AI capability is increasing exponentially and offers tremendous opportunities in many areas, including the design, delivery and management of urban development, infrastructure and energy. The following resources provide valuable insights into AI policy, skills and tools as well as examples of applications in low- and middle-income countries (LMICs).
Research and policy
The Fearless Future: 2025 Global AI Jobs Barometer
PwC’s comprehensive analysis reveals exponential growth in AI skills demand since 2022, with revenue per worker increasing threefold in AI-exposed industries. The research demonstrates significant wage premiums for professionals possessing AI capabilities while dispelling concerns about widespread job displacement. This intelligence could prove particularly relevant for GCIEP countries seeking to understand economic transformation opportunities through strategic AI adoption in infrastructure sectors.
Responsible AI for LMICs whitepaper
This comprehensive research collaboration examines how LMICs can adopt AI to serve local communities while achieving development goals. Drawing from projects across India, Indonesia, Africa and South America, the whitepaper identifies implementation challenges, early solutions and policy implications. The research directly addresses contexts similar to those of GCIEP partner countries, offering valuable insights into appropriate technology transfer and local adaptation strategies.
GSMA study on AI and startups in LMICs
This authoritative study in collaboration with UrbanEmerge and FCDO maps 450 AI startups across Africa and South and Southeast Asia, examining business models, innovation barriers and ethical considerations in LMIC contexts. The research provides evidence-based analysis of AI’s potential for social good while acknowledging risks of deepening inequalities.
Skills and tools
AI Skills Hub
The UK’s national AI skills development platform provides personalised diagnostic tools and training pathways for organisations seeking to implement AI responsibly. The hub offers skills mapping, identifies capability gaps and delivers sector-specific guidance for deployment. Given GCIEP’s emphasis on building local capacity, this resource offers valuable frameworks for developing AI competency within partner country institutions managing infrastructure projects.
ConsultAI traffic and parking platform
This AI-powered consultation analysis tool transforms public feedback from transport schemes into decision-ready reports within hours rather than weeks. The platform processes thousands of responses and identifies key themes around parking, safety, accessibility and environmental concerns while maintaining full accountability and compliance with statutory requirements. The technology demonstrates practical applications for GCIEP’s urban transport interventions where community engagement remains essential but resource intensive.
AI Transforming Urban Planning
TPXimpact explores how artificial intelligence revolutionises urban planning processes through enhanced data analysis, predictive modelling and citizen engagement. Their insights highlight opportunities for more responsive, evidence-based planning decisions that can accelerate sustainable development outcomes.
Climate Resilience Demonstrator (CReDo)
Energy Systems Catapult’s platform demonstrates how digital twin technology and AI analysis can improve infrastructure resilience against extreme weather events. The system integrates data from energy, water and telecommunications networks to predict vulnerabilities and optimise investment decisions. This technology directly supports GCIEP’s climate adaptation objectives by enabling more intelligent infrastructure planning and management across interconnected urban systems.
AI Localism resources
GovLab’s comprehensive platform examines how cities develop local AI governance frameworks in response to gaps in national regulation. The initiative provides evidence-based analysis of successful local AI policies, offering training modules and best practice examples for municipal leaders. This resource proves particularly valuable for GCIEP’s work with city governments seeking to implement AI solutions while maintaining democratic accountability and community engagement.
Technical standards
UN establishes AI governance mechanisms
The United Nations announcement outlines two new international mechanisms: the Independent International Scientific Panel on AI and the Global Dialogue on AI Governance. These bodies will bridge cutting-edge research with policymaking while providing inclusive platforms for global cooperation on AI benefits and risks. The initiative offers GCIEP countries opportunities to engage with international AI governance frameworks and contribute to discussions particularly relevant to development contexts.
Examples and case studies: AI for sustainable infrastructure in cities in LMICs
AI-enhanced green building materials development
Faced with environmental pressures from traditional construction, Nigeria’s sector is turning to AI for solution-driven innovation. By harnessing machine learning and computer vision, local materials are analysed to devise low-impact composites that meet both sustainability criteria and industry standards. This approach enables more cost-effective, greener buildings, pushing the industry towards practices that are as environmentally responsible as they are structurally sound.
