{"id":170871,"date":"2026-08-25T10:52:43","date_gmt":"2026-08-25T09:52:43","guid":{"rendered":"https:\/\/www.intelligentcio.com\/eu\/?p=170871"},"modified":"2026-09-03T08:30:03","modified_gmt":"2026-09-03T07:30:03","slug":"bringing-energy-tech-and-intelligence-together-in-the-ai-era","status":"publish","type":"post","link":"https:\/\/www.intelligentcio.com\/eu\/2026\/08\/25\/bringing-energy-tech-and-intelligence-together-in-the-ai-era\/","title":{"rendered":"Bringing energy tech and intelligence together in the AI era"},"content":{"rendered":"\n

Steven Brown, Vice President, EcoStruxure IT Line of Business, Schneider Electric, on why connecting power, cooling, buildings and IT through Energy Intelligence will be critical to the operation of AI-native data centres.<\/em><\/p>\n\n\n

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Steven Brown, Vice President, EcoStruxure IT Line of Business and Data Center Software at Schneider Electric<\/strong><\/figcaption><\/figure><\/div>\n\n\n

Artificial Intelligence is changing far more than the workloads running inside data centres. The shift towards generative, agentic and physical AI is fundamentally changing how facilities are designed, powered and operated.<\/p>\n\n\n\n

For decades, operators could rely on relatively stable environments. Power demand was predictable and cooling systems were engineered around known workloads. Operational technologies such as building management systems (BMS), electrical power management systems (EPMS) and data centre infrastructure management (DCIM) software each performed their role effectively, largely within their own domain. However, that operating model is rapidly reaching its limits.<\/p>\n\n\n\n

Power swings<\/strong><\/p>\n\n\n\n

Today\u2019s AI clusters introduce highly dynamic workloads, unprecedented rack densities and emerging power architectures that require infrastructure to respond continuously rather than periodically. At the same time, operators face growing pressure to improve resilience, optimise energy use and make faster operational decisions, often while managing increasingly complex facilities with fewer specialist resources.<\/p>\n\n\n\n

The challenge is no longer a lack of data. Modern data centres already generate vast amounts of operational information. The real challenge is turning that data into intelligence that understands how the entire facility operates as one connected system.<\/p>\n\n\n\n

This requires a different approach \u2013 one that brings energy technology and intelligence together in a single platform.<\/p>\n\n\n\n

From isolated systems to connected operations<\/strong><\/p>\n\n\n\n

Historically, separating operational technologies into silos made perfect sense.<\/p>\n\n\n\n

BMS controlled environmental conditions, EPMS monitored power infrastructure, DCIM platforms focused on IT assets and capacity planning and SCADA systems managed industrial processes. Each was designed to optimise a specific operational domain and, for many years, that was enough. The problem now is that AI workloads do not behave in isolated domains.<\/p>\n\n\n\n

Rapid or cascading increases in GPU utilisation immediately affect power demand. Higher electrical loads influence cooling performance. Cooling decisions affect rack temperatures. Those changes can have implications for workload placement, infrastructure resilience and operational efficiency, often within seconds.<\/p>\n\n\n\n

Understanding any one of these systems in isolation is no longer sufficient. Hyperscale, colocation, neocloud and enterprise data centre operators increasingly need to understand the relationships between them. This represents a significant shift in how today\u2019s data centres need to be managed.<\/p>\n\n\n\n

AI is exposing the limits of traditional operating models<\/strong><\/p>\n\n\n\n

Three industry trends are accelerating this transition.<\/p>\n\n\n\n

The first is workload volatility. AI training and inference create highly dynamic power profiles that fluctuate far more rapidly than traditional enterprise applications. Infrastructure now has to respond to continuous variation rather than predictable peaks.<\/p>\n\n\n\n

The second is density. Higher rack power has accelerated the adoption of liquid cooling and new generations of AI infrastructure are increasing operational complexity across every part of the facility.<\/p>\n\n\n\n

Schneider Electric\u2019s own AI-ready infrastructure reflects this shift, highlighting the growing importance of power, cooling, rack design and integrated operational management as AI deployments scale.<\/p>\n\n\n\n

The third is simply scale. Across every region, operators are trying to deliver new capacity faster than ever before while navigating constraints around power availability, skills and sustainability. Success increasingly depends on making better operational decisions rather than simply adding more infrastructure.<\/p>\n\n\n\n

Many organisations have responded by introducing AI-powered analytics within individual operational systems. These solutions undoubtedly provide valuable insight, but they often remain focused on a single domain.<\/p>\n\n\n\n

Making individual systems more intelligent does not automatically create an intelligent data centre. The real challenge lies in connecting operational context across the entire facility.<\/p>\n\n\n\n

Why operational intelligence matters<\/strong><\/p>\n\n\n\n

Most operators can already see what is happening inside their infrastructure. The more difficult question is understanding why it is happening and what will happen next.<\/p>\n\n\n\n

If temperatures begin to rise within an AI cluster, is the underlying cause an increase in workload, a change in cooling distribution or an emerging electrical constraint?<\/p>\n\n\n\n

