Tech Features

ICT CHAMPION AWARDS 2026: FIELD NOTES — FROM HYPE TO HABIT

Published

5 months ago

February 3, 2026

Award winners and industry leaders stand on stage at Integrator Media – the ICT Champion Awards 2026 in Dubai, holding trophies beneath the ICT Champion Awards backdrop.

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.

Tech Features

ENGINEERING INTELLIGENCE IN EDUCATION: PREPARING YOUNG WOMEN FOR FUTURE TECH LEADERSHIP

Published

1 week ago

June 23, 2026

Integrator Web-Admin

Dr Esraa Khatab, Assistant Professor at the School of Mathematical and Computer Sciences, Heriot-Watt University Dubai

As we celebrate International Women in Engineering Day (INWED), attention is increasingly focused on how to prepare young women not only to participate in engineering but to lead its future. In a world shaped by artificial intelligence, sustainability challenges, and rapid digital transformation, education must go beyond technical instruction. It must cultivate what we can call engineering intelligence, a combination of technical expertise, problem-solving ability, creativity, and leadership confidence.

For young women, this preparation is most effective when education is intentionally designed to inspire, support, and position them as future innovators and decision-makers.

Inspiring Young Women Through Meaningful Learning

Engaging young women in engineering begins with making learning relevant and purposeful. When engineering is connected to real-world challenges, such as improving healthcare systems, designing sustainable cities, or developing climate solutions, it resonates strongly with students who are motivated by impact.

Project-based learning plays a key role here. When young women work on designing smart applications, building prototypes, or solving community challenges, they begin to see themselves as capable engineers contributing to society. Thes experiences move engineering from an abstract concept to a meaningful pathway where their ideas matter.

Initiatives such as the UAE’s “One Million Arab Coders” and international programs like “Girls Who Code” have successfully introduced thousands of young women to coding, AI, and digital innovation. These initiatives are powerful not just because of the skills they teach, but because they create an early sense of belonging in technology-driven environments.

Mentorship: Unlocking Potential and Building Confidence

For young women, mentorship is a transformative element of engineering education. It provides not only guidance but also reassurance, helping students navigate academic and career pathways with clarity and confidence.

Connecting young women with mentors, whether through universities, industry partnerships, or outreach programs, offers them valuable insights into emerging fields such as artificial intelligence, robotics, and renewable energy. These relationships make career paths more tangible and achievable.

In classroom settings, mentorship can be embedded into learning through project collaborations and industry engagement. When young women receive feedback from

professionals, present their ideas, and engage in real-world problem-solving, they begin to develop both confidence and professional identity.

Mentorship also nurtures leadership. By observing and interacting with experienced professionals, young women gain exposure to decision-making, teamwork, and innovation processes, essential components of future tech leadership.

Expanding Opportunities Through STEM Outreach

STEM outreach initiatives are vital in reaching young women early and sustaining their interest in engineering pathways. Programs that focus on hands-on, creative engagement, such as robotics competitions, coding bootcamps, and innovation labs, are particularly effective in building confidence and curiosity.

These initiatives create safe and encouraging environments where young women can experiment, take risks, and learn collaboratively. Importantly, they shift the narrative from simply learning technology to actively creating it.

Digital platforms have further expanded opportunities for young women in engineering. Virtual labs such as “MIT OpenCourseWare” and interactive simulations (e.g., PhET) allow learners to experiment and build practical skills remotely, with research showing strong gains in engagement and motivation. Online hackathons, including initiatives like the “UAE InnovAIte AI” Hackathon, provide young women with collaborative spaces to design real-world solutions using emerging technologies. At the same time, AI-powered tools such as “Khan Academy’s Khanmigo” offer personalized guidance, helping learners build confidence through continuous, self-paced support.

Together, these platforms create flexible and inclusive pathways that enable young women to actively engage, experiment, and grow within today’s rapidly evolving technological landscape. By introducing young women to emerging technologies early, outreach programs help them build familiarity and confidence in fields that will define the future of work.

Encouraging Young Women to Lead in Emerging Fields

Emerging engineering domains, such as artificial intelligence, smart systems, biotechnology, and sustainable energy, offer significant opportunities for innovation and leadership. Encouraging young women to explore these areas requires intentional effort within education systems.

This can be achieved through:

Early integration of advanced topics: Introducing AI, data science, and sustainability concepts at foundational levels.
Interdisciplinary approaches: Encouraging young women to apply engineering skills in healthcare, environmental science, and social innovation.
Experiential learning: Providing opportunities for internships, research projects, and innovation challenges in emerging fields.

These experiences allow young women to build not only technical expertise but also the confidence to navigate complex, real-world challenges. They begin to see themselves as contributors to cutting-edge developments, rather than observers.

Building Confidence and Leadership Identity

For young women to thrive in engineering, education must also focus on building confidence and leadership skills. This includes creating environments where their voices are heard, their ideas are valued, and their contributions are recognized.

Encouraging young women to lead team projects, present their work, and participate in competitions helps them develop essential soft skills such as communication, collaboration, and critical thinking.

Representation also plays an important role. Highlighting the achievements of women engineers and innovators, both globally and within local communities, reinforces the message that leadership in engineering is both attainable and expected.

Importantly, leadership development should be embedded into the learning journey. Innovation challenges, entrepreneurship programs, and community-based projects provide platforms for young women to take initiative and drive impact.

Looking Ahead: Empowering Young Women to Shape the Future

The future of engineering will be defined by those who can think creatively, solve complex problems, and lead with vision. Preparing young women for this future is not just about education, it is about empowerment.

By combining meaningful learning experiences, strong mentorship, expanded outreach, and opportunities in emerging technologies, we can create an ecosystem where young women thrive as engineers and leaders.

As we celebrate INWED, the focus is clear: to ensure that young women are equipped not only with skills, but with the confidence and ambition to lead. When this happens, they do more than contribute to technological advancement, they shape it.

Tech Features

FIVE WAYS UAE WORKFORCE PLANNING IS CHANGING IN 2026

Published

2 weeks ago

June 19, 2026

Integrator Web-Admin

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

Published

3 weeks ago

June 11, 2026

Integrator Web-Editor

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.