AI-powered predictive maintenance for telecom infrastructure
Across Africa, maintaining mobile network reliability in great distances and challenging environments requires new approaches. Artificial intelligence – feeding on live data streams from network sites and aerial images – empowers engineers to anticipate faults before they cause outages. As a result, not only have service interruptions reduced, but operators are enjoying improved operational efficiency and significantly reduced power consumption.
AI-enhanced public housing infrastructure management
South Africa’s vast housing stock often faces wear and tear that outpaces traditional maintenance approaches. AI applications, together with sensor networks, continually monitor structural health, picking up subtle signs of developing faults that might otherwise be missed. Early warnings allow repairs in good time and mean fewer acute failures, benefiting both residents and municipal budgets.
AI-enhanced waste sorting for circular materials
The challenge of mixed waste and inefficient manual sorting persists in Asian cities striving for circular economy goals. AI, equipped with computer vision, surveys recycling facilities in real time, sorting materials with unprecedented accuracy and learning from contamination patterns. Governments and enterprises now possess far better tools for minimising landfill, boosting recoverable material rates and keeping human labour out of hazardous environments.
AI-enhanced sustainable construction materials development (global)
Traditional construction methods and materials need a rethink for sustainability. Globally, AI-driven molecular analysis is facilitating the design of advanced materials, from energy-saving concretes to self-healing composites. The sector has witnessed reduced waste and longer-lasting infrastructure, bringing together innovation and responsible resource usage.
AI-driven green building design optimisation
Designing buildings that go beyond the minimum in energy performance is a multilayered challenge. By processing thousands of design options against climate and occupancy data, AI identifies layouts that optimise light, ventilation and energy use. This has shifted architects’ options towards truly sustainable structures.
Vietnam AI urban air quality monitoring
Vietnam’s cities confront urban pollution from industrialisation and longer traffic lines. Smart sensor grids, coordinated via AI, now watch over air quality continuously, synthesising readings to forecast problematic zones before pollution peaks. These insights arm communities and planners with the intelligence to implement responsive measures for safeguarding health and adjusting zoning plans.
Philippines smart building energy management
Rising energy prices and the drive for renewables mean Philippine cities must manage buildings more carefully. AI-integrated controls digest weather, occupancy and solar input data to direct systems – foreseeing surges and refining usage hourly. With this technology in place, building managers find themselves able to cut costs and emissions, while comfortably catering for occupant needs.
Ho Chi Minh City smart grid management (Vietnam)
Urban expansion in Vietnam is pressing for more sophisticated power distribution. AI, tethered to smart grid and meter infrastructure, constantly weighs supply, weather forecasts and consumption behaviour to orchestrate distribution across homes and public spaces. Blackouts have become less frequent, and energy from renewable sources is used more intelligently.
The rapid expansion of Lusaka, with its sprawling informal areas, has necessitated a modern method for mapping. AI-driven aerial analysis now provides city planners with near-instant identification of roads, dwellings and settlement patterns. This fresh accuracy empowers policymakers to target resources where they are needed most and lays the groundwork for bringing more amenities to overlooked communities.
Lagos AI-driven traffic management
Congested roadways have long stifled Lagos’s economic and environmental aspirations. Through smart traffic signals, computer vision and data-driven planning, AI helps synchronise flows and reroute vehicles in real time. Everyday commutes have become less stressful, fuel use has dropped and the data gathered offers important guidance for future city development.
Jakarta Smart City flood control system
Flooding threatens Jakarta’s people and businesses year after year, prompting the city to invest in smarter responses. By linking hundreds of sensors and AI algorithms, officials can spot rising risks hours before floodwaters arrive, using predictive analytics across rainfall and water level data. Quick and targeted interventions now shield millions, while planners gain new insight to refine drainage and resilience strategies in the years to come.
AI-powered digital mapping for national development (Zambia)
Zambia’s capital Lusaka epitomises Africa’s rapid urbanisation challenges, with over 70% of residents living in informal settlements while government mapping capabilities remained frozen with topographic surveys from the 1980s. In response, Ordnance Survey and Zambia’s Ministry of Lands harnessed AI driven machine learning to analyse aerial imagery over 420 km², automatically classifying buildings, roads, vegetation and water features. This AI-driven base map now enables the government to update maps swiftly, guide infrastructure investment in sanitation and utilities, and support land audits and housing policy in informal settlements. Its success earned awards for urban planning innovation and offers a scalable model for sustainable development across other fast-growing cities.
Published
26/09/25