Technical Policy Manager - Responsible AI Deployment

AVEVA to highlight real-time industrial intelligence for mining operations. AVEVA, a global leader in industrial software, will be participating in the Egypt Mining Forum, taking place at The St. Regis New Capital in Cairo, from 28 to 29 September 2026. The company will demonstrate how mining organisations across Egypt and North Africa can gain real-time visibility across the mining value chain to make smarter decisions faster. Under the theme, "Connect your mine from planning to production", AVEVA will showcase how a connected digital thread linking people, systems and data across the mining value chain can provide a unified view while connecting information across traditionally siloed stages such as exploration, mine planning, engineering, construction, operations, processing, logistics and optimisation. Egypt is targeting an increase in mining's contribution to the national gross domestic product from well under 1% today to around 5%-6% by 2030, as part of wider sector reforms and Egypt Vision 2030. This ambition is supported by the government's recent decision to restructure and create an economic entity for the mining sector - a move intended to streamline investment and attract new capital into the sector. Exploration opportunities for gold, phosphate and other minerals are being opened up under an open-sector mining model, with the government pressing for greater domestic processing to add value before export. This shift, from extraction toward integrated and value-added production, depends on the kind of end-to-end operational visibility that AVEVA will demonstrate at the forum. Speaking on its participation, Khaled Salah (pictured), vice-president for Africa at AVEVA, says: "Egypt's mining sector is entering a new phase of investment, modernisation and growth. It is focused on attracting investment, improving productivity, modernising operations and building a sustainable mining industry. AVEVA supports these objectives by helping mining companies accelerate digital transformation, improve operational efficiency, strengthen sustainability performance and unlock greater value from mining assets. "We look forward to engaging with industry leaders and highlighting how digital technologies can help Egypt's mining sector achieve greater resilience, efficiency and sustainability, while unlocking long-term value across the industry." Glenn Kerkhoff, industry principal for Metals, Minerals and Mining at AVEVA, will join the strategic panel, "Developing Competitive and Responsible Gold Value Chains from Mine to Market", on 28 September, 16h00-16h40. The discussion will explore how greater collaboration across the gold ecosystem can support a commercially competitive and responsibly governed value chain connected to international bullion markets. At the event, visitors will be able to explore live demonstrations of the AVEVA PI System, which captures, contextualises and delivers trusted real-time industrial data across the operation; and the AVEVA Unified Operations Centre, which brings data from disparate systems into a single operational view for enterprise-wide situational awareness. Together, these solutions will demonstrate how industrial data can be connected and visualised to provide a more unified view of mining operations and support informed decision-making. Industry experts from AVEVA will be available at Stand 1C22 to share examples on how industrial data can be connected, visualised and transformed into actionable operational insights through the AVEVA PI System and AVEVA Unified Operations Centre.

Industrial intelligence platforms like AVEVA CONNECT: A buyer's guide for manufacturers. Platforms like AVEVA CONNECT promise to unify a manufacturer's engineering data, operational data, and analytics into one system spanning design, build, operate, and optimize. For a mid-sized manufacturer evaluating one of these platforms, the pitch is compelling - and the fine print matters more than the pitch does. This guide walks through what these platforms actually do, where they genuinely help, and where a leaner, purpose-built layer often serves a growing manufacturer better. What an industrial intelligence platform actually does. At its core, a platform like AVEVA CONNECT ingests data from cloud applications, edge data stores, historians, remote IIoT sensors, and MES systems, then applies contextualization, visualization, and modeling on top. The promise is to bridge two worlds that traditionally sit apart: engineering information - the CAPEX side, covering design and asset data - and operational information, the OPEX side covering day-to-day execution and performance. On top of that data layer sits an analytics and AI layer: first-principles models blended with machine learning, plus purpose-built modules for things like maintaining stable process operating bands, batch-to-batch consistency, predictive maintenance, and predictive energy or throughput. AVEVA's own presentation materials cite a familiar industry statistic here - data scientists typically spend roughly 80% of their time gathering, cleaning, and preparing data before any modeling work begins. That is the specific problem these platforms are built to solve. Where this genuinely helps. For large, multi-site operations, platform consolidation has a real, demonstrated payoff. GSK's vaccine manufacturing case - 12 global sites running a shared operations platform and historian - reported a 60% reduction in local qualification effort and reached full return on investment after five site rollouts. That is the kind of result that only shows up once you have enough sites, enough data variety, and enough internal capacity to run a genuinely enterprise-wide rollout. The same logic applies to newer, capital-intensive verticals. Semiconductor fabrication is a clear example: roughly 18 new fab construction projects were reported starting in 2025, with operations