Tag Archives: CriticalThinking

8. AI-Driven Servant Leadership and Job Satisfaction

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Part 8 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article The good shepherd: Linking artificial intelligence (AI)-driven servant leadership (SEL) and job demands-resources (JD-R) theory in tourism and hospitality by Han et al. (2026)

Can an AI ever truly be a “servant leader,” or is the concept limited to the human capacity for genuine empathy and community building? This research examines AI servant leadership in the hospitality and tourism sector, finding that AI’s cognitive dimensions—specifically conceptual skills and empowering—are significantly more effective for boosting employee job satisfaction by reducing job demands and clarifying role ambiguities. AI servant leaders excel at data analysis, task execution, and providing precise guidelines, mimicking human thinking using data analysis protocols. However, the AI leader demonstrates profound limitations in its capacity to convey authentic emotional responses and empathy, leading to a failure in crucial dimensions like “emotional healing” and “creating value for the community”.

The analytical strength but emotional weakness of AI leaders necessitates that human critical thinking focuses intensely on the socio-emotional and community gaps within the workforce. Critical thinking is required to determine the optimal boundary conditions for AI leaders and ensure that technology enhances, rather than diminishes, human connection and communal values. The “so what” is that human leaders must apply critical judgment to synthesize AI’s cognitive efficiency with the necessary emotional support and communal values, especially since AI leaders may not grasp the concept of shared values in a community.

The authors, H. Han, S. H. Kim, T. A. Hailu, A. Al-Ansi, S. M. R. Loureiro, and J. Kim, suggest that while AI’s cognitive functions are supported by their study, its emotional responses to employees are limited, reinforcing the need for human leaders. How must human leaders strategically apply their emotional intelligence to complement AI’s conceptual efficiency, especially in service-driven environments like hospitality?

Reference: Han, H., Kim, S. H., Hailu, T. A., Al-Ansi, A., Loureiro, S. M. R., & Kim, J. (2026). The good shepherd: Linking artificial intelligence (AI)-driven servant leadership (SEL) and job demands-resources (JD-R) theory in tourism and hospitality. International Journal of Hospitality Management, 133, 104470. https://doi.org/10.1016/j.ijhm.2026.104470

7. Influence of Leadership on Human–AI Collaboration

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Part 7 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article Influence of Leadership on Human–Artificial Intelligence Collaboration by Zárate-Torres et al. (2025)

In the newly emerging hybrid workforce, what defines the essential boundary between the cold logic of an algorithm and indispensable human judgment? This research proposes a conceptual model where leadership acts as an ethical and strategic mediator in the Human Intelligence (HI)–Artificial Intelligence (AI) relationship, defining a crucial hybrid space of cooperation. The core finding establishes that while AI provides algorithmic efficiency based on data processing, HI remains necessary for interpretation, experience, and contextual judgment. Leadership modulates this relationship, shifting from mere supervision toward an essential role in co-creation. The model posits that effective leadership must integrate ethical governance mechanisms and establish a balancing mechanism to algorithmic efficiency through cognitive adaptability.

The introduction of this HI-AI hybrid space fundamentally reinforces and redefines human critical thinking as the ultimate strategic and ethical function. Critical thinking is embodied in the leader’s role of translating automated decisions into comprehensible language for teams, ensuring algorithmic transparency, and contextualizing decisions ethically. The essential need for human critical thought is derived from the fact that it is the only mechanism capable of putting automated decisions “in real context through human judgment and reasoning”, thereby guaranteeing organizational resilience beyond technical capability.

The authors, R. Zárate-Torres, C. F. Rey-Sarmiento, J. C. Acosta-Prado, N. A. Gómez-Cruz, D. Y. Rodríguez Castro, and J. Camargo, suggest that leadership acts as the axis that brings together human and technological systems, creating highly flexible, efficient, and ethically overseen interaction. As your organization integrates AI, how are you explicitly training leaders to be effective translators of algorithmic logic into human-centric direction? Share your strategies.

