Exploring the Role of Artificial Intelligence in Architecture & Planning

Hosted by the Architecture and Urbanism Research Hub at the University of Lagos, this CPD session brought together three distinguished practitioners from the Royal Institute of British Architects and the Commonwealth Association of Architects to explore the rapidly evolving role of AI across architecture, urban planning, and environmental policy. The discussion was framed around the intersection of:

• Ecology and environmental planning
• Urbanisation and climate change
• Data-driven technologies and AI
• Equitable and sustainable development

The central premise was that AI is moving from a speculative future technology to a practical tool for analysing cities, managing resources, and supporting planning decisions. The session critically examined both the opportunities AI presents for expanding architectural agency and its systemic risks, including its own carbon footprint and the threat of deskilling. In some of the concluding discussions, the session noted how AI’s adoption raises questions about ethics, governance, and applicability to local contexts.

Outline 

• Part 1 — Scene Setting (Peter Oborn, CAA): The global urbanisation challenge, the Commonwealth’s built environment capacity gap, and the role of AI as a strategic response, illustrated by a mapping pilot in Lusaka, Zambia.
• Part 2 — AI and the Architecture Profession in the UK (Adrian Malleson, RIBA): Findings from the RIBA AI Report including current adoption rates, emerging use cases, perceived threats and opportunities, and the ‘Competency Principle’ for responsible AI use.
• Part 3 — AI, Design Intelligence, and the Lagos Context (Nenpin Dimka, RIBA): A critical examination of AI’s infrastructure demands and environmental cost, the data centre landscape in sub-Saharan Africa, and the Makoko case study as a lens for exploring expanded architectural agency through AI.
• Part 4 — Panel Discussion: All three presenters reflected on the implications for education, practice, and the future of the profession, including questions on standardisation, job displacement, low-carbon materials, and the importance of retaining critical human judgment.

Learning Outcomes

Some of the key discussion topics from the session included:

• Articulate the scale and urgency of rapid urbanisation in the Commonwealth, including the projected doubling of the urban population to two billion by 2050, and understand why AI is increasingly positioned as a necessary tool to bridge critical capacity gaps in architecture, planning, engineering, and built environment policy.
• Understand key findings of the RIBA AI Report, including the rapid growth in AI adoption among UK practices (from 41% in 2024 to 59% in 2025), the primary use cases across the design workflow, and the risks—including role displacement, imitation, intellectual property concerns, and the potential for non-professionals to perform regulated work.
• Apply the ‘Competency Principle’ to professional practice: AI should only be deployed for a task where the practitioner possesses sufficient underlying knowledge to critically evaluate the accuracy and quality of the output, providing both a safeguard and a professional boundary.
• Evaluate the environmental cost of AI infrastructure—including the energy, water, and land demands of data centres—and critically assess the tension between AI’s potential to support climate action and its own significant carbon footprint.
• Analyse a real-world example of AI being applied to urban planning challenges in the Global South, specifically the Ordnance Survey/International Growth Centre mapping of 300,000 structures across 400km² of Lusaka in three weeks, and consider how similar approaches could support sustainable urbanisation across Commonwealth nations.
• Reflect on the Makoko case study in Lagos as an example of the limitations of architectural agency in informal settlements, and consider how AI-enabled data modelling and simulation might strengthen evidence-based advocacy to better inform policy decisions and protect vulnerable communities.
• Recognise the systemic barriers—including policy misalignment, outdated building codes, lack of enforcement, education gaps, and the digital divide between the Global North and Global South—that must be addressed alongside technological solutions if AI is to deliver equitable and sustainable outcomes.
• Understand the Gartner Hype Cycle as a framework for positioning AI maturity, and appreciate Amara’s Law: that we tend to overestimate the effect of technology in the short run and underestimate it in the long run—providing a basis for measured, critical engagement with AI rather than uncritical enthusiasm or rejection.

The session’s Key Takeaways are:

• AI will significantly reshape architecture and planning, especially in urban analysis and design exploration.
• The technology should augment human expertise rather than replace it.
• The Global South has unique opportunities and risks in adopting AI-driven planning tools.
• Addressing data gaps and digital infrastructure limitations is essential.
• Ethical governance and professional leadership are required to ensure AI supports inclusive and sustainable urban development.

