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ICT CHAMPION AWARDS 2026: FIELD NOTES — FROM HYPE TO HABIT
By Subrato Basu, Global Managing Partner, The Executive Board with Srijith KN Senior Editor, Integrator Media.
On 28 January 2026, Integrator Media hosted the 18th edition of the ICT Champion Awards at the Shangri–La Dubai Hotel, bringing together the region’s ICT ecosystem for an evening designed to celebrate milestones, recognise innovation, acknowledge ecosystem leaders, and foster community.
The programme—aligned with INTERSEC 2026—spotlighted organisations making measurable impact across enterprise solutions, critical infrastructure, cybersecurity, and public-sector technology.
By 7pm, the Shangri-La Dubai’s Al Nojoom Ballroom had the feel of a ‘state of the union’ for regional ICT—CXOs, partners, and platform leaders in one room, with AI dominating every board agenda. This wasn’t just an awards evening; it was a moment to take stock: are we still experimenting with AI, or are we ready to operationalise it at scale?
Across conversations at tables and in the corridors, the same theme surfaced: experimentation is easy—operational confidence is the hard part.
Opening keynote: “Is AI ready for us in the UAE—and what next?”
The evening’s tone was set by Mr. Maged Fahmy, Vice President, Ellucian MEA, who opened with a deliberately provocative question: Is AI ready for us in the UAE? What made the question stick wasn’t the technology—it was the implication that leadership models are now the constraint.
His message wasn’t framed as a technology debate—it was framed as a leadership test.
As a leader in enterprise technology for education and public-sector institutions—where trust, governance, and outcomes are non-negotiable—Fahmy’s ‘hype to habit’ message landed with particular weight.
His argument was simple: the UAE is past AI curiosity. The next phase is habit—repeatable, governed AI embedded in day-to-day work. The real question is no longer ‘Can we do a PoC?’ but ‘Can we run this reliably, measure it, and scale it?’
We’re moving from Generative AI (creating content) to Agentic AI (executing work). That shift changes leadership: fewer people doing repeatable steps, more orchestration of workflows across systems—with humans focused on judgement, risk, and exceptions.
For example, an agent can triage a service request, propose the fix, route it for approval, execute the change, and only escalate the ‘weird 3%’ to a human owner.
Leadership reality check: are we still leading like it’s 2022?
He also offered a leadership reality check: if your operating rhythm still assumes long cycles, manual coordination, and slow approvals, you’ll struggle in 2026. Strategy can’t be an annual exercise; it must become a live set of decisions, guardrails, and feedback loops.
AI gives the “how”; humans must own the “why”
His framing landed: AI increasingly gives you the how—options, sequencing, automation. But leaders must own the why—purpose, priorities, ethics, and accountability. In an agentic era, that ‘why’ is what keeps speed from becoming risk.
He also anchored AI’s value in a more human currency: time. Yes, AI drives efficiency. But the real prize is what leaders do with the time they get back: better customer interactions, faster decision-making, more innovation, and more space for creative work that machines cannot replicate.
Talent gaps, transformation, and “sovereign AI”
The keynote did not gloss over constraints. Fahmy flagged the talent gap that emerges when adoption rises faster than capability—especially in AI engineering, cybersecurity, governance, and change leadership. His call was practical: the future workforce isn’t only “AI builders,” but AI challengers—people who can validate outputs, pressure-test recommendations, and govern autonomous workflows.
He also introduced the importance of sovereign AI in the GCC context—where nations like the UAE and Saudi Arabia are thinking deeply about data residency, cultural alignment, regulatory control, and strategic autonomy. The point wasn’t simply “host it locally,” but to build AI that is trustworthy in local context: aligned to language, norms, governance expectations, and national priorities.
In practical terms, sovereign AI means keeping sensitive data and model control within national boundaries, enforcing local governance and auditability, and ensuring outputs reflect language, culture, and regulatory expectations.
