By Afroz Nawaf, Founder of point a.cademy, Middlesex University Dubai

The typical career no longer follows a straight line. Alongside the traditional ‘study, then work’ pathway, something more fluid has emerged: learning, work, learning again. New skills and adapted roles. Back to learning.

By 2030, 39 per cent of workers’ core skills will change. It tells us something that the industry already feels: the pace of work has outrun the pace of learning. Students, skilled practitioners and hiring managers are asking one fundamental question: how do you move at the speed of change?

Three groups are already showing us what it can look like.

 Young people finishing secondary school can test their interests before committing to a pathway, building real work alongside practitioners and making far more informed decisions about what and where they want to study.

For students already at university, capability can be built in parallel with their degree: an engineering student learns to use AI for rapid prototyping, a business student applies AI to research and forecasting, a design student adds content creation or UX certification, while a film student develops AI-enabled workflows alongside their craft.

Mid-career professionals learn in compressed bursts. Someone pivoting industries takes a short course while maintaining their job. Micro-credential enrolments are up nearly 50 per cent year-on-year in 2026. People want capability built in layers, at their own pace, while maintaining work and life.

All three groups point to the same reframe. It’s not just about moving at the speed of change but doing so without abandoning depth. The answer emerging in the market is a fundamental shift in how learning is structured, shaped around people’s time, resources and ambitions.

When point a.cademy opened in early 2026, as an enterprise within Middlesex University Dubai, the market responded decisively. Our capability-building academy offers short, intensive courses in Film, Content, Design and AI, taught over one to five days, at industry standard. Within the first month, 500+ learners signed up, with multiple pathways booking out completely. 240 courses have been completed, with 37.5% of eligible learners continuing into further courses. This continuation rate matters. Learners aren’t stopping after one certificate, they are stacking capability and moving to the next course.

What we validated from these first cohorts is that different people move through compressed learning at fundamentally different rhythms. Some absorb rapidly through immersion, then need time to process. Others build gradually, testing each step. Some need tangible output, a project or a prototype, before concepts land, while others need conceptual grounding before they can engage. In a compressed learning environment, personalisation becomes particularly important, giving us the room to build on the different ways people engage with and apply knowledge. This is why we design courses around eight distinct learning personas, from the tentative newbie who needs confidence-building and the hands-on maker who learns through doing, to the serial pivoter, the purpose-seeker, the sponge who learns through rapid immersion, the chaos creative, the conceptual thinker, and late bloomer who takes their time. Each reflects a different way of engaging with learning.

When a three-day intensive respects the person, their rhythm, motivation and way of thinking, moving at speed does not mean losing the individual; it means creating learning experiences that respond to how different people engage, process and apply knowledge. Research supports this. In a review of personalised adaptive learning research, 59 per cent of studies reported improved performance.

The proof is in the applied work. More than 100 Middlesex University Dubai staff completed certifications through point a.cademy. These are not certificates simply hanging on walls; one staff member redesigned key internal processes using the Design for Storytelling frameworks they learned, creating more compelling messaging for prospective students. Another improved digital services with AI tools. A third redesigned administrative processes, cutting student ID card processing time by 74%. This is what moving at the speed of change looks like in practice: learn, apply, deliver, iterate. Not learn and apply later.

The human element matters more, not less, as AI reshapes every role. The people who move at market pace are not those who simply use AI. They bring human judgement, creativity, ethical thinking and specialist knowledge to it. That capability requires continuous, applied learning in parallel with work.

Education institutions that recognise this are expanding their role into lifelong learning ecosystems, creating end-to-end learning loops that allow people to enter, return and continue building capability at different stages of their lives. Short courses, studios and industry experiences can sit alongside rigorous degree education, extending a university’s reach beyond traditional cohorts and creating a broader community of lifelong learners. Institutions such as Middlesex University Dubai are already exploring this model, connecting academic foundations with applied, continuous learning experiences that allow their communities to keep evolving long after a single programme ends.

The market is moving. The question is no longer whether learning will change. It has. The real question is how education systems will evolve to meet it: how do learners move at the speed of change without losing the individual in the rush?

