Real-world impacts
To illustrate the immense capabilities of AI, we need only consider the role it can play in disaster preparedness. AI-powered tools now provide actionable flood forecasts by combining models that predict river levels with those identifying areas impacted by them, resulting in projections up to seven days in advance of an extreme weather event.4 For example, during recent flooding in Somalia, AI platforms delivered risk maps and daily forecasts, enabling targeted actions like risk communications and the floodproofing of critical infrastructure. This demonstrates how AI can significantly enhance disaster response efforts and community resilience.
Agriculture is another key area where the deployment of AI has already provided benefits, as it can assist farmers in forecasting weather and crop yields, developing sustainable pest control methods and predicting soil erosion, as well as suggesting mitigation measures. These capabilities aim to optimise agricultural practices, promote sustainable crop growing and enhance food security.
Additionally, AI has started reshaping materials engineering by rapidly screening billions of potential low-carbon compounds to replace high-emission materials like steel, cement and plastics. AI-driven labs test selected compounds, with AI interpreting results and designing further experiments, which can drastically shorten research and development timelines.
Transforming businesses with AI
Beyond these sector-specific applications, AI has transformative potential at the company level, unlocking business value and helping to advance sustainable development in multiple ways:2
- Advanced data analysis: Generative AI can extract valuable insights from unstructured data such as text, images and audio and reveal patterns that traditional analytics might miss. This can empower companies to make informed decisions based on comprehensive data.
- Intelligent decision support: Generative AI interprets complex information to support decision-making by providing recommendations, aiming to predict outcomes and offer explanations. In this way, it can help businesses to navigate challenges and develop effective, sustainable strategies.
- Workforce empowerment and efficiency: Generative AI can enhance productivity by acting as a virtual assistant, automating routine tasks like content creation and data analysis. If successfully implemented, this allows teams to focus on strategic initiatives while ensuring consistency and quality in automated outputs, driving innovation and fostering a culture of sustainability.
By applying these AI capabilities in the right way, companies can accelerate sustainable development across several key areas:2
- Enhancing operational efficiency and reducing environmental impacts: AI can optimise the use of resources and improves worker effectiveness, leading to cost savings. It also offers possibilities to lower the environmental footprint of businesses by minimising waste and energy consumption.
- Strengthening sustainable value chains and driving innovation: By analysing supply chain data, AI can help companies to address environmental and social impacts more efficiently, promoting responsible sourcing and comprehensive lifecycle assessments. It can also accelerate innovation in areas like green finance and sustainable product design, fostering breakthrough technologies for sustainable development.
- Risk management and resilience: AI can enable companies to anticipate, assess and mitigate risks related to climate and environmental changes as well as social factors. By analysing trends and predictive data, AI can help to build resilience within operations and supply chains, and to ensure that businesses can adapt to evolving sustainability demands.
Navigating the challenges of AI implementation
While AI holds great promise, it also entails certain risks that require careful management. Without responsible oversight, AI could cause environmental and social harm, while unequal access to technology could widen the digital divide, hindering progress toward the SDGs.
Generative AI introduces new opportunities but also risks like biased outputs, factual errors, misuse and increased resource consumption, as well as societal impacts, such as job displacement and the spread of misinformation. Additionally, the underrepresentation of marginalised groups in AI training data can perpetuate bias and discrimination, especially in critical areas like healthcare, housing and social services.
Bridging the gap: Responsible AI for a sustainable future
AI has significant potential to accelerate progress towards the achievement of the SDGs by 2030. With many of the 17 SDGs not currently on track, AI can contribute to practical solutions in critical areas like climate action, sustainable agriculture and disaster preparedness. Real-world applications − such as AI-driven farming techniques that optimise agricultural practices and AI-enhanced disaster readiness tools − demonstrate how AI can make a tangible difference. By effectively integrating AI into our global strategies and fostering collaboration among governments, businesses and communities, we can address complex challenges more efficiently. Embracing and harnessing AI's capabilities today can help us to bridge the gap between our current efforts and the ambitious targets we have set ourselves in order to secure a sustainable and equitable future.