“use [AI] It may be transformative, but to succeed, companies need leadership from the top. ” – Erik Brynjolfsson, Stanford Digital Economy Lab
Artificial intelligence is no longer a novelty of siloed tools, course modules, or experimental classrooms. It has a 360-degree presence. It reshapes research over and over again, disrupts traditional tasks, changes student experiences, and changes the nature of knowledge creation.
The question for institutional leaders is not whether they are involved in AI, but how to effectively lead in landscapes where AI ubiquitity is a new norm.
Strategic Order
In higher education, AI is not a departmental experiment or technical elective. It is the evolving layer of the institution itself. From enterprise systems to curriculum design, this technology penetrates all layers of business education. A thriving institution will not only teach AI, but will treat it as a strategic partner across all functions.
This requires a change in mindset, from adopting reactive technologies to integration placed on a proactive mission. AI should map to institutional strategic objectives, whether it is educational innovation, research output, student outcomes, or management efficiency.
At its core, this is a matter of leadership. Deans and Presidents need to model AI flow ency, create incentives for experiments, and build institutional capabilities to deploy AI ethically, effectively, and on a large scale.
The ubiquitous curriculum: surpassing elective subjects
The days when AI was limited to elective or data science tracks are over. AI must be embedded throughout the business curriculum. Financial courses must address algorithmic trading. The ethics module must tackle the dilemma brought about by generative AI and surveillance capitalism. Marketing, operations, and HR-each discipline must integrate tools, case studies, and critical thinking frameworks that reflect the AI-driven business environment.
The ubiquitousness of this curriculum means moving beyond theoretical involvement. Students must be given hands-on experience with real AI tools and platforms. Microcredence, AI sandboxes, and experiential learning are essential to building both abilities and confidence.
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AI literacy must be a fundamental element. Students need to understand both technical basics and ethical implications.
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Critical thinking about AI – bias, fairness, transparency – should inject ethical reasoning courses.
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A hands-on experience with AI tools, platforms, and real-world applications builds both competence and confidence.
Teachers and Research: Empowering Innovation
For AI integration to take root, teachers must be empowered. Business schools need to provide access to targeted professional development, peer support models, and tools that help teachers incorporate AI into their education and research. This is not to turn all faculty into data scientists. It's about allowing them to explore how AI intersects with disciplinary expertise.
On the research front, AI must be both a method and a subject. Whether using AI to analyze large datasets or critically examining the impact of AI on corporate governance, business schools should actively support interdisciplinary, impact-driven research agendas.
Institutions must promote interdisciplinary collaboration on AI and ensure that ethical, social and humanitarian perspectives are central to AI-related research.
Operations and Governance: Leading with examples
AI is also changing the way in which the institutions themselves operate. AI-driven innovations ripen with admission, student advice, faculty employment and quality assurance. Intelligent systems can automate repetitive tasks, surface actionable insights, and support strategic decision-making at all levels.
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Admissions, student advice, faculty recruitment, budget planning, and campus services are increasingly driven by AI-enhancing systems.
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Predictive analytics can support students' retention and success strategies.
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Intelligent automation improves the efficiency of everyday processes and allows staff to free themselves up to focus on higher value tasks.
But there is a responsibility to be ubiquitous. Governance structures must address ethical deployment, bias mitigation, data privacy, and transparency. This requires clear policies on responsible use of AI, training programs, and campus-wide dialogue.
Institutional reputation: a new frontier
AI presents a new frontier for institutional excellence and reputation. Future-looking universities distinguish themselves by demonstrating thoughtful, ethical, and human-centered AI integration in education, research, governance and community engagement.
Accreditation bodies and external stakeholders are already beginning to expect agencies to clarify their AI strategies as not only a marker of compliance, but also as a marker of relevance and innovation.
Calling for leadership
AI Ubiquitty is more than just a technological change. It's cultural. It calls for visionary, comprehensive, and deeply involved in both AI promises and dangers.
Business schools have a unique opportunity to prepare students in a world where AI is embedded in every business process, from supply chains to the Strategy Office. The question for leaders is no longer “We should not employ AI.” But “How will AI lead the world already everywhere?”
This conversion does not occur overnight. However, by treating AI as a more strategic and mutually opposed priority than departmental experiments, advanced education leaders can ensure that their institutions remain relevant, respected and ready.
Lakshmi Goel is Associate Professor and Dean of Information Systems at the Coggin College of Business at the University of North Florida. Isabelle Fagnot is Associate Dean of Quality and Accreditation and Professor of Information Systems Management at Kedge Business School. Benjamin Stevenin is sometimes the director of business solutions and partnerships in higher education.
