Can We Trust AI to Solve Sustainability?
- Morgan Woodward
- Jun 10
- 4 min read
Updated: Jun 10
This article was inspired by the themes explored during Insig AI’s most recent roundtable discussion: ‘Can We Trust AI to Solve Sustainability’. This session was chaired by Diana Rose, our Head of ESG Solutions, and brought together leaders across finance and sustainability for a candid exchange of ideas. A summary of the roundtable key takeaways is available in PDF format at the end of this article.
The future of AI is often framed in two extremes: Utopia or Dystopia. On the one hand, some consider that AI is being built for the betterment of human society. Meanwhile, others see it as inherently perpetuating an extractivist system that is dependent on unsustainable resource use.
Like a flint stone, a calculator, or the internet, AI is merely a tool. A tool’s power is given to it through the intentions and actions of its user. Harnessing the power of AI will undoubtedly speed up innovation. But we cannot rely on it, in its current form, to “solve” anything – not cancer, not sustainability. It is vital that we reflect on what AI is built for and who is using it.
Trustworthy AI
Intentionality and centring the human in the AI debate is key to understanding if we can ‘trust’ AI to solve sustainability. The European Commission's ethics guideline on ‘Trustworthy AI’ highlights this clearly. Human agency and oversight are core requirements, alongside valuing diversity, non-discrimination, fairness, and environmental and societal wellbeing.
However, is this reflective of the actual use of AI today?
AI in the Real World
We are already overshooting 6 of the 9 planetary boundaries. Despite claims that AI has been built for people, to help people, we must remain cautious about the trajectory of its current implementation. It has the potential to accelerate innovation and automate processes at unprecedented speeds. But as with any tool, its impact will be heavily influenced by the intentionality behind its use.
We risk using AI and Large Language Models (LLMs) to pursue short-term gains, at the cost of long-term harm. If capital flows into innovations that encourage this mindset, we are sure to overshoot all 9 of the planetary boundaries and leave the earth much worse off. For example, as our energy demands increase as a result of AI integration, we must not begin to re-justify the continued demand for fossil fuels.
However, with the right thinkers and a systems thinking approach to AI, we can begin to unlock its potential for meaningful, sustainability-focused innovation.
Positive Use of AI
With increased understanding of the importance of biodiversity, nature has increasingly come to the forefront of discussion: from corporate disclosures on nature (TNFD) to Biodiversity Net Gain targets in development. However, in nature related fields, the inability to monitor biodiversity impact has become a huge barrier to innovation. Without people on the ground monitoring biodiversity and deforestation, significant instances of greenwashing often go undetected.
However, AI has started to change the game. Through predictive models and satellite imagery, we are now able to compare corporate disclosure on nature targets versus on-the-ground behaviours. Deforestation has become visible and transparency triggers stakeholders. This kind of visibility can prompt accountability and change.
Insig AI sits at the intersection of artificial intelligence and sustainability, contributing a unique perspective to this evolving debate. At the core of our business, our use of AI is embedded with a sustainable ethos. We want to maintain a critical and reflective mind when discussing these important topics. We use LLMs to make sustainable ESG reporting and data practices far easier, in a very personalised way, so consultants, corporates and asset managers can move away from resource intensive (yet vital) reporting practices to focus on making real impact.
Leadership and Investment
During our roundtable, we explored why many of the positive contributions made by AI seem to relate back to measurement. One answer might be the perception that nature-based solutions are riskier investments and decrease capital flow to those projects.
Are we scared to invest in technology that could create fundamental shifts in the way we interact in the world?
We need compelling evidence and storytelling on sustainability issues to challenge the status quo and shift human behaviour. AI will inevitably play a role in this storytelling and the direction innovation will take.
However, to push the boundaries of sustainable management and AI implementation, it is vital that relevant leadership is put in place. Board level decision-makers need information, context and incentives to lead effectively on sustainability. This shift might inspire innovation that moves beyond measurement and into a sphere of innovation that could result in paradigm shifts in sustainability.
Key Takeaways:
AI is a tool. While iterations become increasingly able to innovate for themselves, we need to use the tool in the right contexts for the right problems.
Its power to have a negative or positive impact for sustainability depends on how it is used.
Our current global economic model is fundamentally unsustainable. AI can’t fix that.
We need leadership to steer AI towards intentional, practical and sustainability-focused innovations.
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