beginning in 2026 and 2027, and improving facility and equipment reliability alone can lift production efficiency by more than 10% without adding new tools, according to McKinsey. A facility monitoring and control system spanning gas, electrical, chemical, water, mechanical utility, cleanroom HVAC, safety, and energy management is a genuinely platform-shaped problem - too many interdependent subsystems for a point solution to handle well. Gigawatt-scale AI data centers are the newest version of the same pattern. AVEVA has partnered with NVIDIA to build lifecycle digital twin architecture specifically for facilities at that scale, combining industrial operational modeling with accelerated computing to simulate power consumption, cooling load, and environmental performance before and during construction. Again, the common thread is scale and interdependency large enough that no single point tool could realistically cover it. Where the gap opens for a growing manufacturer. The pattern in both examples above is scale: many sites, many subsystems, enough budget and enough internal engineering capacity to run a multi-year platform rollout properly. That is a very different starting point from a single-site or two-site manufacturer trying to get real-time visibility into one production line. This is where the honest tension sits. A full industrial intelligence platform is built to unify everything - which is exactly why it takes real time and real internal capability to deploy well. For a company that needs to know, right now, whether an approval is stuck, a threshold has been breached, or a batch is drifting out of spec, a platform-first approach can mean months of integration work before the first useful alert ever fires. A leaner, event-driven decision layer takes the opposite starting point: instead of unifying all data first and building insight on top, it listens for the specific events that already matter - a sensor threshold crossed, an approval pending, a batch parameter drifting - and acts on them directly, on top of the systems you already run. It does not replace a platform like AVEVA CONNECT; it solves a narrower, more immediate problem without asking you to wait for the full unification project to land first. How to evaluate what you actually need. Count your sites and subsystems, honestly. A single-site operation with a handful of interdependent subsystems is not yet the case a full platform was built to solve. A dozen sites with shared historians and shared qualification requirements, like GSK, is. Separate "we need visibility now" from "we need a unified data foundation eventually." Both are legitimate goals, but they are not the same project, and conflating them is how platform rollouts stall for a year before delivering a single usable alert. Check who will run the integration. The 80% data-prep burden the platform vendors themselves cite does not disappear when you buy the platform - it moves into your implementation project. Know, concretely, who on your team or which partner is carrying that work, and what it costs in months, not just budget. Ask what "optimize" actually means for your operation today. The full lifecycle framing - design, build, operate, optimize - is the right end state for a capital-intensive, multi-site business. For a smaller operation, "optimize" this year may simply mean catching the one recurring failure mode that already costs real money, not building out all four phases at once. Start with the narrowest version of the problem that is actually costing you money today. A missed approval, a quality drift caught too late, a threshold breach nobody saw in time - these are solvable with a targeted event-driven layer in weeks, not the multi-year timeline a full platform rollout implies. None of this is an argument against platforms like AVEVA CONNECT - for the right scale of operation, the GSK-style payoff is real. It is an argument for being honest about which stage your company is actually at before committing to either path.

ASUS advances AI Factories with integrated infrastructure and operational governance. Sep 03, 2026, 12:10 ET Uniting accelerated computing, simulation-validated deployment and AI governance into one continuous AI factory platform SEOUL, South Korea, Sept. 3, 2026 /PRNewswire/ - At ASUS AI Tech 2026, ASUS today redefined AI infrastructure from systems that run AI to platforms that build and operate AI factories. Working with NVIDIA and a growing ecosystem of technology partners - including Aleria, AMD, Crusoe, Foxlink, IBM, Intel, Samsung, Schneider Electric and WD - ASUS is enabling enterprises to move beyond standalone infrastructure to production-ready AI factories where performance, operational resilience and AI governance are engineered across the entire lifecycle. ASUS also reinforced its unified AI infrastructure platform spanning accelerated computing, networking, storage, deployment software, AI operations and governance. By integrating infrastructure engineering, deployment and operational intelligence into a continuous platform, ASUS helps organizations shorten time to first token, accelerate time to revenue and optimize infrastructure efficiency while maintaining trusted AI at scale. This strategic evolution solidifies the role of ASUS as a pivotal end-to-end AI infrastructure partner, transforming computing capacity into tangible, business-driving AI innovation. "ASUS is now comprehensively embracing its All in on AI strategic initiative, dedicated to embedding AI across infrastructure, commercial solutions, and innovative physical devices. As AI factories evolve into always-on production environments, infrastructure performance and AI governance can no longer be addressed separately. Infrastructure is the foundation of AI production itself. From the decisions that shape the first token to the controls that govern AI at scale, ASUS is engineering for what comes next - bringing