Judgment Before Policy

By Ajoy Kumar Dey

• Self-aware leaders understand the reasons behind the decisions they make and the ways those decisions influence the culture of their organizations.
• In the age of AI, effective deans will move away from compliance and toward institutional stewardship by modeling ethical practice, creating spaces for dialogue, and aligning institutional incentives with integrity.
• Because no institution can anticipate all the challenges that will arise, deans should stop asking “What should our AI policy say?” and start asking “How can we consistently make better decisions?”

 
As more business schools integrate generative AI into their courses and operations, school leaders must develop clear guidelines about classroom use, research integrity, assessment protocols, and disclosure requirements. But the technology is evolving faster than institutional regulations or administrative offices can keep up.

It’s a problem faced by leaders all over the world. Scholars have argued that, in complex and rapidly changing environments, organizations cannot rely exclusively on formal rules; they must turn to top executives who are capable of exercising informed judgment under conditions of uncertainty. In other words, they need self-aware leaders.

Such leaders recognize that policies alone cannot sustain integrity because everyone else in the organization takes their cues from those in the top office. Ethical leadership research consistently shows that leaders shape organizational culture through role modeling, social learning, and the signals they send about acceptable behavior. They communicate institutional priorities through the issues they discuss, the questions they ask, the trade-offs they make, and the achievements they celebrate.

In today’s educational environment, where changes in AI outpace the institution’s ability to formulate policies, self-aware leadership becomes a business school’s most important governance capability. And a self-aware dean becomes a school’s most strategic advantage.

The Self-Aware Leader

To understand the impact of self-awareness, it helps to know that leadership researchers identify two different types. Individuals with internal self-awareness understand their own values, assumptions, motivations, and biases. Those with external self-awareness recognize how their decisions and behaviors are perceived by others and how their actions shape organizational responses. When leaders have both types of capabilities, they recognize not only why they make the decisions they make but also how those decisions influence the culture of the institution.

Importantly, self-awareness is not an innate personality trait reserved for a few exceptional leaders. It is a capability that develops through reflection, feedback, experience, and continuous learning. Leaders who regularly question their own assumptions, seek diverse perspectives, and remain open to constructive challenges are well equipped to respond to unfamiliar situations with sound judgment rather than habitual reactions.

When it comes to AI governance, the challenge for leaders is not to keep pace with every technological development but to exercise responsible judgment when policies cannot anticipate every dilemma. Top executives must shift away from command-and-control approaches where they provide all the answers. Instead, they must facilitate learning, invite collective sense-making, and create conditions under which good judgments can emerge.

Before making decisions, these leaders will not simply ask, Is this permitted? Instead, they will pose more foundational questions: What values are we protecting? What behaviors will this decision encourage? How will this choice shape our institutional culture?

Leaders develop self-awareness by questioning their own assumptions, seeking diverse perspectives, and remaining open to constructive challenges.

As leaders of business schools, deans must make decisions that balance innovation with academic integrity, institutional reputation with educational quality, and short-term efficiency with long-term societal responsibility. For instance, they might need to decide whether AI-generated teaching materials should be encouraged, how much AI assistance is acceptable in faculty research, or whether existing assessment methods reflect genuine learning. Such decisions require more than technical expertise; they require self-awareness.

Therefore, in the age of AI, today’s most effective business school leaders will move beyond compliance and toward institutional stewardship. They can do this by adopting three complementary practices: modeling ethical AI use, creating spaces for dialogue, and aligning institutional incentives with integrity.

1. Modeling Ethical AI Use

Leadership begins with example. Faculty members and students are more likely to emulate what leaders consistently do than what policies prescribe. As noted above, ethical leadership research suggests that organizational norms are established through role-modeling, where leaders demonstrate the behaviors they expect from others.

For deans, this begins with transparency. Consider a dean who uses generative AI to prepare the first draft of a strategic plan. Rather than concealing its use, the dean openly explains that AI helped organize ideas but that the final document reflects human judgment, institutional priorities, and careful verification of facts and references. This simple act communicates that AI is a tool that supports critical thinking but does not replace it.

Role-modeling also requires intellectual humility. AI systems often produce persuasive but inaccurate outputs. Self-aware deans encourage colleagues to question AI-generated materials, including market analyses or accreditation summaries. These deans also verify AI-assisted reports before sharing them with governing boards, accreditation teams, alumni, or external partners. They make it clear that responsibility for decisions always rests with people, not algorithms.

When leaders consistently demonstrate transparency, humility, and accountability, they communicate an important message: Responsible AI use is measured not by how frequently technology is adopted but by how thoughtfully it is applied.

2. Creating Spaces for Ethical Dialogue

Because AI-related dilemmas evolve continuously, no policy manual can anticipate every future scenario. Self-aware deans create opportunities for dialogue so that faculty, students, professional staff, and external stakeholders can interpret emerging challenges together.

This dialogue begins with shared governance. Regular faculty meetings should include discussions on responsible AI use in curriculum design, admissions, assessment, placement, accreditation, international collaborations, executive education, and research partnerships. Faculty participation in institutional decision-making not only improves policies but also builds ownership of those policies.