If additional compute capacity is provisioned for a customer, will the existing power and cooling infrastructure continue to operate within safe limits?<\/p>\n\n\n\n

If maintenance is required on one electrical asset, what are the downstream implications for cooling resilience, redundancy and workload availability?<\/p>\n\n\n\n

The good news is the necessary information often already exists. What is missing is the operational context that connects those individual observations into a complete understanding of how the facility behaves as one system. This is where the industry is now beginning to evolve.<\/p>\n\n\n\n

Bringing energy tech and intelligence together<\/strong><\/p>\n\n\n\n

The next generation of operational environments will not simply collect more information. They will be able to understand and reason with it and then take action.<\/p>\n\n\n\n

Rather than treating power, cooling, buildings and IT as independent systems, they create a shared operational model that recognises how every asset relates to the wider environment.<\/p>\n\n\n\n

This is similar to looking at the difference between a traditional paper map and a modern navigation system. A map allows you to plot a route but does not account for the changing conditions around you. A navigation system continuously interprets real-time environments \u2013 traffic, road closures or unexpected delays \u2013 and adjusts the route.<\/p>\n\n\n\n

AI-native data centre operations apply the same principle, understanding the new conditions across the facility to help operators determine the appropriate next action.<\/p>\n\n\n\n

This is the foundation of Energy Intelligence. Instead of analysing isolated data points, Energy Intelligence creates operational context. It understands the relationships between assets, systems and events, allowing operators to move beyond monitoring towards prediction, optimisation and autonomous decision support.<\/p>\n\n\n\n

Equally important, this connected intelligence must be built on secure and open foundations. As data centres become increasingly critical national infrastructure, resilience, cybersecurity and interoperability become inseparable from operational performance.<\/p>\n\n\n\n

Our wider strategy reflects this convergence of electrification, automation, AI and secure operational technologies across the full lifecycle of digital infrastructure.<\/p>\n\n\n\n

The result is a fundamentally different way of operating as operational data becomes connected rather than simply collected. Systems become contextual rather than isolated.<\/p>\n\n\n\n

Infrastructure becomes intelligent rather than merely automated.<\/p>\n\n\n\n

From vision to implementation<\/strong><\/p>\n\n\n\n

This thinking sits behind Schneider Electric\u2019s EcoStruxure Foresight platform<\/a>.<\/p>\n\n\n\n

Rather than introducing another operational application, Foresight is designed to unify previously separate operational domains into a single AI-powered environment.<\/p>\n\n\n\n

By bringing together power and building management, it enables decisions to be made using a shared understanding of the entire facility instead of disconnected streams of information.<\/p>\n\n\n\n

The objective is not simply better visibility. It is faster engineering, more informed operational decisions, improved resilience and greater efficiency across increasingly complex environments.<\/p>\n\n\n\n

It is this approach that creates an open and scalable operational environment capable of delivering measurable improvements in engineering productivity, operational efficiency and uptime.<\/p>\n\n\n\n

Building AI-native data centres<\/strong><\/p>\n\n\n\n

AI is forcing the industry to rethink not only the infrastructure supporting high-density workloads, but the intelligence responsible for managing it.<\/p>\n\n\n\n

As power, cooling, buildings and IT become increasingly interdependent, the future belongs to connected operational environments built on shared context rather than isolated systems.<\/p>\n\n\n\n

No single organisation will deliver that future alone. It will require open platforms, strong partner ecosystems and a commitment to interoperability that allows technologies from across the data centre to work together as one operational environment.<\/p>\n\n\n\n

The data centre has become one of the world\u2019s most important pieces of critical infrastructure and the way it is managed must evolve accordingly.<\/p>\n\n\n\n

Bringing energy technology and intelligence together is no longer simply an opportunity for innovation. It is becoming the foundation of the AI data centre.<\/p>\n","protected":false},"excerpt":{"rendered":"

Steven Brown, Vice President, EcoStruxure IT Line of Business, Schneider Electric, on why connecting power, cooling, buildings and IT through Energy Intelligence will be critical to the operation of AI-native data centres. Artificial Intelligence is changing far more than the workloads running inside data centres. The shift towards generative, agentic and physical AI is fundamentally […]<\/p>\n","protected":false},"author":58,"featured_media":170872,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[21064,21513,55,218,20673,13,93,24],"tags":[22787,19517,577,19012,1389,9394,26535,26534,26536,774],"class_list":["post-170871","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-data-centre-technologies","category-data-centres","category-editors-choice","category-expert-opinion","category-main-story-newsletter","category-top-stories","category-used","tag-ai-data-centres","tag-ai-infrastructure","tag-artificial-intelligence","tag-cooling-systems","tag-data-centre-operations","tag-dcim","tag-ecostruxure-foresight","tag-energy-intelligence","tag-power-management","tag-schneider-electric"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/170871","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/users\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/comments?post=170871"}],"version-history":[{"count":8,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/170871\/revisions"}],"predecessor-version":[{"id":171122,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/posts\/170871\/revisions\/171122"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/media\/170872"}],"wp:attachment":[{"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/media?parent=170871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/categories?post=170871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.intelligentcio.com\/eu\/wp-json\/wp\/v2\/tags?post=170871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}