Strategy ownership, authority, and misinformation
In 2026, he argued, leaders must be explicit about who owns strategy when decisions are increasingly shaped by AI systems. If an agent can recommend, negotiate, or trigger actions at speed, the organisation needs clarity on authority: approval thresholds, auditability, escalation paths, and responsibility when something goes wrong.
He also linked AI strategy directly to misinformation risk—not as a social media issue alone, but as an enterprise challenge: hallucinations, deepfakes, synthetic fraud, manipulated signals, and decision contamination. The answer, he implied, is not fear—it’s governed adoption: controls, verification, identity assurance, and clear human accountability.
He closed with a grounded reminder that landed strongly with the awards theme: the winners in 2026 won’t be defined by the “fastest AI,” but by the clearest purpose—and by the culture they’ve built to sustain transformation.
Panel discussion: “Seamless Intelligence” — when AI becomes invisible (and unavoidable)
The panel discussion, moderated by Srijith KN (Senior Editor, Integrator Media), brought the theme down from keynote altitude into product and platform reality. The session, titled “Seamless Intelligence: How AI and Dataare Powering the Next Generation of Intelligent Experiences,” featured:
- Mr. Rishi Kishor Gupta, Regional Director (Middle East & Africa), Nothing Technology
- Ms. Bushra Nasr, Global Cybersecurity Marketing Manager, Lenovo
- Mr. Nikhil Nair, Head of Sales (Middle East, Turkey & Africa), HTC
- Ms. Aarti Ajay, Regional Lead Partnerships (Ecosystem Strategy & Growth), Intel Corp
One way to read the panel: infrastructure decides what’s possible, security decides what’s safe, and experience decides what gets adopted.
The discussion converged on one powerful idea: in the next phase, the user shouldn’t “see” the intelligence—it should dissolve into the experience. The ambition is not “AI features,” but AI-native interactions that feel natural, predictive, and frictionless across devices and contexts.
Infrastructure: where does intelligence actually run?
From the infrastructure angle, the panel stressed that “AI everywhere” requires deliberate choices about where compute happens—on device, at the edge, or in the cloud—and how workloads move across that spectrum. This included clear emphasis on the hardware stack (CPU/GPU/NPU) and what it takes to scale AI responsibly.
“AI won’t scale on slogans; it scales on architecture—device, edge, and cloud—each with different cost, latency, and security trade-offs.”
Trust: security, fear factor, and the “moving data center”
From the trust perspective, the panel highlighted the growing “fear factor” around devices and autonomy: more sensors, more data, more models—more attack surface. A memorable analogy landed well: the modern connected vehicle increasingly behaves like a moving data center, raising the bar on governance, identity, and resilience.
“Every new AI capability is also a new attack surface—security has to be designed in, not bolted on.”
Human experience: AI as an experience, not a tool
On the human side, the conversation explored how AI will increasingly show up as experience—wearables, ambient assistance, multi-sensory support, and interactions that augment how people see, decide, and act. The subtext was clear: if AI is going to become ubiquitous, it must become intuitive—and aligned to what humans actually value.
“AI is becoming an experience, not an app—supporting how we see, decide, and act, often without the user noticing the machinery behind it.”
Consumer reality: “make human life smarter” and “declutter your life”
From the consumer device lens, the message was refreshingly plain: AI should help make human life smarter—not noisier. That includes automation that reduces cognitive load and helps people “declutter” their day-to-day, rather than introducing another layer of complexity.
The moderator wrapped the session with a sober economic note: as the stack expands from devices to cloud subscriptions and services, the cost of modern digital life rises—making it even more important that AI delivers tangible value, not just novelty.
“If AI doesn’t declutter your life, it’s not helping.”
Executive Board Commentary: The real shift is “delegation”—not adoption
If there was one undercurrent in the room, it’s that we’ve moved past the question of whether AI is “interesting.” The real question now is: what can we delegate—safely, repeatedly, and at scale—without degrading trust? That’s why the keynote’s emphasis on moving beyond PoCs into governed, repeatable operating models felt so relevant.
