Tech Features
ASUS Techsphere Forum: Empowering Business Leaders Through Next-Gen Hardware Innovation
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).

Subrato Basu, Managing Partner, Executive Board

Srijith KN,
Senior Editor,
Integrator Media
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.
Tech Features
How sustainable materials and AI are shaping NEOM, Masdar City, and Dubai’s new developments
NEOM, Masdar City and Dubai, cities that have long been a symbol of wealth and ambition, are not just building new skylines, they’re attempting to redefine what a city can be. With construction sector being one of the largest contributors to global emission, Middle East, flush with capital, ambitious projects, and new masterplans is testing a simple hypothesis: Can the region radically lower the carbon and resource footprint of entire cities through sustainable materials and Artificial Intelligence (AI)?

MSc Global Sustainability Engineering
Heriot-Watt University
Governments and developers, in the Gulf, are shifting policies and procurement practices toward low-embodied-carbon alternatives: recycled aggregates, low-carbon concrete, engineered timber, high-performance insulation and off-site modular systems that dramatically cut waste. According to Grand View Research, in 2024, the global green building materials market was estimated to be worth hundreds of billions of dollars, and it is forecast to grow. Moreover, the GCC green building materials market alone reached an estimated USD 10.6 billion in 2024 and, according to an IMARC Group report, is expected to grow significantly as demand for sustainable inputs scales up.
NEOM’s energy and utilities arm, Enowa, explicitly emphasises circular systems and positions the project as a 100%renewables-powered ecosystem that integrates water, energy and industrial systems from the outset. It combines Industry 4.0 technologies with circular economy principles that force the choice of materials toward those that can be reused or easily recycled, while promoting off-site fabrication techniques that shrink construction waste.
For more than a decade, Masdar City has been offering a working prototype of what happens when sustainable material choices meet a systems approach, translating low-carbon urban design into practice. It pairs demonstrable clean energy capacity with district cooling systems, solar generation, and energy-efficient building envelopes with planning that reduces transport demand. Masdar’s broader organisation, its parent group, has also been scaling fast. Its report highlighted growth in clean energy capacity and an organisational push into integrated, low-carbon urban projects. The Masdar model is a reminder that reliable renewable supply makes higher-embodied, energy-intensive solutions (for example, electric construction equipment charged by renewables).
But materials alone won’t be enough, this is where AI becomes a multiplier. AI tools now enable topology optimisation for material efficiency, predict and prevent waste by logistics algorithms (supply chain forecasting, demand matching). In operations, machine learning drives HVAC optimisation (manage buildings in real time, predictive maintenance). For projects on the scale of NEOM or Masdar, with thousands of buildings, millions of square meters and complex infrastructure, AI systems can turn millions of data points into continuous efficiency gains. NEOM and related initiatives are already integrating AI for water, energy and materials planning, while Oxagon’s industrial model assumes broad adoption of automation and AI in production.
Dubai’s trajectory shows how regulation and market amplify these technological shifts and incentives accelerate adoption. Municipal green building regulations, alongside certifications such as LEED and local green building systems, have driven a rapid uptake of sustainable construction practices, pushing developers to pursue energy-efficient envelopes, reduced water use, and green materials. According to Dubai Municipality, the city’s policy environment, paired with developers’ appetite for premium assets that offer lower operating costs and resilience to climate risk, creates an ecosystem where sustainable materials and smart building systems are not only environmentally desirable but financially sensible.
The Grand View Research estimates show the Gulf’s green-building sector and related materials markets expanding rapidly, with market valued in the mid-to-high tens of billions of dollars and forecast to double-digit compound annual growth rates in the coming five years. That inflow of capital matters because sustainable materials often carry higher up-front cost but deliver lower lifecycle costs, while AI and automation substantially reduce construction and lifecycle operating overruns. In other words, together they improve the return profile for long-term investors.
Yet ambition collides with practical constraints. Supply chains for low-embodied materials must scale quickly; while those in the region remain sensitive to cost, logistics, and local standards. Skilled labour in advanced assembly and data-science expertise to drive AI systems are limited and must be cultivated. Governance questions are also pressing: who owns the data generated by smart urban systems, how is privacy protected, and how do we ensure that AI allocates resources such as water, energy and mobility fairly. These are governance design problems, solvable, if tackled deliberately.
There are three pragmatic approaches for solving them. First, governments and project sponsors can accelerate local manufacturing of green materials through incentives and public-private partnerships. Second, procurement rules should favour lifecycle carbon and circularity over the lowest upfront price; that shifts incentives toward durable, reusable materials and off-site fabrication. Third, data-governance frameworks must be established from the outset: transparent rules about ownership and enable third-party innovation without commercial capture.
