Dr Fadi Alhaddadin, Director of MSc Information Technology (Business), School of Mathematical and Computer Sciences, Heriot-Watt University Dubai

Urbanisation is accelerating at an unprecedented pace, placing immense pressure on cities to become more efficient, sustainable, and resilient. Today, urban areas account for most of the global energy consumption and greenhouse gas emissions, making them central to addressing climate and resource challenges. In response, cities around the world are transitioning from traditional infrastructure systems to advanced, technology-driven models. The evolution from smart grids to fully integrated smart cities marks a new phase of urban innovation.

At the core of this transformation lies the smart grid. Unlike standard energy systems, smart grids use digital communication technologies to enable real-time interaction between energy providers and consumers. This two-way communication allows for more efficient electricity distribution, improved demand management, and the seamless integration of renewable energy sources such as solar and wind. As a result, smart grids not only reduce energy waste but also enhance reliability and support decentralised energy systems. They form the foundational layer upon which broader smart city systems are built.

However, the true power of smart cities emerges from the convergence of multiple technologies. The Internet of Things (IoT), artificial intelligence (AI), and big data analytics work together to create highly interconnected urban environments. IoT devices ranging, from sensors and smart meters to connected infrastructure continuously collect data on various aspects of city life, including energy usage, traffic flow, air quality, and public services. This data is then analysed by AI systems, which generate insights and enable real-time decision-making.

Through AI-driven analytics, cities can predict energy demand, optimise transportation networks, and detect infrastructure issues before they escalate. For example, intelligent traffic management systems can reduce congestion and emissions by dynamically adjusting traffic signals based on real-time conditions. Similarly, predictive maintenance systems can identify potential failures in utilities or transportation networks, minimising disruptions and reducing operational costs.

One of the most significant benefits of smart city technologies is their contribution to sustainability. Energy-efficient buildings equipped with smart systems can automatically regulate lighting, heating, and cooling based on occupancy and environmental conditions. Smart transportation solutions, including connected public transit and electric mobility systems, help reduce carbon emissions and improve urban mobility. Furthermore, integrated resource management systems enable cities to optimise the use of energy, water, and other essential services, supporting a more sustainable urban ecosystem. A notable example in the Middle East is Masdar City, which has been designed as a sustainable urban development powered by renewable energy and smart technologies. The city integrates energy-efficient buildings, smart grids, and intelligent transportation systems, demonstrating how digital innovation can support low-carbon urban living.

The Middle East is increasingly positioning itself as a global leader in smart city development through ambitious national strategies and large-scale projects. In Dubai, smart city initiatives focus on digital governance, artificial intelligence, and integrated urban services to enhance efficiency and citizen experience. Similarly, Saudi Arabia’s NEOM project represents a transformative vision of a fully automated and sustainable urban environment powered by advanced technologies. These initiatives highlight the region’s commitment to leveraging innovation to address urban challenges and drive future economic growth.

Beyond environmental benefits, smart cities are designed to enhance the quality of life for their residents. Digital platforms enable more accessible and efficient public services, from healthcare to administrative processes. Smart health systems can improve patient care through remote monitoring and data-driven diagnostics, while intelligent safety systems enhance security through real-time surveillance and rapid emergency response. These advancements contribute to more convenient, inclusive, and liveable urban environments.

Resilience is another critical dimension of smart cities. As urban areas face increasing risks from climate change, natural disasters, and infrastructure strain, the ability to adapt and respond effectively becomes essential. Smart grids play a key role in enhancing energy resilience by supporting decentralised power generation and rapid recovery from outages. Meanwhile, data-driven systems allow city authorities to anticipate and prepare for potential disruptions, improving overall crisis management and response capabilities.

Despite their many advantages, the development of smart cities is not without challenges. The integration of interconnected systems raises concerns about cybersecurity and data privacy, as large volumes of sensitive information are collected and processed. Additionally, the high cost of implementing advanced infrastructure and the need for standardised systems can pose significant barriers. Addressing these issues requires strong governance, clear regulatory frameworks, and collaboration between governments, private sector stakeholders, and technology providers.

In conclusion, the transition from smart grids to smart cities represents a fundamental shift in how urban environments are designed and managed. By leveraging the combined capabilities of IoT, AI, and data-driven infrastructure, cities are becoming more efficient, sustainable, and resilient. This transformation is not only redefining urban systems but also shaping the future of how people live, work, and interact within cities. As this evolution continues, smart cities will play a crucial role in addressing global challenges and improving the overall quality of urban life.

By Alfred Manasseh, Co-Founder and COO of Shaffra

For much of the past two years, AI has been discussed mainly in terms of pilots, productivity, and experimentation. But in moments of uncertainty, the conversation changes. This is when AI needs to move beyond pilots and into execution. When pressure rises, what matters most is speed, consistency, and coordination. The real question is whether institutions have the operational capacity to respond clearly, maintain continuity, and support decision-making under pressure.

