Connect with us

Tech Features

ASUS Techsphere Forum: Empowering Business Leaders Through Next-Gen Hardware Innovation

Published

on

ASUS Techsphere Forum - Group Photo
  • By: Subrato Basu, Managing Partner, Executive Board &
  • Srijith KN, Senior Editor, Integator Media


The line on the opening slide— “Every company will be an AI company”—wasn’t tossed out as a provocation. At the ASUS Techsphere Forum 2025 in Dubai, it landed as an operating instruction. The message across keynotes, the Intel segment, and two candid panels was strikingly consistent: AI stops being theatre the moment you standardize three things—the workspace (where people actually work), the runtime (so models are portable), and the portfolio (so you manage dozens of use cases like a product backlog, not a parade of proofs-of-concept).

A quick reality check on market size so we’re not drinking our own Kool-Aid: the global AI market in 2025 is roughly $300–$400B, depending on scope (software vs. software + services + hardware). Reasonable consensus ranges put 2030 at ~$0.8–$1.6T. In other words, still early—but already too big to treat as a side project.

A wide-angle shot of the ASUS Techsphere Forum

ASUS: PUT AI ON THE ENDPOINT—AND MAKE IT GOVERNABLE

ASUS’s enterprise stance is disarmingly practical. As Mohit Bector, Commercial Head (UAE & GCC) at ASUS Business, framed it, the fastest way to make AI useful is to put it where the work happens (the endpoint) and to make it governable. Concretely, that means:

  • NPUs for on-device inference (privacy, latency, battery life).
  • Manageability (fleet policy, remote control, security posture you can actually audit).
  • Longevity (multi-year BIOS/driver support) so IT can set an AI-ready baseline and keep it stable.

ASUS thinks about the modern workplace as an Enter → Analyse → Decide loop, this is where the workday actually speeds up—quietly, relentlessly, at the endpoint:

  • Enter: the device captures signals—voice, docs, screens, forms, sensors.
  • Analyse: retrieval-augmented reasoning + analytics produce options, risks, and rationales.
  • Decide: humans choose; agents act—raise tickets, update ERP/CRM—with audit trails.

It isn’t about one blockbuster use case. It’s about standardizing the canvas, so small wins compound every week.

ASUS Techsphere Forum 2025 - Panel 1
Panel 1 – From Data to Decisions: Leveraging AI Across Industries

INTEL: FROM SLOGAN TO STACK (AND WHY THE AI PC MATTERS)

Intel’s deck made the “every company will be an AI company” claim implementable. Four slide-level words—Open, Innovative, Efficient, Secure—double as a buyer checklist:

  • Open: less cost, no lock-in. The same models should move across CPU/GPU/NPU and PC → Edge → Datacentre/Cloud without rewrites.
  • Innovation: treat AI PCs with NPUs, edge systems, and cloud clusters as one continuum.
  • Efficient: lead on performance per dollar and per watt; energy and cost are first-class design goals.
  • Secure: your data and your models are IP; run locally when you should, govern tightly when you don’t.

A “Power of Intel Inside” platform slide stitched this together:

  • AI software & services: OpenVINO as the portability layer to convert/optimize/run models across heterogeneous silicon.
  • AI PC: always-on, private inference for day-to-day assistants.
  • Edge AI: near-machine intelligence for vision and time-series use cases.
  • Datacentre & cloud AI: scale-out training/heavy inference (fraud graphs, multimodal analytics, enterprise RAG).
  • AI networking: the fabric that keeps it all moving—securely.

Why the fuss about the AI PC? Because it’s the next enterprise inflection after Windows and Wi-Fi. Slides mapped tangible outcomes:

  • Productivity: faster info-find, auto-drafts, note-taking.
  • Communication: translation, live captioning, dictation, transcription.
  • Collaboration: smart framing, background removal, eye tracking, noise suppression—without pegging the CPU.
  • IT operations: endpoint anomaly detection, VDI super-resolution, remote screen/data removal.
  • Security: client-side deepfake detection, anti-phishing, ransomware flags.

Under the hood, Intel’s definition is a division of labour: CPU for responsiveness and orchestration, GPU for high-throughput math/creation, NPU for low-power sustained inference—the always-on stuff that makes assistants truly useful. Add vPro + Core Ultra and you get the fleet controls and long-term stability IT actually needs.