Curriculum committees should debate fundamental questions such as, What capabilities should graduates possess in an AI-enabled world? These capabilities might include technical proficiency alongside analytical reasoning abilities, quantitative competence, systems thinking, ethical judgment, communication skills, empathy, teamwork, and an appreciation of lifelong learning. Curriculum design should ensure that, even if students use AI to complete a task, they retain the capacity to think independently, integrate knowledge across disciplines, and exercise sound managerial judgment.

Students also should have opportunities to discuss the responsible use of AI. Potential organizational settings for these conversations include hackathons, debate societies, student councils, social impact activities, events at innovation and entrepreneurship centers, and committee meetings on discussing diversity or preventing sexual harassment. Such forums encourage students to see AI not simply as a productivity tool but as a technology with ethical, social, managerial, and even environmental implications.

Researchers, too, should have opportunities to debate AI-related topics such as research integrity, responsible AI use, authorship, peer review, and data transparency.

Through discussion forums, students come to see AI not simply as a productivity tool but as a technology with ethical, social, managerial, and even environmental implications.

It’s important that these groups meet in psychologically safe environments where they can express concerns, acknowledge uncertainty, and discuss failures without fear of embarrassment or retaliation. Open conversations strengthen institutional learning far more effectively than punitive responses meted out after problems arise.

Consider an MBA program where faculty members disagree about acceptable AI use in assessments. Instead of issuing an immediate directive, the dean establishes a cross-functional working group of faculty, students, instructional designers, and quality assurance specialists. The group develops guiding principles, conducts pilots of revised assessments in selected courses, gathers evidence on student learning, and reviews the outcomes after one semester before recommending institutional policy. The resulting approach is more robust because it is informed by evidence, collective reflection, and shared ownership.

The lesson is straightforward: Dialogue creates adaptive institutions, whereas rules alone often produce organizations and workforces that comply without commitment.

3. Aligning Incentives With Integrity

Perhaps the most influential leadership responsibility is ensuring that institutional reward systems reflect the school’s genuine values. As Steven Kerr’s classic observation reminds us, organizations often reward one set of behaviors while hoping for another. In AI-intensive environments, this misalignment becomes even more consequential because technology can accelerate both excellence and unintended consequences.

Self-aware deans examine whether existing incentive systems encourage responsible judgment or simply reward measurable outputs. They pay attention to three areas in particular:

• Faculty promotion criteria. If advancement depends primarily on publication counts, use of AI may help increase writing efficiency without necessarily improving research quality. By contrast, evaluation systems that recognize methodological rigor, transparency, reproducibility, interdisciplinary collaboration, educational innovation, and societal impact encourage scholarship that strengthens institutional credibility rather than merely increasing output.
• Awards for teaching excellence. These honors can recognize faculty members who use AI transparently to enrich learning and who also design assessments that foster critical thinking, reflection, creativity, and problem-solving. At the same time, institutions should celebrate and learn from faculty innovations and pilot projects that did not achieve their intended outcomes. In this way, deans signal that continuous improvement is more important than maintaining an appearance of infallibility.
• Assurance of learning processes. Faculty can design processes that show how students have used AI to demonstrate reasoning, ethical decision-making, communication, collaboration, and the integration of knowledge across complex managerial situations.

Ultimately, organizational culture is not determined by mission statements or policy documents but by the actions and outputs that leaders consistently measure, reward, and tolerate. Over time, these everyday signals become the norms that shape behavior across the business school, even during eras of rapidly changing technology.

From AI Governance to Ethical Stewardship

As AI continues to evolve, business schools inevitably will revise their policies, update their curricula, and introduce new governance mechanisms. Yet no policy can anticipate every ethical dilemma that will arise. The most resilient institutions will be those that move from asking What should our AI policy say? to How can we consistently make better decisions?

Organizational culture is not determined by policy documents but by the actions and outputs that leaders consistently measure, reward, and tolerate.

One practical way to achieve this shift is to embed reflective questioning into institutional decision-making. Before introducing new AI-related policies, technologies, or academic practices, self-aware leaders can pause to consider four questions.

• What institutional value are we protecting? Every policy should reinforce the school’s educational mission and core values, whether those relate to academic integrity, student learning, scholarly excellence, inclusion, or societal impact.
• What behaviors will this decision encourage? Policies shape incentives. Leaders should consider not only the intended outcomes but also the behaviors that faculty, students, and staff may adopt in response.
• em>Who might be unintentionally disadvantaged? To ensure that governance remains inclusive and equitable, deans should reflect on issues of access, fairness, workload, disciplinary differences, and diverse stakeholder perspectives.
• Would this decision still make sense if AI changed tomorrow? Technologies evolve rapidly, but institutional values endure. Decisions grounded in foundational educational principles are more likely to remain relevant than those tied to the capabilities of a particular AI tool or platform.

These four questions do not eliminate uncertainty, nor do they replace institutional policies. Instead, they encourage leaders to approach governance as a continuous process of reflection, learning, and adaptation.

These questions remind us that responsible AI governance will not be determined by sophisticated institutional policies. Rather, it will be defined by leaders who possess enough self-awareness to model ethical behavior, foster open dialogue, guide judgment, and align institutional incentives with integrity.

These leaders will create cultures where responsible decisions become part of everyday academic practice. They will embrace innovation without compromising the educational mission or the public trust.