Reference: Zárate-Torres, R., Rey-Sarmiento, C. F., Acosta-Prado, J. C., Gómez-Cruz, N. A., Rodríguez Castro, D. Y., & Camargo, J. (2025). Influence of Leadership on Human–Artificial Intelligence Collaboration. Behavioral Sciences, 15(7), 873. https://doi.org/10.3390/bs15070873

6. Impact of AI on Corporate Leadership

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Part 6 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article Impact of Artificial Intelligence on Corporate Leadership by Nguyen and Shaik (2024)

In the pursuit of AI-driven efficiency, are corporate leaders inadvertently sacrificing core human values like privacy and fairness? This research explores the profound dual impact of Artificial Intelligence (AI) on corporate leadership, detailing both transformative advantages and critical associated risks. Key findings show that AI significantly enhances positive leadership outcomes in four domains: communication (e.g., seamless collaboration via Slack), personalized feedback systems, optimized tracking mechanisms, and data-driven decision-making. However, the adoption introduces severe negative impacts, specifically algorithmic bias (citing Amazon’s biased recruiting tool) and substantial data privacy concerns. The paper proposes leveraging Local Large Language Models (LLMs) and techniques like federated learning to mitigate these privacy issues.

Successfully navigating the dual nature of AI necessitates advanced critical thinking centered on ethical oversight and risk management. Leaders must exercise critical judgment not only to maximize AI benefits but, crucially, to mitigate potential risks stemming from AI errors and biases. The “so what” for critical thinking is the imperative to establish and adhere to stringent ethical guidelines and accountability to protect the organization and its employees from unintended consequences. This continuous critical verification reinforces that technological prowess must be subordinate to human trust and ethical decision-making.

The authors, Daniel Schilling Weiss Nguyen and Mudassir Mohiddin Shaik, suggest that responsible AI adoption requires a delicate equilibrium between leveraging AI’s transformative potential and mitigating the associated risks. How do you structure your internal AI governance framework to proactively catch algorithmic biases before they impact human capital decisions? Let’s share best practices.

Reference: Nguyen, D. S. W., & Shaik, M. M. (2024). Impact of Artificial Intelligence on Corporate Leadership. Journal of Computer and Communications, 12(4), 40–48. https://doi.org/10.4236/jcc.2024.124004

5. How Will AI Evolve Organizational Leadership?

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Part 5 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article How Will Artificial Intelligence (AI) Evolve Organizational Leadership? Understanding the Perspectives of Technopreneurs by Zaidi (2025)

Will the next generation of leadership be defined by human intellect, or by the sophistication of the algorithms they manage? Expert interviews confirm that AI mandates fundamental, real-time shifts in leadership philosophies, transforming leaders into “tech-savvy leaders” committed to continuous learning. The primary finding is that AI enables the automation of routine tasks (like organizing and scheduling), allowing human leaders to pivot toward higher-level responsibilities, such as creative thinking, employee development, and bridging the human-technology gap. Crucially, the study reinforces the irreplaceable value of human judgment, noting that AI lacks intuition, a moral compass, and a ‘soul’, meaning it cannot settle complex business problems alone.

This transformation intensely reinforces the need for advanced human critical thinking in both decision-making and ethical oversight. Leaders must use critical thought to effectively become the guardians of powerful machines. This requires them to critically understand the truth of the algorithms they use, including their limitations and capabilities, especially within the company’s decision chain. The key critical function is determining “where people can be taken out of the loop, and where they can be involved,” ensuring automation doesn’t lead to an organizational philosophy that neglects human well-being.

The author, Syed Yasir Abbas Zaidi, suggests that AI coaching is further enhancing tomorrow’s AI congruent business leaders, fundamentally altering how leaders make decisions and transforming future team dynamics. How do we standardize the measure of a leader’s “tech savviness” to ensure they maintain critical, ethical oversight over the AI systems they deploy? Let’s discuss.

