What Will Cities Look Like in 2035? The Future of Smart Urban Life
Saudi Arabia – Ismail Hamoumi is a leading Smart City and infrastructure strategy expert with extensive experience shaping sustainable urban transformation across Africa and the Middle East. A passionate advocate for technology-enabled governance, he has worked with public agencies, development banks, and private partners to align digital innovation with tangible infrastructure delivery. From pioneering city data platforms and mobility strategies to advancing PPP frameworks for smart utilities, Ismail’s work bridges the gap between policy, engineering, and citizen engagement. His mission: to help emerging cities become more connected, inclusive, and resilient.
In this exclusive conversation, he shares his insights on scaling smart city initiatives, financing innovation, and building institutional capacity to deliver the next generation of urban infrastructure across the region.
Evolution of Smart Cities in Emerging Markets.
Q1. You’ve positioned yourself at the intersection of infrastructure, urban policy, and technology as a Smart City expert. What, in your view, distinguishes “smart city” initiatives in emerging markets (especially in Africa or MENA) from those in more mature economies? What are the biggest opportunities and pitfalls?
In emerging markets, rapid urbanization remains a defining feature of growth. Yet, it continues to coexist with persistent gaps in public services. In this context, Smart City strategies appeared as a tool to attract foreign investment and project a renewed urban identity. Entire cities designed from scratch, such as Kenya’s Konza Technopolis, have emerged as emblematic milestones, proposing alternative narratives of modernity. Starting with a blank page enables to plan integrated environments that combine economic activities, housing, leisure and mobility within a unified digital ecosystem. We also observe how they become advanced enclaves of innovation that coexist with broader systemic deficiencies. In the absence of a wider urban strategy, these smart city initiatives deepen inequalities between urban spaces and weaken the continuity of public services.
In contrast, more mature economies, with slower demographic dynamics and consolidated urban systems, have followed a more incremental path. There, Smart City strategies have evolved less as isolated projects and more as institutionalized frameworks for public policy innovation. Digital technologies have been gradually embedded into existing urban infrastructures. Over time, this pragmatic approach has fostered the emergence of legal and regulatory foundations, including ambitious data protection principles the GDPR, that anchor Smart City development in broader governance practices.
Bridging Policy, Technology & Physical Infrastructure.
Q2. Many smart city projects falter because there’s a disconnect between urban policy, digital systems, and the on-ground built environment. How do you ensure alignment across those domains when advising or driving projects?
For too long, the Smart City has been understood as a sum of digital tools and technological gadgets. This techno-centric view has led to repeated strategic disillusions. When the interaction between urban policy and digital systems is not anticipated, technology evolves without institutional adaptation and innovation risks becoming decorative.
The experience of projects such as Songdo in South Korea stands as a cautionary example. Designed as a showcase, it struggled to fulfil its promises precisely because it treated the city as a laboratory of innovation rather than a living, social, and cultural ecosystem. The urban experience was a secondary consideration, overshadowed by a focus on technological implementation.
In this regard, designing a Smart City strategy requires a holistic re-examination of existing urban policies, service delivery mechanisms and administrative models. It begins with a comprehensive diagnostic, mapping assets, policies, and institutional capacities. It progressively evolves into a shared roadmap built with all relevant stakeholders. This process questions how governance operates, how infrastructures are managed, where decision-making flows occur and how data is effectively used. This is why we advocate for problem-led rather than technology-led design.
Data Governance, Privacy & Citizen Trust.
Q3. As cities deploy sensors, surveillance, real-time analytics, and citizen platforms, issues of data governance, privacy, and trust become critical. How do you approach these challenges, especially in cities with less regulatory maturity?
The boundary between the Smart City and the emerging notion of Safe City could be increasingly porous. The proliferation of urban sensors, surveillance infrastructures, algorithmic systems and real-time data analytics has contributed to the progressive establishment of what some scholars describe as a dataveillance urban system. Such developments raise ethical and legal questions concerning the collection, storage and use of massive volumes of data in urban spaces.
To tackle this question, some cities have made this algorithmic city visible by signaling the presence of sensors and cameras and disclosing their purpose and the nature of collected data. Other cities have produced data charters and AI frameworks, co-produced with researchers and local partners. These instruments, while often non-binding, provide flexible frameworks articulating the values, principles and operational boundaries guiding local data and AI use.
Tangible Infrastructure vs Digital Overlay.
Q4. In your experience, which infrastructure investments (roads, utilities, public transport, sanitation) deliver the highest multiplier when combined with digital overlays (IoT, AI, urban analytics)? Can you share a project example where such synergy yielded measurable gains?
Sectoral evaluations have confirmed that productivity gains and cost savings are tangible in specific operational domains such as mobility, utilities and waste management.
In the field of mobility, the integration of real-time data streams, demand prediction models and algorithmic maintenance scheduling has reconfigured operational logics. Similarly, in utilities, the deployment of smart meters, leak detection systems and intelligent grids constitutes an example. These systems enable continuous feedback between consumption patterns, network performance and maintenance operations, producing cumulative efficiency gains over time. Waste management, often a less visible but financially significant domain, has likewise benefited from algorithmic route optimization and dynamic resource allocation, yielding demonstrable operational economies.
The common denominator among these sectors is their asset-intensive nature and the recurrence of their operations. In such contexts, digital optimization does not create entirely new infrastructures but rather enhances the productivity and reliability of existing ones. The “smartification” of assets and modest digital investments in monitoring and control generate returns through improved coordination and reduced downtime.
Resilience & Future Proofing.