If NEOM, Masdar City and Dubai’s new districts can scale these approaches, the payoff will be tangible: lower lifecycle emissions, less construction waste, healthier indoor environments, and long-term savings for investors and taxpayers. The Middle East can move beyond being a market for imported technology to becoming a global crucible for sustainable urban practices, provided policymakers, developers and technologists align incentives and share data and best practices.
NEOM, Masdar and Dubai’s new districts are more than national statements; they are testbeds whose lessons could reshape how cities are built globally. If they get it right, prioritising lifecycle outcomes, scaling green materials, and embedding AI from design to operations, Middle East will be measured not only in square metres and skylines, but in the tonnes of embodied carbon avoided and the megabytes of intelligence that keep cities efficient and humane. The world will, for once, be watching not only to admire, but to learn.
Tech Features
Smart Grids: Powering the Middle East’s Renewable Energy Future
Dr. Mutasim Nour, Director of MSc Energy, School of Engineering and Physical Sciences, Heriot- Watt University Dubai
The usage of green energy has soared in the Middle East in recent years, highlighting the region’s futuristic and sustainable approach to socio-economic growth. According to a report by Rystad Energy, by 2050 renewable energy sources, including hydro, solar, and wind are expected to constitute a staggering 70 percent of the region’s power generation mix – a massive jump from the five percent recorded at the end of 2023. The UAE stands out in particular, ranking 10th globally in per capita solar capacity in 2023, with an impressive 708 watts per capita as per the World Future Energy Summit 2025 report. From a modest 12 MW in 2012 to an ambitious 6.1 GW in 2023, the UAE’s solar capacity has grown rapidly. Saudi Arabia is also making significant progress in this domain, with over 17 major renewable projects producing 41.2 million MWh annually that are aimed at fulfilling nearly 66 percent of residential energy needs.
These diversification efforts are accelerators of economic development as well as environmental well-being. However, green energy relies on variables that often fluctuate such as temperature, season, and wind intensity. This makes balancing supply and demand a complicated task requiring innovative solutions. The most promising one has been found in Smart Grids, which are an upgraded version of the traditional power network. These grids use digital technologies to monitor, predict, and respond to energy demand in real time, and enable two-way interactions where consumers can also produce energy (through solar panels, for example) and feed it back into the system. Their components include an Advanced Metering Infrastructure (AMI), grid automation and control, energy storage, and demand response programs that help them deliver superior results.
Smart grids are more flexible, efficient, and reliable compared to traditional grids and have helped significantly strengthen the renewable energy infrastructure in the Middle East. Saudi Arabia, for instance, has been developing AI-powered smart grids to integrate renewable energy and modernise infrastructure as part of its Vision 2030 initiative. It has already automated 32% of its electricity distribution network and installed more than 11 million smart meters to further meet its goal of achieving net-zero emissions by 2060.
The UAE has also emerged as a pioneer in renewable energy innovation. Under the UAE Energy Strategy 2050, the nation aims to triple its renewable energy contributions by 2030 and achieve a 50 percent clean energy mix by 2050, aided by a substantial investment of AED150-200 billion. The Department of Energy in Abu Dhabi also recently announced the first legally binding clean and renewable energy target in the Middle East called the Clean Energy Strategic Target 2035. This regulatory framework dictates that 60 per cent of the emirate’s electricity will be generated from clean and renewable sources by 2035, and there will be up to 75 per cent reduction in carbon emissions per MWh produced by the electricity sector. Energy storage solutions to achieve this goal, due to which the Department of Energy has signed a Memorandum of Understanding with the State Grid Corporation of China to build a strong and highly efficient smart energy and power system.
In Dubai, progress in green energy is being led by the Dubai Electricity and Water Authority (DEWA), which has executed a $1.9 billion smart-grid initiative to deliver high standards of reliability and energy management. The smart grid initiative has helped DEWA achieve some remarkable outcomes: in 2023, line losses in electricity transmission and distribution networks were reduced to 2 percent, compared to 6-7 percent in Europe and the US. Additionally, water network losses dropped to 4.6 percent, significantly lower than approximately 15 percent reported in North America.
Even as smart grids transform the energy landscape, there are challenges that hinder the ability to effectively scale them up. These include:
- Technical interoperability: Smart Grids run on a complex mix of sensors, meters, and communication devices that are often made by different manufacturers. Ensuring that all data between these components is compatible and integrated correctly is often a difficult feat.
- Cybersecurity: The reliance on digital communications and internet-based technologies in Smart Grids bring a new set of challenges with them. There is increased vulnerability to cyber-attacks that can lead to power outages, data breaches, and even structural damage to grid infrastructure.
