As Middle Eastern cities invest heavily in smart mobility, intelligent transport systems, and connected infrastructure, reliable communication networks are becoming the foundation of urban resilience. In this exclusive interview with Technology Integrator, Thibaut Faivre, Head of MEAI Sales & Programme Delivery for Public Safety and Security at Airbus Defence and Space, discusses the technologies enabling real-time situational awareness, inter-agency collaboration, and mission-critical connectivity across the region’s rapidly evolving mobility ecosystem.

As Middle Eastern cities accelerate smart mobility and connected infrastructure projects, how important is real-time communication and situational awareness becoming for modern transportation and emergency response ecosystems?

As Middle Eastern cities accelerate their journey toward becoming global hubs for smart mobility, the role of communication is undergoing a fundamental shift from a supportive function to the very backbone of urban resilience. For modern transportation and emergency response ecosystems, real-time communication is no longer merely about voice connectivity; it is about the seamless flow of high-capacity data. Traditional narrowband systems, while reliable for voice, cannot accommodate the digital evidence, live video feeds, and precise geolocation data that now sit at the heart of frontline decision-making. In a region defined by rapid urban expansion and high-security profiles, situational awareness means having the ability for command centres to see incidents in real time rather than reacting to them after the fact. This “resilient intelligence” ensures that as infrastructure becomes more connected, the agencies protecting it can operate with a level of clarity that matches the complexity of the environment they serve.

How could technologies such as GINA Software’s Tactical AVL and Unified Command Interface reshape the way emergency services respond to large-scale road incidents, traffic disruptions, and mobility-related crises?

The integration of GINA Software’s specialised modules into our Agnet and TETRA ecosystems represents a significant leap in how emergency services manage large-scale mobility crises. By turning complex data into life-saving action, these modules allow first responders to operate within a unified digital interface. The Tactical AVL tool provides dispatchers with granular visibility of assets and personnel, ensuring that units are deployed with maximum efficiency during major road incidents where every second is vital for clearing traffic and saving lives. Simultaneously, the Smart CAD (IMS) module supports high-level decision-making by consolidating mapping, field data, and reporting into a single interface, removing the cognitive load of managing disparate systems. Perhaps most importantly, the Unified Command Interface facilitates coordination between different agencies and networks. This reduces the communication barriers that often plague large-scale operations, ensuring that police, medics, and transport authorities are all working from a single, synchronised and common operational picture.

With connected vehicles and intelligent transport systems continuing to evolve, do you see public safety communication networks eventually becoming deeply integrated into future smart mobility frameworks?

We are seeing a definitive convergence where public safety communication networks are becoming deeply embedded into the wider smart mobility framework. The transition from legacy narrowband to mission-critical broadband (4G/5G) is the catalyst for this integration. As vehicles and infrastructure become more intelligent, the communication architecture must scale accordingly to support AI-enabled intelligence and automated workflows. For instance, AI can now be used to detect anomalies in real-time video feeds or automate resource allocation, allowing first responders and control centres to act proactively rather than reactively. This digital transformation ensures that public safety tools are not isolated silos but are instead natively integrated into the data-driven workflows of the cities they protect. By leveraging shared data rather than isolated radio channels, future smart mobility frameworks will benefit from a level of inter-agency coordination that was previously impossible.

In high-density urban environments, where every second matters during emergencies, how critical is interoperability between agencies, fleets, transport authorities, and first responders?

In dense urban environments, the speed of response is often dictated by the fluidity of information across different organisations. Interoperability between agencies, transport authorities, and first responders is a strategic priority, particularly within the Gulf Cooperation Council (GCC) region. Airbus is actively supporting this by creating gateways between respective communication systems to facilitate interstate and inter-agency collaboration. Public safety professionals operating in high-stakes environments rely on their tools to work across boundaries without friction. A smooth transition to broadband must ensure that interoperability, tactical management, and scalability are designed into the system from the outset. This allows agencies to communicate across organisations seamlessly and adjust operational priorities in real time as missions evolve. Without this level of technical and operational certainty, the benefits of high-speed data cannot be fully realised in a crisis.

As the automotive and mobility sectors become increasingly software-defined and data-driven, what role will secure communication architectures play in ensuring safer and more resilient transportation networks across the region?

As the automotive and mobility sectors become increasingly software-defined, the role of secure communication architectures is to ensure that the “intelligence” of the network never fails. Resilience in the Middle East is a unique challenge due to vast geography, the regional security threats and the exposure to climate-driven incidents, which means terrestrial networks alone are sometimes insufficient. Airbus addresses this through a layered approach that integrates satellite connectivity with terrestrial broadband. Solutions such as Agnet over Satcom ensure that mission-critical communications remain operational even in remote areas or during major disasters that disable standard infrastructure. Furthermore, for local incident scenes where network coverage might be overloaded or temporarily unavailable, tools like Agnet Direct allow teams to stay connected via off-network direct mode. This hybrid architecture, which combines the mission-critical reliability of TETRA with the high-speed data capabilities of 4G and 5G, creates a robust foundation for the next generation of secure, data-driven transportation networks across the region.

