17 March 2026
Q: How is AI changing the way sustainability teams work?
A: People tend to see the world through particular lenses. Perspectives are shaped by the context of our experience, our surroundings, our needs, and the tools we have at our disposal. The saying is very apt that to the person with a hammer, everything looks like a nail.
In the rapidly emerging, exciting era of AI, decision-makers will need to be very clear-headed in selecting the best tool for the job. In sustainability as with most things, it’s important to clarify the objective, before selecting the method for achieving it. That sounds obvious, but when I was 7 years old, my parents gave me a tool box for Christmas. From that moment on, I looked at everything around me in terms of what I could cut up with a saw or hit with a hammer. For some things, like making a small set of shelves or hanging pictures on the wall, the saw and hammer were ideal. For making my bed, taking the dog for a walk, or laying the table for dinner, they were less useful.
So takeaway #1 is: clarify what you want to achieve, and then choose the best tool to help you get there.
AI can, of course, help clarify your objectives. However human expertise and critical thinking remain essential, and it doesn’t make sense to defer too much high-level, strategic decision-making to AI. AI is great for increasing efficiency, saving time and speeding up execution, and supporting trend analysis and scenario generation. However, it is not a supplement for sound prioritisation and good judgement. It should also be remembered that hallucinations (when AI presents as fact information that is fundamentally wrong) can lead organisations down dangerous ‘rabbit holes’, triggering bad decisions based on flawed or false information. Effective use of AI requires both the right tools, and strong contextual understanding.
Q: How will AI change environmental strategy and targets?
A: Our perspective is that AI is not the major driver of climate and sustainability strategy change. Geopolitics has been a far more powerful change agent in sustainability and climate strategy in recent months, with many organisations deprioritising sustainability and diverting attention, discussion, and budgets elsewhere. AI may reflect and encourage those changed priorities through its algorithms, but the original driver is geopolitical. However AI itself may also drive geopolitical changes, especially as pressure increases for governments to secure access to resources such as critical minerals, on which AI depends.
Takeaway #2 is: geopolitics is more likely than AI to shape climate and sustainability strategy, but AI may also drive geopolitical change.
AI will also affect sustainability indicators, improving efficiency on the one hand, but also creating new environmental impacts (such as increased energy demand, carbon emissions, water use, and critical mineral extraction and processing). There is an important role for sustainability professionals in helping senior colleagues understand the hidden environmental costs of AI. What decision-makers choose to do with this information is, of course, up to them. However, emerging conversations about responsible AI ought to include all available information about the environmental footprint of AI, as well as the difficulty of obtaining reliable data from both users and providers. Data transparency is severely lacking on emissions, water use, land-use change, and the impacts of critical mineral extraction and processing. There is a role for proponents of responsible AI in pushing for increased levels of data transparency on these issues.
Bernice Maxton-Lee
Executive Education Lecturer, Climate and Sustainability
Mina Rezaeian
Lecturer in Sustainability and Strategy