I decided to stay in the industry and continue my career elsewhere. The direction was right, although neither I nor anyone else could have described exactly what the market would look like twelve years later. We could see the growth of data, but not the precise workloads, equipment or power densities that would follow.

I think about that experience often because data centre developers are again making long-term decisions amid considerable technological uncertainty, now at far greater scale and pace. A facility may cost more than a billion dollars, take years to build and operate for fifteen years or longer. Meanwhile, the AI technology it is intended to support can change while it is still under construction. We are building 15-year assets for technologies that can change every six months. That is the defining challenge of the AI era.

Much of the industry discussion is understandably about meeting immediate demand. Customers want capacity, power is difficult to secure and nobody wants to miss a period of exceptional growth.

However, I am concerned that we are allowing the urgency of AI deployment to narrow the way we judge infrastructure. We hear how many megawatts a project will deliver, how quickly it can be energised and what rack densities it will support. We hear much less about whether the same facility will be straightforward to maintain, safe to operate and capable of adapting once the first generation of equipment is no longer there. The biggest risk in AI infrastructure is no longer underbuilding or overbuilding; it’s building for the wrong generation of AI.

Forecasting used to mean estimating how much capacity a customer might need. Today, the uncertainty extends much further into the technical design. Developers must form a view on future rack densities, cooling methods, electrical distribution and the type of customer the facility is likely to serve. Those choices affect one another, and they are being made before the industry knows which AI architectures will become established.

An anchor tenant can reduce some of this uncertainty. It gives the project a defined requirement and supports the commercial case for investment, which is why finding the right anchor tenant has become almost as important as securing land and power. Nevertheless, there is a risk in treating the first customer’s requirements as a permanent description of the asset. A facility designed very closely around one platform may work extremely well at launch and then prove difficult to use for the next platform.

I would therefore begin a new design by asking where change is most likely to occur and how much of that change the facility can absorb. This does not mean installing every possible system in the first phase. Overbuilding can damage returns just as surely as underbuilding can restrict growth. The practical objective is to create a route between stages: modular power capacity, cooling systems that can accommodate different operating conditions, monitoring that provides a useful view across the facility, and enough physical and electrical flexibility to make later expansion realistic.

There is often pressure to minimise capital expenditure by installing only what present demand supports. Financially, that can be reasonable. It becomes a problem when future capacity exists only as an assumption in a presentation. Adding critical power or cooling inside a live facility is disruptive and usually more expensive than expected. If the expansion route has not been engineered at the beginning, it may not be a route at all.

Some of the strongest projects I have seen were not the cheapest in their first phase. Their advantage became apparent later, when demand changed and the owner still had choices. In a market where nobody can forecast the workload mix with confidence, retaining choices has a measurable commercial value.

The industry should apply the same reasoning to redundancy. We have become accustomed to describing reliability through additional systems and paths. If one component fails, another is available; if one path is isolated, another continues to carry the load. The engineering logic is sound, but the operational result depends on how the complete system behaves.

Every additional layer has to be controlled, tested and maintained. It creates more operating states and more interfaces between equipment. During normal conditions, the architecture may appear straightforward. During maintenance or a fault, operators have to understand which elements are genuinely independent, how controls will respond and whether an action in one part of the system changes the risk somewhere else.

One of the most useful lessons from my early career was that reliable equipment does not necessarily add up to a reliable facility. Individual products may perform exactly as specified while the wider system still fails because of an integration issue, an incomplete procedure or a maintenance error. The interfaces are often where the surprise occurs. This is difficult to capture through a list of component specifications because the quality of each product is not the question; the question is whether their combined behaviour has been properly understood.

For this reason, I am cautious when complexity is presented as resilience without equal attention to operability. A design can have impressive redundancy on paper and still leave the operating team uncertain during an abnormal event. That uncertainty matters because decisions then have to be made under pressure, perhaps with incomplete information and little time to test an assumption.

Maintainability should be discussed while the architecture is still being developed. It is too late to discover at handover that routine work requires difficult switching arrangements or leaves the facility in a condition the operators do not fully trust. The people who will run the site need an effective role in design reviews, and the design needs to be assessed through realistic operating and maintenance scenarios rather than only against its intended steady state.

AI did not create the power challenge; it simply accelerated a transition that the industry could postpone for years but can no longer ignore. Efficiency improvements were pursued within relatively familiar AC power architectures for many years. The industry reduced conversion losses, improved equipment performance and lowered PUE. This work remains relevant, but high-density AI has changed the scale of the power distribution problem inside the facility.

