AI for Sustainable Development: Three Lessons from UNDP's Global Network

The Choice of Sustainable Development In The Age of AI

Are we on the brink of an AI-led renaissance, or sleepwalking into deeper inequality and fewer freedoms? That question is no longer theoretical. Across UNDP's global network, AI is moving from pilot to production. The difference between progress and harm is in the choices we make and how we build.

Date: September 24, 2025

What AI is already delivering in development

• Health: AI-enabled portable X-ray units are accelerating tuberculosis screening in remote areas while upskilling frontline workers in Tajikistan, Turkmenistan, and the Pacific.
• Nature: In 60+ countries, AI is used to compare national policies with global biodiversity targets so governments can spend less time on document review and more on strategy and engagement.
• Information integrity: eMonitor+ deployments in 25+ countries help analyze hate speech, disinformation, and tech-facilitated gender-based violence.
• Crisis response: In Ukraine, Myanmar, and Haiti, AI supports displacement forecasts, damage assessment, and debris analysis. An Anticipatory Data Hub combines real-time analytics with conversational interfaces so 7,500 practitioners can query risks and access API-based insights.

Three lessons for IT and development teams

1) Multidisciplinary teams beat shiny tech

Effective AI work blends policy, domain, data, and engineering skills with "translators" who bridge product and public interest. This prevents elegant solutions that miss real constraints.

Example: Smallholders need practical traceability to meet the EU Deforestation-free Regulation. UNDP piloted AI-generated data layers for coffee traceability across Ecuador, Colombia, and Costa Rica with cooperatives, officials, forest experts, supply-chain operators, and digital teams. The outcome wasn't the flashiest model-it solved a real compliance problem. See the regulation overview for context: EU Deforestation-free Regulation.

2) Government ownership makes it stick

Projects last when national institutions own the stack and skills. Open standards, open source, and a clear handover plan are essential.

Example: The Data in Climate Resilient Agriculture (DiCRA) platform provides open geospatial datasets and algorithms for climate-resilient farming. Launched with the UNDP Accelerator Lab in India and the Government of Telangana, it is now hosted by India's National Bank for Agriculture and Rural Development-ensuring maintenance, scaling, and policy integration.

3) Governance is a feature, not an afterthought

Institutions need practical tools to build safe, secure, trustworthy AI: procurement guidance, oversight checklists, red-teaming protocols, and training for legislators and civil servants. UNDP and partners are supporting this capacity building through training, peer networks, and policy guidance.

The environmental cost needs just as much attention as model accuracy. Data centers and AI workloads demand significant energy and water. The International Energy Agency projects a steep rise in electricity use from data centers by the end of the decade. For reference, see the IEA's overview of data centers and ICT energy trends: IEA: Data centres and data transmission networks.

A practical playbook you can run this quarter

• Form the team: policy lead, domain expert, product manager, ML engineer, data engineer, designer, and privacy/security lead. Assign a translator to manage trade-offs.
• Pick outcome-first use cases: tuberculosis triage, debris mapping, traceability checks, or policy consistency scans. Define success metrics that matter to citizens, not dashboards.
• Start with public data and DPGs where possible. Document data provenance, consent, and retention. Write data contracts that include equity and environmental clauses.
• Ship an API before a UI. Keep interfaces simple for frontline workers. Plan for low-connectivity and offline modes.
• Build in oversight: model cards, data statements, audit logs, explainability notes, and human-in-the-loop checkpoints where risk is high.
• Measure resource use: track energy, cooling water, and inference cost per decision. Prefer efficient models, scheduled inference, and regional hosting that meets sustainability goals.
• Secure procurement: require risk assessments, red-teaming evidence, and incident response plans. Mandate accessibility and language support.
• Plan ownership from day one: budget for local maintenance, train public sector teams, and set a realistic exit and upgrade path.

Traceability that works for smallholders

For commodities like coffee and cocoa-major drivers of deforestation-traceability is becoming non-negotiable. Smallholders need simple workflows: geotag plots, verify boundaries, link to supply lots, and flag deforestation risk with clear remediation steps.

Practical stack: satellite-derived land-use layers, mobile data collection, a lightweight rules engine for risk flags, and an export that meets EUDR requirements. Keep the model explainable so farmers and buyers can act on it.

AI for crisis and public services: patterns that scale

• Forecasting: combine earth observation, mobility signals, and administrative data for displacement and damage forecasts. Validate with ground truth early and often.
• Triage: use low-latency models for screening (e.g., TB X-rays) and route cases to human experts with clear thresholds.
• Policy analysis: use NLP to compare national strategies with global targets. Flag gaps; keep humans in charge of interpretation.
• Information integrity: monitor toxic narratives with transparent taxonomies. Share aggregate insights with civil society and platforms where appropriate.

From pilots to ecosystems

UNDP's AI Sprint supports countries with strategies, training, data infrastructure, and responsible AI advisory. The Hamburg Declaration on Responsible AI for the SDGs signals a shared direction: AI must serve people and the planet, and reduce inequality rather than amplify it.

If you're upskilling teams for delivery and oversight, explore curated learning paths by job role: AI courses by job.

Bottom line

AI can widen gaps or solve real problems. Multidisciplinary teams, public ownership, and serious governance decide which path we take. The time to build with care-and at scale-is now.