Tech Features
THE YEAR AI WENT MAINSTREAM
Talal Shaikh, Associate Professor, Heriot-Watt University Dubai
In 2025, artificial intelligence crossed a threshold that had little to do with model size or benchmark scores. This was the year AI stopped feeling like a product and started behaving like infrastructure. It became embedded across work, education, government, media, and daily decision-making. The shift was subtle but decisive. AI moved from something people tried to something they assumed would be there.
From my position at Heriot-Watt University Dubai, what stood out most was not a single breakthrough, but a convergence. Multiple model ecosystems matured at the same time. Autonomy increased. Regulation caught up. Infrastructure scaled. And nations began to treat intelligence itself as a strategic asset.
From one AI story to many
For several years, public attention clustered around a small number of Western firms, most visibly OpenAI and Google. In 2025, that narrative fractured.
Google’s Gemini models became deeply embedded across search, productivity tools, Android, and enterprise workflows. Their strength lay not only in conversation, but in tight coupling with documents, spreadsheets, email, and live information. AI here was designed to live inside existing habits.
At the same time, Grok, developed by xAI, took a different path. With real-time access to public discourse and a deliberately opinionated tone, it reflected a broader shift in design philosophy. AI systems were no longer neutral interfaces. They carried values, styles, and assumptions shaped by their creators. That diversity itself was a sign of maturity.
By the end of 2025, users were no longer asking which model was best. They were choosing systems based on fit, trust, integration, and intent.
The rise of agentic AI
If generative AI defined earlier years, agentic AI defined 2025.
In 2023, most people experienced AI as a chatbot. You asked a question, it replied, and the interaction ended. In 2025, that interaction became continuous. An agent does not simply respond. It reads context, sets sub-goals, uses tools, checks results, and decides what to do next.
A chatbot drafts an email. An agent reads the full thread, looks up past conversations, drafts a response, schedules a meeting, and follows up if no reply arrives. A chatbot explains an error. An agent runs tests, fixes the issue, commits code, and opens a pull request.
This transition from response to agency turned AI from a helpful assistant into an operational participant. It also shifted risk. As systems gained the ability to act, questions of oversight, auditability, and failure containment moved from academic debate into everyday management.
A shift I saw first in the classroom
This change was not abstract for me. I saw it unfold directly in my classrooms.
Only a short time ago, many students dismissed AI-assisted coding with a familiar phrase: “It hallucinates.” They were not wrong. Early tools often produced code that looked correct but failed logically. Students learned quickly that blind trust led to wasted hours.
In 2025, that language faded.
Students now approach AI differently. They no longer ask whether the model is correct. They ask why it produced a solution, where it might fail, and how to constrain it. In one recent lab, a student debugging a robotics control pipeline did not reject the AI output after a failed test. He used it to generate alternative hypotheses, compared execution traces, and isolated the fault faster than traditional trial and error would allow.
At one point, a student stopped and said, “It is not hallucinating anymore. It is reasoning, but only if I reason with it.”
That sentence captures 2025 better than any benchmark.
From skepticism to supervision, in industry
The same shift is visible among our alumni now working in software engineering, fintech, data science, and robotics. Several who once warned juniors not to trust AI code now describe it as a first-pass collaborator. They use it to scaffold architectures, surface edge cases, and speed up documentation, while keeping final judgment firmly human.
The concern is no longer hallucination. It is over-reliance.
AI moved from being treated as an unreliable shortcut to being treated as a junior colleague, fast, useful, and fallible, requiring supervision rather than dismissal.
Sovereign AI, two models of power
One of the clearest signals that AI went mainstream in 2025 was the divergence in how regions approached it.
In much of the West, the year was framed as a corporate contest. Product launches, market share, and valuation battles dominated headlines. Innovation moved fast, driven by competition between private firms.
In the Middle East, and particularly in the UAE, the framing was different. AI was treated as national infrastructure.
The UAE’s investment in sovereign models such as Falcon and Jais reflected a belief that intelligence, like water or electricity, must be secured, governed, and trusted within borders. This was not about isolation. It was about resilience, data sovereignty, and long-term capacity. Dependence without control came to be seen as a strategic risk.
In 2025, this idea matured. Sovereign AI stopped being a slogan and became a planning principle. While the West debated which company would win, the UAE focused on ensuring that the capability itself remained accessible, accountable, and locally anchored.
When culture embraced AI
Another signal of mainstream adoption arrived from outside the technology sector.
