For some, the United Nations Sustainable Development Goals (SDGs) and the 2030 Agenda for Sustainable Development is the golden compass that directs all our decisions and actions to achieve global sustainability goals. Others perceive the SDGs and 2030 Agenda to be a bit of ‘bullsh#t’ that only looks good on paper. Speak to the latter group and many will mention that we simply cannot collect, understand and analyse the volume of sustainability data enough for us to set realistic science-based targets – and we are swinging without aiming.
We are also quickly learning that any sustainability targets that we set – whether on a country or organisational level – it needs to be driven by two things; science data, and the ability to implement the things to make progress towards these targets.
We now have Artificial Intelligence (AI), and this changes things. How can we use AI to set new science-based sustainability targets, process and present data more accurately, and facilitate rapid learning – so ‘sustainability’ is not only determined and managed by the science and engineering fraternities, but rather by everyone? If this is what we need, then rather than to fear AI, let’s use it to rapidly advance our sustainability agendas.
Let’s move our attention to AI for a moment. This is another controversial topic, mostly sparked by fear of making humans redundant in many fields of work. But, if we are really honest with ourselves, AI opens up so many doors….for efficiency, for visualisation, for rapid learning, and importantly in the context of sustainability, data processing, modelling and visualisation. We simply cannot ignore the power of AI if we want to understand our progress towards the SDGs and the UN’s 2030 Agenda. We are five years away from 2030, and the SDGs will need to be updated.
Here is a bold prediction…perhaps all of the new global sustainability goals will be determined by AI using its ability to process existing global data while using this to model future sustainability trends in a lot of detail. Hectic….can we trust AI with such an important task?
While many of us still build up our trust in AI, let’s explore some possibilities with using AI for sustainability:
Creating more citizen scientists
AI will create more ‘citizen scientists’ with more everyday citizens expanding their ability to understand and use in-depth science data, anywhere at any time. Expanding our ability to capture, process, and visualise sustainability data is essential for making informed decisions – and now this will not only be in the hands of data scientists and engineers, but everyone.
At formula D_, we are already witnessing the emergence of this trend, with an increasing number of commissioned projects focused on urban placemaking – integrating design elements in public spaces that essentially capacitated visitors as citizen scientists.
Take, for example, our work on Echoes of Parow, an interactive experience designed to inspire curiosity and engagement in the historical and social fabric of the early Jewish community in Parow. While not explicitly tied to sustainability, this project embodies the same principles of using data to tell compelling, immersive stories that inform, educate, and inspire action – whether about cultural heritage or environmental issues.
When we think about the possibilities that come with using AI to communicate science data, we start to see the incredible opportunities for better interactive and immersive public experiences. Complex datasets can be transformed into visually compelling, interactive experiences that allow a broader audience to grasp key insights without requiring technical expertise. AI-driven dashboards, immersive AR/VR experiences, and large-scale digital visualisations help bridge the gap between raw data and actionable knowledge. With experiences like these, we want to inspire anyone walking through a public space at any given time.
Facilitating rapid learning by making voluminous and complex data accessible
One of the biggest challenges in sustainability is making vast amounts of data accessible and understandable to a wider audience. AI-assisted learning experiences can simplify complex sustainability data, enabling more people to engage meaningfully with urgent sustainability issues. Interactive models and predictive analytics can illustrate the potential impacts of different decisions and actions that citizens take, making the learning process more dynamic and participatory.
Citizen science projects, supported by AI, may allow people to contribute their own valuable data, not just to improve visitor engagement, but to also improve the way we address sustainability issues – especially in public spaces. This will also ensure that sustainability is more inclusive and community-driven.
By integrating AI with experiential learning, we can equip society with the tools and understanding necessary to drive meaningful sustainability action. The intersection of design, science, technology, and public engagement holds immense potential for shaping a more informed and proactive global community. This is the capacity we need to make more progress on the global sustainability goals – future ones included.
