Abstract

This study examines how Artificial Intelligence (AI) is reshaping strategic leadership and governance in intermediary organizations undergoing digital transformation. It argues that AI enhances decision-making, operational effectiveness, and adaptive governance while upholding human accountability and ethical standards. Rooted in hybrid intelligence and the dynamics of automation versus augmentation, the study conceptualizes AI adoption as a coevolutionary process between human and machine capabilities. It introduces a four-phase framework: (1) Awareness and Strategy, (2) Experimentation and Prototyping, (3) Integration and Governance, and (4) Scalability and Continuous Innovation. Early stages foster AI literacy and tools such as internal dashboards and prompt-engineering systems. Phase three surfaces strategic and ethical concerns - including bias, reskilling, and vendor dependence - while phase four anticipates institutional scaling supported by multi-stakeholder governance. The methodology involves a qualitative, exploratory case study of a single intermediary institution. Sources include internal documentation, semistructured interviews with key personnel, and a targeted literature review on augmented leadership and ethical AI governance. This triangulated approach enables a nuanced view of the socio-technical dynamics of AI integration. The case contributes to the literature by extending theories of human-AI collaboration to intermediary contexts, which are typically underexplored. Through a structured and iterative lens, the framework supports both replicability and scalability in multi-actor environments. The study enriches academic discourse on digitally enabled leadership and responsible AI adoption by offering theoretical and practical insights on co-evolving governance mechanisms, strategic agency, and ethical resilience.

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