One more practical bit I liked: Intel AI Assistant Builder—a portal to stand up local assistants/agents (with RAG) that can run on the PC fleet first, shrinking time-to-value from months to days/weeks and letting you prove the full E-A-D loop before you scale heavier jobs to edge/cloud.

When the “100M AI PCs by 2026” slide hit the screen, heads tilted from curiosity to calculation. The figures—bullish vendor projections (~100M by 2026; ~80% AI-capable by 2028)—invite a haircut, but the signal is unmistakable: endpoint AI is becoming the default.

ASUS Techsphere Forum 2025 - Panel 2
Panel 2 – AI-Powered Workspaces and the Future of Work

WHAT THE PANELLISTS REALLY TAUGHT US

RAKEZ (Free Trade Zone)

Posture: Execution-first. Make AI practical on the shop floor and trustworthy in the back office—governed from day one.

What they drive:

  • Diagnostics (OEE baselines, defect maps) + data-readiness scans (MES/ERP) so pilots don’t stall.
  • Reference lines/sandboxes where vendors prove accuracy, safety, throughput before purchase.
  • Template playbooks: CV-QC, predictive maintenance, warehouse vision, invoice extraction/3-way match—each with SOPs, KPIs, integration steps.
  • Curated vendors + shared services (labelling, model hosting/monitoring, SOC for AI) to reduce MSME cost/complexity.

MSMEs: “Bookkeeping-in-a-box” to clean ledgers and free cash; pre-negotiated PoC packs (fixed price/timeline, acceptance metrics); compliance starter kit (consent, retention, safety, escalation).

Enterprises: Multi-site rollout playbooks, edge + cloud reference architectures (identity-aware RAG, policy-constrained agents), and assurance artifacts (model cards, change control, audit trails).

Outcome lens: OEE ↑, FPY ↑/DPMO ↓, MTBF ↑/MTTR ↓, faster close cycles, fewer incidents—AI that moves the P&L and passes audit.

Note – FPY — First Pass Yield; OEE — Overall Equipment Effectiveness; DPMO — Defects Per Million Opportunities; MTBF — Mean Time Between Failures (repairable systems); MTTR — Mean Time To Repair

Oracle (Consulting / Applications cloud)

Posture: AI belongs inside the workflows where finance, HR, supply chain, and service teams live. Expect talk tracks like: ground answers in your own records (RAG with policy), instrument before/after outcomes, and treat AI features as part of ERP/HCM/CX—not a sidecar chatbot. The ask from buyers: prove the Enter → Analyse → Decide gains in real workflows (FP&A forecasting lift, supplier risk scoring, HR talent match quality).

Zurich Insurance (BFSI)
Posture: AI as a force for good, scaled with governance. Think hundreds of use cases: claims triage, fraud/anomaly detection, internal knowledge bots—human-in-the-loop where stakes are high, and IoT-style prevention to reward good behaviour. The key is measurement: fewer false positives, shorter cycle times, clearer audit trails—and elevated roles, not replaced ones.

Group-IB (Cyber / Threat Intel)

Posture: AI to defend—and defend against AI. SOC copilots that summarize and enrich alerts, deepfake/phishing detection, behaviour analytics across identities and endpoints, and the emerging discipline of security of AI (prompt-injection defences, LLM gatewaying, data loss controls for AI apps). If you’re rolling out agents, involve your security team early.

Dhruva Consultants (Tax Tech Transformation)

Posture: RegTech + AI to reduce compliance cost and risk. Document AI to normalize invoices/contracts, anomaly detection for mismatches and fraud flags, and a pragmatic “bookkeeping-in-a-box” on-ramp for MSMEs. Non-negotiables: auditability, versioning, segregation of duties for anything that touches filings.

Prime Group (Labs/Certification)

Posture: Risk-scored processes—every lab step tagged with expected outputs, data access, and fallbacks. Near-term wins: smarter scheduling and test selection; long-term horizon: a Mars-ready lab by 2050 aligned with the UAE’s space ambitions. It’s operational excellence today, exploration mindset tomorrow.

