From streamlining ESG processes, to consuming vast amounts of energy, AI provides both opportunities and threats to the sustainability of industry. Julia Binder and José Parra Moyano of IMD unpack the issues
Artificial intelligence is both a game-changer and a potential liability for business sustainability. On one hand, it offers transformative opportunities, optimising energy use, streamlining supply chains, and driving climate innovation. On the other, AI itself can be a sustainability challenge, with its energy-intensive computations and ethical concerns. For business leaders, AI is not inherently good or bad for sustainability, it all depends on how it is developed, deployed, and managed. To navigate this landscape, businesses must take a structured approach. The most immediate opportunities lie in quick wins and fixes, while AI’s transformative power is best realised in reshaping complex systems. Looking ahead, its greatest potential may lie in scaling sustainable innovations that drive long-term impact.
The quick wins
For businesses seeking to improve their sustainability efforts, AI offers several low-hanging fruits that can deliver immediate results. One of the first areas to address is AI’s own environmental footprint. Training large-scale AI models consumes vast amounts of energy. A 2021 study found that training a single AI model could emit as much carbon as five cars over their entire lifetimes. Companies such as Google and Microsoft are tackling this issue by developing energy-efficient AI models and shifting toward cloud computing powered by renewable energy. Businesses leveraging AI must ensure their AI infrastructure aligns with their sustainability goals.
However, while the training of AI models is extremely intensive in terms of electricity, using them may not be so. In fact, generating an image or a video using GenAI may be less impactful from an environmental point of view, than travelling to take the picture or recording a video. Thinking of the counter-factual, i.e., the “how would we have done this otherwise?” can be very helpful to have a complete picture of the actual impact of using, rather than training, an AI model.
Another quick win comes in the form of AI-powered sustainability reporting. Companies face increasing pressure to disclose their ESG performance, but reporting can be complex and resource-intensive. AI streamlines this process by automating carbon accounting, analysing vast amounts of data, and identifying areas for improvement. AI-powered tools can detect discrepancies in sustainability claims, helping businesses avoid accusations of greenwashing while ensuring compliance with regulatory frameworks.
AI also helps businesses and consumers navigate sustainability-related data overload. Today, companies collect massive amounts of environmental and operational data, but often struggle to derive actionable insights. AI-powered analytics platforms enable businesses to cut through the noise, identifying risks and opportunities more efficiently. For example, financial institutions use AI to assess climate-related risks in investment portfolios, while manufacturers apply AI to monitor real-time emissions and optimise resource use.
“One of the first areas to address is AI’s own environmental footprint“
AI’s ability to influence consumer behaviour also presents an immediate opportunity. Businesses can use AI to personalise sustainability-driven recommendations, making it easier for consumers to choose eco-friendly products and services. Retail giant IKEA, for instance, employs AI-driven recommendation engines that suggest products based on a customer’s sustainability preferences. AI-powered nudges, such as dynamic pricing or real-time carbon footprint tracking, can guide consumers toward more sustainable choices, benefitting both businesses and the planet.
AI’s role in industry-level sustainability
While AI’s quick wins provide a strong foundation, its true power lies in transforming complex systems. Many of the world’s sustainability challenges, such as decarbonising energy, optimising supply chains, and creating circular economies, require an integrated approach that AI is uniquely suited to facilitate.
In the energy sector, AI is revolutionising how electricity is produced, distributed, and consumed. Renewable energy sources such as wind and solar are inherently variable, but AI enhances their efficiency by predicting fluctuations in supply and demand. AI-driven smart grids balance energy loads, reducing waste and increasing grid stability. Google’s DeepMind AI, for example, improved the energy efficiency of its data centres by 40% through predictive modelling, a concept that can be applied to broader energy-intensive industries.
In transportation and logistics, AI is helping companies reduce emissions and optimise operations. AI-powered route optimisation enables logistics firms to cut fuel consumption and streamline deliveries. UPS, for instance, uses AI-driven logistics software that minimises left-hand turns, reducing fuel consumption and emissions while saving the company millions of dollars annually. The aviation and shipping industries are also exploring AI solutions to optimise flight paths and cargo routes, significantly cutting their carbon footprints.
Agriculture, a major contributor to deforestation and greenhouse gas emissions, is undergoing a transformation with AI-driven precision farming. By analysing satellite imagery and sensor data, AI helps farmers optimise water usage, reduce fertiliser dependency, and predict crop yields with greater accuracy. AI-powered autonomous tractors and drones further enhance sustainability by reducing soil disruption and lowering operational costs. These advancements not only make agriculture more sustainable but also increase food security in a rapidly changing climate.
