As technologies continue to evolve, their potential impact on the workforce is increasingly important. This shift challenges not only how we lead but also how we define jobs, workflows, and the value of human contributions. While some view AI as capable of surpassing human intelligence, others see it as a tool designed to augment human efforts. To better understand this dynamic, we must distinguish between knowledge and wisdom and consider how this distinction shapes the evolving nature of work.
Wisdom operates at multiple levels, from individual decision making to broader societal responsibilities. Unlike knowledge, which AI can accumulate and process at unprecedented speeds, wisdom involves ethical discernment, moral responsibility, and the ability to navigate complex, uncertain situations. As humans, we bear the responsibility of ensuring that AI-driven developments align with values such as fairness, justice, and empathy – qualities that AI lacks. This responsibility becomes even more pronounced at a leadership level, where decisions impact not only individuals but entire organizations and societies. However, if the wisdom’s foundational values are not reflected beyond the leadership level, then its impact is sub-optimized.
While leadership styles (e.g., transformational, transactional) describe how leaders motivate and guide others, practical wisdom refers to the ability of leaders to navigate complex situations with sound judgment (Rooney & McKenna, 2022). Practical wisdom, also referred to as phronesis, is regarded as a leadership quality that positively influences economic, social and environmental outcomes (Nonaka & Takeuchi, 2021). Exhibited by responsible leaders, it prioritizes a greater purpose and cultivates conscious leadership and culture contributing to the common good and long-term meaningful impact within and beyond the organization (Nonaka & Takeuchi, 2021), representing a distinctly human strength.
Why Leadership and Job Design Must Evolve in the Age of AI: The Role of Leadership Humility in Navigating AI’s Impact on Work
While phronesis enables ethical discernment in complex situations, humility is the virtue that makes such discernment possible-by keeping leaders open to uncertainty, self-reflection, and the limits of their own knowledge.
As artificial intelligence increasingly mediates decision-making in the workplace, leadership humility has become an essential counterweight to overconfidence in algorithmic outputs. Unlike technical competence or data literacy, humility is an ethical orientation that invites questioning, cultivates trust, and resists the illusion of infallibility. In AI-integrated environments, where systems often obscure their own limitations, humble leadership is critical for surfacing ethical concerns and maintaining human accountability.
Humble leaders demonstrate a willingness to admit what they do not know, to seek feedback across levels of authority, and to revise decisions in light of new evidence. Research confirms that expressed humility enhances team learning, psychological safety, and ethical climate-outcomes essential for responsible AI use (Owens et al., 2013; Morris et al., 2005). When leaders model humility, they legitimize critical reflection and create space for employees to raise concerns about biased data, automation risks, and value misalignments.
This virtue also plays a pivotal role in distinguishing between knowledge and wisdom in the evolving world of work. While AI systems increasingly outperform humans in processing information and recognizing patterns, they cannot exercise judgment. Knowledge alone-especially when embedded in machine systems-does not confer wisdom. Wisdom requires contextual awareness, empathy, and moral reasoning, all of which depend on the leader’s ability to remain open, attentive, and grounded in humility (Glück et al., 2013). Without humility, knowledge risks becoming detached from ethical responsibility.
In a workforce transformed by AI, humility is not merely a personal trait but an organizational necessity. It tempers the impulse to automate without reflection and fosters the kind of deliberative leadership required for long-term trust, equity, and resilience. This capacity for humility creates the foundation upon which emotional and cultural intelligence can operate effectively–enabling leaders to not only reflect ethically, but to respond empathetically and lead adaptively in diverse, AI-driven work environments.
Practical wisdom, or phronesis, is rooted in a deep understanding of context following a human-centered perspective. Leaders demonstrate this quality through various capacities in times of organizational change, all the way from regulating their own emotions to balancing competing values to act wisely. Emotional intelligence (EQ) and cultural intelligence (CQ) can be viewed as important expressions of this broader wisdom.
EQ enables leaders to recognize, understand, and manage their own emotions as well as those of others, leading with empathy and creating strong interpersonal relationships. CQ extends this capability further by equipping leaders with the capacity to navigate cultural changes to lead strategic efforts with an organizational culture that is resilient to change and leverages its organizational assets. Both EQ and CQ support leaders in making context-sensitive decisions that respect individuals as collective perspectives, driving a culture of mutual respect and strategic agility.
While neither EQ nor CQ alone forms the full scope of practical wisdom, they are foundational capabilities that support its development. As EQ and CQ are capabilities, not virtues, they contribute to how leaders perceive and respond to human dynamics but do not by themselves ensure wise or ethical outcomes. A leader can be emotionally or culturally intelligent yet still act in ways that are self-serving or shortsighted. What distinguishes phronesis is its grounding in moral discernment and a commitment to human flourishing, not just skill, but judgment anchored in purpose.