This is the step-change many organisations underestimate: adoption is a technology story; delegation is an operating model story. In an agentic era—where systems don’t just generate answers but initiate actions—the enterprise doesn’t need more demos. It needs a way to decide: what tasks can be automated end-to-end, what must stay human-led, and what requires a hybrid “human-in-the-loop” pattern?
A useful lens: the “Delegation Curve”
Think of your AI journey as a curve with three stages:
- Assist (copilot) – AI helps humans do the work faster (drafting, summarising, analysing).
- Act (agentic) – AI executes steps across workflows (triage → route → approve → action), escalating exceptions.
- Assure (governed autonomy) – AI operates with clear authority limits, auditability, and continuous controls (especially critical in regulated sectors and national infrastructure contexts).
Most enterprises are still celebrating Stage 1, experimenting in Stage 2, and under-investing in Stage 3. Yet Stage 3 is where operational confidence is built—and where reputational risk is avoided.
The missing KPI: “Trust latency”
The panel made it clear that infrastructure, security, and experience all shape whether “seamless intelligence” is adopted in the real world.
But the deeper measurement leaders should add is trust latency: how long it takes an organisation to trust an AI outcome enough to act on it without manual re-checking.
In practical terms, the most important AI metrics in 2026 won’t be model accuracy in isolation. They’ll look like:
- Time-to-trust (how quickly decisions can be taken without repeated human verification)
- Exception rate (the “weird 3%” humans must handle)
- Containment rate (how often an agent resolves end-to-end without escalation)
- Governance velocity (how quickly policy, approvals, and controls keep up with agent speed)
This is where leadership becomes the constraint—or the advantage.
Sovereign AI isn’t just residency; it’s “accountability at the boundary”
The keynote’s introduction of sovereign AI resonates strongly in the GCC because the stakes aren’t only technical. They are cultural, regulatory, and strategic.
The next phase of sovereign AI will be defined not by where data sits, but by where accountability sits—who can inspect, audit, override, and certify AI behaviour, especially when agents trigger actions across systems.
Sovereign AI done well will become a competitive advantage: it makes cross-sector adoption easier because it offers confidence by design—clear boundaries, policy alignment, and traceability.
The “AI dividend” test: what are you doing with the time you saved?
A subtle but powerful keynote point was that AI’s real asset is time.
The leadership question is what you do with it. In organisations that win, the reclaimed time becomes: better customer experience, sharper decision-making, faster innovation cycles—and more human attention where it matters.
In organisations that struggle, that time gets lost to rework, re-checking, and governance friction—because trust was never engineered into the operating model.
The new perspective to carry forward
At ICT Champion Awards, the celebration of winners implicitly reinforced the real benchmark for 2026: repeatability. Not “who has the flashiest AI,” but who can run it reliably with trust, governance, and measurable outcomes.
So perhaps the most useful question to take forward is this:
What are the first 3 workflows in your organisation that you are willing to delegate to agentic AI—end-to-end—under clearly defined authority, auditability, and exception handling?
That’s also what the ICT Champion Awards ultimately celebrated: not technology theatre, but execution maturity. The winners weren’t simply early adopters—they were organisations demonstrating innovation with outcomes, leadership with accountability, and scale with governance. In a year defined by agentic possibilities, the Awards served as a reminder that the real competitive edge is operational confidence—systems that work, controls that hold, and teams that can sustain change. Hype is easy; habit is earned.
Dr Heba El-Shimy, Assistant Professor (Data and AI), Mathematical and Computer Sciences, Heriot-Watt University Dubai
Healthy societies, institutions, or teams operate best when comprising a healthy balance between males and females. A landmark study by Boston Consulting Group (BCG) with the Technical University of Munich uncovered that companies with above-average gender diversity generated around 45% of their revenues from innovative products, compared to only 26% as innovative revenues for companies with below-average gender diversity. These findings are echoed in the scientific field. A 2025 study by Nature analyzing 3.7 million US patents revealed that inventing teams with higher participation of women are associated with increased novelty in patents. Research by the Massachusetts Institute of Technology confirms that teams with more women exhibit significantly higher collective intelligence and are more effective at solving difficult problems. These studies tell one clear story: that participation of women in innovative and scientific fields is not only desirable — it is a strategic national asset.
