Perspectives on AI’s role and impact on higher education remain remarkably diverse, as they should with any emerging technology embedded in different social and institutional contexts. In business schools, the debate over artificial intelligence and its interaction with sustainability is particularly complex. Both are subject to conceptual stretching. AI encompasses vastly different technologies, from machine learning systems to large language models. Sustainability ranges from carbon accounting to circular business models and social inclusion. When definitions vary, findings naturally diverge.
This conceptual ambiguity partly explains the uncertainty surrounding AI adoption in business education. Concerns about the environmental footprint of AI, particularly the energy consumption of large language models, often dominate public discussion. Yet the deeper question is not whether AI has an environmental impact – it does – but whether business schools can harness it as an enabler of sustainable transformation rather than view it solely as a threat.
A 2026 study by CarringtonCrisp found widespread AI use in business schools, with students having greater familiarity than faculty. Notably, one third of faculty respondents indicated that their institutions lacked clear AI policies. Most strikingly, only three per cent of respondents found university AI courses genuinely helpful.
This is a sobering finding. AI courses are proliferating across universities yet their perceived impact remains limited. The problem might not be supply, but relevance. If AI is framed narrowly as a technical subject rather than as a transformative capability embedded in organisational strategy, sustainability and entrepreneurship, expectations will inevitably diverge from outcomes.
At my institution, we have approached AI not as a stand-alone technical topic but as a skill integrated into strategy, operations, education, research and venture-building. In practice, this has meant encouraging responsible AI experimentation, embedding large language model (LLM) tools into teaching and administrative workflows and offering executive programmes focused on the strategic use of AI in organisations. Our most popular programmes have not been abstract courses on AI theory but practical workshops demonstrating how leaders can use LLM tools to improve decision-making, innovation and resource efficiency.
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Importantly, we extend these conversations beyond the university. In addition to collaborating with our established business partners, we work with secondary school leaders to develop shared best practices. Sustainable AI adoption is not achieved through isolated expertise but through cross-sector learning.
How AI enables sustainability in practice
Yet meaningful AI adoption is ultimately less about technical tools than about human collaboration. Technology alone does not produce sustainability; communities do. To foster practical innovation, we have hosted two AI defence hackathons sponsored by the Estonian Ministry of Defence. Our most recent event focused on resource optimisation in defence systems, including material reuse, circular supply chains, product redesign and sustainable disposal.
Over three days, interdisciplinary teams developed prototypes addressing real-world challenges. One winner, Jälle Technologies, advanced graphene-like materials to reduce the thermal signature of defence clothing. Another team presented drone-based logistics solutions to increase front-line efficiency. Another developed an LLM-powered assistant to enhance resilience in military communications. A sustainability prize was awarded to another team for its smart solution to ballistic plate reuse.
These initiatives demonstrate that AI and sustainability can reinforce one another when innovation is guided by clear societal goals. Moreover, our engagement does not end with events. Through our venture-building platform, we provide selected teams with access to a venture studio pre-programme, bridging experimentation and commercialisation.
Equally important is fostering critical reflection and imagination for potential future trajectories. For the past six years, we have organised an international summer school on digitalisation and sustainability, bringing together students, scholars, policymakers and entrepreneurs from four continents. Participants have included a Nobel laureate, senior European Union officials, founders of AI and energy start-ups and executives from institutions such as the Nordic Investment Bank. We also organise a scenario-planning workshop, where we explore how AI reshapes business models while simultaneously intensifying energy demands and environmental trade offs.
The central premise is straightforward. Artificial intelligence is transforming business models. It is also reshaping the sustainability challenge. LLMs require significant computational resources yet they also enable more precise optimisation of supply chains, energy systems and circular processes. The outcome depends on governance, incentives and leadership.
How can business schools scale AI adoption?
First, integrate AI horizontally rather than isolating it in specialised courses. Embed it in strategy, operations and sustainability discussions.
Second, prioritise human collaboration across sectors. Public and private partnerships, hackathons and cross-community learning create shared ownership of responsible AI solutions.
Third, combine experimentation with critical reflection and imagination. Business schools must cultivate both entrepreneurial capability and societal awareness.
If approached thoughtfully, AI can become a catalyst for sustainable transformation rather than an environmental liability. The responsibility and opportunity lie with business schools to shape that trajectory.
Meelis Kitsing is rector of Estonian Business School.
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