By cultivating both these intelligences and embedding them within a values-driven mindset, leaders deepen their ability to act with humility, integrity, discernment, and compassion, qualities essential to navigating the uncertainty of digital transformation and ones that AI, with its current capabilities, cannot replicate.
The Knowledge-Wisdom Divide
A useful way to understand the evolving relationship between humans and AI is through the dichotomy between AI knowledge and wisdom:
- AI Knowledge: Understanding the present capabilities and limitations of artificial intelligence technologies and tools.
- AI Wisdom: Understanding AI’s capabilities and limitations, being aware of its impacts, and committing to use it to bring about benefit and not harm.
Knowledge represents the ability to acquire, store, and apply information. AI thrives in this domain, excelling in tasks requiring speed and precision, such as analyzing datasets or identifying patterns. Wisdom, however, transcends knowledge. It involves discernment, judgment, and the ability to navigate complex, context-dependent situations. Practical examples highlight this difference:
- Healthcare: AI’s diagnostic tools provide doctors with data-driven insights, but only human wisdom can translate those insights into patient-centered treatment plans.
- Aviation: Captain Chesley “Sully” Sullenberger’s life-saving decision to land a plane on the Hudson River exemplifies wisdom informed by experience and emotional intelligence. (AOPA Miracle on the Hudson)
As AI systems become more integrated into decision-making processes, the question of moral responsibility becomes even more critical–yet increasingly difficult to discern. When AI makes a mistake, who is accountable – the developer, the organization, or the technology itself? This ambiguity highlights the need for clear ethical frameworks and human oversight to ensure accountability is maintained. In industries like healthcare, finance, and law enforcement, assigning moral responsibility isn’t just theoretical. It has real-world implications for trust and justice.
The 2018 fatal accident involving a self-driving Uber vehicle highlights the real-world consequences of AI’s struggle with complex moral scenarios (CNN Uber Self Driving Car Death). Without human judgment, such decisions remain incomplete and potentially dangerous. This incident underscores that ethical decision‑making in AI systems is not just about technical proficiency, but about leadership phronesis and humility. It requires recognizing what cannot be known or controlled, listening to internal skepticism, and embedding ethical caution into design and testing. Without such humility, organizations risk prioritizing innovation goals over public safety.
Rethinking Work in the AI Era
AI’s ability to automate repetitive tasks invites organizations to rethink the value of work. At the same time, an over-reliance on automation may lead to missed opportunities to create meaningful roles that leverage uniquely human capabilities. As AI reshapes industries, organizations must strategically integrate these technologies while safeguarding their core identity and cultural values. (Raisch and Krakowski, 2021) highlight the “automation-augmentation paradox,” where firms must balance AI-driven automation with human augmentation to ensure their unique values and traditions are not eroded. Similarly, (Kellogg, Valentine, and Christin, 2020) explore how organizations can preserve cultural norms and established workflows while incorporating AI systems. Their research emphasizes that AI adoption should be carefully managed to align with existing practices rather than disrupt them entirely.
Clarifying Differences: Reskilling, Upskilling, and Rethinking Jobs
To adapt to the AI era, organizations must differentiate between three approaches:
- Reskilling: Preparing individuals for entirely new roles by teaching them new skills.
- Upskilling: Enhancing current employees’ abilities to meet evolving demands within their existing roles.
- Rethinking Jobs: Reimagining roles, workflows, and organizational design to maximize the synergy between AI and human efforts.
For example, healthcare organizations use AI to streamline logistics and improve patient care workflows. However, unless jobs are restructured to integrate human judgment, these efforts fall short of achieving their full potential. In addition, an imbalance between a Machine on the Team and a Doctor in the Loop creates a higher risk for poorer outcomes and exposes clinicians and healthcare organizations to legal exposure.
Overcoming Automation Over-Reliance
Businesses often prioritize automation for efficiency without considering the broader implications. This approach can lead to:
- Missed Opportunities: Roles emphasizing creativity, empathy, and strategy remain underdeveloped.
- Organizational Gaps: Automation may replace tasks but leave critical gaps in decision-making and adaptability.
AI should not be deployed in a way that exacerbates inequality or replaces jobs without a clear plan for economic transition. Instead, organizations must proactively design jobs that foster human-AI collaboration.
The frequency and manner in which AI tools are used can either emphasize or diminish human connection within organizations. The risk of role replacement doesn’t just affect job security. It can foster alienation, breaking and ultimately eroding the unique organizational culture that defines and binds an organization together. This dynamic highlights the need for intentional integration of AI that supports rather than undermines human relationships.
