Artificial intelligence is often associated with efficiency and optimisation. Yet behind the scenes, AI systems consume significant energy resources particularly large-scale machine learning models.
If business education aims to prepare responsible leaders, sustainability must be part of the AI conversation.
Understanding AI’s Environmental Impact
Training and deploying AI systems can involve:
- High computational power
- Energy-intensive data centres
- Significant carbon emissions
While AI can help optimise supply chains or reduce waste, its own footprint must be measured and managed.
Bringing Sustainability into the AI Curriculum
Educators can introduce sustainability in AI by:
- Comparing carbon footprints of different AI deployment strategies
- Discussing “build vs. buy” decisions in AI procurement
- Exploring energy-efficient AI design principles
- Analysing ESG reporting implications of AI infrastructure
Students should learn to ask:
Is this AI solution environmentally justified relative to its benefits?
Aligning AI with ESG Strategy
Companies are increasingly evaluated on Environmental, Social, and Governance (ESG) metrics. AI decisions now influence:
- Corporate sustainability reporting
- Investor confidence
- Public perception
By integrating environmental awareness into AI education, we foster leaders who can balance innovation with responsibility.
Sustainable AI is not anti-innovation, it is innovation done right.