Core Curriculum Topics

• Sustainable Architecture: Central to this session: AI is examined as a mechanism for climate-responsive design, carbon reduction analysis, performance simulation, and environmental impact modelling. The session directly addresses climate change mitigation and adaptation, and the role of the built environment in achieving net zero targets in both the Global North and Global South.
• Places, Planning & Communities: The session explores AI’s application to sustainable urbanisation at city scale, including the mapping of informal settlements, secondary city planning, and evidence-based urban policy. The Lusaka and Makoko case studies illustrate how AI can support better planning, housing provision, and community resilience in rapidly growing Commonwealth cities.
• Design, Construction & Technology: The session advances understanding of AI as an emerging and rapidly adopted technology across the architectural design process, from concept generation and specification writing to code checking, performance simulation, and generative design. It also addresses the ethical, professional, and workforce implications of integrating AI into practice, and the importance of maintaining critical competency.

SDG Learning Outcomes

• SDG11: Sustainable Cities & Communities – This is the primary SDG addressed by the session. Speakers directly confronted the challenge of making rapidly growing cities—particularly in the Commonwealth—inclusive, safe, resilient, and sustainable. The Lusaka mapping pilot and the Makoko case study both illustrate how AI can be deployed to address housing deficits, inadequate infrastructure, informal settlement, and urban governance failures.
• SDG13: Climate Action – Climate change is a thread running throughout the session: from the vulnerability of rapidly urbanising Commonwealth cities to climate impacts, to the carbon cost of AI infrastructure itself, to AI’s potential to support net zero targets through performance modelling, passive design optimisation, and material selection. The session explicitly calls on the profession to treat climate action as a core driver of architectural innovation.
• SDG9: Industry, Innovation & Infrastructure – The session highlights how AI is fundamentally reshaping the infrastructure of professional practice—enabling architects to identify infill housing sites, generate code at scale, automate building compliance, and model complex urban systems. It also raises concerns about who controls AI infrastructure, the concentration of data centres in the Global North, and the implications for equitable access to technological innovation.
• SDG10: Reduced Inequalities – Speakers highlighted the deep inequality embedded in current patterns of urbanisation, professional capacity, and digital infrastructure. The session raised questions of environmental justice: that those least responsible for carbon emissions—communities in the Global South—face the greatest climate vulnerability, and that AI must be deployed in ways that reduce rather than entrench inequality, including through open-access knowledge sharing and capacity building.

The following CPD questions forms part of the learning guide for this session. As different Institutions of Architecture across the Commonwealth have different CPD reporting requirements, it is suggested that you retain a copy of your responses to these questions for your records.

1. The session presented data showing that cities which are urbanising most rapidly are currently among the least carbon-intensive, but that this will change dramatically without sustainable planning interventions. In your own practice or context, how are you currently integrating climate-adaptive design thinking into your projects, and what role could AI play in strengthening that response—particularly for projects serving communities most vulnerable to climate impacts?
2. Dimka highlighted that AI infrastructure—including data centres—demands significant quantities of energy, water, and land, and that the environmental cost of AI is itself a sustainability concern. As an architect engaging with AI tools in your practice, how do you weigh the carbon cost of AI against its potential to deliver more sustainable design outcomes? What steps, if any, should the profession take to advocate for greener AI infrastructure?
3. Oborn described how AI was used to map 300,000 informal structures across 400km² of Lusaka in three weeks, providing a planning foundation where none previously existed. Reflecting on your own region or practice context, where do you see the most significant opportunities for AI-enabled data analysis to support sustainable urbanisation, climate resilience planning, or evidence-based environmental policy?
4. The Makoko case study raised the question of whether better data modelling and simulation—enabled by AI—could have strengthened architectural and community advocacy to avert the demolition of a 19th-century waterfront settlement. In your experience, how well does the architecture profession currently communicate the social, economic, and environmental value of its proposals to policymakers? How might AI tools improve that capacity, and what are the ethical responsibilities that come with it?
5. Malleson cited growing use of AI for environmental impact modelling and performance simulation in UK practice, including wellbeing performance, health outcomes, and the impact of buildings on wider urban systems. How could a broader definition of building performance—extending beyond thermal and acoustic standards to social and environmental outcomes—change the way you approach design briefs, and what competencies would you need to develop to work effectively with these tools?
6. The session closed with a provocation to professional institutes and educators: the architect of the future will require fundamentally different skills. Reflecting on your own continuing professional development, what are the two or three areas of knowledge or competency you most need to develop to practice sustainably and ethically in an AI-enabled built environment—and how will you prioritise those in your CPD plan for the coming year?

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