UAE Women In STEM
The UAE holds one of the world’s most striking gender profiles in STEM education. According to UNESCO data, 61% of graduates in STEM fields are Emirati women, surpassing the Arab world average of 57% and nearly doubling the global average of 35%. At government universities, 56% of graduates are women, and they represent over 80% of graduates in natural sciences, mathematics, and statistics.
These numbers have translated into accomplishments that have captured global attention. The Emirates Mars Mission — the Hope Probe — was developed by a team of scientists that was 80% women, selected based on merit. Noora Al Matrooshi became the first Arab woman to complete NASA astronaut training in 2024. The Chair of the UAE Space Agency and the mission’s Deputy Project Manager is a woman: H.E. Sarah Al Amiri. At Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), female enrolment reached 28% within five years and continues to grow. Women’s talents are being recognised — this is not a mere future ambition, but a present reality.
Scientific Research As An Engine For National Strategy
The ‘We the UAE 2031’ vision sets ambitious goals: doubling GDP to AED 3 trillion, generating AED 800 billion in non-oil exports, and positioning the country as a global hub for innovation, artificial intelligence, and entrepreneurship. The UAE’s rise to the 30th place in WIPO Global Innovation Index 2025 signals a steady pace towards achieving the UAE 2031 vision. Sustaining this ascent requires continued investment into human capital to produce research output, intellectual property, and commercial innovation at a pace matching the ambition. This is precisely where women scientists become indispensable.
Women scientists are already major contributors to the seven priority sectors identified in the UAE National Innovation Strategy: renewable energy, transport, education, health, technology, water, and space. UAE women scientists are research-active in climate science, sustainable materials, clean energy systems, AI-driven diagnostics in healthcare, and environmental monitoring — all crucial sciences that the national development commitments depend on.
Knowledge economies are built on the ability to generate, apply, and commercialize research locally — reducing the dependence on imported technologies and creating self-sustaining innovation ecosystems. When a researcher at UAEU develops patented computational methods for drug design, as Dr. Alya Arabi recently did with four patents spanning AI-driven pharmaceutical development and medical devices, that is intellectual property created on UAE soil, addressing healthcare challenges that would otherwise require imported solutions. When women scientists at Masdar City and Khalifa University advance research in solar energy systems, carbon captured materials, or sustainable desalination, they are producing foundational science that the UAE’s Net-Zero 2050 Strategy depends upon.
Masdar’s WiSER (Women in Sustainability, Environment and Renewable Energy) programme has graduated professional young women from over 30 nationalities, closing the gap in the global sustainability workforce. In healthcare, women scientists are active in the areas where AI, genomics, and precision medicine converge. The Emirati Genome Programme, M42’s Omics Center of Excellence, and the Abu Dhabi Stem Cells Center all represent domains where locally produced research can reduce the country’s reliance on imported diagnostics and therapeutics.
From these examples, it is clear that women scientists’ and researchers’ contributions are a central pillar of the national R&D ecosystem.
A Regional And Global Perspective
The UAE’s experience is instructive for the wider region. Across the Arab world, up to 57% of STEM graduates are women, yet the MENA region maintains one of the lowest female workforce participation rates globally at 19%. Saudi Arabia’s Vision 2030 has made notable progress, with women’s workforce participation reaching 36.2% and women now comprising 40.9% of the Kingdom’s researchers. The challenge across the GCC and MENA is consistent: converting educational attainment into sustained professional participation and research output. Globally, only one in three researchers is a woman, and parity in engineering, mathematics, and computer science is not projected until 2052. UNESCO’s 2026 International Day of Women and Girls in Science theme — “From Vision to Impact” — captures this urgency well.
