By Dr Nurul Noraziemah Mohd Pauzi, Construction Materials, Concrete Technology, Energy Management Universiti Teknologi MARA
The Smart City Dream Meets Reality
Smart cities envision coordinated traffic, data-driven waste management, and energy-optimised buildings – all powered by Artificial Intelligence (AI). AI enables efficiency, sustainability and responsiveness; however, turning this vision into reality is complex. Data silos, legacy infrastructure, funding gaps, talent shortages, and governance challenges often slow progress.
This tension between aspiration and execution drives today’s debate. Governments and industries must address financing, modernisation, regulation, skills and risks. Using a SWOT analysis with the F.I.R.S.T® framework (Finance, Infrastructure, Regulations, Skills and Technology) and benchmarking Singapore, Thailand and Vietnam, we see that success depends not only on technology but on overcoming systemic barriers to ensure inclusive, resilient urban development.
AI remains the backbone of smart cities, powering data, analytics, and decisions. From optimising traffic and mobility to using predictive analytics in energy grids, AI reduces waste and boosts sustainability for citywide transformation.
AI supports environmental management by monitoring air quality, predicting pollution, and improving waste systems. In governance, it streamlines e-services and boosts transparency. Public safety benefits from smarter surveillance, predictive policing, and responsive emergency management, while healthcare advances through telemedicine, predictive analytics, and the efficient use of hospital resources. For buildings and infrastructure, AI optimises energy, manages facilities, and extends asset lifespan with predictive maintenance. As Figure 1 shows, AI enables cities to shift from fragmented operations to holistic, efficient, and sustainable systems.
Cracking the Code with F.I.R.S.T®
The adoption of AI in smart cities can be understood through the F.I.R.S.T. framework – Finance, Infrastructure, Regulations, Skills and Technology – which captures both opportunities and challenges. Table 1 presents a SWOT analysis based on focus group discussions with industry players and government bodies, reflecting real-world implementation rather than theoretical considerations.
The SWOT analysis shows that while AI offers major opportunities for urban transformation, systemic issues hinder its adoption. International schemes like Horizon Europe support projects;, but, SMEs face funding and sustainability challenges. Infrastructure is constrained by legacy systems, though prototypes and pilots offer a gradual path forward.
Regulations build trust through growing governance frameworks, yet silos and fragmented oversight hinder integration. Incremental reforms and pilots can balance innovation with security. Skills show strong digital design expertise, but AI talent shortages limit progress – though they also create opportunities for upskilling and new roles in cybersecurity and analytics.
Technology enables greener, smarter cities through IoT, AI and smart architecture, but raises risks of cybersecurity vulnerabilities and ethical concerns. Success in smart city development will depend on governments, industries and communities addressing weaknesses, seizing opportunities, and managing risks from rapid change.
Lessons from Our Neighbours
AI adoption in smart cities is growing across the region as governments modernise infrastructure, strengthen governance, and prepare for the digital future. Insights from Singapore, Thailand, and Vietnam – benchmarked in Table 2 through official policies and reports – show how each positions itself in the global AI–smart city landscape.
The benchmarking shows each country’s unique path to AI-enabled smart cities. Singapore, under its National AI Strategy, leads with strong policies, funding, and private sector collaboration, though siloed logistics remain a challenge. Thailand’s National AI Strategy (2022–2027) and Smart City programme aim to target 100 Smart Cities, but regional disparities cause uneven adoption despite National Science and Technology Development Agency (NSTDA)and The Board of Investment of Thailand’s (BOI) support.
Vietnam, through its National AI Strategy (2021–2030), focuses on urban management and governance, but fragmented data and a lack of standards remain obstacles, even as its digital agenda drives rapid adoption. The benchmarking highlights two key lessons: leaders still struggle with data integration, and success relies on political will, investment, and collaboration. For Malaysia, the takeaway is to align policies with practical steps while advancing funding, skills, and governance with technology.
The Road Ahead
The path to AI-enabled smart cities is a complex one. The F.I.R.S.T framework highlights strengths in funding, technology and design, but also weaknesses in legacy infrastructure, siloed data, and skill shortages. Benchmarking Singapore, Thailand and Vietnam reveal that even strong strategies face integration gaps, uneven adoption, and fragmented governance.
For Malaysia and other emerging economies, success demands foresight: financing to support SMEs and long-term projects, scalable pilots for infrastructure, and regulations that strike a balance between trust, security, and flexibility. Investing in people through upskilling, reskilling, and new AI roles is critical.
Safeguarding trust is equally vital, with ethics, accountability, and cybersecurity as priorities. Strong governance, cross-sector collaboration, and lessons from peers will help align vision with action. With this, Malaysia can lead sustainable, resilient AI-driven urban development and realise the aspiration of smart, equitable, future-ready cities.
References
1. World Economic Forum. Future of Urban Development and Services. World Economic Forum; 2023. https://www.weforum.org/
2. United Nations Human Settlements Programme (UN-Habitat). World Cities Report 2022: Envisioning the Future of Cities. UN-Habitat; 2022. https://unhabitat.org/
3. Organisation for Economic Co-operation and Development (OECD). Data Governance in Smart Cities. OECD Publishing; 2023. https://www.oecd.org/
4. European Commission. Horizon Europe Framework Programme. European Commission; 2022. https://research-and-innovation.ec.europa.eu/funding/fundingopportunities/funding-programmes-and-open-calls/horizon-europe_en
5. Rolls Royce. Intelligent Digital Systems for Infrastructure. Rolls Royce; 2021. https:// www.rolls-royce.com/
6. International Telecommunication Union (ITU). AI for Good: Scaling Pilot Projects for Smart Cities. ITU; 2023. https://aiforgood.itu.int/
7. Smart Nation and Digital Government Office. Singapore National AI Strategy. Government of Singapore; 2019. https://www.smartnation.gov.sg/
8. Digital Economy Promotion Agency. Thailand National AI Strategy and Action Plan (2022–2027). Government of Thailand; 2022. https://www.depa.or.th/
9. Ministry of Science and Technology, Vietnam. National Strategy on Research, Development and Application of Artificial Intelligence (2021–2030). Government of Vietnam; 2021. https://en.most.gov.vn/
10. World Bank. Digital Skills and the Future of Jobs in Asia. The World Bank; 2022. https://www.worldbank.org/
11. United Nations Educational, Scientific and Cultural Organization (UNESCO). Recommendation on the Ethics of Artificial Intelligence. UNESCO; 2021. https:// unesdoc.unesco.org/
