As annual planning season arrives, organizations everywhere are wrapped up in budgets, forecasts, and the inevitable need to justify next year’s spending. Yet for many, data center software still adds unnecessary complexity to this process – spread across different systems, consolidating key insights is more challenging, and often more time-consuming than it should be. The result is lost efficiency at a time when clarity matters most.

But when used strategically, software can become an invaluable asset in tasks like annual planning. Real-time visibility into the health, performance, and risks of critical infrastructure enables teams to make better-informed decisions around budget justification and resource allocation.

And if software can already deliver this level of insight, it naturally raises the question of how much further these capabilities can go. Increasingly, these tools also provide a foundation for broader facility modernization efforts – especially as AI moves toward the Edge and brings with it a host of new demands on power, cooling, and overall management.

Jon Gould, director of business development for software at Schneider Electric, plays a key role in shaping the data center software ecosystem, and has made it his mission to ensure that every offering – from data center infrastructure management (DCIM) to building management systems (BMS) and electrical power monitoring systems (EPMS) – receives the attention it deserves.

From monitoring to modeling

When it comes to DCIM, nearly everyone has a slightly different definition of what it truly encompasses.

In its early days, DCIM was largely synonymous with asset tracking. But over time, its role has extended well beyond logging equipment and monitoring real-time operations so that today these systems provide forward-looking insights to help operators understand not only what is happening, but what could happen – for better or for worse.

Information technology (IT) and operational technology (OT) play essential, complementary roles in this evolution. IT systems process, store, and deliver information (equipment like servers, storage, and compute hardware) while OT systems provide the physical environment that keeps them running (power distribution, UPS systems for cooling, and such). DCIM sits between these layers to provide visibility and tracking to ensure safe and efficient operation.

Modern data centers are increasingly relying on the convergence of IT and OT. Rather than being monitored as separate domains, they are brought together through a unified, holistic view. As these systems influence each other more directly, the boundaries between them blur, and DCIM becomes the integration layer where they finally meet. Gould explains:

“Power capacity planning, for example, is a very complex topic, and you need a lot of data and the right software to do it properly. We’re seeing the bridge between IT and OT become very real – especially as we move toward liquid cooling at the chip or rack level, creating almost a physical connection between the two.”

Digital twins, increasingly integrated into advanced DCIM platforms, sit right at the intersection of IT, OT, and DCIM, representing the next evolutionary step that turns monitoring and management into simulation and prediction. Because digital twins rely on real-time IT and OT data feeds to behave as realistic models of the facility, they demand a comprehensive, interconnected view of the environment.

This requires understanding how every component – from compute resources to power systems to downstream applications – interacts within a larger chain of dependency. The data center architecture, the IT networks that interlink it, and the remote Edge applications it supports all form a single digital ecosystem.

Mike Oakes, Schneider’s director of software and technical sales for its secure power division, emphasizes:

“Our equipment exists across the ecosystem, and we look at the whole install base as it spans from the data center out to the Edge – that way we’re protecting the whole path from the data center to the user.”

The possibilities are endless

AI workloads, with their fluctuating, volatile profiles, have fundamentally changed how we design and operate data centers. While much of AI’s long-term impact on data centers – and digital infrastructure more broadly – is still uncertain, what the industry can do is adapt to this new operating profile, from changes in physical layout to shifts in power architecture and beyond.

One challenge, as Gould points out, is the sheer number of operators it would take to manage data centers at these escalating capacities – especially as kilowatt demands continue to rise year after year.

“We’re going to have to leverage software to allow us to be more efficient from a use-of-people-hours perspective,” he explains.

When you consider the data center as an ecosystem – with an overwhelming abundance of data generated at every point along the journey from the facility itself to the end user – the potential for AI to analyze complex patterns and issues is even clearer. As Oakes explains:

“There is an incredible amount of data to be collected, analyzed, and contextualized.

"We’re always looking at new ways to incorporate AI into our EcoStruxure IT platform that views data centers as a holistic ecosystem so we can leverage that data, whether it’s to improve customer experience, improve efficiency or resiliency, or another way that we can enhance the journey.”

Key opportunities for AI and machine learning include automating operations such as cooling. By inputting desired SLA values into the software, an embedded AI model can determine optimal fan speeds and set points at the rack level. At the same time, operators can achieve greater efficiency, realize meaningful energy savings, and reduce operational risk across the facility.

The intersection of data center and Edge

Edge facilities have typically been viewed as less critical than large, centralized data centers. However, over time, these strategically located sites are becoming indispensable, and the rapid adoption of AI applications is pushing their importance even further.

More workloads, greater heat generation, increased cooling, and higher energy consumption all form a continuous cycle of energy demand and debt. And with latency a top priority, more services are branching out to Edge sites to deliver the performance customers expect. Yet, even at these smaller scales, Edge sites still require the same fundamentals of stable power, reliable cooling, and robust security. Oakes notes:

“What ends up happening is that Edge becomes more and more critical, so there’s less tolerance for downtime. But, often these distributed environments don’t get managed anywhere near the level of a data center. The same level of management that’s happening at the data center needs to happen out at the Edge.”

