Kurnia Candra Wirawan
Telkom Corporate University Center

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Journal : people and behavior analysis pba

AI-Enabled Ambidextrous Leadership for Future-Ready Organizations: A Conceptual Framework for Human–AI Decision Support Richard Alberto; Happy Gery Pangestu; Agung Suhendar; Kurniawan Adhie Thama; Kurnia Candra Wirawan
People and Behavior Analysis Vol. 4 No. 2 (2026): September Volume
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/pba.v4i2.4374

Abstract

Organizations operating in fast-changing digital environments must pursue operational efficiency and innovation simultaneously. This dual demand places ambidextrous leadership at the center of organizational transformation, while recent advances in artificial intelligence (AI) create new possibilities for how leaders sense, interpret, and decide. This paper proposes a conceptual framework explaining how AI-enabled decision support may help ambidextrous leaders balance exploitation and exploration to strengthen organizational future readiness. The framework is developed through a conceptual exploratory design that synthesizes literature on organizational ambidexterity, ambidextrous leadership, AI-enabled decision-making, digital transformation, organizational agility, and organizational resilience. It positions AI as a cognitive and analytical support mechanism rather than a replacement for human leadership. It organizes the proposed process into three connected components: internal strategic input, an AI intelligence core, and strategic output. The paper is conceptual in nature. The framework has not been empirically tested, statistically validated, or shown to improve decision accuracy or organizational performance, and it should not be read as a proven leadership mechanism. Instead, it integrates existing theoretical insights into a structured model that may guide future empirical research and practical experimentation. The paper concludes with theoretical and proposed practical implications, together with directions for the empirical testing that the framework still requires.