In the UAE, that question carries particular weight because resilience, proactiveness, and digital by design have already been established as national priorities. This is no longer a futuristic idea. It is already being implemented across institutions.

This is why the conversation is moving beyond AI as a surface-level capability and closer to the operating core of institutions. In 2024, UAE federal government entities processed 173.7 million digital transactions and delivered 1,419 digital services, with user satisfaction reaching 91%. Once millions of people are interacting with digital systems, resilience depends not only on keeping platforms online, but on making sure information flows remain clear, response times hold steady, and service quality stays consistent under pressure.

Filtering signal from noise

In high-pressure environments, the first challenge is information overload. Fake information, true information, public questions, updates, and warnings all arrive at once, and institutions have to respond without adding confusion. Human teams remain essential because judgment and accountability must stay with people. But people alone cannot process that volume of information at the speed now required.

This is where Autonomous AI Teams become operationally valuable. AI is effective at dealing with large amounts of data, identifying patterns, and helping institutions filter signal from noise. Used properly, that gives leadership a stronger basis for communicating clearly, responding faster, and addressing confusion before it spreads.

Why governed systems hold up

Good governance is what makes AI dependable in sensitive moments. It is not only about speed. It is about consistency in messaging, consistency in how citizens and residents are served, and making sure people are well-informed. In uncertain situations, the public does not only need information. It needs information that is clear, timely, and trusted. Governed AI helps institutions provide that support without losing control or passing ambiguous situations with false confidence.

This is particularly relevant as research has found that six in 10 UAE employees use AI in their daily jobs, while IBM reported that 65% of MENA CEOs are accelerating generative AI adoption, above the global average of 61%.

The UAE can lead this shift because it is building around digital capacity at every layer, from infrastructure to service delivery to workforce readiness. The Digital Economy Strategy aims to raise the digital economy’s contribution significantly by 2031, while broader trade guidance has also framed the ambition as growing from 12% of non-oil GDP to 20% by 2030.

Working model in practice

This is also where Shaffra offers a practical example of how the model is changing. Through its AI Workforce Platform, Shaffra’s Autonomous AI Teams are already saving more than two million manual work hours per month and reducing operational costs by up to 80%. These systems can monitor inbound activity, classify issues, support fraud reviews, prepare draft responses for approval, and help institutions listen at scale to recurring public concerns.

In Shaffra deployments more broadly, this model has also delivered significant time and cost efficiencies across enterprise operations.

That does not replace leadership or human judgment. AI and humans play different roles, and the real value comes when they work together. It gives institutions stronger operational support, with greater speed, consistency, and control when pressure is highest. In the years ahead, the strongest organisations will be the ones that move beyond AI as a productivity tool and build it as a governed resilience layer that stays reliable when uncertainty tests every process around them.

From content creation to media buying, artificial intelligence is quietly reshaping how campaigns are built, delivered, and optimised across the GCC.

In the UAE and across the GCC, artificial intelligence has moved well beyond the stage of experimentation. What was once a buzzword discussed in boardrooms is now deeply embedded in the day-to-day execution of advertising. Brands are no longer testing AI—they are relying on it to run campaigns, generate content, and make increasingly precise decisions about audience targeting and timing.

On the creative front, the shift is particularly visible. AI-powered tools are now capable of producing ad copy, visuals, and even short-form video content at a pace that would have been unthinkable just a few years ago. For marketers operating in a market like the UAE—where campaigns often need to speak to audiences in both English and Arabic, while also resonating across a diverse mix of nationalities, this level of speed and adaptability is more than a convenience. It is becoming a necessity.

Behind the scenes, machine learning has also transformed how media buying is approached. Traditional methods that relied heavily on instinct or retrospective performance reports are steadily being replaced by systems that analyse audience behaviour in real time. These platforms continuously optimise campaign performance, adjusting budgets and placements based on how users interact with content.

In the UAE’s PR ecosystem, brands are already leveraging platforms such as Meltwater, Brandwatch, and Sprout Social to better understand media performance, audience sentiment, and the broader buying landscape.

AI is also influencing emerging advertising formats. Digital billboards, for instance, are becoming more responsive, using live data inputs to tailor content based on factors such as time of day, location, and audience movement. Similarly, augmented reality experiences are beginning to incorporate behavioural insights, offering more contextual and interactive brand engagements.

Looking ahead, the trajectory appears clear. Advertising is moving towards deeper automation, more intelligent recommendations, and tighter integration between creative tools and analytics platforms. The industry is shifting from a model centred on broadcasting messages to one that focuses on responding to audiences in real time, with context and precision.

In this evolving landscape, AI is no longer just an enabler, it is becoming the foundation on which modern advertising is built.