This need becomes even more pronounced at unmanned Edge sites, where IT teams monitor equipment remotely and cannot access or service it as quickly. Gould explains:

“For many of our customers, Edge is in some cases just as critical as the data center. If a service underperforms, this triggers support just as quickly as if a server goes down. Schneider as a whole understands the Edge space really well, which has enabled us to offer hardware and software for all of these environments; we don’t limit ourselves to data center, Edge, cooling, or BMS individually – we have offers for all of it and each component of an overall system.”

Much like first responders, data center teams manage a mission-critical ecosystem where failure can have wide-ranging consequences. In emergency services, a duty of care extends from leadership to the people and communities they protect; in a similar vein, data center operators carry a duty of care from the boardroom to the very same people who rely on uninterrupted digital services. The two may serve those end users in different capacities – one physically and the other digitally – but the expectation of reliability and responsiveness is shared.

And whether those end users ever see the systems behind the scenes or not, they still depend on them. They deserve the highest possible level of service – whether the network is supporting a hospital room that clinicians rely on, or an office floor that keeps a business running.

Compounding this responsibility, as soon as you start introducing liquid cooling to the rack or chip, likely required by all next-generation AI GPUs, the integration of IT and OT becomes even more vital. With such significant investment in these GPUs, systems handling liquid transfer must be absolutely fail-safe. Gould emphasizes:

“Being able to have a software solution that can tie together leak detection inside the GPU with room temperature, and then trigger automation across the system, is one of the really big changes that AI at the Edge is going to drive. Schneider can deliver this through integrations across the full DCIM, BMS, EPMS, and IT management stack.”

Predictive maintenance is key to achieving this. Oakes recounts that data center operators often exhaust their day before they can fully attend to infrastructure layers like power, cooling, and security.

Simplifying DCIM at the Edge not only helps operators respond quickly and efficiently, but also enables them to anticipate failures using predictive analytics. AI can learn the conditions that lead to failure and automatically generate maintenance schedules. Gould explains that Schneider’s EcoStruxure IT expert system is designed with these varied roles in mind, providing simplicity and ease of use for customers – not just for their current needs, but to support where they plan to go in the future.

There’s a DCIM migration protocol for that

Apple’s campaign “There’s an app for that” was an iconic time in tech that signaled a shift in how we interact with software, indicating that every need could have a digital solution. In the context of data centers and DCIM, this idea translates into the expectation that every piece of equipment should be digitally connected within an integrated system.

Nowadays, just as many of the appliances and tools in our homes already communicate with external facilities in one way or another, data center operators increasingly expect the same seamless experience but at a far greater scale.

“Half of the appliances in my kitchen are connected back to the manufacturer. And say I need a new filter, I can order directly from the manufacturer, so the appliances are communicating between an app and the manufacturer, and there’s this ecosystem of how modern tools work together. I believe we should look to make that experience easier for customers,” says Oakes, adding:

“When we have customers that are on our platforms getting more connected with us, it helps us modernize their spaces better, faster, and more timely.”

Prioritizing a logical digital experience between end users and the systems that generate and manage their data builds a level of rapport that ultimately leads to more efficient operation, maintenance, and modernization.

A comprehensive DCIM system, therefore, allows customers not just to capture measurements from their equipment but to process and leverage that data to continually improve their environment. Oakes continues:

“As we continue to go forward with these platforms, we establish those connections, get all of our data up in the cloud, and work together to ensure they’re staying on top of their maintenance, modernizing in a timely way, and that we’re actively supporting them through the entire process while it’s happening.”

In this model, Oakes envisions a world where customers no longer have to carve out time to determine whether their equipment needs an upgrade. Instead, manufacturers take a proactive role – alerting users, guiding modernization, and streamlining the full lifecycle.

Essentially, when your manufacturer and your DCIM provider are one and the same, equipment management can be automated more effectively, giving operators the headspace to excel in other areas.

Stretching your software

Where DCIM functionality takes a meaningful leap forward is in its use as an audit or assessment tool upfront. In this way, even if teams don’t have the capacity to constantly research evolving regulatory requirements or shifting industry expectations, vendor-neutral DCIM software can deliver valuable business intelligence directly to the customer.

Particularly important is the ability to stay ahead of cybersecurity standards, as Gould explains:

“Within our system, we have an analytics report that will run cybersecurity scans and take all of the best practices to say ‘here are some ways you can make your environment more secure, why you might want to replace this hardware’ and to help you understand if your equipment even has the ability to meet modern cybersecurity standards.”

Ultimately, the Schneider experts emphasize that having a single DCIM platform – one that not only supports your immediate data center needs, but also anticipates those you may face tomorrow – is a fast track to data center efficiency. And in today’s economy, efficiency equals success.

Learn more about Schneider Electric’s DCIM software Ecostruxure IT here, or request a free Power Infrastructure Assessment of your environment here.