Journal of Intelligent Strategic Management

Journal of Intelligent Strategic Management

AI-Driven Innovation via Transfer Learning for Identifying Recurrent Strategic Patterns

Editor-in-Chief Lecture

Authors
1 PhD in Business Administration, University of Tehran, Tehran, Iran.
2 Management Department, Nabi Akram Institute of Higher Education, Tabriz, Iran.
3 Associate Professor, Department of Business Administration, North Tehran Branch, Islamic Azad University, Tehran, Iran.
4 Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
Abstract
Recent advances in artificial intelligence, particularly the emergence of foundation models and transfer learning techniques, have opened new avenues for innovation and analysis in strategic management. This review article explores the role of transfer learning in identifying recurrent strategic patterns—recognized in the strategy-as-practice perspective as the foundation of organizational decision-making and long-term strategic trajectories. The literature indicates that transfer learning effectively addresses the scarcity of labeled data and the dynamism of business environments by enabling knowledge transfer across domains and industries. The proposed framework consists of four key layers: knowledge representation and extraction through foundation models, domain adaptation to contextualize knowledge, identification and explanation of recurrent patterns using sequence and network analysis, and linking these insights to innovation and strategic decision-making. The discussion highlights opportunities for organizational innovation while addressing critical challenges, including limited explainability, potential transfer of data biases, and the ethical governance requirements of AI. The article concludes that transfer learning can trigger a “cognitive revolution” in strategic management, provided that a balance is maintained between technological capabilities and the human, organizational, and policy dimensions necessary for trust and adoption.
Keywords

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