When a rack requires hundreds of kilowatts, the implications extend beyond the rating of one piece of equipment. Current levels, cable quantities, conversion stages, available space, heat rejection and maintenance access all become more difficult. Electrical and cooling design can no longer be treated as parallel workstreams that meet at the rack. They are parts of the same problem, because almost every decision about delivering power also affects how heat can be removed and how much usable space remains.

I have supported greater adoption of DC power architectures for several years. Previously, I made the case mainly in terms of efficiency. Reducing the number of conversion stages could lower losses and improve overall energy performance. AI has given the discussion a different urgency. Higher-voltage DC distribution may become important because it offers a practical way to deliver very large amounts of power without some of the current, cabling and space constraints that conventional distribution encounters at extreme densities.

I do not expect every facility to move to the same architecture, nor do I think DC should be treated as the latest universal solution. Existing systems, customer equipment, regulation, operational capability and project economics will all influence adoption. What has changed is the reason for examining it. The conversation is moving from whether DC offers an efficiency improvement to whether established architectures can remain practical as the power required at rack level continues to rise.

This is also why energy availability will define the next phase of digital growth. The industry has spent decades discussing advances in servers, storage and networking as the drivers of capacity. Increasingly, the limiting factor is whether reliable power can be secured and converted into usable compute capacity at an acceptable cost. A company may have access to capital and the latest IT platform, but neither can compensate for a grid connection that will not arrive or a facility unable to distribute the available power effectively.

There is a visible imbalance in the way many projects allocate attention and money. Buildings, generators, UPS systems and cooling equipment receive enormous investment. The operating workforce and the systems that give them visibility across the facility often receive less consideration, even though they will be responsible for protecting that investment every hour of the year.

Operational capability is easy to undervalue before a facility is live. It is difficult to place a number on the outage that an experienced operator prevents, or the equipment life gained through better maintenance and control. Problems become visible later, when teams have incomplete information, procedures do not reflect the actual system or an issue in one discipline is not recognised as part of a broader pattern.

Digital tools can improve that situation if they support decisions rather than simply generate more data. Operators need to understand individual equipment and how capacity, energy performance and resilience are changing across the system. A large volume of alarms is not operational visibility if the relationships between them remain unclear.

The operating model should consequently be developed alongside the engineering design. Staffing, competence, maintenance philosophy and system information affect what level of complexity can be managed safely. A technically possible architecture is not automatically an appropriate one for every organisation. If the operating team cannot test it, maintain it and respond to it confidently, the gap will eventually become a business risk.

This point also belongs in the sustainability discussion. Much of a facility’s environmental performance is determined through ordinary engineering and operational decisions: conversion losses, cooling set points, equipment life, maintenance quality and the ability to reuse infrastructure for changing workloads. A building that requires substantial rework after a few years is not a particularly sustainable outcome, even if its first-year figures were strong.

My decision in 2014 was based on a broad conviction that digital infrastructure demand would grow, not on an accurate prediction of the systems we are installing today. There is a useful lesson in that distinction. We need a view of where the market is moving in order to invest, but we should not confuse confidence in the direction with certainty about the destination.

The data centre industry clearly needs more capacity, and AI will account for a significant share of it. The difficult work is deciding which parts of the current requirement should define a fifteen-year asset and which should be allowed to change. In my experience, that decision cannot be made by engineering or finance in isolation. A technically elegant solution can fail commercially if it cannot support customer outcomes, while a low-cost solution can become expensive if it removes the owner’s ability to respond later.

I began my career believing that the strongest technical proposal would be recognised automatically. Over time, I learned that customers do not invest in technology for its own sake. They invest because they need capacity, continuity, manageable risk and the ability to serve their own customers. Engineering expertise becomes valuable when it helps them achieve those outcomes and understand the consequences of the choices in front of them.

As AI development accelerates, there will be pressure to make each project answer the immediate market as precisely and quickly as possible. Some of that pressure is unavoidable, and some of it is healthy. But a data centre will live through several technology cycles, changes in customer demand and countless maintenance events. We should judge its design with those years in mind, rather than allowing the requirements of its first day to become the limits of its future.

Long before artificial intelligence became the defining conversation in our industry, I learned that the success of an infrastructure project rarely depends on technology alone. Reliable power, local execution and the ability to navigate permitting and grid access were often far more decisive than any technical specification. A design could be flawless on paper and still fail because those fundamentals weren’t in place.

