Business schools must embed AI across teaching and strategy, not isolate it in standalone courses
Business schools are failing to teach AI effectively despite widespread adoption. Only 3 percent of respondents in a 2026 CarringtonCrisp study found university AI courses genuinely helpful, even though AI use is common across campuses and students often know more than faculty.
The problem is not supply. AI courses are proliferating. The issue is that most universities treat AI as a technical subject rather than as a capability embedded in strategy, operations, and sustainability.
This matters because AI carries real environmental costs-large language models consume significant energy-yet they also enable precise optimization of supply chains and circular processes. How business schools teach AI shapes whether graduates see it as a threat or a tool for sustainable business.
Three approaches that work
Integrate AI horizontally across the curriculum. Rather than offering standalone courses, embed AI discussions into strategy, operations, and sustainability classes. This signals to students that AI is not a specialized domain but a capability they need across roles.
Prioritize collaboration across sectors. One institution hosted AI defense hackathons where interdisciplinary teams developed prototypes for real-world challenges: graphene materials to reduce thermal signatures, drone logistics solutions, and systems for ballistic plate reuse. These events created shared ownership of responsible AI solutions between academia, government, and industry.
Combine experimentation with critical reflection. Practical workshops on how leaders use AI tools to improve decision-making and resource efficiency draw larger audiences than abstract theory. Equally important is hosting scenario-planning sessions where participants explore trade-offs: LLMs enable supply chain optimization but demand more computational resources.
Why faculty adoption matters
A third of faculty respondents said their institutions lack clear AI policies. Without institutional clarity, individual instructors struggle to decide when and how to use these tools in teaching and assessment.
The solution is not top-down mandates but practical support. Executive programs focused on strategic AI use, LLM tools integrated into teaching workflows, and responsible experimentation create buy-in among faculty who otherwise see AI as external pressure.
Business schools also need to extend these conversations beyond campus. Collaborating with secondary school leaders on shared best practices ensures that responsible AI adoption becomes a cross-sector norm, not an isolated university initiative.
The outcome depends on leadership
AI will reshape business models. It will also intensify energy demands and environmental trade-offs. Whether AI becomes a catalyst for sustainable transformation or an environmental liability depends on governance, incentives, and the decisions made by business school leaders today.
Learn more about AI for Education and AI for Executives & Strategy to develop institutional approaches that work.
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