Tag Archives: HybridLeadership

19. The AI-Enabled Leadership Framework: Unifying Strategy, Ethics, and Human Judgment

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The following is the conceptualisation of the research-based series exploring AI’s impact on leadership.

If AI is a strategic imperative, can leaders afford to treat ethics and data fluency as mere compliance checklist items? The ultimate challenge of the AI era is synthesis: bringing together disparate technical, ethical, and human demands into a coherent strategic model.

Flow chart detailing The AI-Enabled Leadership Framework Model, structured across three phases: Foundation (Blue), Action (Purple), and Sustainability (Green). The model identifies 14 strategic steps, starting with a Red Target icon for the core Vision and including areas like Governance, Ethics, Upskilling, and Feedback.
The complete strategic framework illustrating 14 steps for guiding leadership development and decision-making in AI-transformed organizations.

The “AI-Enabled Leadership Framework Model” achieves this synthesis by structurally integrating the critical requirements identified across contemporary leadership research into three interdependent sections. First, it establishes the Foundation by mandating Executive Data and AI Fluency and Governance, Risk & Guardrails. This addresses the strategic necessity of understanding ML essentials and generative architectures, enabling leaders to ask sharper questions about systems and data. Second, the AI-Enabled Leadership in Action section addresses the core behavioral shift, requiring Adaptive Leadership Traits and Ethical and Inclusive Decision-Making. This is the most critical function: ensuring human judgment balances utilitarian AI output with necessary human values. Third, the Sustainability and Continuous Growth section manages the long-term journey through Transformational Capability and Feedback Loops. This aligns with the Dynamic Managerial Capabilities (DMC) view, ensuring the organization can actively Pilot → Refine → Scale → Iterate to maintain competitive advantage in volatile environments. The framework functions as an integrated blueprint, transforming theoretical mandates into a practical roadmap for leading hybrid human–AI organizations.

This structural approach directly addresses the primary research gap: the fragmentation of literature across technical, behavioral, and ethical domains. It elevates critical thinking from an abstract concept to a structured, repeatable process embodied in the Ethical and Inclusive Decision-Making phase (Goal 9). This critical function serves as the essential mediator, putting automated decisions into real-world context through human judgment. Furthermore, by emphasizing Trust, Transparency, and Human Oversight (Goal 10), the model compels leaders to address the risks of algorithmic bias and ethical missteps, ensuring that the powerful “double-edged sword” of AI is wielded responsibly.

I would like to suggest that this framework provides a strategic roadmap for organizations to navigate the transformative potential of AI. How does your organization currently measure and reward leaders who successfully balance AI-driven efficiency with ethical mediation and human oversight? Let’s share approaches.

10. The Impact of AI on Evolving Leadership Theories

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Part 10 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article The Impact of AI on Evolving Leadership Theories and Practices by Frimpong (2025)

Can leadership remain fundamentally “human” when data-driven AI challenges the traditional reliance on intuition, experience, and emotional perception? This paper argues that the rise of AI compels leadership theories to evolve, necessitating the development of hybrid leadership models that fuse AI’s analytical strengths with indispensable human qualities like empathy and ethical judgment. The core finding highlights that AI enhances leadership effectiveness by automating routine tasks, optimizing workforce performance, and providing valuable data-driven insights. However, this shift introduces critical risks, including algorithmic bias, a lack of transparency, and the potential erosion of human-centric attributes like emotional intelligence.

The emergence of this hybrid model places critical thinking at the core of human responsibility, transforming it into a necessary ethical safeguard. Leaders must critically interpret AI’s analytical insights within complex organizational and ethical contexts. The crucial takeaway is the need for human leaders to scrutinize the algorithms themselves, ensuring they reflect ethical frameworks, are fair, and are accountable, actively preventing the perpetuation of biases embedded in training data. This critical oversight ensures that the efficiencies gained do not diminish the foundational human attributes of trust and compassion.

The author, Victor Frimpong, suggests that integrating AI into leadership must be conducted with caution and responsibility, safeguarding fundamental human attributes like ethical decision-making. How can we ensure that “hybrid leadership” means augmentation of human judgment, not merely automation of human ethical responsibility? Join the debate.

Reference: Frimpong, V. (2025). The Impact of AI on Evolving Leadership Theories and Practices. Journal of Management and Work, 2024(4), 400–417. https://doi.org/10.53935/jomw.v2024i4.1100