Why Advanced Network Infrastructure Is the Backbone of the Digital Economy

Artificial intelligence (AI) is reshaping the digital economy at an unprecedented pace. Across industries, organizations are embracing AI to unlock new efficiencies, accelerate innovation, and create more personalized experiences. Yet behind every AI application, cloud platform, and digital service lies a critical foundation that often receives far less attention than the technologies it enables: the network infrastructure that connects the modern data center.

As digital transformation accelerates, data centers have become some of the most strategic assets in today’s economy. They support everything from enterprise applications and cloud services to digital government platforms and AI workloads. Their growing importance is reflected in the scale of investment flowing into the sector. The Middle East hyperscale data center market is expected to grow from USD 4.61 billion in 2025 to USD 16.38 billion by 2031, expanding at a CAGR of 23.53 percent. At the same time, investment in data center infrastructure is expected to reach unprecedented levels. McKinsey estimates that meeting future compute demand could require as much as $6.7 trillion in global data center investment by 2030. These figures highlight not only the growing demand for digital services, but also the increasing importance of the infrastructure that supports them.

However, the AI era is creating challenges that extend well beyond adding more computing power. The performance of modern data centers is increasingly determined by the efficiency of the networks operating within them. Importantly, such efficiency encompasses more than high-volume throughput and continuous uptime. It incorporates stringent requirements around data privacy and integrity, alongside strict performance metrics like deterministic latency and rapid service provisioning.

Unlike traditional applications, AI and high-performance computing workloads generate enormous volumes of east-west traffic as data moves continuously between servers, storage systems, GPUs, and CPUs. Training large AI models requires thousands of processors to communicate simultaneously, making low latency and high-capacity connectivity essential to maintaining performance. As AI models become larger and more sophisticated, the demand for 400 Gigabit Ethernet (GE) and 800GE optical networking architectures is growing rapidly to support the scale and speed these environments require.

This shift is changing the way data center infrastructure is designed. AI-ready environments require networks capable of scaling seamlessly across thousands of servers while maintaining deterministic latency, intelligent traffic engineering, and ultra-high throughput. In effect, the network is becoming just as critical as the computing resources themselves.

Meeting these requirements demands innovation across IP routing, data center switching, and optical transport technologies. Advances in routing silicon and switching platforms are helping operators build networks that can support increasingly complex workloads while maintaining efficiency and reliability. Technologies such as Nokia’s FP5 network processor silicon deliver the high-capacity performance required for modern digital infrastructure while improving energy efficiency compared with previous generations. Similarly, the Nokia 7250 Interconnect Router portfolio is designed to support hyperscale environments through high-density Ethernet connectivity and open networking architectures that enable efficient scaling as demand grows.

Across the Middle East, operators are already evolving their networks to prepare for the next wave of AI-driven growth. Nokia has been collaborating with leading service providers across the entire MEA market, primarily in the Gulf, but across Africa as well, on IP and optical network modernization initiatives aimed at increasing capacity, improving resilience, and supporting growing cloud and data traffic demands. Recent projects in the region, including a 1Tbps data center connectivity deployment spanning hundreds of kilometers in Saudi Arabia, AI-powered optical network automation trials in the UAE, and enhanced cloud interconnection capabilities for hyperscale environments, illustrate how network infrastructure is being modernized to meet rising data and AI demands. These efforts reflect a broader regional focus on building digital infrastructure capable of supporting long-term economic and technological growth.

Performance, however, is only one side of the equation. Sustainability is becoming an equally important consideration as data center capacity expands.

According to the International Energy Agency’s Electricity 2026 report, electricity demand from data centers worldwide is expected to more than double by 2030, driven largely by AI workloads and accelerated computing requirements. As operators balance performance objectives with sustainability commitments, energy-efficient networking infrastructure will play a critical role in reducing operational costs and limiting environmental impact.

This is where advances in networking technology can make a meaningful difference. Modern silicon innovations and optical transport platforms are enabling operators to deliver significantly higher capacity while consuming less power, helping support both traffic growth and sustainability goals. As data volumes continue to rise, achieving greater efficiency across the network will become increasingly important.

The stakes are particularly high in the GCC, where governments are investing heavily in digital infrastructure to support AI, cloud computing, and smart city initiatives. The UAE, Saudi Arabia, and Qatar are positioning themselves as regional hubs for hyperscale cloud providers and AI research centers, creating new opportunities for innovation and economic diversification.

