Views on the role of AI and its impact on higher education remain surprisingly diverse. This is the case with emerging technologies embedded in different social and institutional contexts. Business schools are having discussions about: artificial intelligence interaction with sustainability is particularly complex. Both are subject to conceptual extension. AI encompasses vastly different technologies, from machine learning systems to large-scale language models. Sustainability ranges from carbon accounting to circular business models and social inclusion. Different definitions naturally lead to different findings.
This conceptual ambiguity explains some of the uncertainty surrounding AI implementation in business education. Concerns about the environmental impact of AI, particularly the energy consumption of large-scale language models, are often at the center of public debate. But the deeper question is not whether AI will have an impact on the environment – and it does – but whether business schools can leverage AI as an enabler for sustainable change, rather than just seeing it as a threat.
2026 study by CarringtonCrisp found that AI is widely used in business schools and that students know more about it than faculty. Notably, one-third of faculty respondents said their institution lacked clarity. AI policy. Most surprisingly, only 3% of respondents found AI courses at university really useful.
This is a sobering discovery. AI courses are proliferating across universities, but the perceived impact remains limited. The problem may not be supply, but relevance. When AI is narrowly framed as a technical subject, rather than a transformative capability embedded in organizational strategy, sustainability, and entrepreneurship, expectations will inevitably diverge from results.
At my institution, we have approached AI not as a separate technical subject, but as a skill integrated into strategy, operations, teaching, research, and venture building. In practice, this means encouraging responsible AI experimentation, incorporating large-scale language modeling (LLM) tools into educational and administrative workflows, and offering executive programs focused on the strategic use of AI within organizations. Rather than abstract courses on AI theory, our most popular programs are practical workshops that show leaders how to use LLM tools to improve decision-making, innovation, and resource efficiency.
Importantly, we extend these conversations beyond the university. In addition to working with established business partners, we work with secondary school leaders to develop shared best practices. Sustainable AI adoption will be achieved through cross-disciplinary learning, not through isolated expertise.
How AI actually enables sustainability
But meaningful AI adoption ultimately depends on human collaboration, not technological tools. Technology alone does not create sustainability. The community does. To foster practical innovation, we hosted two AI defense hackathons sponsored by the Estonian Ministry of Defense. Our latest event focused on resource optimization in defense systems, including material reuse, circular supply chains, product redesign, and sustainable disposal.
Over three days, a multidisciplinary team developed a prototype that addressed: real world challenges. One of the winners, Jälle Technologies, has developed a graphene-like material to reduce heat signatures in defense clothing. Another team introduced a drone-based logistics solution to increase efficiency in the field. Another company developed an LLM-powered assistant to increase resiliency in military communications. The Sustainability Award was awarded to another team for a smart solution to the reuse of ballistic plates.
These efforts demonstrate that AI and sustainability can mutually reinforce each other when innovation is guided by clear social goals. Furthermore, our involvement does not end with the event. Through our venture-building platform, we provide selected teams with access to Venture Studio’s pre-programming, bridging experimentation and commercialization.
Equally important is nurturing critical consideration and imagination of potential future trajectories. For the past six years, we have organized an international summer school on digitalization and sustainability, bringing together students, academics, policy makers and entrepreneurs from four continents. Participants include Nobel Prize winners, senior European Union officials, founders of AI and energy startups, and executives from institutions such as the Nordic Investment Bank. We will also host scenario planning workshops to explore how AI can reshape business models while enhancing energy demand and environmental trade-offs.
The central premise is simple. Artificial intelligence is transforming business models. The sustainability agenda is also being reframed. Although LLM requires large amounts of computational resources, it also enables more precise optimization of supply chains, energy systems, and circular processes. Results depend on governance, incentives and leadership.
How can business schools scale up AI adoption?
First, we integrate AI horizontally rather than separating it into specialized courses. Incorporate it into your strategy, operations, and sustainability discussions.
Second, prioritize human collaboration across sectors. Public-private partnerships, hackathons, and cross-community learning create shared ownership of responsible AI solutions.
Third, it combines experimentation, critical reflection, and imagination. Business schools must develop both entrepreneurial abilities and social awareness.
If approached thoughtfully, AI can become a catalyst for sustainable change rather than an environmental liability. Business schools have the responsibility and opportunity to shape that trajectory.
Mielis Kitzing is the Dean of the Estonian Business School.
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