The strategic alignment between The Walt Disney Company and OpenAI marked a moment when AI entered the core of global culture. Disney does not adopt technologies lightly. Its value lies in storytelling, world-building, and intellectual property sustained over decades.
This move was not about automating creativity. It was about scale and continuity. Modern story worlds span films, series, games, theme parks, and personalised digital experiences. Managing that complexity increasingly requires intelligent systems that can assist across writing, design, localisation, and audience interaction.
When a company whose primary asset is imagination treats AI as foundational, it signals that intelligent systems are no longer peripheral to creative industries. They are becoming part of how stories are built, maintained, and experienced. In that sense, 2025 marked the moment AI became cultural infrastructure, not just technical tooling.
Work changed quietly
Another sign of mainstreaming was how little drama accompanied adoption. Professionals stopped announcing that they were using AI. They simply expected it.
Developers assumed code assistance and automated testing. Analysts assumed rapid summaries and scenario modeling. Marketers assumed content generation and performance analysis. Students assumed access, but outcomes increasingly depended on how well they could guide, verify, and critique what AI produced.
This created a new divide. Not between technical and non-technical people, but between those who could reason with AI and those who delegated thinking to it.
What this means for universities
For universities, 2025 closed the door on treating AI as optional.
Every discipline now intersects with intelligent systems. Engineers must understand ethics and regulation. Business graduates must understand automation and decision support. Creative fields must grapple with authorship and originality. Researchers must design methods that remain valid when AI is part of the workflow.
At Heriot-Watt University Dubai, this pushes us toward assessment that rewards reasoning over polish, and education that teaches students not just to use AI, but to supervise it.
The real shift
AI went mainstream in 2025, not because it became smarter, but because society reorganised around it. Multiple models coexisted. Agents acted with growing autonomy. Nations planned for sovereignty. Culture adapted. Classrooms recalibrated trust.
The next phase will not be defined by faster models alone. It will be defined by judgment.
That is the quieter, more demanding challenge left to us after the year AI went mainstream.
Tech Features
FIVE WAYS UAE WORKFORCE PLANNING IS CHANGING IN 2026
The UAE is entering a more complex phase of workforce growth. Hiring momentum remains strong, with the country recording a Net Employment Outlook of 60% for Q2 2026, placing it among the strongest employment markets globally. Yet the main challenge for companies is whether their employment structures, immigration planning, compliance systems, and HR leadership can support growth at scale.
Aethra Advisory, a UAE-based global hiring strategy and mobility architecture firm, outlines five shifts companies should prepare for as compliance, immigration, and HR become more connected.
HR is becoming workforce architecture
HR can no longer be treated as an administrative function focused only on recruitment, onboarding, contracts, and employee relations. In 2026, HR leaders are expected to help design the workforce model itself. That includes where a company hires, which employment structures it uses, how talent moves across borders, and where compliance risk may appear. A hiring decision is now linked to visa eligibility, payroll structure, sponsorship, worker classification, relocation timelines, and long-term operating needs.
Many companies still hire first and address structure later. The consequences often emerge months afterwards, when employment models become costly, difficult to manage, or unable to support growth.
AI is entering recruitment and workforce planning
Companies are using AI to screen CVs, match candidates to roles, automate outreach, schedule interviews, assess skills, and generate workforce insights. Used well, it can make hiring faster and more consistent, especially in high-volume recruitment environments.
A 2025 field experiment involving around 37,000 applicants found that 54% of candidates assessed through an AI-assisted recruitment pipeline passed the final human interview, compared with 34% of candidates assessed through a traditional pipeline. However, AI does not replace human judgement. Companies still need clear hiring criteria, documented decision-making, oversight and an understanding of how recommendations are generated and reviewed.
Companies are moving into global talent systems
Many companies make the UAE a base for regional and international expansion due to its business-friendly policies and strategic location. Local companies are hiring across borders, global firms are entering the UAE, and leadership teams are being built across multiple jurisdictions. In fact, the cross-border workforce and migration solutions market is projected to reach $11.37 billion by 2033, growing at an annual rate of 11.8%.
For employers, hiring can no longer be treated as a local HR process. Companies must make deliberate decisions about how they enter new markets and engage talent. Some may use an Employer of Record to hire quickly, while others may establish a local entity to gain greater control. In some cases, relocating and sponsoring employees will be the right approach or engaging contractors or building a longer-term market entry structure may be more suitable. Each route carries different implications for cost, compliance, operational control, and future scalability.
