From installation to predictive maintenance: The new service backbone of AI data centers

AI is changing almost everything about how we design and operate data centers. Ultra-dense IT, liquid cooling and new power architectures are pushing facilities into territories where margins for error are shrinking fast. At the same time, operators face a shortage of skilled technicians and electricians, while digital services are expected to run 24/7 with near-zero tolerance for downtime.

From a manufacturer’s perspective, the growing complexity of AI-ready data centers is exposing new behaviours in critical power equipment that were rarely visible before, making services an integral part of long-term performance.

In this context, service is no longer just an after-sales add-on. It has become a structural backbone of the data center lifecycle: from design support and commissioning, through day-to-day operation, to optimization and upgrades.

AI data centers: Why services are now mission-critical

AI workloads bring together several shifts at once: much higher rack densities, more dynamic load profiles, new forms of cooling, and tighter integration between electrical and digital systems. A single misconfiguration in the power chain can have much wider consequences than would have been the case in a traditional facility.

This is happening at a time when many operators struggle to recruit and retain experienced operations and maintenance staff. The personnel on site often have to cope with hybrid environments that combine legacy air-cooled rooms with liquid-ready zones, energy storage, and multiple software layers for control and monitoring.

In such an environment, services are not a ‘nice to have’. Instead, they are one of the main levers that allow operators to bridge the gap between design intent and actual performance, and to keep infrastructure aligned with changing business requirements.

As AI data centers grow more complex, the difference between a design that works on paper and infrastructure that performs in reality often lies in the quality of services that support it.

Commissioning in the AI era: Where designs meet reality

Commissioning has always been a critical moment in the life of a data center. In AI facilities it becomes even more decisive. The days when commissioning could be treated as a simple functional checklist are gone.

Bringing together high-power UPS systems, complex switching devices, liquid-cooled racks, and integrated monitoring inevitably create more interactions. In these environments, the electrical power chain becomes the backbone that conditions both availability and cooling performance, placing unprecedented demands on how critical power equipment is commissioned and validated.

If commissioning is rushed or incomplete, configuration or coordination issues may remain hidden until the first AI workloads hit the infrastructure. When they do, step loads and rapid ramps can expose weaknesses very quickly.

Correct parameter settings on UPS and switching devices are particularly important: transfer modes, overload thresholds, protection coordination, and bypass logic all have a direct impact on how the system behaves under stress. These details can be the difference between a controlled response and a cascading fault.

New digital approaches are also reshaping how commissioning is delivered. Remote commissioning – where expert engineers connect securely to configure and validate systems – can help standardise procedures, reduce travel, and ensure that the same level of expertise is applied across multiple sites.

From monitoring to proactivity: Operating AI power chains in real time

Once the first workload is live, the focus shifts from project mode to operation. Traditionally, maintenance has been mostly reactive: something breaks, a ticket is opened, and a technician is dispatched. In an AI environment, that approach is no longer sustainable.

Continuous monitoring turns the power chain into a live, observable system rather than a black box that is only inspected during scheduled visits or after an incident. Connected UPS, switching devices and distribution equipment stream operational data in real time – load levels, temperatures, events, alarm histories, battery status and more – into central dashboards or remote monitoring centers.

This enables operators and expert service teams to receive automated alerts as soon as key parameters move outside normal ranges. Instead of dispatching technicians ‘just in case’, remote diagnosis allows engineers to analyse the situation first, confirm the root cause and then decide whether an onsite intervention is truly needed and how urgent it is.

The operational benefits are significant: mean time to repair (MTTR) is reduced because the issue is often pre-analysed before anyone travels to site, and many minor problems can be resolved remotely. Site visits become less frequent and more targeted, focusing resources where they add the most value.

Visibility on the health of critical equipment improves, as trends and anomalies can be tracked over weeks and months. Perhaps most importantly, this continuous flow of data makes it possible to detect ‘weak signals’ – small but persistent deviations in temperature, load or battery behaviour – and act before they escalate into failures.

Connected services transform maintenance from a cost center into a strategic tool. We no longer just repair when something fails – we use data to anticipate, to optimise and to extend the useful life of critical assets, while ensuring that our remote connections and data handling comply with our customers’ cybersecurity requirements.

Reducing human risk: Procedures, standardisation and skills

As architectures become more intricate, human error remains one of the main residual risks. AI-ready infrastructures combine complex electrical designs, liquid cooling circuits, high-density rack layouts, and multiple software layers such as EMS, BMS and DCIM. Operating and maintaining such systems safely requires clear procedures and a high level of discipline.

Structured service frameworks can help reduce this risk. Standardised test protocols, step-by-step procedures for switching and bypass operations, and well-defined roles and responsibilities all contribute to safer operations. Training and knowledge transfer are equally important. As staff turnover increases, ensuring that new team members are brought up to speed on both the technology and the procedures becomes a continuous task.

Here again, service is a way to compensate for market-wide skills shortages. By combining onsite teams with external expertise, operators can maintain a high level of resilience even when their internal resources are limited.

Services as a strategic lever for AI-ready resilience

In an AI-driven era, service strategy is as important as the choice of UPS topology, cooling technology or energy storage. Commissioning, monitoring, maintenance, and training are not isolated activities. Together, they form a continuous backbone that supports the entire lifecycle of the data center.

Well-designed service models help operators improve availability, optimise energy performance and make better use of the assets they already have. They also provide a framework for adapting to future changes in IT loads, regulations and sustainability targets.

For AI data centers, services are no longer an afterthought. They are the mainstay that keeps complex power and cooling architectures aligned with the digital services they are intended to deliver.

AI is changing almost everything about how we design and operate data centers – from ultra-dense IT and liquid cooling to new power architectures. For more information and support, get in touch here