- Regulatory barriers: Current regulations and policies often need to be adapted for the dynamism of smart grids. A clear and streamlined framework makes adoption easier and attracts investments into this technology.
- Consumer awareness: Consumers can be skeptical of the advantages a smart grid presents, especially due to data privacy concerns and doubts regarding wireless communication. Initiatives like community education and incentivisation can go a long way in increasing consumer acceptance and support.
Smart grids also depend on a high initial investment and regular infrastructure upgrades to function properly. To address these challenges, governments across the world must formulate a comprehensive strategy that outlines the investment, infrastructure, and education required for smart grid networks in their region. A streamlined approach and clear objectives can revolutionise green energy integration and help mitigate climate change. With smart grids, consumers are empowered to become a part of the energy ecosystem and foster a culture of conservation and sustainability.
Tech Features
HR-led Initiatives to nurture women-led TECH startups

Professor Fiona Robson,
Head of the School of Social Sciences & Edinburgh Business School
HR is no longer just about recruitment and retention – there is a growing trend towards taking a more creative and innovative approach. This can involve looking at talent through different lenses rather than seeing it as a hierarchical talent management process focusing on vertical promotions within the organization.
In an age where HR rightly have a seat at the strategic table for decision making, they have the opportunity to ensure that appropriate levels of funding and expertise are used to develop a forward-looking talent strategy that goes beyond the norm. Artificial intelligence (AI) brings a plethora of opportunities for organisations to be braver in how they identify potential talent. Using AI to identify talent can be a useful starting point but when it comes to areas such as innovation and identifying an entrepreneurial mindset, it may be more difficult to pinpoint the traits which could indicate potential to innovate.
Where HR teams start to consider and plot non-traditional pathways they may be able to recruit and retain employees with diverse skillsets. Taking an entrepreneurial path opens up the talent pool as it isn’t as focused on people looking at the next hierarchical step up within the organisation. This is important as there are usually resource constraints about how many vacancies are available to fill at the highest pay grades in the organisation. These new pathways should provide opportunities for women to shine in different types of projects and recognise the strategic importance of creative thinking and innovation.
Providing testing opportunities
Depending on the level of finance available there are some additional resources that could be provided. Innovation sprints or challenges can be a great way to test out ideas and receive feedback from different groups of stakeholders. They may facilitate prototyping and identify issues that were not previously considered. Internal technology venture labs can also provide a safe environment to test out ideas and proof of concept. Collaborating with Universities who are experienced in running labs and sprints can be very beneficial, they may also have access to funding to support the development of new products and services. However, in order to be truly successful, they need to ensure that there is a sustainable follow up process before the momentum deflates.
Inclusive Procurement and Equitable IP Policies
Organisations can seek to lead the way and exhibit good practice by reviewing their procurement policies where practicable to ensure that they are inclusive. Examples may include having provision for flexible payment terms which would make it easier for those at the beginning of their entrepreneurship journey. Access to specialist support which could help women to set up their businesses in a more timely way could also break down some of the perceived barriers. Often the processes around procurement can be rather cumbersome so the provision of training which shows exactly how to navigate it could be helpful. Forward looking organisations might seek to approve a process whereby women entrepreneurs are given priority with their applications.
For many women, even thinking about intellectual property (IP) and patents can seem overwhelming if they have never had exposure to this world before. The introduction of simplified processes could act as a springboard to attract more women, particularly if the timeline can be expedited so that there is a shorter gap between the initial idea and when it is approved. Having access to real case studies showing how this happens would also be reassuring. In large organisations, the HR team may be able to encourage the legal team to provide some initial advice so that prospective entrepreneurs get a realistic insight into whether their proposal has merit or not.
Commercial Advantage Through Policy
HR can make a name for itself by encouraging innovation through widening participation and breaking down barriers to encourage, support and recognise innovation. For some HR professionals this may be quite a shift for them so they may also require some reskilling and retraining.
It would be good practice for organisations to regularly review their HR policies to ensure that they reflect the changing eco-system and that there are appropriate diversity clauses within the library of policies, procedures and practices. As part of this, ensuring that there is awareness of bias and how this can sneak into processes unconsciously and inadvertently disadvantage women. The establishment of women’s networks would be a proactive approach and could help them at all of the different stages of developing and executing their entrepreneurial ideas.
Providing funding opportunities may be one of the most impactful decisions that an organisation can make. Obviously this would need to have transparent parameters around it but it could be the difference between an idea being turned into practice or not. If the funding allocation is governed by stakeholders with appropriate expertise in different areas i.e. finance, law, governance and people, this would reduce the organisational risk of investing in small new businesses. Where there isn’t a potential conflict of interest, organisations could also make a significant impact by facilitating market entry and opening doors within networks and supply chains.
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