Employment models are becoming more hybrid
As companies scale, informal arrangements become harder to manage. A single UAE business may now have locally sponsored employees, remote workers, consultants, contractors, relocating workers, etc. This gives companies more flexibility, but also creates operational risk when obligations are not understood from the start. Worker classification, payroll treatment, benefits, visa eligibility, contract terms, management control, and termination rules can vary depending on how a person is engaged. Employers need clear structures defining employment status, work location, applicable law, and how each relationship is governed.
Regulation is influencing hiring decisions
In the UAE, hiring depends on more than finding the right candidate. Companies need the right regulatory setup before they can move quickly. Licensing gaps, unclear sponsorship routes, incomplete documentation, or a mismatch between the role and the employment structure can still delay a strong hire.
This makes compliance and immigration planning an early hiring priority. Companies should understand the requirements before entering a market, confirming a hire, or committing to a relocation timeline.
Tech Features
Networks Must Evolve Before AI Can Scale
Rohit Chowdhary, Head of Advanced Consulting Services at Nokia, sat down with The Integrator to share insights into the company’s vision for enabling the AI supercycle. He outlined how Nokia’s end-to-end portfolio spans everything from AI-ready connectivity and energy-efficient 800G data centre networking to intelligent, self-optimising home Wi-Fi experiences powered by AI.
A key focus of the discussion was Nokia’s shift from strategic advisory to real-world execution through its dedicated Automation Excellence Practice, helping operators translate ambitious transformation roadmaps into measurable outcomes. The conversation also highlighted the growing importance of integrated, intelligent and secure networks that can support rising AI workloads, eliminate infrastructure bottlenecks and unlock tangible business value, while maintaining the highest standards of security, privacy and resilience
Could you begin by telling us about your role at Nokia and the journey that brought you here?
I lead Nokia’s Advanced Consulting Services business across Europe, the Middle East and Africa. My journey with Nokia spans nearly seventeen years, beginning at a time when consulting was largely focused on network transformation initiatives. Over the years, I have worked closely with operators around the world on transformation programmes, analytics adoption, customer experience management and digital modernization.
As the industry evolved, so did our consulting focus. Following the Nokia and Alcatel Lucent merger, we established what is today known as Advanced Consulting Services. The organization now spans several domains, including security, business monetization, cloud and technology transformation, autonomous operations, and data and AI.
More recently, we launched an Automation Excellence Practice. The idea was simple. Customers often appreciated our strategic blueprints but needed practical expertise to implement them. Today, we have specialized engineers who combine telecom expertise, AI capabilities and software development skills to turn strategic visions into real automation pipelines, AI-driven workflows and production-ready use cases. Our role is to help customers move from concept to measurable business outcomes.
Nokia is often associated with connectivity, but the company is increasingly talking about AI readiness. How does Nokia’s infrastructure portfolio support this transition?
AI is creating what we describe as an AI supercycle. It is transforming everything from data centres and cloud infrastructure to network architectures and edge computing. Supporting this shift requires a complete ecosystem rather than isolated technologies.
Nokia’s portfolio addresses this across multiple layers. On the network side, we continue to innovate in radio technologies, including AI-RAN capabilities developed alongside strategic partners such as Nvidia. We also have a strong optical networking and IP portfolio that enables the high-capacity connectivity required between data centres, edge locations and cloud environments.
One area that excites me is our innovation in data centre networking. We are introducing highly efficient coherent optical technologies and advanced switching platforms that significantly reduce infrastructure footprints while improving performance and energy efficiency. These innovations are becoming increasingly important as organizations invest in AI factories, AI grids and large-scale inference environments.
Beyond connectivity, we also provide intelligent automation layers through our autonomous networking platforms, enabling operators to manage complex, multi-vendor environments more efficiently and intelligently.
What are some of the biggest infrastructure bottlenecks you see operators and enterprises facing as AI adoption accelerates?
One of the biggest challenges is understanding that AI infrastructure is not just about compute power. Organizations often focus heavily on GPUs and processing capabilities, but connectivity can quickly become the limiting factor.
You can deploy the most powerful AI infrastructure available, but if the network cannot support the required data movement between racks, data centres and edge locations, performance suffers. This is where intelligent networking becomes critical.
At Nokia, we are helping customers design what we call AI-ready connectivity. This includes high-capacity optical networking, intelligent routing and the seamless interconnection of compute environments. As AI workloads become increasingly distributed, the ability to move data efficiently becomes just as important as the ability to process it.