As the FIFA World Cup 2026 final approaches this week, we reflect on a tournament that transformed how millions experienced the sport, from living room stadiums to quiet spaces in packed arenas

As we count down the final hours before this week’s showpiece final, the FIFA World Cup 2026 has delivered 103 matches across 16 cities, and with it, a reimagining of what “experiencing football” means.  Hisense served as the official and exclusive Video Assistant Referee (VAR) Review TV Provider for the entire tournament across the United States, Canada, and Mexico. Every controversial offside call. Every penalty review that had fans screaming at their screens. Every red card confirmation that shifted the momentum of a knockout match. The technology referees used to make those match-defining decisions ran on Hisense RGB MiniLED displays. The Video Operation Room in Zurich was upgraded specifically with these screens because VAR officials needed “clear and authentic restoration of live match footage.”

And it delivered.

Two parallel revolutions unfolded across this tournament: one that transformed homes into legitimate viewing destinations, and another that finally opened stadium doors to millions who’d been locked out for decades.

Hisense made an argument before kickoff: the home viewing experience could, in some ways, surpass what you’d get at the stadium itself. If the technology was precise enough for officiating decisions scrutinized by billions and debated across social media within seconds, it was good enough for living rooms worldwide.

For those who invested in the L9Q TriChroma Laser TV, everyday living spaces became premium match-day destinations throughout the tournament. With ultra-large displays up to 200 inches, fans followed every run, pass, tackle, and goal with remarkable clarity.

The flagship UXS RGB MiniLED TV, powered by breakthrough RGB MiniLED technology that delivers exceptional color accuracy, brightness, and contrast, brought fans closer to every moment on the pitch and created a more immersive and lifelike viewing experience for sports, entertainment, and gaming.

The Party Everyone Could Finally Join

For millions of fans living with autism, PTSD, dementia, anxiety, and other sensory processing conditions, the stadium experience had remained firmly out of reach, a party they could hear from outside but never truly join. This tournament changed that.

At this year’s tournament, all 16 host stadiums featured dedicated sensory rooms, making this the first-ever Sensory Inclusive FIFA World Cup. Hisense collaborated with FIFA and KultureCity to install these spaces across every venue in the United States, Canada, and Mexico, and they were used.

As Hisense continues pushing boundaries, making every match feel bigger, every celebration more immersive, and every memory more unforgettable, one truth has emerged from this tournament: the hierarchy of World Cup viewing has been expanded, making room for everyone who loves the beautiful game.

This week, as billions watch the final from living rooms with 300-inch screens and fans with sensory needs take their seats in the stadium, football’s promise will be fulfilled. The beautiful game. Finally, for everyone.

Artificial intelligence is no longer a future technology. It has become part of our everyday lives almost overnight. Whether we are writing emails, analysing data, generating code, creating presentations, or conducting research, AI has fundamentally changed how knowledge is created and consumed.

For higher education, this represents one of the biggest disruptions since the arrival of the internet.

Much of today’s conversation revolves around a simple question: Will AI replace educators?

I believe we are asking the wrong question.

The real question is whether universities can reinvent themselves quickly enough to prepare graduates for an AI-first world.

Having worked extensively with generative AI technologies, I see AI not as a replacement for education, but as an extraordinary opportunity to redefine it. From One-Size-Fits-All Learning to Personalized Education.

Traditional education has largely been built around standardized delivery: one lecturer, one classroom, one pace, and one curriculum for every student.

AI changes that equation.

For the first time, every learner can potentially have access to an intelligent learning companion available 24 hours a day. AI tutors can explain difficult concepts, generate additional practice exercises, adapt explanations to different learning styles, provide immediate feedback, and support students until genuine understanding is achieved.

Instead of asking students to adapt to education, education can finally adapt to students. This has important implications for accessibility, allowing high-quality learning experiences to reach individuals regardless of geography or socioeconomic background.

In many ways, AI has the potential to become the great equalizer in education.

Teaching Students How to Think; Not What to Memorize

At the same time, AI forces universities to rethink their educational philosophy.

When information is instantly accessible, memorization becomes less valuable.

Future graduates will be judged less by what they know, and more by how effectively they can solve problems, evaluate evidence, think critically, collaborate, communicate, and exercise sound judgement. This means assessment methods must evolve as well.

Rather than rewarding students for reproducing information that AI can generate in seconds, universities should increasingly emphasize authentic projects, real-world problem solving, teamwork, creativity, ethical reasoning, and applied learning. Ironically, AI may push higher education to become more human, not less.

Educators Are Becoming AI-Enabled Mentors

There is growing concern that AI will eventually replace lecturers. I see the opposite happening.

The educator’s role is becoming even more important; but it is changing.

Rather than acting primarily as transmitters of knowledge, educators are evolving into mentors, coaches, facilitators, and critical thinking partners who help students interpret information, challenge assumptions, and develop professional judgement.

To do that effectively, universities must invest heavily in AI literacy. Faculty need more than basic familiarity with AI tools. They must understand how these systems work, their limitations, their biases, and how they can be integrated responsibly into teaching, assessment, and research. AI literacy is rapidly becoming as fundamental as digital literacy was twenty years ago.

