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02 January 2026 AIOps will unlock the next generation of hybrid infrastructure Simon NinanSVP Business Strategy
Discover the benefits of IT/OT convergence, plus our roadmap for achieving it in your own data center operations.
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Long story short Hybrid is now the defining model for IT infrastructure as data center operators are required to balance vast volumes of data and large-scale workloads across increasingly distributed environments. But to realize its full potential, organizations need to embrace the latest advances in AIOps. Why it matters In a world of skyrocketing compute demand and rapid technological change, managing distributed workloads effectively has never been more important. It’s the difference between success and failure in a complex competitive landscape. The complete picture A recent report from CoreSite found that 98% of IT leaders have adopted or plan to adopt a hybrid IT model. Added to this, the benefits that hybrid infrastructure can unlock for businesses – increased flexibility, scalability and adaptability to name but a few – are well known. Probably not new news, so why are we still so focused on talking about hybrid? Short answer – until now, the full potential of hybrid and multi-cloud environments has been left unrealized. Amidst the proliferation of GenAI tools, digital twins, and analytics, workloads have changed. And the question is not now whether to deploy hybrid and multi-cloud environments, but “how” to deploy, operate and optimize them. To do that, we must turn to the same transformational technologies that data centers are powering. Just as hyperscale compute is powering AI models, AI tools are in turn enabling the next generation of operations in hybrid and multi-cloud environments. It’s a virtuous circle – the same technologies that demand superior infrastructure also enable it. AIOps is at the heart of this latest generation of hybrid infrastructure. It provides the flexibility to take on new workloads in new ways, simplifying complexity by bringing disparate systems and data together - from on-premise systems to the edge. This unlocks a range of operational opportunities, from advanced automation and machine learning to real-time analytics and insight generation. Together, they enable data centers to keep up with growing demand while ensuring systems run smoothly.
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Case study Southwest Power Pool and NVIDIA In partnership with Southwest Power Pool and NVIDIA, we’re using AI to transform the U.S. power grid’s interconnection process. Our integrated solution, combining Hitachi iQ’s accelerated computing with proprietary AI algorithms, is reducing generator interconnection analysis times by 80%.
Get the most out of your data with AIOps Data is everywhere. Today’s data centers are awash with it across every layer of their operations. They are continually gathering it, from sensors embedded in GPUs and cooling systems to real-time updates on IT system performance. This means more visibility into traffic patterns, application workloads, and even the physical environment in which the systems operate.
“AI is now the brain of the modern data center, but to live up to that role it needs the capability to pull all the available operational data together.” Whereas five-to-ten years ago, operators were focused on gathering data from their infrastructure, the challenge is increasingly how to store, secure, and use the sheer volume and variety of data effectively. A 2024 study from SAP Global found that 80% of organizations are seeing an increase in data quantities. So how can they get a return on that data, bringing it to bear in decision making and operational efficiencies? And how do they ensure that it’s safeguarded and managed in line with regulatory requirements and customer expectations? More data, both structured and unstructured, means more complexity. Some of it may be private or sensitive, requiring particular access restrictions. And it may be protected by regional legislation like Europe’s GDPR, which requires user data to be stored and processed in region - this is one of many reasons for the shift to hybrid environments. It’s vital that organizations start with a comprehensive data management strategy before adopting sophisticated AIOps systems. AIOps refers to the use of AI technologies to manage operations, be that data, processes, or equipment. In the context of hybrid infrastructure, it can act as an intelligent layer that unifies disparate environments. That enables you to manage on premise systems alongside any number of connected clouds, data centers, and edge systems, seizing the opportunities that data offers; be that autonomous operations or advanced analytics. AI is now the brain of the modern data center, but to live up to that role it needs the capability to pull all the available operational data together. Gartner recently noted that the most urgent GenAI challenge for data centers will be data synchronization across the hybrid cloud. If achieved, this can then inform and automate decisions about everything from where to allocate workloads to liquid cooling and energy use.
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Case study HMAX HMAX uses AI-driven orchestration to optimize workloads across IT and OT systems. This is exactly what modern hybrid data centers are doing: balancing compute, storage, energy, and facility operations in real time to maximize efficiency, resilience, and performance.
Delivering next-generation hybrid infrastructure While the benefits and challenges of AI-driven hybrid data centers are clear in theory, the realities of delivery remain complex. The technology exists. But it requires a 360-degree approach with AI at the core, integrating multiple subsystems and intelligently orchestrating them. We recommend employing an ‘expanding, concentric circles’ approach. Essentially, you start small, at the rack. Here, there are different components, electromechanical and IT. Get them sharing and interacting. Next, focus on cooling. And then, it’s the energy piece, including grid interconnection, power generation, and energy storage. As with laying the right data foundations for your operations, this step-by-step approach helps avoid the pitfalls of complex hybrid environments. This methodology can also help to ensure that return on investment (ROI) is baked into operators’ approach to innovation. From the start, decision making should reflect both IT and business priorities, and implementation needs to be continually optimized for cost effectiveness. Next is the question of balancing workload distribution. Where data and workloads are hosted should be defined by your particular requirements. This can vary wildly: at Hitachi, one of our recent projects runs 80% on-prem. Consider the characteristics of your applications too. Are they compute intensive? Are they provided by third parties who should only access certain data? Supplier risk is a consideration here – today’s partner could be tomorrow’s competitor.
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Case study BMW Group BMW Group has partnered with Hitachi Vantara since 2012 to manage hybrid infrastructure across 10 countries — from Germany to China to the United States. Using intelligent automation and predictive analytics, BMW’s infrastructure automatically moves workloads to optimal systems, achieving 30% faster migrations with zero downtime.
Crucially, don’t forget to think beyond the infrastructure, aligning any technological changes with your strategic objectives. Too often, businesses invest in the architecture without thinking about how they’re going to monetize it. “Too often, businesses invest in the architecture without thinking about how they’re going to monetize it.” What’s next for the data centers of tomorrow? By the end of the decade, we’ll have fully autonomous, AI-driven data centers. This is the natural progression of the existing robotization of data centers. Because of the scale of their investment and R&D, hyperscalers will get there first. But we are already having these conversations with colocators too. The workloads they run will be increasingly complex, and customers will expect a resilient, application-ready approach that ensures uptime and performance. Governments will continue to regulate hybrid cloud environments and push for greater digital sovereignty. We welcome more development here. As it stands, Europe is the most advanced on data regulation. Often, where it leads others follow and we are likely to see more GDPR equivalents elsewhere. Equally, some countries may look to gain a competitive advantage with regulation that act as a facilitator for innovation, rather than a blocker. Wherever the future leads, the organizations that thrive will be the ones that lay the foundations and then embrace the latest AIOps solutions to enhance their data center operations.
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Own the conversation Technological innovation, especially in the data center space, must be guided by a deeper sense of humanity. How do we balance our commitment to scale and efficiency alongside a commitment to creating systems that serve people and safeguard the planet? Ask the big question: Are we missing the long view when it comes to data centers? Amid the rush to invest in digital infrastructure, do we have a holistic vision for how this transformation will deliver lasting value? For systems, for ROI and for the people and businesses they’re meant to serve. Disrupt your feed: In many parts of the world, access to data center infrastructure remains out of reach and digital equality is not yet a reality. As we build for the future, we need to ask: are we narrowing the digital divide, or quietly widening it? Drop this fact: Data quality is holding back almost half of AI projects from delivering ROI.
