2024). Theory is all you need: AI, human cognition, and causal reasoning. Strategy Science, 9(4), 346–371. DOI: 10.1287/stsc.2024.0189
[9] Gomez, C., Cho, S. M., Ke, S., Huang, C.-M., & Unberath, M. (2025). Human-AI collaboration is not very collaborative yet: A taxonomy of interaction patterns in AI-assisted decision making from a systematic review. Frontiers in Computer Science, 6, 1521066. DOI: 10.3389/fcomp.2024.1521066
[10] Huber, D. M., & Alexy, O. (2024). The impact of artificial intelligence on strategic leadership. In Z. Simsek, C. Heavey, & B. C. Fox (Eds.), Handbook of research on strategic leadership in the Fourth Industrial Revolution (pp. 108–136). Edward Elgar Publishing. DOI: 10.4337/9781802208818.00012
[11] Kanbach, D. K., Heiduk, L., Blueher, G., Schreiter, M., & Lahmann, A. (2024). The GenAI is out of the bottle: Generative artificial intelligence from a business model innovation perspective. Review of Managerial Science, 18(4), 1189–1220. DOI: 10.1007/s11846-023-00696-z
[12] Kanitz, R., Gonzalez, K., Briker, R., & Straatmann, T. (2023). Augmenting organizational change and strategy activities: Leveraging generative artificial intelligence. The Journal of Applied Behavioral Science, 59(3), 345–363. DOI: 10.1177/00218863231168974
[13] ALAMPALLY, J. (2024). Real-Time and Near-Real-Time Analytics in Healthcare Data Ecosystems. Journal of Computer Science and Technology Studies, 6(1), 314-324.
[14] Kim, J.-S., & Seo, D. (2023). Foresight and strategic decision-making framework from artificial intelligence technology development to utilization activities in small-and-medium-sized enterprises. Foresight, 25(6), 769–787. DOI: 10.1108/FS-06-2022-0069
[15] Kim, J. Y., Hasan, A., Kueper, J., Tang, T., Hayes, C., Fine, B., Balu, S., & Sendak, M. (2025). Establishing organizational AI governance in healthcare: A case study in Canada. npj Digital Medicine, 8, Article 522. DOI: 10.1038/s41746-025-01909-3
[16] Krakowski, S., Luger, J., & Raisch, S. (2023). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425–1452. DOI: 10.1002/smj.3387
[17] Nagraj, A. (2022). Modernizing Legacy Banking Systems: Migration Strategies and Cost Optimization in Financial Enterprises. Frontiers in Computer Science and Artificial Intelligence, 1(1), 43-52.
[18] Asthana, A. N., & Charan, N. (2023). Minimising catastrophic risk in the chemical industry: Role of mindfulness. European Chemical Bulletin, 12, 7235-7246.
[19] Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2024). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 75, Article 102716. DOI: 10.1016/j.ijinfomgt.2023.102716
[20] Mukherjee, C. Ai-Driven Personalization of Power System Learning Modules Using Student Personas based on Behavioral Analysis of Grid Performance.
[21] Nadia, N. Y., Rabby, H. R., Arif, M. H., Tanvir, M. I. M., Ahmed, M., & Firdaus, S. (2025, October). Scalable RNN-Based Transfer Learning for Patient Sentiment Monitoring in Telehealth Platforms. In 2025 IEEE 2nd International Conference on Computing, Applications and Systems (COMPAS) (pp. 1-6). IEEE.
[22] Takon, A. (2025). Explainable AI for Threat Modelling and Decision Support in Engineering Assets. Journal of Cyber-Physical Security and Robotics, 1(02), 46-52.
[23] Mukherjee, C. (2025). Combating digital media piracy with agentic ai: Leveraging video transcription and character recognition for automated enforcement. Authorea Preprints.
[24] Anifowose, K. (2025). Development and Validation of AI-Assisted Analytical Methods for Biochemical Compound Detection in Pharmaceutical Chemistry. Journal of Applied Pharmaceutical Sciences and Research, 8(4), 41-52.
[25] Mukherjee, C. (2025). Use of Agentic AI with OpenAI and Prompt Engineering and State-of-the Art Machine Learning Algorithm to detect the patterns in IOT Device Network Intrusion Attacks. Authorea Preprints.
[26] Ravikumar, V. (2025). Therapeutic Bot: Ethical Concerns in AI therapy for Neurodivergence. J Int Scient Re Rep.
[27] Mukherjee, C. (2025). Use of Agentic AI with LLM and Prompt Engineering and State-of-the Art Machine Learning Algorithm to detect the patterns in IOT Device Network Intrusion Attacks. TechRxiv. August, 6.
