Strategy in the Age of Artificial Intelligence: From Analysis to Final Decision-Making

Pourshahabi, Vahid

doi:10.22034/jmsmo.2026.601821.1058

Strategy in the Age of Artificial Intelligence: From Analysis to Final Decision-Making

Articles in Press

Document Type : Original Article | English

Author

Vahid Pourshahabi

Associate Prof., Department of Management, Zah.C., Islamic Azad University, Zahedan, Iran.

10.22034/jmsmo.2026.601821.1058

Abstract

Purpose: This study investigates the boundaries of artificial intelligence participation in strategic decision-making and explains why final decision-makers must remain human—a fundamental question unresolved despite AI's growing analytical capabilities.
Methodology: This qualitative research employed a multiple case study design. Using purposive and snowball sampling, 23 senior and middle managers from leading AI-adopting Iranian organisations across manufacturing, financial services, information technology, and consumer goods sectors were interviewed. Data collected through semi-structured interviews and document analysis were analysed using thematic analysis with an abductive approach, following Gioia, Corley, and Hamilton's (2013) three-stage coding method.
Findings: Analysis revealed 64 first-order concepts, 14 second-order themes, and 4 aggregate dimensions. Problem structuredness and system transparency emerged as key contextual determinants. Human-AI interaction dynamics—conditional trust, experience-based judgment, control-accountability alignment, and epistemic de-skilling—shape managerial responses to AI recommendations. The Iranian context presents both structural barriers and local opportunities. Four criteria for determining AI participation boundaries were identified: problem structuredness, outcome irreversibility, data availability, and ethical accountability.
Conclusion: The three-level conceptual model demonstrates that while AI can effectively support all strategy stages, final decision-making must remain human due to ethical-legal accountability, context-based experiential judgment, unforeseeable consequences, and the need to cultivate managerial wisdom. The findings contribute theoretically to human-AI interaction literature and offer practical implications for managers and policymakers.

Keywords

Strategic Decision-Making

Artificial Intelligence

Human-AI Interaction

Problem Structuredness

Control-Accountability Alignment

Subjects

The Use of Artificial Intelligence in Organizations and Management

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