Understanding AI’s role in shaping sustainable development outcomes: Asking the right questions

The rapid emergence of AI, and especially generative AI, is one of the most significant technological shifts of our lifetime. It is already changing how we work, how institutions function, how people access services, how systems interact, and how knowledge is produced and shared.

While AI has significant potential in helping us respond to some of the most critical problems of our time, from climate change and food insecurity to inequality, institutional fragility, achieving sustainable development outcomes is not an inevitable consequence of wide-spread AI adoption. Whether AI accelerates sustainable development or deepens existing inequalities will depend on how we choose to design, govern, and apply it.

Already we can observe how AI can support development work in many ways: Just within the UN system, its applications entail helping institutions analyse information, improve access to services, strengthen early warning systems, support climate and risk modelling, detect fraud, improve knowledge management, or make public systems more responsive. In Tajikistan, Turkmenistan, and The Pacific, AI-enabled portable X-ray machines are improving Tuberculosis screening in remote communities whilst simultaneously expanding frontline workers' digital capacities. While in places like Ecuador and Columbia in South America, coffee traceability is being improved by AI data consolidation in efforts to reduce deforestation and aid sustainable coffee production.

These challenges are too complex to be addressed through traditional approaches alone. It is difficult to imagine meaningful progress on them without also thinking seriously about how technology can be used more effectively, more responsibly, and at scale. But possibility alone is not enough. The promise of AI does not automatically translate into positive impact. Its use must be deliberate, and it must begin with a set of fundamental questions.

• What do we want AI to help us achieve?
• Which problems are genuinely suited to AI-enabled approaches, and which are not?
• What risks emerge when powerful technologies spread faster than institutions, safeguards, and cultures of use can adapt?

Our ability to deploy AI responsibly depends not only on understanding the technology, but on understanding why we need it, where it can add value, and what conditions must be in place for it to be useful. AI adoption is therefore not simply a matter of choosing a tool. It depends on the conditions under which the tool is introduced, the safeguards around its use, and the institutional capacity to learn from it, govern it, and course-correct when needed.

Questions of trust, accountability, data privacy, cybersecurity, transparency, bias, and inclusion sit at the heart of responsible adoption. So too does the question of sustainability itself, particularly as more powerful models bring greater energy and resource demands.

This is where development actors need to look both outward and inward. There are important debates taking place at the global and national levels on the regulation of AI. These debates are necessary. But for institutions working on sustainable development, the questions are also immediate and practical:

• What governance arrangements do we need internally?
• What applications are relevant to our mandates and development outcomes?
• Are we ready to adopt these tools responsibly?
• Are we prepared for the cultural and organizational change that meaningful adoption requires?

Readiness will also look different across organizations and countries. Some are still building basic digital foundations. Others are thinking about AI strategies, governance frameworks, road maps, or safe spaces for experimentation. For development actors, the task is not to adopt AI for its own sake, but to understand what kind of institutional readiness is needed for AI to be useful, responsible, and sustainable.

Once these foundations are in place, the question of application becomes much clearer. The strongest outcomes are likely to come from combining human expertise, contextual understanding, and institutional responsibility with tools that can extend what people and organizations are able to do.

Ultimately, the future of AI for sustainable development will be shaped by the choices institutions make now. Our ability to understand the interplay between governance, adoption, and application, and to respond to it with purpose and care, will determine whether AI can be harnessed as a force for sustainable development.

This is the space the Digital Transformation for Sustainable Development Academy seeks to explore: where development actors can ask the harder questions of purpose, readiness, responsibility, and practical use. The Academy will bring together policymakers, government officials, practitioners, and global experts from across the UN system and beyond to examine how AI can be operationalized responsibly, governed effectively, and applied in ways that respond to real sustainable development priorities.

If you are working to shape the future of digital transformation for sustainable development, we invite you to join us and be part of the conversation. Sign up here. Limited scholarships are available.