On the consumer side, Nokia has been showcasing AI-driven Wi-Fi management capabilities. How does this improve the end-user experience?
The home network has become far more complex than it was a few years ago. Consumers expect flawless connectivity across multiple devices, applications and services.
Our AI-enabled Wi-Fi solutions continuously monitor network performance and user experience. They can identify coverage gaps, detect congestion, analyze interference patterns and even recommend or automatically implement corrective actions.
The goal is to create a self-optimizing network environment where many issues can be resolved autonomously before they impact the user. This reduces support requirements for service providers while delivering a more consistent and reliable experience for customers.
The Middle East is witnessing an unprecedented surge in data centre investments. How do you see this shaping Nokia’s opportunities in the region?
The Middle East has emerged as one of the most dynamic markets globally for AI infrastructure investments. Governments and enterprises are actively investing in sovereign AI capabilities, advanced data centres and digital ecosystems.
This creates significant opportunities, not only for Nokia but for the broader technology industry. The success of these initiatives depends on having secure, scalable and efficient connectivity between compute resources, cloud environments and end users.
Our role is to help customers build these foundations. Whether it is data centre interconnectivity, optical networking, intelligent routing or autonomous operations, Nokia’s technologies are designed to support the scale and performance requirements of AI-driven economies.
As data volumes continue to grow, security and data sovereignty are becoming increasingly important. How is Nokia addressing these concerns?
Security is deeply embedded into Nokia’s strategy and innovation roadmap. As a European technology company, trust, resilience and security have always been fundamental principles in how we design and operate our solutions.
While we continue to invest heavily in AI innovation, we are equally focused on strengthening security capabilities across our portfolio. This includes advanced network security architectures, AI-driven threat detection and preparations for future technologies such as quantum-safe networking.
We are actively engaged with industry bodies, standards organizations and ecosystem partners to help define the next generation of secure digital infrastructure. As AI becomes increasingly pervasive, security must evolve alongside it, and that is an area where Nokia continues to invest significantly.
Looking ahead, what excites you most about the future of AI-driven networks?
What excites me most is the convergence of AI, automation and connectivity. Networks are evolving from passive transport layers into intelligent platforms that can learn, adapt and optimize themselves.
The future will be defined by autonomous operations, AI-native networks and real-time decision-making at scale. Organizations that successfully combine these capabilities will unlock entirely new business models and levels of operational efficiency.
For us, the opportunity is not just about deploying technology. It is about helping customers transform the way they operate, innovate and create value in an increasingly AI-driven world.
Tech Features
WHY AUDIO CLARITY MATTERS FOR THE CONTINUITY OF EDUCATION, WORSHIP, AND COLLABORATION IN THE MIDDLE EAST
Spokesperson – Yassine Mannai, Associate Sales Director at Shure MEA
Across the Middle East, continuity is being shaped by the quality of connection people experience every day. In classrooms, places of worship, and collaborative workspaces, that connection often begins with one essential factor: audio clarity. At Shure, we recognised this gap early and understood its growing importance across these environments.
When sound is clear, people stay present. Students follow lessons more easily, engage with greater confidence, and absorb information with less strain. This becomes especially important in hybrid learning environments, where every participant needs to feel equally included, whether they are in the room or joining remotely. Research cited by Shure shows that poor audio affects one-third of all virtual meetings, while four out of five common video conferencing frustrations are linked to audio issues such as background noise, echo, dropouts, and difficulty hearing others.
The same reality carries into places of worship. The ability to hear with clarity shapes how messages are received, how people remain attentive, and how connected they feel to the moment itself. In these spaces, sound supports focus, presence, and the overall quality of the experience.
In workplaces and institutional settings, audio has become central to how teams communicate and make decisions. Strong collaboration depends on being able to hear and respond without friction. As hybrid work continues to reshape professional life, the need for dependable communication systems has become more visible. [1] Shure’s regional insight, referencing IDC research, notes that 67% of professional workers are now at least partially remote, underlining how important it is for institutions to support communication across distributed teams. That understanding has been reflected in the solutions across our portfolio, including the MXA920 Ceiling Array Microphone for hybrid learning, the MXA320 Table Array Microphone for collaboration environments, and the DCA901 Broadcast Microphone Array for places of worship, where audience capture can bring greater depth to livestream experiences.
Across the region, institutions are moving toward smarter, more adaptable spaces where audio performance, system simplicity, and digital integration work together more effectively. Reliable audio has become part of how organisations sustain engagement, support participation, and deliver a better experience for the people who rely on them every day.
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