[28] Takon, A. (2025). 3D Object Detection and Localization for Industrial Threat Monitoring. Well Testing Journal, 34(S3), 850-880.
[29] Mukherjee, C. (2025). Harnessing large language models and ai agents for child behavior analytics in day care: a proof of concept for next-generation parental insight using simulated data. Machinery and Production Engineering, 174(2870), 26-34.
[30] Mukherjee, C. (2025). Combating digital media piracy with agentic ai: Leveraging video transcription and character recognition for automated enforcement. Authorea Preprints.
[31] Takon, A. (2024). Data-Driven Threat Intelligence for Energy and Critical Asset Management. International Journal of Technology, Management and Humanities, 10(04), 253-266.
[32] Kola, J. N. Longitudinal Cohort Intelligence for Self-Insured Employer Groups: A Predictive Framework for Healthcare Cost Trajectory Modeling and Proactive Risk Intervention.
[33] Adepoju, S. A., & Adepoju, M. A. (2024). From Portals to Case Graphs: A Reference Architecture and Benchmark for Safety Investigation Operations with Agentic Orchestration.
[34] Takon, A. (2024). Data Science Approaches to Asset Integrity Management in Offshore and Onshore Oil and Gas Operations. Multidisciplinary Innovations & Research Analysis, 5(2), 17-31.
[35] Kola, J. N. (2011). An Integrated Framework for Data Mining and Distributed Database Optimization in Resource-Constrained Network Environments. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 2(02), 82-86.
[36] Ravikumar, V. (2014). Fair and optimal resource allocation in wireless sensor networks.
[37] Naidu, K. J. (2014). Secure OLAP Reporting Architectures: Integrating Role-based Access Control and Query Execution Plan Optimization for Enterprise Analytical Environments. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 5(02), 155-159.
[38] López-Solís, O., Luzuriaga-Jaramillo, A., Bedoya-Jara, M., Naranjo-Santamaría, J., Bonilla-Jurado, D., & Acosta-Vargas, P. (2025). Effect of generative artificial intelligence on strategic decision-making in entrepreneurial business initiatives: A systematic literature review. Administrative Sciences, 15(2), Article 66. DOI: 10.3390/admsci15020066
[39] Marasani, Y. (2025). Explainable AI Frameworks for Patient-Level Claims Data Analytics. J Artif Intell Mach Learn & Data Sci, 8(1), 3382-3390.
[40] Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2, 603–609. DOI: 10.1007/s43681-022-00143-x
[41] Nagraj, A. (2024). GraphQL in Wealth Management Platforms: Optimizing Data Access and Performance. British Journal of Multidisciplinary Studies, 2(1), 16-24.
[42] Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, Article 114542. DOI: 10.1016/j.jbusres.2024.114542
[43] Moro-Visconti, R. (2025). Is artificial intelligence a new stakeholding agent? Human-Intelligent Systems Integration, 7, 3–16. DOI: 10.1007/s42454-025-00069-9
[44] MARASANI, Y. (2024). Enterprise Readiness for Generative AI: The Critical Role of Data Engineering. Frontiers in Computer Science and Artificial Intelligence, 3(2), 59-71.
[45] Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2025). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Management Decision, 63(10), 3501–3532. DOI: 10.1108/MD-10-2023-1946
[46] Sriharan, A., Sekercioglu, N., Mitchell, C., Senkaiahliyan, S., Hertelendy, A., Porter, T., & Banaszak-Holl, J. (2024). Leadership for AI transformation in health care organization: Scoping review. Journal of Medical Internet Research, 26, e54556. DOI: 10.2196/54556
[47] MARASANI, Y. (2023). Machine Learning Models for Predicting Patient Treatment Switching Using Claims Data. Frontiers in Computer Science and Artificial Intelligence, 2(1), 59-66.
[48] ALAMPALLY, J. (2024). Enhancing data quality and trust in AI systems through robust data engineering. Frontiers in Computer Science and Artificial Intelligence, 3(1), 120-130.
[49] Mukherjee, C. (2025). Use of Agentic AI with LLM and Prompt Engineering and State-of-the Art Machine Learning Algorithm to detect the patterns in IOT Device Network Intrusion Attacks. TechRxiv. August, 6.
[50] Sakthivel, A. (2025). Agentic Ai In the Enterprise: How Autonomous Ai Systems Will Reshape Business Strategy, Operations, and Leadership. Well Testing Journal, 34(S3), 767-785.