Preparing Graduates for an AI-First Workforce

Perhaps the biggest transformation is happening outside the classroom. Virtually every profession; from healthcare and finance to engineering, education, law, and government; is being reshaped by AI.

Graduates entering the workforce will collaborate with intelligent systems every day. This requires a new combination of technical and human capabilities. Understanding AI, data, automation, and digital technologies will become essential across disciplines. Equally important will be creativity, emotional intelligence, leadership, adaptability, ethical decision-making, and lifelong learning. The most successful professionals will not compete against AI. They will learn how to work alongside it.

Looking Ahead

The future university may look very different from today’s institution. Degrees are likely to become more modular and flexible, complemented by stackable micro-credentials that allow professionals to continuously update their skills throughout their careers.

Immersive technologies such as virtual and augmented reality will create richer learning experiences, while learning analytics will enable institutions to identify struggling students earlier and provide personalized support. Education will become increasingly global, connected, and lifelong.

The Human Advantage

Despite all these technological advances, one thing remains unchanged. Education has never been solely about transferring knowledge. It is about inspiring curiosity, building confidence, developing character, nurturing empathy, and preparing individuals to make meaningful contributions to society.

No algorithm can replace the inspiration of a great teacher or the mentorship that shapes a student’s future.

AI should not diminish the human element of education. It should amplify it.

The universities that thrive over the next decade will not be those that simply adopt AI tools. They will be those that successfully combine technological innovation with the uniquely human qualities that no machine can replicate. Because ultimately, the future of higher education is not about artificial intelligence. It is about human intelligence; enhanced by AI, guided by educators, and applied to solve the world’s most complex challenges.

Aurora Flow represents a significant technical milestone by solving one of the most challenging problems facing scale commercial deployment of video intelligence: the ability not just to analyse what is happening across a sequence of movement over time, but to understand whether further review or action is required. This extends Eluviant’s existing platform that has been proven in production for years; an unsupervised self-learning engine that flags genuinely unforeseen events and a vision language model (Aurora) that has sat inside the live alerting decision for the past 18 months.

By recognising movement patterns and contextualising behavioural sequences as they unfold, Aurora Flow unlocks use cases that were previously out of reach for organisations operating in the world’s most secure and sensitive environments. Equipment tampering, unsafe climbing in dangerous environments, and dangerous driving are just a few of the behaviours that can be more accurately identified as they happen using AI video understanding.

Rafik Lamri, Regional Director, META at Eluviant, said: “We believe Aurora Flow is a frontier AI model in surveillance and a step change in what video intelligence can deliver, moving beyond detection and into genuine understanding and evaluation of behaviours and actions in complex live environments. It addresses a challenge that traditional video analytics has struggled to solve efficiently: the ability to understand what is happening in the moment when a single still frame is not enough. Things like fighting, climbing and theft have typically required human eyes to detect them accurately – now we can help operators focus on what needs their urgent attention by putting AI into the alert decision.”

Founded in 2017, Eluviant has spent nearly a decade proving that existing surveillance infrastructure can be so much more than a security measure. Today, organisations across every sector are recognising the untapped operational value sitting within their existing camera networks, driving a growing market for enterprise-scale video intelligence which is expected to be worth $30bn by the end of the decade. In the Middle East, UAE and Saudi Arabia lead the video surveillance market which is valued at USD 4.3 billion¹.

“The Middle East’s enterprise-scale video intelligence market is experiencing high growth due to smart city initiatives, large-scale infrastructure projects and government-mandated security measures,” Lamri added. “With our advances in video AI, we are making it far easier for organisations to manage large numbers of camera feeds. Using Aurora Flow, we were able to reduce 4,000 potential events in a day to just 7 verified alerts – 0.2% of the total volume. But this technology isn’t just empowering operators to reduce control room workloads and respond more quickly to incidents; it is enabling them to harness their existing footage as a rich data source for decision-making across their operations.”

Formerly IntelexVision, the company has also announced a full rebrand, and today steps forward with a new name and brand identity as Eluviant that reflects both the rapid evolution of video intelligence in the age of AI and the company’s ambition to continue pushing the boundaries of what’s possible in computer vision for video surveillance.

Eluviant’s technology is trusted across a range of demanding operational environments, from retail and critical infrastructure to smart cities. Whether supporting loss prevention, anomaly detection or rapid incident response, its solutions are built to meet the needs of organisations that require reliable, scalable AI across complex, multi-camera environments in real-time.

Eluviant works with enterprise customers representing some of the largest organisations in their sectors, and more than 60 technology and commercial partners. With over 250 deployments across five continents, Eluviant customers include Airbus, DP World, Prosegur and Vodafone.

The Pharmaceutical Research and Manufacturers Association in Gulf (PHRMAG), the region’s leading innovative biopharmaceutical research companies, and the Authority of Social Contribution – Ma’an, the Abu Dhabi Government’s official channel to receive social contributions, has announced a strategic collaboration aimed at enabling access  for oncology and rare disease patients most in need to innovative solutions and modern treatments, within an integrated framework aligned with the national health insurance system.