That lesson shapes the way I look at today’s AI race. Everywhere I go, I hear conversations about GPUs, liquid cooling and ever higher rack densities. Those conversations matter. But I sometimes wonder whether they’re making us overlook a much simpler reality. The biggest constraint facing AI infrastructure isn’t computing. It’s the infrastructure that makes computing possible in the first place.

Our industry is making a familiar mistake. We’re pouring enormous energy into optimising the visible parts of the system while paying too little attention to the foundations that determine whether the system can exist at all. I have watched this pattern repeat for three decades, and it rarely ends well.

The organisations that build successful infrastructure aren’t necessarily those with the most advanced technology. More often, they’re the ones that understand where the real risks lie before everyone else does. Today, that means recognising that the conversation about AI is becoming, increasingly, a conversation about power.

One of the reasons I worry about the current direction of the industry is that we have become very good at measuring progress without measuring what actually determines success.

Every industry develops its own language of reassurance. In ours, it’s easy to find comfort in metrics, certifications and benchmarks because they create the impression that complexity can be reduced to a handful of numbers. They’re useful, and I’m certainly not arguing against measurement. The danger comes when the metrics become the conversation rather than a tool to inform it. We start optimising for what we can quantify instead of asking whether we’re measuring the constraints that will define the outcome.

Power Usage Effectiveness is a good example. For years it has been one of the most influential indicators in our industry, and understandably so. It encourages better engineering and greater operational efficiency. But efficiency is not resilience, and it is certainly not readiness. A facility with an outstanding PUE still depends on one fundamental assumption: that the electricity it was designed to consume will actually be available when the doors open. That assumption has become fragile.

Consider what AI has done to the numbers. A conventional enterprise rack drew perhaps five to ten kilowatts. The latest AI training racks demand well over a hundred, and the roadmaps point higher still. Meanwhile, developers in parts of Europe are being quoted five years or more for a grid connection. When demand multiplies by ten and supply moves at the speed of transmission projects, the strategic question changes. In many markets today, the first competitive advantage is no longer a better design or a more efficient cooling architecture. It’s access to reliable, affordable power before everyone else starts looking for the same resource.

That’s why I believe the industry is approaching an inflection point. The questions that determined success over the last decade won’t determine success over the next one. We can keep refining the metrics that helped us optimise yesterday’s infrastructure, but we shouldn’t mistake them for the metrics that will decide tomorrow’s investments.

For much of my career, people assumed the challenges we faced in the Middle East and Africa were unique to the region. Reliable grid capacity couldn’t always be assumed. Utility upgrades often moved at a different pace from commercial expectations. Regulatory frameworks evolved quickly, local supply chains varied enormously from one market to the next, and every major project demanded a far deeper understanding of the local operating environment than the technical specification alone could provide.

Many people viewed those conditions as exceptions. I never did.

I remember a project early in my years building this region where the facility was ready months before the utility connection was. Every technical milestone had been met, and none of it mattered until we sat with the utility, understood their constraints, and built a bridging plan around their timeline rather than ours. The customer never mentioned the specification again. What they remembered was that we solved the problem. Deliver enough projects like that, across markets as different as Riyadh, Cairo, Lagos and Nairobi, and you develop a particular instinct for risk. A technically correct design is not automatically a deliverable one. You learn to ask certain questions at the very beginning of a project, because experience tells you those questions become expensive if they’re postponed.

That perspective has become far more relevant as AI transforms the economics of digital infrastructure. The challenges many regarded as characteristic of emerging markets are now appearing in mature economies with surprising speed. Developers across Europe are waiting years for grid connections. Utilities are struggling to keep pace with unprecedented demand. Governments are stepping into decisions about energy allocation, permitting and strategic infrastructure. And in my own region, the direction of travel has reversed entirely. Saudi Arabia and the UAE are announcing AI campuses measured in gigawatts, backed by sovereign capital and, crucially, by energy systems planned alongside the compute rather than after it. The lessons this region learned the hard way are now the lessons the whole industry needs.

This is why I believe the industry has entered a fundamentally different phase of its development. For years, competitive advantage came primarily from building more efficiently than your competitors. Better engineering, shorter lead times and stronger commercial execution usually created a meaningful edge. Those capabilities remain important, but they’re no longer sufficient on their own, because they assume the underlying resources are available to everyone. Increasingly, they are not.