The Way Forward: From Vision To Impact
As an academic working at the intersection of artificial intelligence and healthcare research in Dubai, I witness this potential daily — in students who arrive with rigour and ambition, in researchers producing work that stands alongside the best globally, and in a national ecosystem that increasingly treats women’s scientific participation as a strategic priority rather than a social courtesy. But policies alone do not produce innovation. What produces innovation is funding, access to facilities, clear pathways from research to commercialisation, and the recognition that a woman scientist publishing a patent in the UAE is building national capability in exactly the same way as the infrastructure projects that make headlines.
Sustained commitment is key — from governments, institutions, and the private sector — to ensure that every woman scientist in this region has the funding, the platforms, and the pathways to convert her research into national impact. When women scientists thrive, nations innovate faster. The UAE understands this. Now it must ensure the rest of the ecosystem does too.
Dr Maheen Hasib, Global Programme Director for BSc Data Sciences, School of Mathematical and Computer Sciences, Heriot-Watt University Dubai
Artificial intelligence (AI) and data science are no longer distant or experimental ideas. They quietly sit behind many of the decisions that shape our everyday lives: how patients are diagnosed, how job applications are filtered, how loans are approved etc. These systems increasingly influence who gets opportunities and who does not. That reality makes one question impossible to ignore: who is building the algorithms that shape our future?
As a Programme Director for the Data Sciences programme at Heriot-Watt University, this question is not just academic for me, it is deeply personal. Every year, I meet capable, curious, and motivated young women who are genuinely interested in data science. Yet many hesitate. Not because they lack ability, but because they are unsure whether they truly belong in the field. Too often, they do not see people (like themselves) reflected in AI research, technical teams, or leadership roles. And that absence matters.
When bias in AI feels uncomfortably familiar
AI systems are often described as objective or neutral, yet they are trained in data shaped by human history, something that is far from neutral. When training data reflects existing gender imbalances, AI systems can replicate and even magnify those patterns. This has led to technologies that perform less accurately for women, fail to capture women’s health needs, or disadvantage women in recruitment and evaluation processes.
For many women, these outcomes feel uncomfortably familiar. They echo everyday experiences of being overlooked, misunderstood, or underrepresented. In most cases, this is not the result of deliberate exclusion. It is the consequence of design choices made without diverse perspectives at the table.
Why representation goes beyond numbers
Representation in AI and data science is often discussed in terms of statistics or diversity targets. But at its core, representation is about perspective. When women are involved in developing AI systems, they help shape how problems are defined, what data are considered relevant, and which risks are taken seriously.
From an academic perspective, diverse teams produce more robust research and better-tested models. From a human perspective, they help ensure that AI systems work for the full range of people they are meant to serve. Inclusion improves both technical quality and social impact, it strengthens the science and the society it serves.
Women and the future of ethical AI
Many women working in AI are already at the forefront of discussions around fairness, transparency, explainability, and responsible data use. These are not peripheral concerns; they are central to building trustworthy AI. Ethical AI requires asking difficult questions: Who might be harmed when a system fails? Whose data is missing? Who is affected by design decisions that seem minor on the surface?
By advocating for human-centered approaches, women in AI are helping shift the field beyond purely performance-driven metrics toward systems that balance innovation with responsibility.
Education, encouragement, and visibility matter
At Heriot-Watt University Dubai, we make a deliberate effort to encourage women to pursue data science, not just as a degree, but as a long-term career. This means creating supportive learning environments, highlighting female role models, and openly discussing the wide range of paths that data science can lead to. Students need to see that success in AI does not follow a single template.
Equally important are spaces where women can connect, share experiences, and feel supported. As an ambassador for Women in Data Science, I have seen how such events play a vital role. They create visibility, build confidence, and remind women that they are not alone. We need more of these initiatives, not as one-off celebrations, but as sustained platforms for mentorship, networking, and growth.
Encouraging women in AI is not about lowering standards or meeting quotas. It is about recognizing that inclusive participation leads to better research, more ethical technologies, and systems that genuinely reflect the societies they shape.