The collaboration brings together public and private sector to address a pressing issue, with a shared commitment to providing long-term impact, and continued care delivery for targeted patients. It further strengthens family stability resilience when facing health challenges, while contributing to broader social cohesion and supporting the objectives of the Year of the Family.

H.E. Abdullah Al Ameri, Director General of the Authority of Social Contribution – Ma’an, said:
“Our collaboration with PHRMAG represents a strategic step towards establishing a long-term and integrated healthcare system for oncology and rare disease patients in Abu Dhabi, reflecting our commitment to supporting key social priorities that matter to community members, particularly in the healthcare sector.

“At the Authority of Social Contribution – Ma’an, we are committed to directing social contributions and efforts towards creating tangible impact in the lives of the community members, including patients with complex medical conditions. This collaboration reflects a model of integrated roles between the public and private sectors, by leveraging the expertise of companies within ‘PHRMAG’ and unifying their efforts through the Authority’s platform, which ultimately contributes to enhancing access to specialised healthcare services, improving quality of life, and reinforcing the values of shared responsibility and social solidarity.”

The Authority will oversee allocation of contributions in line with agreed project milestones. A dedicated committee will also be established to monitor the initiative’s progress through monthly meetings aimed at assessing developments, providing the necessary strategic guidance, and reviewing progress achieved, ensuring effective collaboration and continuous knowledge exchange between both parties throughout the duration of the project.

H.E. Mohammed Abdullah Al Awadi, Executive Director of the Health System Financing Regulation Sector at the Department of Health – Abu Dhabi, said: “We are thrilled to witness the collaboration between the Authority of Social Contribution – Ma’an and PHRMAG, which will contribute to supporting our mission of ensuring accessible, world-class healthcare for community members, accelerating innovation and research within the healthcare sector, and advancing the early detection and treatment of rare diseases. This collaboration contributes to building a healthier society with longer, better wellbeing for individuals, while further strengthening Abu Dhabi’s position as a leading destination for innovation in life sciences.”

Sameh El Fangary, Chairman of PHRMAG, added: “As an industry association representing innovative pharmaceutical companies, we are committed to partnering with Abu Dhabi’s health and social authorities to ensure continuity of patients having access to treatment and care when needed. This collaboration with the Authority of Social Contribution – Ma’an reflects our shared ambition to co-create sustainable solutions that improve access to high-quality medical care for those who need it the most from oncology and rare disease patients.”

The partnership reflects Abu Dhabi’s ongoing commitment to strengthening collaboration between the public and private sectors, as an effective approach to addressing complex healthcare challenges.

-END-

About the Authority of Social Contribution – Ma’an

Established in 2019 by the Department of Community Development Abu Dhabi (DCD), The Authority of Social Contribution – Ma’an is the Abu Dhabi government’s official channel to receive social contributions, dedicated to uniting community efforts and fostering a culture of giving by collecting contributions, directing them towards social priorities, empowering social enterprises, and promoting volunteering to build a cohesive community.

The Authority supports projects that address social priorities in health, education, environment, infrastructure, and social services, aiming to nurture a collaborative and active community by connecting individuals and entities in the public, private, and civil society spheres to support their communities.

Contributions made to the Authority of Social Contribution – Ma’an are transparently deployed in full to social projects led by key partners meaning benefactors can maximise the impact their funds have in driving community engagement and providing access to essential resources, programmes, and funding for organisations across Abu Dhabi to achieve their Corporate Social Responsibility and sustainable development goals.

By Amjad Alqaqaa – Vice President – MEAI

Power sectors around the world are undergoing rapid transformation. Digital technologies, advanced materials, and the shift towards lower-carbon energy are reshaping how power plants and critical infrastructure are designed, operated, and maintained. Yet one persistent challenge continues to hold the sector back: a shortage of people with the right engineering and technical skills.

As the UAE continues to advance its ambitions as a leading hub for innovation and technology, there is an increasing need to strengthen and future-proof STEM capabilities to keep pace with evolving industry demands. According to a report by STEM workforce consultancy SThree, 40% of STEM professionals in the UAE believe that upskilling and reskilling are the most effective ways to boost productivity and competitiveness. While more than a third (32%) point to skills shortages as a barrier to productivity, highlighting a clear gap between workforce capabilities and industry needs.

Additionally, data from the Hays 2026 US Salary & Hiring Trends Guide indicates that companies in the UAE are starting to slow down recruitment and instead are investing in the skills of their existing workforce, with around 42% of employers prioritising upskilling over hiring.

Research from LinkedIn also suggests demand for green skills is rising much faster than supply, highlighting a widening gap between the skills needed for the energy transition and the talent currently available in the workforce.

For power generation companies, this is more than a recruitment issue. Skills shortages can impact equipment reliability, delay maintenance programmes, and slow the deployment of new technologies. In a sector where uptime, safety, and efficiency are critical, having the right expertise in place is essential.

At the same time, interest in STEM subjects among young people has fallen in recent years.  This weakens the future talent pipeline. This means companies must do more to attract and develop STEM talent.

Showing young people what engineering looks like today

One of the challenges is perception. Many young people still associate engineering with traditional industrial roles, rather than the highly advanced, technology-driven careers available today.