Practical Steps to Address This Challenge
- Ask Bigger Questions: How can AI augment rather than replace human capabilities? What roles require uniquely human skills, such as judgment and creativity? Allow for human creativity to take on new forms.
- Restructure Roles: Integrate AI into workflows while maintaining human oversight for context-dependent decisions.
- Foster Human Connection: Design roles that emphasize collaboration, creativity, and empathy to counterbalance AI’s impersonal nature (ChatGPT Personality Test)
- Strengthen Psychological Safety: Encourage open dialogue about AI’s impact on jobs. Only 40% of employees feel comfortable discussing how AI may affect their roles with managers (IBM Institute for Business Value, 2023). Upskilling efforts must include communication and change readiness, ensuring employees feel safe speaking up during periods of transformation.
Beyond Automation: Synergy Between Humans and AI
The future of work is not a zero-sum game. It requires collaboration between AI and humans, combining their strengths to achieve transformative outcomes:
- Healthcare: AI augments diagnostics, while doctors bring empathy and ethical care.
- Education: AI personalizes learning, but teachers inspire and mentor students. Generative AI, for example, can guide self-directed projects, acting as a catalyst for exploration (Harvard Creative Computing).
- Supply Chain Management: AI optimizes operations, while leaders ensure ethical sourcing, crisis management, and empathy (König et al., 2020). In fields like supply chain and economics, understanding both the technological capabilities and the broader implications for human interaction is essential.
However, over-reliance on AI tools can lead to cognitive offloading, reducing critical thinking skills among users (MDPI Study; Kosmyna et al., 2025). To mitigate this, organizations can adopt frameworks for responsible AI use that encourage and thoughtful engagement and critical reflection.
Responsible AI: A Call-to-Action
Organizations must lead with intention to harness AI responsibly. Consider these actionable steps:
- Invest in Reskilling/Upskilling: Prepare workers for roles requiring creativity and strategic thinking that complements algorithmic capabilities
- Adopt Ethical Frameworks: Ensure fairness, transparency, and accountability in AI systems. For example, collaboration and trust frameworks in higher education around generative AI offer valuable models for ethical AI integration (APRU Whitepaper).
- Champion Human-Centric Cultures: Value human judgment as a cornerstone of decision-making.
- Rethink Work: Develop jobs and workflows that align human and AI strengths.
By doing so, organizations can unlock the potential of AI without sacrificing the irreplaceable qualities of human wisdom.
Closing Thoughts
Innovation is a means, not an end. As AI transforms the workplace, we must do more than adapt. We must rethink. This means redefining leadership, redesigning jobs, and rebuilding workflows to harness the best of both human wisdom and machine intelligence. Practical wisdom, emotional intelligence, and cultural awareness aren’t just soft skills. They’re strategic imperatives that AI cannot replace. By embedding these human capacities into our organizations, we don’t just prepare for a future of work-we help shape it. The path forward demands humility, creativity, and intention to ensure that technology serves humanity, not the other way around.
For organizations seeking to integrate AI ethically and responsibly, resources like AI 2030 (https://ai2030.org/) offer valuable guidance.
About the Authors
Anastasia “Tracy” Raissis is a global leader in Responsible AI, governance, risk, and compliance, with over three decades of experience across fintech, banking, and emerging technologies. As Head of Strategy at AI 2030, Founder of Achillia Group, and adjunct faculty at UCNJ, she champions the integration of transformative innovation with ethical, strategic, and regulatory excellence to drive responsible growth, strengthen public trust, and shape future-ready governance.
Zakaria Laaraj is a cultural intelligence practitioner with international experience supporting organizations and institutions in navigating change. As the Founder of Global New Ventures and Senior Advisor at AI 2030, he drives initiatives that promote sustainable development through cross-sector collaboration and human-centered innovation.
Cole Short is an Associate Professor of Strategy at Pepperdine University’s Graziadio Business School. His research blends financial and language data to examine governance, stakeholder strategy, and innovation. He advises executives on emerging technology and serves on AI 2030’s Advisory Board.
Colleen Lyons is an AI Ethicist and Founder of AXIA AI Advisory, Professor (ADJ), at Drexel College of Medicine, FDA ethicist, and AI 2030 Global Fellow. As the creator of AI Voice-as-a-Competency™ and the Volatile, Uncertain, Complex, Chaotic, & Ambiguous (VUCCA)™, she addresses the accountability gap in the AI Governance (Responsible, Trustworthy, Ethical AI).
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