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.

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.

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.

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.

In the Gulf’s fast-evolving retail landscape, one thing is clear: attention is everything. With consumers moving between screens, stores, and digital channels in seconds, capturing that attention outdoors is becoming a high-stakes game. That’s why LED display technology is rapidly becoming the new storefront essential, especially when paired with interactivity.

Retailers across the UAE, Saudi Arabia, and Qatar are investing in large-format LED displays that do more than just promote products; they invite shoppers in. Whether it’s a vivid display on a flagship store’s exterior or an interactive screen at a luxury mall, brands are embracing motion, light, and tech to cut through the noise. Across malls in Dubai, Doha, and Riyadh, it’s no longer uncommon to see storefronts come alive with animations, responsive visuals, or even gesture-based content.

“Retailers today are competing not just for sales, but for attention, and in this region, that means making a bold visual impact,” said Zac Liang, General Manager – Gulf Area, Unilumin Group. “That’s why more brands are investing in outdoor LED displays that don’t just advertise, they engage.”

While many regions are adopting this trend, the Middle East is scaling fast. According to Grand View Research, the digital signage market in the Middle East and Africa is expected to grow from USD 1.66 million in 2024 to USD 2.80 million by 2030, with the GCC leading the charge thanks to infrastructure development, smart city strategies, and a strong mall culture. This growth is being fueled by the rising demand for immersive experiences, particularly in high-traffic outdoor retail environments.

The shift isn’t just about visuals; it’s also about interactivity. LED displays equipped with touchscreens, motion sensors, and augmented reality are turning passive browsing into active engagement. Shoppers can explore digital lookbooks, scan QR codes for real-time offers, or interact with content that responds to their presence. These experiences help bridge the online-offline divide, giving brands a powerful edge in driving foot traffic and customer engagement.

“Interactivity is no longer a luxury; it’s a necessity,” Liang added. “Our clients in the Gulf are asking for displays that do more than play content. They want screens that connect, respond, and adapt in real time.”

Unilumin has been at the forefront of this transformation. The company made waves by being the first in the LED industry to introduce MIP/COB technology for outdoor displays in China; the technology is now making its way into major Middle Eastern markets. At the 19th Hangzhou Asian Games, Unilumin deployed over 4,200 square meters of LED screens across key venues. Its outdoor COB display at West Lake, the world’s first outdoor high-brightness COB screen, not only lit up the event but became part of the visual narrative of the games.

That same energy is now flowing into the Gulf, where malls, airports, and open-air retail zones are hungry for solutions that combine aesthetics, interactivity, and performance. From arch-shaped LED portals in Dubai to street-facing media walls in Doha, the region is becoming a live canvas for digital storytelling.

The future of outdoor retail in the GCC isn’t just about visibility; it’s about visibility with purpose. Interactive LED displays give brands the power to stop shoppers mid-scroll, pull them off the sidewalk, and get them through the door. In a market where first impressions are everything, those few seconds on the street could mean the difference between a passerby and a purchase.

 

By Mansour Al Ajmi, CEO of X-Shift

Sustainable AI practices are no longer optional—they are essential for shaping technology that benefits both people and the planet. As artificial intelligence transforms industries from healthcare to transportation, the challenge is to ensure its growth is ethical, environmentally responsible, and socially inclusive. This means addressing not only energy efficiency and carbon reduction but also governance, fairness, and long-term societal impacts.

Why Sustainable AI Practices Go Beyond the Environment?

AI is now deeply embedded in investment strategies, medical diagnostics, media platforms, and public infrastructure. While reducing energy usage is vital, true sustainability also requires ethical governance and the elimination of bias.

For example, biased training datasets can unintentionally reinforce social inequality. Studies, such as those from the MIT Media Lab, have shown that some AI systems perform poorly with diverse populations, highlighting the risk of discrimination. Addressing this means conducting regular algorithmic audits, enforcing transparency, and ensuring diverse representation in AI development teams.

The Environmental Impact of AI

Training advanced AI models consumes enormous computational resources. The process can generate carbon emissions equivalent to hundreds of long-haul flights. To counter this, tech leaders are investing in renewable energy and designing energy-efficient processors and cooling systems.

However, sustainable AI practices should become the default, not the exception. From sourcing materials responsibly to rethinking hardware infrastructure, the focus must be on green innovation by design.

Embedding Sustainability at the Strategic Core

Sustainable AI practices work best when integrated into an organization’s core strategy. Aligning AI solutions with the UN’s Sustainable Development Goals (SDGs) can directly support climate action, reduce inequalities, and promote responsible consumption.

In the Middle East, initiatives like Saudi Arabia’s Vision 2030 and the UAE Strategy for Artificial Intelligence demonstrate how sustainability and AI can align with national priorities. These strategies not only meet ethical standards but also deliver competitive advantages, building consumer trust and fostering innovation.

Governance for Responsible AI

Strong governance is key to ensuring sustainable AI practices are upheld. Regulatory frameworks, such as the European Union’s AI Act, guide transparency, accountability, and fairness.

Governance should enable innovation while preventing harm. Public-private partnerships, global cooperation, and industry alliances are critical to creating ethical, scalable, and resilient AI ecosystems.

Preparing the Workforce for the AI Era

McKinsey estimates that AI adoption could displace up to 800 million jobs by 2030. Sustainable AI practices must include reskilling and upskilling initiatives to ensure inclusive economic growth.

By investing in training programs, organizations can help employees transition to new roles in AI-related fields. This proactive approach strengthens workforce agility and supports long-term resilience.

Leadership’s Role in Driving Sustainable AI Practices

AI can significantly advance sustainability goals, from optimizing supply chains to reducing environmental waste. Companies like Unilever are already using AI to achieve greener operations, proving its real-world potential.

Yet leadership commitment is essential. Executives must set measurable goals, model ethical behavior, and integrate sustainability into company culture. This ensures that sustainability is not a side project but a core business value.

The Shared Responsibility for a Sustainable AI Future

Creating a sustainable AI future requires collaboration between individuals, corporations, and governments. Citizens should stay informed and question how AI affects them. Companies must embed sustainability into their AI strategies, while governments need to establish policies that encourage responsible innovation.

By acting now, we can ensure AI evolves as a force for good—advancing technology without sacrificing ethics, equity, or environmental stewardship.

Check out our previous post on WHX Tech 2025 to Drive Global Digital Health Transformation