Today’s engineers work with advanced digital tools, automation systems, and real-time monitoring technologies. In the power sector, they help keep turbines, pumps, and other critical systems running efficiently. They also work on challenges linked to sustainability, energy efficiency, and emissions reduction.

To address this gap, employers must play a more active role in educating emerging talent about the career opportunities in the sector. That means working more closely with schools, colleges, and universities to showcase the wide range of careers available across engineering and energy.

Partnerships between industry and academia play an important role here. For example, John Crane works closely with the University of Sheffield to support research and PhD programmes in areas such as materials science and engineering. Collaborations like this help connect academic research with real industrial challenges and encourage more students to consider careers in engineering.

These partnerships also help ensure that new research translates into practical solutions that can support industries such as power generation.

Why apprenticeships matter

Alongside academic pathways, apprenticeships are another key way to attract new talent into engineering.

They offer a practical, accessible route into engineering, allowing individuals to gain hands-on experience while working towards recognised qualifications. For employers, apprenticeships provide an opportunity to develop skills aligned to real operational needs, from maintenance and reliability engineering to digital and software capabilities.

But apprenticeships are not only for new recruits. They can also help people who are already in work develop new skills. Programmes linked to areas such as leadership, project management, and digital technologies allow employees to adapt as roles change and technology evolves.

This matters because the skills challenge is not only about bringing new people into the sector. It is also about helping the existing workforce build the capabilities needed for the future.

Building the right skills through training partnerships

Developing a skilled workforce requires more than internal programmes alone. Strong partnerships with external training providers are essential to ensure employees gain the specialist knowledge needed in highly technical environments.

Working with a network of training providers enables organisations to deliver structured learning alongside on-the-job experience. This approach ensures that training remains aligned with real operational challenges, including maintaining equipment reliability, improving efficiency, and meeting evolving safety standards.

Reaching a broader talent pool

Engineering companies need to widen their outreach and look beyond traditional recruitment channels. This includes engaging with students earlier and encouraging people from different backgrounds to consider technical careers.

In addition, requalification programmes are increasingly important in some regions. For example, in the Czech Republic, targeted requalification initiatives are helping individuals transition from other industries into engineering roles, providing a practical route to address skills shortages while bringing valuable experience into the sector.

Ensuring training programmes cater to a wide range of people with varying levels of experience can upskill new and existing workers and build a healthier talent pipeline. Providing that support is an investment that helps create a stronger, more resilient workforce in the long term.

Building the workforce of the future

The power sector plays a central role in driving the global energy transition. In the Middle East, this transition is expected to drive demand for a wide range of engineering roles, particularly in renewable energy, grid infrastructure, and related technologies, highlighting the need for targeted training and workforce development programmes to equip both new entrants and existing workers with relevant technical skills.

Engineers and technicians will be needed to maintain power plants, improve equipment performance, and develop new energy technologies. But these goals will only be possible if the industry has access to the right skills.

To achieve this, companies must think differently about talent. Strengthening collaboration with educators, improving outreach to diverse talent, and offering practical training routes such as apprenticeships all play an important role in addressing the STEM skills gap.

Apprenticeships alone will not solve the skills gap. But when combined with research partnerships and targeted workforce development, they can play a major role in rebuilding the STEM talent pipeline. By investing in people and skills today, the power sector can build the workforce it needs to support a more reliable and sustainable energy system for the future.

The AI revolution is not on the horizon; it is already transforming how we work, solve everyday problems, and interact both with one another and with technology. From generative models to agentic systems capable of disrupting entire industries, artificial intelligence has advanced at a pace that few institutions, businesses, or governments are fully prepared for. What once felt like a distant technological possibility has become a structural force shaping labour markets and economies. As a result, one of the most pressing questions facing societies is no longer whether AI will change the world, but whether it will make it fairer. Increasingly the answer depends not only on the technology itself, but on the choices organisations and governments make about how its benefits are shared.

AI has the potential to unlock unprecedented prosperity. Yet history shows that technological revolutions rarely distribute their rewards evenly. Without deliberate intervention, the benefits of AI risk concentrating in the hands of a small number of large technology firms, highly skilled professionals and capital owners. This pattern has already emerged in earlier waves of digital transformation, where wealth and opportunity accumulated disproportionately in regions best positioned to adapt. For AI to foster equality rather than widen disparity, policymakers must treat inclusion as an ex-ante design principle rather than an ex-post correction.

The first crucial step for achieving fairness is improving the data that AI systems rely upon. Algorithms are only as representative as the information used to train them. When datasets exclude marginalised or underrepresented communities, AI risks reinforcing existing biases. Organisations and governments developing AI algorithms should prioritise collecting data from communities historically overlooked in policy design, such as rural populations, low-income groups, minority communities and those outside the formal labour markets. More inclusive datasets lead to fairer systems, more effective public services and policy decisions that better reflect the realities of entire populations, rather than just their most visible segments.

