In a world facing escalating environmental challenges, the intersection of artificial intelligence (AI) and sustainability offers promising avenues for innovation and impact. According to a recent report by the European Commission, the EU is investing €200 billion in AI initiatives, highlighting the strategic importance of this technology in addressing global sustainability challenges. But how can we ensure that AI contributes to a greener and fairer future?
A recent panel discussion brought together experts from WWF Sweden, AI Sweden, LTP Labs, and Her Venture Hub to explore this question. This article distills key insights from this conversation, examining both the transformative potential of AI in sustainability and the considerations necessary for responsible implementation.
The Intersection of AI and Sustainability
The discussion was framed around two critical perspectives:
- How can AI support, scale, and accelerate sustainability-driven innovation?
- How can we ensure we use AI in a responsible and sustainable way?
These questions represent two sides of the same coin – leveraging AI as a powerful tool for environmental and social good while being mindful of its limitations and potential downsides.
Understanding the Framework
As Roberto from LTP Labs emphasized, AI is not merely a 2024 phenomenon but has been evolving under different names (machine learning, deep learning) for years. The recent emergence of generative AI represents just one facet of this technology’s capabilities.
Anders Hagström from AI Sweden provided a helpful framework for understanding AI’s capabilities, categorizing them as:
- Sensing the world (through various technologies)
- Understanding the world (using prediction and reasoning models)
- Creating in the world (generative AI applications like ChatGPT)
- Controlling the world (through agents and robotics)
This framework highlights how AI already permeates many aspects of our lives, often invisibly.
Örjan Jansson, Global Programme Manager for Climate Innovations at WWF Sweden, introduced the “iceberg model” of sustainability transformation. This model suggests that the most significant barriers lie beneath the surface, in societal structures, governance, and individual behaviors. These deeper issues, such as disinformation, polarization, and knowledge resistance, often impede progress. AI offers unique possibilities to tackle these challenges, providing leverage that no other technology can.
AI’s Environmental Footprint: The Green Peculiarity
An important consideration when using AI for sustainability is its own environmental impact. Anders highlighted what he termed the “green peculiarity” of AI – its environmental footprint is real but often invisible and easily ignored. Key environmental considerations include:
- High energy consumption: With two-thirds of energy still produced from fossil fuels, AI’s computational demands have a substantial climate impact
- Water usage: Data centers, which power AI applications, consume vast amounts of water for cooling
- Hazardous substances: The hardware needed for AI systems often contains problematic materials
- E-waste and hardware footprint: The production and disposal of devices contribute to environmental pollution
- Hidden infrastructure costs: Cloud services and other supporting systems have invisible environmental impacts
- Rapid scaling: AI’s fast adoption can amplify negative impacts if not managed properly
The invisible nature of this footprint means it often gets pushed aside, even as AI is deployed to solve other environmental problems. This paradox requires careful consideration – AI can help solve sustainability problems but also creates new ones.
Key Applications of AI in Sustainability
The panel highlighted several areas where AI is already making significant contributions to sustainability:
Environmental Protection
AI is playing a crucial role in climate innovation programs, helping to fact-check information, combat misinformation, and enforce environmental laws. Örjan Jansson emphasized AI’s unique ability to address disinformation and polarization, which are significant barriers to green transformation.
AI can also assist businesses in making sustainable choices by providing accurate and reliable information. For example, AI-powered tools can analyze corporate reports to detect greenwashing, cross-reference data sources to verify environmental claims, and monitor real-time ESG performance metrics.
Business Transformation: Reducing Food Waste
Roberto shared a powerful example of AI application in sustainability: using predictive analytics to reduce food waste in grocery stores. By anticipating when products would approach their expiry date and creating targeted promotional strategies, they improved process performance by 20 percentage points.
Similar approaches in consumer electronics prevented products containing batteries and heavy metals from ending up in landfills. These practical applications demonstrate how AI can drive tangible environmental benefits when properly applied.
Reshaping Industries: Fashion’s Transformation
Anders shared how AI could help reverse environmentally problematic practices in the fashion industry. Rather than using AI to boost sales in traditional models (which can lead to more waste), it could enable on-demand production where items are manufactured only after being sold, eliminating overproduction that typically ends up as discounted sale items.
Social Sustainability Through AI
Luísa Baltazar from Her Venture Hub highlighted AI’s potential in scaling social impact initiatives. She described using AI to provide personalized support to women entrepreneurs globally – something impossible to achieve at scale through human effort alone.
AI can also analyze research and data to anticipate economic stress factors affecting vulnerable populations, enabling preemptive support during recessions or other global challenges.
Combating Misinformation and Polarization
Orian from WWF Sweden discussed how AI could address deeper societal barriers to green transformation, particularly disinformation and polarization. One prototype developed during a hackathon could analyze political debates in real-time, fact-checking statements and identifying rhetorical strategies that divert from logical discussion.