The organisations that will define the next decade are unlikely to be those with the most impressive product portfolio or the most persuasive marketing. They’ll be the ones that understand infrastructure as an ecosystem rather than a collection of technologies. They’ll know how utilities make investment decisions, how governments think about strategic energy security, how local execution partners influence operational performance over decades, and why relationships built years before a project begins often matter more than negotiations conducted once procurement starts.

That’s one of the reasons I remain cautious whenever I see announcements of enormous AI capacity accompanied by aggressive delivery timelines. I’ve spent too many years watching infrastructure projects move from concept to reality to believe capital alone determines outcomes. Capital is essential, but infrastructure obeys a different set of rules. Electricity arrives when the grid is ready, not when the investment committee approves the budget. Permits are granted when institutions complete their work, not when shareholders demand growth. Operational capability is built over years, not acquired in the final months before commissioning.

None of this makes me pessimistic about the future of AI infrastructure. On the contrary, the opportunities ahead are extraordinary. But our industry needs to become more honest about what it takes to deliver them. Technology is only one part of the equation. Infrastructure has always been the art of aligning technology with physical reality and institutional reality. The companies that recognise that today won’t simply build more data centres. They’ll build the infrastructure that makes the next generation of AI economically and operationally sustainable.

If this sounds like an argument for investing more in technology, then I’ve probably made my point badly. The opposite is true. The industry already knows how to build extraordinary technology. The real challenge is delivering extraordinary outcomes, and those two things are not the same.

Some of the toughest decisions I have made over the years had very little to do with selecting equipment. They were decisions about whether to stop a project when commercial pressure was pushing us to continue, whether to challenge a customer’s assumptions before they became expensive mistakes, or whether to absorb short term pain to protect a relationship that mattered over the long term. Those moments never appear in a technical specification, yet they often determine whether a project succeeds or fails.

That’s one reason I’ve grown sceptical of the way our industry talks about partnerships. We use the word constantly, but too often we describe relationships that are fundamentally transactional. A supplier delivers equipment, the customer signs the acceptance certificate, and both parties move on. There’s nothing wrong with that model if the objective is simply to complete a purchase. It is far less effective when the objective is infrastructure expected to operate reliably for the next twenty years.

Real partnerships are usually tested when somebody must deliver unwelcome news. They’re built when a vendor is prepared to say, “I don’t think this is the right decision,” even when that advice may delay a project or reduce short term revenue. They’re strengthened when both sides solve problems together rather than argue about contractual obligations after something has gone wrong. Those conversations are uncomfortable, but they’re also the moments when trust is created. In my experience, customers rarely remember the project that went exactly according to plan. They remember how you behaved when it didn’t.

The same principle applies inside organisations. As infrastructure projects become larger and more strategically important, technical excellence alone is no longer enough. The decisions that shape these investments increasingly involve finance, operations, energy providers, regulators and government stakeholders, each bringing different priorities and different definitions of risk. Success depends on aligning those interests long before construction begins. By the time the equipment arrives on site, many of the decisions that will determine the outcome have already been made.

That’s why execution has become the industry’s greatest competitive advantage. Not execution in the narrow sense of delivering a project on time, but execution as the ability to connect engineering with commercial judgement, long term relationships, and an honest understanding of how infrastructure is actually delivered. Technology can be copied. Products improve with every generation. Execution is built slowly through experience, and it’s much harder to replicate than any innovation on a product roadmap.

If there’s one prediction I feel increasingly confident making, it’s this: we’re about to stop thinking of electricity as a utility and start treating it as a strategic asset. That change may sound subtle, but it will reshape the economics of digital infrastructure more profoundly than any single advance in computing.

For decades, access to power was largely assumed. It was an input into the business model rather than a source of competitive advantage. Companies competed on engineering capability, commercial execution and operational excellence because those were the variables they could influence most directly. That assumption is breaking down. As AI accelerates demand for high density compute, reliable electrical capacity is becoming scarcer, more valuable and far more strategic than most organisations anticipated.

The implications extend well beyond the data centre industry. Governments are beginning to view energy infrastructure through the lens of economic competitiveness and national capability. Utilities find themselves at the centre of investment decisions that would once have been driven almost exclusively by technology companies. Corporate boards are asking questions about energy security that would have been considered operational details only a few years ago. The conversation is changing because the constraints are changing.

That’s why I believe we’ll look back on this period as a turning point. Not because AI transformed the technology inside the data centre, impressive though those advances are, but because it forced the industry to recognise something it had gradually overlooked. Digital infrastructure has never been independent of physical infrastructure. We simply reached a point where that dependence became impossible to ignore.