Another equally important aspect is how governments distribute the productivity gains and wealth generated by AI into broader societal benefits. Different regions are experimenting with alternative approaches. In parts of the Middle East, including the United Arab Emirates, economic gains from technological advancement are often channelled through state-led investment strategies rather than relying solely on traditional taxation and redistribution mechanisms. While VAT and other taxes exist, governments often reinvest a significant share of national income derived from natural resources and state-owned enterprises directly into infrastructure, public services, education and economic diversification. This approach builds long-term national capability by funding human capital development, strengthening digital infrastructure and fostering new sectors that create employment and opportunity.

Such strategies highlight an important principle: AI benefits do not need to be redistributed after inequality has emerged. They can be embedded in development strategies from the outset. By investing in education, digital skills and access to technology, governments expand the number of people able to participate in the AI ecosystem rather than merely compensate those left behind. China, for example, has made substantial investments in AI education and research capacity, recognising human capital as central to technological leadership. Every year 100,000 selected teenagers are funnelled into elite science talent streams across top high schools. These “genius classes” systematically train students to excel in international maths, physics, chemistry, biology and computer science competitions.

The pace of the AI revolution makes this challenge more urgent than previous technological transitions. Earlier industrial transformations unfolded over decades, allowing societies time to adapt institutions and labour markets. AI development in recent years has gained pace. Breakthroughs that once took years are now emerging within months, with new capabilities rapidly spreading across sectors from healthcare diagnostics and financial analysis to logistics and defence industries. This acceleration has been further intensified by the present-day AI race to achieve Artificial General Intelligence (AGI), amid a widespread belief that the first government to reach this milestone will gain a decisive strategic advantage. Organisations at the forefront of AI development are reluctant to slow for fear of falling behind geopolitical or commercial rivals. Meanwhile, many governments are hesitant to introduce AI regulation, concerned that premature constraints could hinder innovation and weaken their competitiveness in the pursuit of AI leadership.

However, the path forward requires a global perspective. While governments should encourage innovation, they must also recognise that AI technology will diffuse across borders. Hence governments worldwide should collaborate towards a global AI governing body, or at the very least, agree on minimum safety and fairness standards for AI deployment. The EU AI Act provides an important foundation by identifying unacceptably high-risk AI applications that should be prohibited. When forming such regulatory frameworks, governments should seek guidance from leading AI scientists to ensure they fully understand where the principal risks originate. Indeed, many prominent experts in the field argue that regulation is failing to keep pace with AI innovation.

Allowing AI technology to evolve without placing guardrails in place early risks embedding structural inequalities, particularly in labour markets, education access and capital distribution. Ultimately, the debate about AI and inequality is not primarily about algorithms; it is about governance. Technology reflects the priorities of the societies that deploy it. If policymakers treat AI purely as an engine of leadership and economic growth, its benefits will likely accrue to those already best positioned to capture them. But if AI development is guided by a clear commitment to inclusion through better data, wider access and sustained investment in human capital, it has the potential to expand opportunity on a global scale. As AI reshapes labour markets, workers will need opportunities to develop capabilities that complement intelligent systems rather than compete directly with them. Access to AI infrastructure, computing resources, data and digital connectivity must not be confined to a small group of corporations or wealthy regions.

The direction of the AI revolution is not predetermined. The question is not whether AI will transform our world, but whether governments and institutions will act quickly and thoughtfully enough to ensure that its benefits are broadly shared. In the race to build increasingly powerful systems, equal attention must be given to building the social and economic frameworks that will ensure the future is genuinely fair.

Across the GCC, AI is becoming more operational. The conversation has moved beyond whether organisations are testing AI and toward how deeply these systems are being embedded into daily work. McKinsey’s finding that 84% of GCC organisations have adopted AI in at least one business function shows the region’s strong momentum, but the more important shift is where this technology is now creating measurable value.

AI is beginning to operate inside real enterprise workflows, where productivity, cost, speed, service quality, and governance can be measured. This practical shift means AI is being judged less by novelty and more by whether it can reduce manual work, improve response times, and support better execution across organisations.

Where AI is being deployed

AI deployment is gaining traction in structured, high-volume functions where it can remove this coordination burden and give employees more capacity for skilled output. Asana’s research has found that around 60% of time is spent on “work about work,” such as chasing updates, attending unnecessary meetings, and switching between tools.

Customer service teams are using AI for automated query handling, routing, escalation management, and multilingual support. Operations teams are applying AI to order processing, workflow coordination, and SLA monitoring.

In HR, AI is supporting CV screening, interview scheduling, and onboarding orchestration. In finance, it is being used for invoice processing, reconciliation, and anomaly detection. Sales teams are also applying AI to lead qualification, follow-ups, CRM hygiene, and pipeline updates.

Regional governments are also preparing the workforce for this reality. Digital Dubai recently launched the AI Workforce Transformation Program, known as AI+, to help train 50,000 government employees for an AI-ready workforce.

Three phases of AI workforce evolution

AI use across the workforce can be understood in three phases. First, AI acts as an assistant through copilots, chat interfaces, summarisation, drafting, search, and advisory tools that improve individual productivity. Second, AI becomes an operator, completing defined tasks across CRM, HR, finance, customer service, and operations systems within controlled boundaries. Third, AI develops into a workforce layer, where systems are assigned roles, KPIs, access rights, escalation pathways, and governance controls. At this stage, Autonomous AI Teams operate as governed digital employees, helping structure, assign, monitor, and improve work.