The built environment, responsible for nearly 40% of global carbon emissions, is another area where AI is making a profound impact. AI-driven design software is helping architects create energy-efficient buildings by analysing climate conditions and material properties. Smart building management systems leverage AI to optimise heating, cooling, and lighting, leading to significant reductions in energy use. Companies investing in AI-powered building technology not only reduce their environmental footprint but also achieve long-term cost savings.
Supply chains, often fragmented and opaque, are becoming more transparent and efficient with AI. Businesses can use AI to trace raw materials, detect inefficiencies, and optimise production processes. AI-driven monitoring systems help companies ensure that suppliers adhere to ethical and environmental standards, mitigating risks related to deforestation, forced labour, and excessive carbon emissions. Unilever, for example, uses AI to track palm oil sourcing, ensuring its supply chain aligns with sustainability commitments.
AI accelerator: Scaling sustainable innovation
Beyond optimising existing systems, AI has the potential to unlock entirely new sustainability solutions. From AI-designed materials to biodiversity conservation, its role in driving innovation is just beginning. AI is accelerating climate modelling, making it possible to simulate complex environmental scenarios with greater accuracy. Traditional climate models take years to develop, but AI-enhanced simulations can analyse massive datasets in real time. This capability allows policymakers and businesses to make more informed decisions about carbon reduction strategies and disaster preparedness. AI is also being used to optimise carbon capture technologies, making them more efficient and scalable.
Material science is another area where AI is leading groundbreaking advancements. AI-driven research platforms analyse chemical compositions and molecular structures to identify sustainable alternatives to high-carbon materials. For example, AI has been instrumental in developing biodegradable plastics, energy-efficient batteries, and low-carbon cement. These innovations are crucial for businesses looking to reduce their material footprint while maintaining product performance.
Biodiversity protection and ecosystem restoration efforts are also benefiting from AI’s analytical capabilities. AI-powered drones and satellite imagery help track deforestation, detect illegal fishing, and monitor endangered species in real time. Conservation organisations use AI to analyse vast amounts of ecological data, improving habitat restoration efforts and protecting vulnerable ecosystems. Businesses engaged in land use and natural resource extraction can leverage these technologies to minimise environmental harm and enhance their sustainability credentials.
“AI’s true power lies in transforming complex systems“
The circular economy, which aims to reduce waste and extend product life cycles, is another frontier where AI is making an impact. AI-powered waste management systems optimise recycling processes by identifying valuable materials in waste streams. AI-driven reverse logistics platforms help businesses recover and refurbish products, enabling more sustainable consumption patterns. Companies exploring AI-powered circular business models not only reduce waste but also create new revenue streams from reusing materials.
Sustainability enabler, not a panacea
AI is neither a magic solution nor a guaranteed threat; its impact depends on how businesses choose to implement it. Companies that prioritise responsible AI development, align AI applications with sustainability goals, and mitigate associated risks will emerge as leaders in the transition to a more sustainable economy. AI’s ability to process vast datasets, optimise resource use, and drive innovation makes it a powerful tool for sustainability, but only if it is deployed responsibly.
To maximise its impact, businesses must ensure that AI operates efficiently, minimising its own energy consumption and environmental footprint. AI must also be integrated across industries, breaking down silos in energy, mobility, supply chains, and agriculture to drive system-wide efficiencies. Beyond optimisation, AI must be a catalyst for sustainable innovation, accelerating breakthroughs in material science, climate modelling, and circular business models that fundamentally reshape how industries operate.
The businesses that succeed in embedding AI into their sustainability strategies will not only reduce their environmental impact but also create new economic opportunities, strengthen resilience, and build trust with consumers and regulators.
AI’s role in sustainability is still evolving, but those who take a strategic, integrative, and forward-thinking approach will be the ones defining the future of sustainable business.
About the authors
Julia Binder is Professor of Sustainable Innovation and Business Transformation and Director of the Center for Sustainable and Inclusive Business at IMD. José Parra-Moyano is a Professor of Digital Strategy at IMD. Julia is co-editor and José is a contributor to Leading the Sustainable Business Transformation: A Playbook from IMD (Published by Wiley).
Further reading
This article was first published in Business 4.0