Reference: Zaidi, S. Y. A., Aslam, M. F., Mahmood, F., Ahmad, B., & Bint Raza, S. (2025). How will artificial intelligence (AI) evolve organizational leadership? Understanding the perspectives of technopreneurs. Global Business and Organizational Excellence, 44(1), 66–83. https://doi.org/10.1002/joe.22275

3. Impact of AI on Leadership Styles in High-Stakes Environments

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Part 3 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article Exploring the Impact of AI on Leadership Styles: A Comparative Study of Human-Driven vs. AI-Assisted Decision-Making in High-Stakes Environments by Hwang (2024)

When AI handles risk management in high-stakes sectors, does the human leader become obsolete, or is their role simply elevated? This research finds that AI integration in high-stakes environments (such as finance, healthcare, and aviation) fundamentally shifts leadership roles away from centralized control toward decentralization, with a crucial focus on interpretation, oversight, and ethical accountability. AI significantly augments decision-making accuracy and speed by analyzing vast datasets and offering predictive insights, which allows executives to concentrate more on organizational and strategic decisions. However, this augmented efficiency simultaneously introduces critical challenges regarding team trust in AI decisions and the complexities of ensuring algorithmic transparency and managing emotional/ethical nuances.

The profound shift toward decentralized decision-making clearly defines the indispensable requirement for human critical thinking: the leader must become the interpreter of algorithmic outcomes. This critical function bridges the gap between AI’s analytical strength and its inherent lack of contextual sensitivity required for real-world application. Without this interpretive layer, leaders risk losing the necessary ethical grounding, confirming that critical thinking is essential for maintaining human oversight in environments where mere technical accuracy might overlook broader social consequences.

The author, Jinyoung Hwang, suggests that organizational success requires adopting collaborative leadership approaches that blend AI capabilities with essential human judgment. If AI provides a highly optimized, data-driven recommendation, how do you critically ensure that the execution aligns perfectly with human values and existing team dynamics? Share your perspective.

Reference: Hwang, J. (2024). Exploring the Impact of AI on Leadership Styles: A Comparative Study of Human-Driven vs. AI-Assisted Decision-Making in High-Stakes Environments. International Journal of Science and Research Archive, 13(01), 3436–3446. https://doi.org/10.30574/ijsra.2024.13.1.2030

4. Generative AI Use in the Workplace

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Part 4 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article Generative artificial intelligence use in the workplace: implications for management practice by Hernández-Tamurejo et al. (2025)

If trust is the strongest predictor of GenAI acceptance, what happens when managers blindly trust outputs generated from biased data? This mixed-method study finds that trust is the strongest predictor of intention to use Generative AI (GenAI). Crucially, this trust is conditioned primarily by the perception that organizational data management routines are reliable and objective. Interestingly, employee and manager perceptions of information transparency or privacy risk were found not to directly influence trust or usage intention. This raises significant risk, as users might blindly trust GenAI outcomes due to perceived efficiency benefits, leading to doubtful content usage in management decision-making and creating integrity issues in service delivery.

This reliance on perceived data integrity creates a paradox that demands rigorous human critical thinking focused on managing inputs and evaluating outputs. The research emphasizes that trustworthy data governance, not abstract explainability, is the foundation of sustainable GenAI adoption. Critical thinking must, therefore, be deployed to scrutinize both the data sources used by the AI and the factual accuracy of the outputs provided, rather than passively accepting results based on the promise of efficiency. This critical function serves as the essential check against the allure of speed, ensuring managers avoid the irresponsible use of GenAI content in high-stakes decisions.

The authors, Á. Hernández-Tamurejo, R. Bužinskienė, B. Barbosa, A. Miceikienė, and J. R. Saura, suggest that adopting monitoring, calibrated disclosure, and adaptive privacy protocols are concrete managerial levers to strengthen GenAI acceptance. In your experience, is the greatest challenge in AI adoption enforcing transparency, or instilling the critical capacity in staff to question seemingly objective algorithmic output? Share your thoughts.

Reference: Hernández-Tamurejo, Á., Bužinskienė, R., Barbosa, B., Miceikienė, A., & Saura, J. R. (2025). Generative artificial intelligence use in the workplace: implications for management practice. Review of Managerial Science. https://doi.org/10.1007/s11846-025-00949-z

2. AI-Powered Leadership: A Systematic Review

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Part 2 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article AI-powered leadership: a systematic literature review by Aziz et al. (2025).