This application exemplifies how AI might tackle root causes that hold back sustainability progress, though finding funding for such public-good tools remains challenging.
Implementation Strategies and Challenges
To successfully implement AI for sustainability, organizations need to address several key challenges and adopt effective strategies.
Key Principles for Effective AI Implementation
The panelists emphasized several critical principles for organizations looking to leverage AI for sustainability:
1. Start with the Problem, Not the Technology
All panelists stressed the importance of beginning with a well-defined challenge rather than forcing AI into situations where it might not be needed. As Luísa articulated: “Usually we see the potential of a tool and then try to fit that solution into a problem. The approach should be the opposite – this is the social problem, how do we use technology to address it?”
2. Responsible and Transparent Development
Roberto highlighted that AI lacks the ability to consider unintended consequences – it blindly optimizes for the metrics it’s given. Human oversight remains essential, particularly for social applications, to ensure technology doesn’t create new problems while solving others.
3. Collaboration is Essential
The experts emphasized that collaboration accelerates progress. Organizations don’t need to develop everything independently – even competitors can collaborate in areas outside their core products. This approach helps generalize solutions, optimize them with broader use, and accelerate positive impact.
Public-private partnerships, cross-sector initiatives, and knowledge-sharing platforms can help organizations share best practices, pool resources, and address common challenges.
4. Multidisciplinary Teams Drive Success
Luísa emphasized the need for teams that bring together social science and technical expertise, noting these groups often speak “different languages” but must learn to communicate effectively to create meaningful solutions.
5. Question the Environmental Footprint
Anders encouraged organizations to challenge suppliers about the environmental impact of AI services they purchase. Without customer demand for information about AI’s footprint, providers have little incentive to address these concerns.
Leadership Requirements
A recurring theme throughout the discussion was the critical role of leadership in steering AI toward positive sustainability outcomes. Key leadership insights included:
Embrace Uncertainty and Innovation
Luísa urged leaders to take leaps of faith with emerging technologies: “Leaders need to embrace innovation even when they don’t fully understand it. They’re not supposed to understand the technology; they’re supposed to understand and measure the results.”
She also noted that cultural differences play a significant role in AI adoption. What works in the US may not be effective in Europe or the Middle East. Leaders need to be aware of these differences and adapt their strategies accordingly.
Take Control of Your AI Journey
Roberto advised organizations not to wait until AI technologies are fully tested and mainstream: “If you wait until this technology is tested, learned, and everyone is using it, it will be too late for you to create sustainable value for yourself, your company, your workers, and your economy.”
Aim Higher Than Efficiency
Orian encouraged leaders to look beyond merely making existing processes more efficient: “Try to find the bigger good that can be done, and aim for that. Don’t aim too low.”
Future Outlook and Recommendations
Several key trends are shaping the future of AI in sustainability:
- European AI Development Initiatives: The EU is investing heavily in AI research and development, with a focus on ethical and human-centric AI.
- Public Sector Adoption: Anders noted that in Sweden, the public sector is collaborating on AI initiatives to improve services while addressing a projected workforce shortage.
- Social Innovation Opportunities: AI is creating new opportunities for social enterprises to address pressing social and environmental issues, as highlighted by Luísa’s work with Her Venture Hub.
Practical Steps for Organizations
To harness the full potential of AI for sustainability, organizations should take the following steps:
- Identify Specific Problems: Begin by identifying sustainability challenges where AI can provide unique solutions.
- Start with Pilot Projects: Test and refine AI solutions in controlled environments before scaling.
- Measure Impact: Track key performance indicators (KPIs) to assess the environmental and social impact of AI initiatives.
- Build Sustainable AI Practices: Adopt ethical guidelines, prioritize energy efficiency, and ensure AI benefits all members of society.
- Collaborate Across Sectors: Work with other organizations, research institutions, and government bodies to share knowledge and resources.
The Path Forward
The conversation made clear that AI offers tremendous potential for advancing sustainability goals, but realizing this potential requires thoughtful implementation, collaboration, and leadership. Organizations must balance the innovative potential of AI with careful consideration of its impacts.
By starting with well-defined problems, embracing collaboration, building multidisciplinary teams, and maintaining human oversight, we can harness AI as a powerful tool for creating a greener, fairer future – while ensuring the technology itself is deployed responsibly and sustainably.
As we navigate this evolving landscape, one thing is certain: the intersection of AI and sustainability will play a crucial role in addressing our most pressing environmental and social challenges. The question is not whether AI will impact sustainability efforts, but how we will direct that impact toward creating the future we want to see.
To learn more and get involved, join the Global Green Action Day on June 5th (UN Environment Day) and connect with organizations driving AI innovation for sustainability, or contact us to submit a challenge.