How mature AI deployments operate

AI is not replacing entire jobs. It is restructuring work by taking over repetitive tasks within roles. Human teams are shifting toward oversight, exception handling, decision-making, escalation management, and quality control.

Autonomous AI Teams operate as coordinated systems rather than standalone models. They support humans through role-based actions with defined responsibilities, structured access to enterprise systems, clear decision boundaries, controlled autonomy levels, human escalation pathways, performance metrics, auditability, and governance.

From tools to workforce infrastructure

Before scaling autonomous AI systems, executives need clear visibility into decision-making, accountability, risk controls, and human intervention points. Trust grows when productivity gains are measurable and governance is visible. IBM research shows that 77% of UAE senior leaders have already seen significant productivity gains from AI, which reflects growing confidence in its operational value.

Across Shaffra deployments, Autonomous AI Teams have contributed to more than 2 million manual work hours saved monthly across operational workflows. Organisations have reported up to 80% reductions in operational costs, customer service teams can manage up to five times more queries, and HR recruitment cycles that previously took weeks can be reduced to hours.

The future workforce layer

The GCC has a strong appetite for AI adoption, but many organisations still need to redesign workflows and overcome fragmented legacy systems before AI teams can function as part of daily operations. Research showing that 94% of UAE data leaders lack complete visibility into AI decision-making processes reinforces why explainability, governance, and workflow design must develop alongside deployment.

The next phase of AI is about building a governed workforce layer where humans and Autonomous AI Teams execute together with clarity, accountability, and valuable impact.

Contributed by Jeff Jacob, Regional Business Team Lead – ISBG at ASUS Middle East & Africa

AI Creative Ecosystems Are Transforming Professional Workflows from Technical Execution to Intent-Driven Innovation

For decades, professional creativity was defined by a precise, hard-earned technical mastery. To be a digital creator involved understanding the underlying mechanics of software: knowing which shortcut keys to press, how to modify complicated codes, and how to adjust render engines frame by frame manually. Designers studied sophisticated software interfaces. Editors memorised keyboard shortcuts. Architects explored multiple layers of modelling systems. Filmmakers designed workflows around rendering pipelines. But the limits of the digital interface restricted creativity. The creator’s thoughts generated an idea, but their hands spent hours, days, or weeks converting that vision into a language that the computer was able to understand.

Today, that equation is fundamentally changing. Generative AI is ushering in a new era in which the focus shifts from execution to intention. It is changing the laws of professional creativity, propelling us from manual digital workflows to the era of intent-driven innovation.

When an efficient AI model can create complex codes, display hyper-realistic settings from a text prompt, or isolate audio frequencies in seconds, technical project execution becomes commoditised. The fundamental value of the human creator centres on intent, the ability to direct, curate, refine, and orchestrate complicated visions. The world is transitioning from one in which creators are valued for how they code or compile to one in which they are appreciated for what they aim to build and why it is important.

This shift represents a significant challenge for conventional hardware philosophy. For years, the computing industry saw professional machines through a strictly quantitative lens. Traditional parameters for evaluating creative laptops and workstations included processing power, graphics performance, display accuracy, storage capacity, and the most aggressive thermal cooling. These factors remain important, but in an intent-driven environment, passive hardware is no longer enough. If the creative process is to become an ongoing, fluid interaction between human intent and artificial intelligence, the technology must evolve. It must grow into an intelligent partner rather than a mere productivity tool.

This is precisely where the concept of technological design must pivot, a shift that many brands anticipated with the expansion of their AI art ecosystems. Rather than seeing AI integration as a superficial software tool, when it is developed as an intelligent, creative collaborator, it bridges the gap between raw computing capacity and human intuition.

A single campaign today may involve long-form video, short-form social assets, AI-generated photography, interactive experiences, 3D content, spatial design, and linguistic adaptations all at the same time. This requires a whole new level of physical and digital collaboration. The modern hardware anticipates the creator’s next action by using dedicated Neural Processing Units, tailored AI workflows, and fully connected software ecosystems. It optimises system resources based not only on raw CPU load, but also on the cognitive needs of an AI-powered pipeline. Physical control interfaces are no longer just shortcuts for legacy software sliders; they are physical extensions of intent, allowing creators to dynamically scrub through AI-generated iterations, manipulate parameters in real time, and maintain a tactile connection to an increasingly non-linear process.

Furthermore, this evolution alters the perspective on the mobility of professional talent. Intent-driven creativity thrives on cross-disciplinary exploration. A filmmaker may need to create architectural backgrounds on set, or a designer may need to run localised, big language models during a client pitch to iterate on branding concepts in real time. By compressing massive AI computing capabilities into extremely sophisticated, colour-accurate, and portable forms, the modern ecosystem assures that the studio is no longer confined to a single desk.

Yet, despite the excitement around AI, a major misconception must also be addressed. Generative AI does not replace creativity. It reframes where human value fits into the creative process. Historically, technical expertise has been a barrier to entrance. Having the ability to master complex structures determined who could participate in creative industries. AI lowers those barriers, but it also emphasises the importance of distinctively human skills such as judgment, taste, narrative, emotional intelligence, cultural understanding, and strategic thinking.

