Artificial intelligence is increasingly becoming a practical tool for advancing the Sustainable Development Goals (SDGs). Beyond improving business productivity, AI and data technologies can help governments, organisations and businesses understand complex social and environmental challenges and make better decisions.
Startups are playing an important role in this space because they can develop focused solutions for problems such as climate vulnerability, public services, agriculture and responsible supply chains.
Using Data to Understand Local Challenges
One example is Fraym, a geospatial data company that combines survey information with satellite imagery. By analysing large amounts of location-based data, its technology can help identify patterns related to agriculture, climate vulnerability, public health and access to essential services.
The value of this approach is that sustainability challenges are rarely the same everywhere. Localised data can help organisations understand which communities are most vulnerable and where resources may be needed most.
Improving Access to Public Services
Data can also support better planning of infrastructure and public services.
Flowminder, a Swedish nonprofit, combines anonymised mobile data, geospatial information and surveys to understand how populations are distributed. Through its work with GRID3, these insights have been used to help determine where public services can be positioned to improve accessibility, particularly in underserved communities.
This demonstrates how data can move beyond measuring problems to supporting practical decisions about where resources should be deployed.
Connecting Environmental Data
Another example is Artificial Intelligence for Environment & Sustainability (ARIES), an open-source platform designed to improve the integration of environmental data and models.
ARIES can bring together information from remote sensing, environmental models and other data sources to create ecosystem accounts for specific areas. By connecting environmental changes with human activities, such tools can provide a stronger evidence base for environmental and climate-related decision-making.
The broader opportunity is significant: better integration of environmental data can help policymakers and organisations understand not only what is changing, but also how those changes are connected.
Making Supply Chains More Inclusive
AI and data can also contribute to the social dimensions of sustainability.
Ulula, for example, uses mobile surveys and grievance mechanisms to give workers a platform to share concerns about working conditions and other issues within supply chains.
Technology like this can provide companies with information that may otherwise be difficult to obtain, particularly across complex global supply chains. Gender-sensitive data and worker feedback can also support more inclusive sourcing and sustainability strategies.
From Data to SDG Action
These examples share a common principle: data is most valuable when it leads to better decisions.
AI can help organisations process information faster, identify patterns and understand issues that would otherwise be difficult to measure. But technology alone does not deliver the SDGs. Its impact depends on how governments, businesses and communities use the insights generated.
For startups, this creates a significant opportunity. Instead of developing technology simply for technology’s sake, entrepreneurs can focus on solving specific sustainability challenges through better data, smarter analysis and more targeted action.
As the world works towards the 2030 SDG targets, the ability to measure progress and identify where intervention is needed will become increasingly important.
AI may not solve every sustainability challenge, but when combined with reliable data and practical solutions, it can help turn complex global goals into measurable action.