Realizing these ambitions will depend on more than the construction of new data centers. It will require high-performance network infrastructure capable of connecting hyperscale facilities, edge computing sites, enterprise clouds, and national digital platforms into a seamless digital ecosystem. As these national platforms come online, sovereignty, resilience, and security become defining requirements: networks must keep sensitive data and AI inferencing in-country, withstand disruption, and meet the trust standards of mission-critical government and enterprise operations.

As the region continues its digital transformation journey, the conversation around data centers must evolve beyond computing power alone. The future of AI will depend not only on the intelligence of algorithms, but also on the networks that enable data to move securely, efficiently, and on scale. Very significantly, these networks will enable the consumption of a new generation of AI-boosted cloud services, driving the consumption needed to generate the ROI required by this promising AI supercycle. Building AI-ready data centers therefore starts with building AI-ready networks, creating the resilient digital foundations that will power the next chapter of growth across the Middle East.

By Gaurav Mohan, SVP Sales – APAC, India, Middle East & Africa, NETSCOUT

The Middle East is building one of the world’s most advanced digital economies. Across the UAE, Saudi Arabia, Qatar and the wider Gulf, artificial intelligence is moving from experimentation into production. Sovereign cloud strategies are reshaping infrastructure. 5G is powering smart cities,, autonomous services and new digital business models. Yet as organisations accelerate innovation, many are struggling to maintain visibility across these digital infrastructures that gives them the knowledge they need to manage, control and protect their business.

Today’s digital services rarely operate within a single environment. Applications, workloads and services are spread across sovereign clouds, hyperscalers, regional data centres, telecommunications networks and edge environments, each generating its own telemetry, tools and operational workflows. As a result, organisations often gain more data but less understanding of how their services actually behave end to end.

According to Enterprise Management Associates’ Network Management Megatrends 2026 report, 51 percent of enterprises now manage four or more distinct network domains, 38 percent of organisations lack end-to-end visibility across their network domains and even 24 percent acknowledge having areas where their monitoring tools cannot see at all. This highlights a growing paradox that organisations are rich in data but poor in visibility.

That means decisions are made using incomplete information. Incident response slows down, operational risk increases, and it becomes even harder to protect the customer experience. In the Gulf, the challenge is particularly relevant. As data is increasingly localised to meet regulatory obligations, applications and workloads naturally cluster around where that data resides. While this strengthens governance and compliance, it can also fragment visibility if organisations lack a consistent view across multiple environments.

Often the most valuable operational and security information never travels between users and applications. It moves silently between cloud workloads, databases, APIs and servvices inside the infrastructure itself. If organisations cannot observe and understand these interactions, they miss the activity that often matters most.

The conversation is no longer simply about visibility. It is about whether organisations can trust the data used to make operational and AI-driven decisions. The question that must be answered is do they have the trusted operational data that is the authoriative network evidence that gives them the certainty they need to make better, smarter decisions – faster.

High-fidelity network data provides a more accurate and consistent view of network activity, helping teams fill the gaps left by logs, metrics and sampled telemetry. It enables organisations to move beyond assumptions and approximations, allowing teams to understand events as they occur and investigate them with confidence.

The most authoritative source of network intelligence comes directly from network packets, providing  an independent record of how applications, infrastructure and users actually interact. Rather than relying solely on sampled metrics or instrumented logs, it gives teams evidence grounded in observed network activity. The result is a clearer understanding of both operational and security events.

In the Middle East, where regulatory expectations continue to evolve and data sovereignty remains a priority, that level of accuracy carries particular importance. Organisations are increasingly expected to demonstrate resilience, accountability and operational transparency. Meeting those expectations becomes significantly harder when visibility is incomplete.

AI does not eliminate operational uncertaity. In fact, it magnifies and can force-multiply whatever uncertainty already exists. Feed AI incomplete or inconsistent data and it simply automates bad decisions faster. Feed it complete, contextual and trusted network intelligence, and AI becomes more accurate, responsive and reliable.

The Middle East has invested heavily in building world-class digital infrastructure. As AI, sovereign cloud and connected services continue to expand, organisations tha combine comprehensive visibility with trusted, high-fidelity network intelligence will be able to thrive. In the next phase of digital transformation, success will be defined not simply by how much infrastructure organizations build, but by how clearly they can see, understand and act across it with confidence.