compute, connectivity, power, cooling and automation together to turn every watt, every dollar and every minute invested into AI output." said Paul Ju, Senior Vice President, Head of Infrastructure Solution Business Group, ASUS. AI governance starts before the first token Building a trusted AI factory begins long before the first model is deployed. ASUS combines advanced system engineering with the NVIDIA DSX Sim Blueprint to help enterprises design, simulate, validate and optimize complete AI factories before physical deployment. Working with ecosystem partners including Schneider Electric, AVEVA and IBM, ASUS brings compute, networking, storage, power, cooling and facility infrastructure into a unified digital twin environment, enabling organizations to identify constraints, optimize configurations and reduce deployment risk before installation begins. Once deployed, governance extends throughout AI operations. As agentic AI and physical AI become increasingly autonomous, organizations require continuous visibility, oversight, and operational control across models, data, tools and enterprise systems. Beyond the infrastructure management platforms, ACC (ASUS Control Center) and AIDC (ASUS Infrastructure Deployment Center), ASUS significantly extends these capabilities through its comprehensive AI Software Stack and Turnkey AI Applications. Within the AI Software Stack, customers gain direct access to essential tools such as Quota & Billing and an MLOps Portal. These facilitate efficient resource management within the data center, thereby accelerating overall operational speed. At the application level, the AI Software Stack acts as a crucial operational layer and cross-platform software stack. It functions as an 'operational harness', connecting enterprise governance directly with AI services and autonomous agents, enabling secure, manageable, and production-ready deployment of agentic AI. Backed by advanced integration capabilities and a long-standing collaboration with NVIDIA, ASUS enables enterprises to accelerate AI from design to deployment and ongoing operations. Strong business growth reflects increasing market demand for integrated AI infrastructure that shortens time to first token, accelerates time to revenue and supports AI factories ranging from frontier-scale model development to enterprise, agentic and edge AI deployments. * For large-scale AI factories: ASUS AI POD, XA VR721-E3, built on NVIDIA Vera Rubin NVL72, provides a rack-scale platform for high-volume intelligence and low cost token generation delivering 10X the performance per watt versus the prior generation. Complementing this, the enterprise-ready XA NR1I-E12LR and XA NR1I-E12L, based on NVIDIA HGX Rubin NVL8, accelerate high-density AI infrastructure for demanding enterprise training, inference, and post-training workloads. * For agentic AI: The ASUS XA P2N-E2, a 2U NVIDIA MGX platform equipped with two NVIDIA Vera CPUs and supporting up to two dual-slot NVIDIA GPUs, is purpose-built for responsive, energy-efficient agentic reasoning, data processing, and orchestration at scale. * For edge and visual computing: ASUS ESC8000-E12P, featuring NVIDIA RTX PRO 6000 and RTX PRO 4500 Blackwell Server Edition GPUs, extends acceleration for enterprise inference, vision AI, and visual computing from the data center to the edge. Additionally, the compact PE3000N edge-AI platform, powered by NVIDIA Jetson Thor T5000 module, delivers real-time inference, sensor fusion, and autonomous control for robotics and industrial automation environments. * For robust data foundation: The ASUS UF920-E3-RS24 server, based on the NVIDIA STX modular foundation for AI-native storage, together with the ASUS OJ340A-RS60 object storage solution and VS320D-RS26N storage system, provides the resilient data foundation required for enterprise AI. Designed to meet the capacity, availability and data management demands of modern AI pipelines, the portfolio supports AI factories from design validation to production-scale deployment. Customer applications and AI factory implementation Beyond foundational infrastructure, ASUS showcased AI's tangible impact through key partner demonstrations, illustrating real-world applications and emerging AI paradigms. Foxlink demonstrated next-level industrial automation with autonomous robotics, while Aleria highlighted the critical need for Sovereign AI deployments designed to support localized data control and secure operations. This holistic approach, combined with robust ASUS software-hardware integration and Kubernetes platforms, significantly shortens time-to-revenue for our customers. The ASUS vision extends beyond merely selling computing power; we deliver operational AI factories designed to continuously generate business value and drive the token economy. Accelerating AI adoption for transformative growth Looking ahead, ASUS is committed to continuously deepening its collaboration with NVIDIA and a growing global ecosystem of technology partners. By integrating accelerated computing, simulation-validated deployment, and AI harness engineering into one continuous governance framework, we empower organizations to protect their infrastructure investments and maintain control as AI applications grow in complexity and autonomy. Our overarching goal is to help enterprises transition from simply investing in AI to embedding intelligence across their entire operations. This strategic shift is designed to shorten time to revenue, improve efficiency of tokens per watt, significantly reduce resource waste and TCO, and ultimately build AI Factories engineered for peak performance, designed for adaptability, and governed for scalable growth. AVAILABILITY & PRICING ASUS servers are available worldwide. However, the availability of certain other ASUS products is subject to local regulatory requirements. For specific product availability and offerings in your region, please contact your local ASUS representative.