When AI delivers a ‘data-driven’ decision, who is responsible for the social and ethical fallout if it goes wrong? The emergence of Artificial Intelligence (AI) has positioned itself as a critical factor in reshaping organisational dynamics, particularly in the realm of leadership. This systematic literature review investigated the evolving relationship between AI and leadership, focusing on definitions, prevalent themes, and challenges. The findings confirmed a complex range of key challenges in AI-powered leadership, including ethical dilemmas, difficulties in human-AI interactions, implementation hurdles, and long-term risks associated with deep AI integration. The study synthesises findings across diverse disciplines like management and ethics, aiming to advance the understanding of this complex relationship and facilitate scholarly investigations into the AI-powered leadership domain. Although AI offers tools to enhance efficiency and cognitive abilities, a clear, universally accepted definition of AI-powered leadership remains elusive.

The inherent fragmentation in defining AI leadership and the established link to ethical dilemmas underscore the absolute necessity of robust human critical thinking and moral judgment. The true value of critical thought here is its role as an essential safeguard against algorithmic overreach. Leaders must critically clarify how the benefits of AI are achieved while upholding ethical standards and human-centric values. This involves navigating the inherent risk that reliance on data-driven decision-making may fail to adequately factor in crucial ethical and social issues.

The authors suggest that clarifying the challenges presented by the integration of AI into leadership contexts empowers scholars and practitioners to understand the evolving AI landscape and its impact on effective leadership. What steps are organizations taking today to explicitly build human moral judgment into AI-powered decision architecture? Share your insights.

Reference: Aziz, M. F., Rajesh, J. I., Jahan, F., McMurrray, A., Ahmed, N., Narendran, R., & Harrison, C. (2025). AI-powered leadership: A systematic literature review. Journal of Managerial Psychology, 40(5), 604–630. https://doi.org/10.1108/JMP-05-2024-0389

1. Enhancing Top Managers’ Leadership with AI Insights

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Part 1 of 17 of a research-based series exploring AI’s impact on leadership This post summarises the article Enhancing top managers’ leadership with artificial intelligence insights from a systematic literature review by Bevilacqua et al. (2025)

In the AI era, are executive leaders truly adapting, or are they just layering technology over outdated strategic mindsets? Drawing on Upper Echelons Theory (UET), this systematic literature review confirms that AI radically restructures the managerial processes of organizations, making top managers’ leadership a determining factor in AI innovation effectiveness. The study identifies three key research clusters, with a core finding focused on the required AI-driven skills of top managers: data-driven decision-making, agility, and emotional and social intelligence. Successful integration requires leaders to cultivate environments that foster collaboration and knowledge sharing to maximize AI value. The integration necessitates a profound evolution of leadership dynamics, demanding leaders to balance technical capabilities with the ability to handle organizational and sociocultural factors.

The critical finding confirms that data provision alone is insufficient; sophisticated critical thinking is required to translate AI output into legitimate strategic action. The ability to analyze data critically and accurately and extract relevant insights remains crucial for top managers. This critical lens is essential not only for internal process efficiency but, more importantly, for navigating external pressures; top managers must use critical thought to align AI adoption with sociocultural context, ensuring regulatory compliance and ethical use. The critical layer ensures technology adoption, which is influenced by factors like social perceptions and regulations, contributes responsibly to competitiveness.

The authors, S. Bevilacqua, J. Masárová, F. A. Perotti, and A. Ferraris, suggest that the study contributes to UET by integrating AI as a crucial variable that radically transforms leadership and decision-making at the executive level. As AI tools multiply, how can we measure and accelerate the critical skill of translating algorithmic insights into human-centric strategic results? Let’s discuss.

Reference: Bevilacqua, S., Masárová, J., Perotti, F. A., & Ferraris, A. (2025). Enhancing top managers’ leadership with artificial intelligence insights from a systematic literature review. Review of Managerial Science, 19, 2899–2935. https://doi.org/10.1007/s11846-025-00836-7

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