When historians write about the AI boom, I suspect they’ll spend less time on the processors that defined this generation than on the infrastructure decisions that determined where those processors could actually be deployed. Many of today’s announcements will become successful facilities. Some will never move beyond ambitious plans. The difference will rarely come down to the quality of the technology. More often, it will reflect whether the hard work of securing power, building capability and aligning long term infrastructure with long term demand had already been done before the headlines appeared.

That’s the lesson I have taken from three decades in critical infrastructure. Technology changes remarkably quickly. Infrastructure does not. It rewards patience over excitement and preparation over optimism. Those principles aren’t obstacles to the AI era. They’re the conditions that will determine how much of its promise can actually be realised.

Perhaps that’s the paradox our industry now faces. We’re living through one of the fastest technology revolutions in history, yet success will increasingly depend on disciplines that have always moved at a slower pace: energy planning, infrastructure investment and trust.

The companies that understand this won’t simply adapt to the next phase of AI. They’ll help define it.

Every generation believes it’s living through a technology revolution. Very few recognise they’re also living through an infrastructure revolution.

Artificial intelligence will transform the digital economy. It won’t change the fundamental truth that has shaped every major infrastructure project I have worked on: technology creates possibilities, while infrastructure determines which of those possibilities become reality.

In the years ahead, secured megawatts will be traded the way capital once was. Those who locked them in early, and built the capability and relationships to use them well, will hold the currency everyone else is trying to buy. That is what I mean when I say infrastructure is the new currency of AI. And in this region, we have been earning it for thirty years.

Our ambition was to bring together the strengths of two established telecom power portfolios, Delta and Eltek, and create a unified platform for the future. Both product families were mature and successful. Each had its own engineering heritage, design philosophy and loyal advocates. We were not starting with a blank sheet of paper and nor should we have been. The challenge was to decide what deserved to be carried forward.

That distinction matters. In complex engineering organisations, innovation is often understood as the addition of something new: more functionality, more options or another configuration designed to meet a particular requirement. My experience has taught me that the more difficult form of innovation is actually simplification. It requires an organisation to examine everything it has created, identify what genuinely delivers value and be prepared to leave the rest behind. Sometimes the harder challenge is saying that something once needed is no longer necessary because nobody wants to have that uncomfortable conversation, but without it, real simplification is impossible.

I came to the Centric programme with experience spanning test, installation, commissioning, customer support and product management. Over three decades, I have seen telecom power systems from several perspectives: as products being designed and specified, as equipment arriving on site and as critical infrastructure that must continue operating reliably for many years.

My time as an Installation Manager was particularly important. A configuration that appears logical in a catalogue or engineering document can look very different to the people installing and commissioning it. Complexity has a real cost. It consumes engineering hours, extends lead times, increases the possibility of errors and makes systems more difficult to support throughout their lifecycle.

This practical experience shaped the question I kept returning to during the Centric programme: will this make life easier for the customer and the people working with the system?

Before we could answer it, we had to understand the scale of what already existed. Together with management and colleagues across the business, we reviewed the Delta and Eltek catalogues, product options and system configurations. We examined what customers valued, where requirements overlapped and where the existing portfolios could be improved.

There was no shortage of opinions, which was hardly surprising given how much experience and commitment people had invested in the products over the years. Each person approached the programme from a different perspective, whether shaped by regional customer expectations, technical consequences or the long-term demands of managing a product throughout its lifecycle. Bringing these views together meant finding a direction that could deliver progress without losing the continuity customers expected.

The role of Product Management is not simply to collect all these requests and add them to a specification. If every preference becomes a feature and every historical configuration is preserved, the result may satisfy individual stakeholders but fail the wider market. The responsibility is to distinguish between a genuine customer requirement and complexity inherited from the past. It is a fine line to dance and ultimately the goal is to create something everyone will be satisfied with.

This meant that some concepts had to be eliminated because they did not meet the programme’s technical, commercial or strategic objectives. These were not always easy decisions or pleasant conversations. A good idea in isolation is not necessarily right for a common platform. Every option affects manufacturing, documentation, training, sales configuration, installation and long-term support. Product complexity rarely remains contained within the product itself.

At the same time, decisions cannot be made from a conference room alone. Input from Sales Support Engineers and Product Support teams was essential because these colleagues see how systems are applied in the real world. Their experience helped us test our assumptions against actual customer requirements and installation practices. What we had thought impossible, they showed us could be done.