Conclusion
As AI and data science continue to influence our world, we must ask not only what these systems do, but who designs them. Supporting women to study data science, pursue AI careers, and step into leadership roles is essential to building technologies that are fair, responsible, and trustworthy. Through education, visibility, and initiatives, we can help ensure that the future of AI is shaped by many voices.
The future of AI should be one where women do not simply use technology but actively shape it.
It has been two years since the debut of the original HUAWEI FreeClip, Huawei’s first-ever open earbuds that took the market by storm. Its massive popularity proved that the world was ready for a new kind of listening experience. The new HUAWEI FreeClip 2 tackles the hard challenges of open-ear acoustics physics head-on, combining a powerful dual-diaphragm driver with computational audio. It delivers depth and clarity, which was once thought impossible with an open-ear design.
Solving the acoustic limitations of open-ear audio alone would have been sufficient to make the HUAWEI FreeClip 2 our pick for best open-ear audio. But it is way more than that.
Comfortable C-Bridge design
The HUAWEI FreeClip 2 earbuds weigh only 5.1 g per bud, a 9% reduction from the previous generation. This lightweight architecture ensures an effortless experience, perfect for long calls, workouts, and commutes, allowing you to wear them all day without fatigue. The comfort bean is 11% smaller than the previous model, yet the design provides a secure fit that prevents the earbuds from falling out, even during intense activity.
Constructed from a new skin-friendly liquid silicone and a shape-memory alloy, the C-bridge is 25% softer and significantly more flexible than its predecessor. Finished with a fine, textured surface, it ensures a comfortable, irritation-free wearing even after extended use.
Adaptive open-ear listening
The acoustic system has been significantly upgraded, featuring a dual-diaphragm driver and a multi-mic call noise cancellation system. This setup not only delivers powerful sound but also maximises space efficiency. That’s why, despite their small size, these earbuds can deliver substantial acoustic performance.
The Open-fit design of the earbuds demands high computing power to maintain sound quality and call clarity. The HUAWEI FreeClip 2 offers ten times the processing power of the previous generation, serving as Huawei’s first earbuds to feature an NPU AI processor for a truly adaptive experience. The new dual-diaphragm driver includes a single dynamic driver with two diaphragms, effectively doubling the sound output within a compact space to provide a significant boost in volume and bass response.
Furthermore, the earbuds dynamically detect surrounding noise and adjust volume and voice levels in real-time. If the environment is too noisy, the system uses adaptive voice enhancement to specifically boost human frequencies, ensuring you never miss a word of a podcast or audiobook. When you return to a quiet environment, the earbuds automatically settle back to a comfortable volume level.
Crystal clear calls
To ensure call quality in chaotic environments, the HUAWEI FreeClip 2 utilises a three-mic system combined with multi-channel DNN (Deep Neural Network) noise cancellation algorithms. This system intelligently identifies and filters out ambient noise. Thanks to the NPU AI processor, the earbuds automatically enhance voice clarity, ensuring your conversations remain crisp regardless of your surroundings.
Battery life and charging
With the charging case, the HUAWEI FreeClip 2 offers a total battery life of 38 hours, allowing users to enjoy music throughout a full week of commuting on a single charge. On their own, the earbuds last for 9 hours—enough for a full workday of uninterrupted calls. For those in a rush, just 10 minutes of fast charging in the case provides up to 3 hours of playback. For added convenience, they support wireless charging and are compatible with watch chargers.
Rated IP57, the earbuds are resistant to sweat and water. They can easily withstand intense workouts or even a downpour.
Connectivity
The earbuds support dual connections and seamless auto-switching across iOS, Android, and Windows. When connected to EMUI devices, you can even switch audio between more than two devices. Additionally, when connected to a PC, the earbuds allow you to answer an incoming call without disconnecting from or interrupting your conference setup.
It is, quite simply, a pair of earphones reliable enough for the gym, the office, and the commute.
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