This is why the discussion on AI-powered creativity must extend beyond software. Infrastructure matters. Devices matter. Ecosystems matter. Professionals driving the future of creative industries will require technology that can enable sophisticated AI-native tasks while maintaining reliability, portability, security, and precision. The brands that recognise creativity as a human experience enhanced by intelligent technology will be the ones to succeed in the next phase. Every technology leader must now face the same question: in a future where AI can generate practically anything, how can we empower humans to create something meaningful?

The change of professional creativity is a story of structural emancipation rather than human replacement. As generative AI continues to demystify the technical aspects of execution, the primary focus returns to where it always belonged: the depth of human insight and the precision of artistic vision. The future of professional creation belongs to those who can master the art of intent.

There is a line that gets repeated at every tech conference in Dubai, in every government briefing, and across most pitch decks: the UAE is building the future. Artificial intelligence is projected to contribute $96 billion to the UAE’s GDP by 2031, according to PwC and corroborated by the UAE’s own National AI Strategy. The country has invested AED 543 billion in AI since 2024 alone, as confirmed by Omar Sultan Al Olama, the UAE’s Minister of State for Artificial Intelligence. And according to Microsoft’s AI Diffusion Report for Q1 2026, the UAE has become the first country in the world to cross the 70 percent threshold for AI tool adoption among its working-age population. 

These are not vanity metrics. They reflect a deliberate national strategy that has positioned the UAE as one of the world’s most ambitious AI markets and laid the foundations for long-term technological leadership. Yet despite that progress, a disconnect is emerging between the country’s AI ambitions and the day-to-day reality of the people building products within the ecosystem.

The Gap Between AI Infrastructure and AI Adoption

Much of the discussion around AI in the UAE has focused on infrastructure, whether that is sovereign AI models, data center investments, national strategies, or the capital required to support them. These are all essential components of a successful AI ecosystem. However, infrastructure alone does not create products. Founders, developers, and businesses still need the tooling layer that sits between AI capability and real-world execution.

This is precisely the challenge a new generation of AI-native development platforms is trying to solve: embedding software engineering best practices directly into the building process so that users can focus on the product rather than mastering prompt engineering.

One of the clearest examples of this challenge is language. Arabic is spoken by more than 400 million people across 22 countries. Yet developers across the region still rely heavily on tools that were primarily designed for English-speaking users. Researchers at Nature Middle East have previously highlighted how the relative lack of robust Arabic language models continues to create limitations around linguistic nuance, dialects, and cultural context.

At the same time, the developer tools, AI coding assistants, and product-building platforms that define the modern software stack were largely built around Western markets and workflows. They assume a particular type of user, a particular language, and a particular development environment. For many builders in the GCC, those assumptions become a source of friction that compounds throughout the product development lifecycle.

A founder in Dubai building a fintech product for Emirati consumers has to work through documentation written in English, prompts that perform better in English, and interfaces that treat right-to-left text as an afterthought.

The challenge is not that these tools fail outright. Rather, they introduce small points of friction throughout the development process that compound over time, affecting productivity, iteration cycles, and ultimately product delivery. Over time, that friction compounds across teams, product cycles, and entire businesses, becoming the difference between shipping and not shipping.

We’ve Seen This Before

This pattern plays out clearly in payments, an industry where many founders across the region have spent much of their careers. The UAE has built a sophisticated financial infrastructure, but for years, the tooling that sat on top of that infrastructure, the APIs, developer documentation, and integration frameworks, was largely oriented toward Western payment methods, Western card schemes, and Western compliance frameworks. Local founders had to build workarounds. Some of those workarounds were innovative, but workarounds are not a strategy. More often than not, they are a sign that the underlying stack was never designed for the people using it.

The same lesson applies to AI. Infrastructure creates possibilities, but it does not automatically create innovation. Innovation happens when builders can move quickly, efficiently, and confidently on top of that infrastructure. If the tools developers use every day are not designed for the realities of this market, then the UAE’s AI ambitions risk being partially realized by people working around their environment rather than with it.

What Comes Next

There is a real opportunity here to address the gap between the infrastructure the UAE has built and the tools its founders, developers, and businesses actually need.

The UAE has already demonstrated that it can build AI infrastructure at scale. It has invested heavily in research, talent, adoption, and national AI initiatives, creating one of the most ambitious AI ecosystems anywhere in the world.

The next phase of that strategy is not simply building larger models or attracting more capital. It is ensuring that the people responsible for creating products, launching companies, and deploying AI solutions have the tools they need to succeed. It also means reducing dependence on a small number of external AI providers. As AI becomes embedded in critical business and government workflows, questions around privacy, data governance, and long-term resilience become increasingly important. Building capable regional AI ecosystems is not simply about innovation; it is about ensuring that organisations can deploy AI with greater control, confidence, and sovereignty.

The countries that win the next decade of technology are not necessarily the ones that spend the most money. They are the ones where the people doing the building have the right tools for the job.

Infrastructure creates possibility. Tooling turns possibility into innovation. The next phase of the UAE’s AI story will be defined by how effectively it enables the people doing the building.