Q6. Given climate risk, rapid urbanization, and evolving technologies, how do you “future-proof” smart city investments – so that infrastructure doesn’t become obsolete or stranded? What flexibility, modularity, or upgrade pathways do you embed?
The growing urgency of climate change and environmental risk invites a renewed understanding of the Smart City paradigm. In many national and local contexts, environmental public policies remain hindered by a fragmented data landscape. Public officials frequently operate without a coherent data baseline that would enable an analysis of ecological performance or policy impact. Historical datasets are often incomplete and project-level monitoring mechanisms are either inconsistent or insufficient. This structural opacity limits the capacity of public authorities to assess progress against defined environmental indicators or to recalibrate policies based on empirical feedback.
Within this context, the Smart City framework offers the possibility of constructing data-driven governance, a process through which environmental action is informed by sensors, data flows and monitoring systems. In this model, the data strategy establishes an “as-is” diagnostic and ensures the continuity of environmental measurement. Applications such as air quality monitoring, energy consumption tracking, predictive maintenance for green infrastructures and climate-risk modeling constitute the infrastructure through which cities can become self-observing and self-regulating systems.
Moreover, the coupling of Smart City infrastructures with scientific modeling and simulation tools allows cities to move beyond descriptive analytics toward predictive governance. Digital twins, for instance, enable the simulation of future climate scenarios, supporting informed decision-making around resource allocation, infrastructure design and risk mitigation.
Capacity Building & Institutional Change.
Q7. Governments and municipal bodies sometimes struggle to keep pace with tech and data-driven city management. What is your strategy to build institutional capacity, shift mindsets, and embed frameworks so that the “smart” systems integrate sustainably?
Many public authorities continue to face structural and cultural barriers in their engagement with Smart City agendas. The challenge often lies in the persistence of institutional inertia, understood as embedded administrative routines, siloed organizational structures and procedural rigidities that leave little room for experimentation.
A genuine innovation culture requires a critical and reflective stance toward established practices, encouraging administrations to question inherited norms and to interrogate the implicit logics that govern decision-making. It involves shifting from the logic of compliance to one of adaptation.
In our practice, we adopt a human-centered approach, anchoring innovation in the lived experiences, capacities and visions of public actors. The goal is to build a shared understanding: what innovation means for each actor, what constraints they face, and what collective vision can guide a coherent Smart City trajectory.
Moreover, we support the emergence of new roles and administrative architecture. Increasingly, public authorities are appointing Chief Innovation Officers, Smart City Leads and Data Governance Managers to coordinate cross-departmental efforts. Beyond these formal designations, the creation of interdisciplinary teams, internal innovation labs and cross-functional steering committees serve as catalysts for organizational change.
Scaling Pilots to City- or Region-Wide Rollouts.
Q8. Many cities pilot smart solutions (mobility apps, sensor networks, waste management systems), but scaling is hard. What have you found to be the key success factors (technical, organizational, financial) to scale pilots into full city deployments?
Public authorities have frequently relied on experimental projects, so-called test beds, to explore the potential of emerging technologies. These pilots were conceived as laboratories of innovation, enabling limited-scale experimentation prior to broader deployment. Yet, despite their apparent promise, most remained at a demonstrative stage. They were tested, occasionally validated but only rarely institutionalized.
This limited diffusion highlights an inherent ambivalence of the pilot model. Even if experimentation can foster learning by doing, it could also become a substitute for strategic commitment, a way to display innovation without fully engaging. The result has often been a proliferation of fragmented initiatives, administrative fatigue and growing skepticism among both partners and local users.
For these reasons, we consistently advocate for the formulation of an integrated smart city strategy prior to any investment or experimentation. Empirical evidence indicates that the most resilient smart city programs are those built on incremental logics. Successful initiatives have generally relied on progressive investment phases that focus less on pilots but give more space to progressive structural investments. Scaling up thus requires a deliberate selection of technologies that balance sophistication with operational simplicity.
Vision for African / MENA Urban Future.
Q9. If you look ahead 10–15 years across Africa and the Middle East, what kind of urban transformation do you hope to see? What role will smart mobility, governance, and infrastructure play in shaping that future?
The Middle East has already demonstrated remarkable progress in urban innovation and the early integration of promising technologies.
In the future, mobility will be at the forefront of this evolution. Beyond conventional subways, tramways, and buses, autonomous shuttles, electrified transit, hydrogen-powered vehicles, and vertical take-off and landing (VTOL) urban air taxis will be integrated into AI-driven multimodal transportation networks. Predictive algorithms anticipate congestion, optimize routes and dynamically link neighborhoods, cities and regional corridors. Commuting becomes seamless and personalized.
Urban design and environmental adaptation are likewise entering a new era. By 2035, residents wake in neighborhoods where 15-minute city principles are fully operational, granting access to work, schools, shops, healthcare and green spaces without reliance on private vehicles. Streets are shaded by canopies that regulate temperature, while tree-lined corridors and green rooftops mitigate urban heat islands. Sensor networks continuously monitor air, water, and energy flows, adjusting irrigation, lighting and ventilation in real time to optimize comfort and energy consumption.
Public services will also function with unprecedented efficiency. Waste collection, water distribution and energy management are coordinated through integrated platforms that anticipate demand, prevent disruptions and allow rapid intervention when anomalies arise. Citizens interact with these services through intuitive apps, kiosks, or wearable devices, supporting local authorities. Governance is similarly evolving with digital twins that simulate infrastructure projects, environmental impacts and policy changes, enabling anticipatory governance where interventions can be tested before investments are committed.
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