Education (Heriot-Watt University, Dubai)

Posture: candid and useful: human-led pedagogy; AI-assisted admin and decision support. HWU brings talent pipelines (AI/Data Science programs), translational research, and applied robotics capacity (think Robotarium-style ecosystems). This is the repeatable talent + research engine enterprises can plug into—capstones, CPD, joint R&D—that shortens the path from idea to pilot.

WHY UAE HAS A STRUCTURAL ADVANTAGE: RAKEZ × HWU

Local context matters. RAKEZ (Ras Al Khaimah Economic Zone) is more than a location; it’s an adoption on-ramp aligned with MoIAT’s Industry 4.0 programs (ITTI/Transform 4.0). Translation: factories—especially MSMEs—get real help to deploy vision-led quality, OEE analytics, and worker-safety use cases, with policy scaffolding and incentives attached.

Pair that with Heriot-Watt University as a talent/research flywheel and you have a short, well-lit path from concept to production: execution zone + skills engine. That’s a genuine regional edge.

SUMMARY

Techsphere’s most important contribution wasn’t a prediction; it was a design pattern. ASUS gives you the enterprise substrate (AI-ready endpoints you can actually govern). Intel gives you the principles and plumbing (OpenVINO portability; CPU/GPU/NPU continuum; PC → Edge → Cloud). The panellists supplied proof patterns across industries. And the UAE context—RAKEZ for execution, HWU for talent/research—shortens the distance from idea to impact.

If “every company will be an AI company,” the winners won’t be the first to demo—they’ll be the first to standardize. Start at the endpoint, insist on portability, manage a portfolio, and make the Enter → Analyse → Decide loop measurable. That’s how the slide turns into the balance sheet.

_________________________________________________________

  • Glossary of Technical Acronyms
  • OEE — Overall Equipment Effectiveness (measures manufacturing productivity: availability × performance × quality).
  • FPY — First Pass Yield (percentage of units passing production without rework).
  • DPMO — Defects Per Million Opportunities (defect rate in Six Sigma terms).
  • MTBF — Mean Time Between Failures (average time between breakdowns of a repairable system).
  • MTTR — Mean Time To Repair (average time to repair a failed component/system).
  • AI / IT Terms
  • NPU — Neural Processing Unit (specialized chip for AI inference, optimized for low-power sustained workloads).
  • CPU — Central Processing Unit (general-purpose processor for orchestration, responsiveness).
  • GPU — Graphics Processing Unit (parallel processor for high-throughput math and AI training/inference).
  • RAG — Retrieval-Augmented Generation (technique where AI models query external knowledge bases before generating answers).
  • ERP — Enterprise Resource Planning (integrated system for core business processes like finance, supply chain, manufacturing).
  • MES — Manufacturing Execution System (software for monitoring and controlling production).
  • VDI — Virtual Desktop Infrastructure (running desktop environments on centralized servers).
  • SOC — Security Operations Center (hub for cybersecurity monitoring and response).
  • IP — Intellectual Property (protected data, models, or designs).
  • Industry & Enterprise Acronyms
  • BFSI — Banking, Financial Services, and Insurance (industry vertical).
  • FP&A — Financial Planning & Analysis (finance function for budgeting, forecasting, performance analysis).
  • HCM — Human Capital Management (HR technology and processes).
  • CX — Customer Experience (customer-facing processes and software).
  • ITTI — Industrial Technology Transformation Index (UAE Ministry of Industry and Advanced Technology initiative under Industry 4.0).

The ASUS Techsphere Forum, organized by Integrator Media, brought together C-suite leaders from diverse industry verticals to explore how evolving hardware standards are shaping the future of work. The event highlighted the growing role of AI-enabled PCs, showing how advancements in endpoint hardware can directly support business needs. By balancing industry-specific requirements with insights on hardware innovation, the forum offered executives a clear view of how these technologies can enhance productivity and deliver measurable value across the wider business community.

Tech Features

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

Published

on

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.

Continue Reading

Tech Features

FIVE WAYS UAE WORKFORCE PLANNING IS CHANGING IN 2026

Published

on

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.

Continue Reading

Tech Features

Networks Must Evolve Before AI Can Scale

Published

on



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.

Continue Reading

Trending

Copyright © 2023 | The Integrator