AI Ubiquity In Business Education: From Novelty To Necessity

“Harnessing [AI] can be transformational, but for it to be successful, enterprises need leadership from the top.” — Erik Brynjolfsson, Stanford Digital Economy Lab

Artificial intelligence is no longer a siloed tool, a course module, or an experimental classroom novelty. It has become a 360-degree presence—quietly reshaping research, disrupting traditional operations, altering the student experience, and transforming the very nature of knowledge creation.

The question for institutional leaders is no longer whether to engage with AI, but how to lead effectively in a landscape where AI ubiquity is the new norm.

THE STRATEGIC IMPERATIVE 

In higher education, AI is not a departmental experiment or a technology elective; it is an evolving layer of the institution itself. From enterprise systems to curriculum design, the technology permeates every layer of business education. The institutions that thrive will be those that treat AI not merely as a subject to be taught but as a strategic partner across all functions.

This requires a shift in mindset—from reactive technology adoption to proactive, mission-aligned integration. AI must be mapped to the institution’s strategic objectives, whether in pedagogical innovation, research output, student outcomes, or administrative efficiency.

At its core, this is a leadership issue. Deans and presidents must model AI fluency, create incentives for experimentation, and build institutional capacity to deploy AI ethically, effectively, and at scale.

CURRICULAR UBIQUITY: BEYOND ELECTIVES

The days when AI was confined to electives or data science tracks are over. AI must now be embedded across the business curriculum. Finance courses must address algorithmic trading. Ethics modules must tackle dilemmas posed by generative AI and surveillance capitalism. Marketing, operations, HR—each discipline must integrate the tools, case studies, and critical thinking frameworks that reflect the AI-driven business landscape.

This curricular ubiquity also means moving beyond theoretical engagement. Students must be given hands-on experience with real AI tools and platforms. Micro-credentials, AI sandboxes, and experiential learning are essential for building both competency and confidence.

• AI literacy should be a foundational element: students need to grasp both technical basics and ethical implications.

• Critical thinking about AI—bias, fairness, transparency—should infuse ethical reasoning courses.

• Hands-on experience with AI tools, platforms, and real-world applications will build both competence and confidence.

FACULTY & RESEARCH: EMPOWERING INNOVATION

For AI integration to take root, faculty must be empowered—not burdened. Business schools should offer targeted professional development, peer support models, and access to tools that help faculty incorporate AI into their teaching and research. This is not about turning every faculty member into a data scientist; it is about enabling them to explore how AI intersects with their disciplinary expertise.

On the research front, AI should be both a method and a subject. Whether using AI to analyze large datasets or critically examining AI’s impact on corporate governance, business schools must actively support interdisciplinary, impact-driven research agendas.

Institutions must foster interdisciplinary collaboration around AI, ensuring that ethical, social, and humanistic perspectives are central to AI-related research—not afterthoughts.

OPERATIONS & GOVERNANCE: LEADING BY EXAMPLE

AI is also transforming how institutions themselves operate. Admissions, student advising, faculty hiring, and quality assurance are ripe for AI-driven innovation. Intelligent systems can automate repetitive tasks, surface actionable insights, and support strategic decision-making at all levels.

• Admissions, student advising, faculty recruitment, budget planning, and campus services are increasingly driven by AI-enhanced systems.

• Predictive analytics can support student retention and success strategies.

• Intelligent automation can improve efficiency in routine processes, freeing staff to focus on higher-value work.

But with ubiquity comes responsibility. Governance structures must address ethical deployment, bias mitigation, data privacy, and transparency. This calls for clear policies, training programs, and campus-wide dialogue on the responsible use of AI.

INSTITUTIONAL REPUTATION: A NEW FRONTIER

AI presents a new frontier for institutional excellence and reputation. Forward-looking universities will distinguish themselves by demonstrating thoughtful, ethical, and human-centered AI integration—in their teaching, research, governance, and community engagement.

Accreditation bodies and external stakeholders are already beginning to expect institutions to articulate their AI strategy—not just for compliance, but as a marker of relevance and innovation.

A CALL TO LEADERSHIP

AI ubiquity is not just a technical shift; it is a cultural one. It demands leadership that is visionary, inclusive, and deeply engaged with both the promise and the perils of AI.

Business schools have a unique opportunity—and responsibility—to prepare students for a world where AI is embedded in every business process, from supply chains to strategy rooms. The question for leaders is no longer “Should we adopt AI?” but “How do we lead in a world where AI is already everywhere?”

This transformation will not happen overnight. But by treating AI as a strategic, cross-cutting priority—rather than a departmental experiment—higher education leaders can ensure their institutions remain relevant, respected, and ready.

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Lakshmi Goel is an associate professor of information systems and associate dean at the Coggin College of Business at the University of North Florida. Isabelle Fagnot is associate dean for quality and accreditation and professor of management of information systems at KEDGE Business School. Benjamin Stevenin is director of business school solutions and partnerships at Times Higher Education.

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