One of the biggest surprises was discovering how much commonality already existed between the Delta and Eltek platforms. Despite years of separate development, the two portfolios had often arrived at similar answers to the same engineering problems. Beneath different product identities and design traditions, there was a considerable amount of shared thinking.

That discovery changed the character of the programme. Centric did not need to be a compromise between competing approaches. It could become a distillation of their strongest elements.

Of everything we achieved, bringing those concepts together into a single direction is what makes me most proud. It required technical evaluation – certainly – but also patience and trust across the wider engineering organisation. Unifying products means asking people to look beyond the solutions they know and consider what the business and its customers will require in the future.

The first variant, Centric 3000, was launched in July 2026. Reaching that point brought relief, pride and excitement in equal measure. The programme had been under significant time pressure and had taken longer than originally anticipated. Yet with infrastructure products, a launch date is only one measure of success. The greater responsibility is to create something customers will choose to adopt and continue to trust.

Centric is therefore more than a new telecom power product. It represents a step forward in configuration and standardisation. A more coherent range should enable sales offices to respond faster to customer requirements, reduce unnecessary engineering effort and make systems easier to specify, deploy and support.

There is still work ahead. The value of the platform will become more visible as further products and options are introduced. But the principle behind it is already clear. The next generation of telecom power systems will not be defined by how many configurations a manufacturer can offer. It will be defined by how intelligently those systems combine flexibility with simplicity.

After three decades in this industry, I believe that is one of the most important judgements a Product Manager can make. Engineering excellence is not only about what we are capable of adding. Sometimes it is about having the experience and discipline to know what we no longer need.

The urgency is justified. AI has transformed digital infrastructure from an operational necessity into a strategic asset. Decisions once made by engineering teams are now discussed in boardrooms because they influence competitiveness, resilience, and long-term growth.

Yet I believe we are asking the wrong question. Much of the conversation still revolves around deployment speed, power availability and the technologies needed to support increasingly demanding AI workloads. Those are important discussions, but they focus on today’s challenges rather than tomorrow’s risks. The question that concerns me is not how quickly we can build, but whether what we build now will still be the right investment a decade from now.

For decades, data center economics relied on a simple assumption: infrastructure would evolve far more slowly than the technology it supported. Electrical systems, cooling infrastructure, and power distribution were designed with lifecycles measured in decades, while investment decisions assumed those assets would retain their value throughout their depreciation period.

AI has fundamentally changed that equation. Technology is now evolving faster than the investment models designed to support it. Compute requirements continue to shift; power densities keep increasing and every new generation of accelerated computing challenges assumptions that seemed entirely reasonable only a short time ago. This is not a failure of engineering; it is the inevitable consequence of technology moving faster than the infrastructure built to support it.

When people talk about infrastructure risk, they usually mean outages, resilience, or operational reliability. They ask whether critical systems remain available, whether redundancy performs as intended and whether facilities can withstand failure. Those risks have always mattered.

But I believe the defining risk of the AI era is financial rather than technical. It is entirely possible to build a world-class facility that performs exactly as specified, meets every engineering requirement and delivers flawless operational performance, while simultaneously becoming a weaker investment than anyone anticipated when construction began. That possibility deserves far more attention than it receives.

The industry has become obsessed with deployment speed. Projects are celebrated for breaking ground early, reducing delivery schedules and bringing new capacity online ahead of competitors. But speed only creates value if you are deploying the right architecture. Building the wrong architecture faster simply accelerates commitment.

That is an uncomfortable thought because almost every commercial incentive favors speed over reflection. Investors want projects delivered quickly, customers need capacity as soon as possible and suppliers compete to shorten lead times. Few people are rewarded for asking whether today’s architecture will still be the right investment a decade from now.

Historically, flexibility has often been treated as an engineering preference. If budgets allowed, additional headroom could be designed into the infrastructure. Now flexibility is not an engineering luxury but a form of financial risk management.

The organizations that understand this are beginning to evaluate infrastructure differently. Instead of asking whether current design satisfies technical requirements, they ask whether today’s investment preserves tomorrow’s strategic options. Can electrical infrastructure evolve without wholesale replacement? Can cooling systems support technologies that have not yet entered commercial deployment?

Sure, these are engineering questions. But they are also investment questions. That distinction matters because the people making infrastructure decisions have changed. Twenty years ago, these discussions were largely led by engineering organizations. If a design met the technical requirements, the investment case was often straightforward.

As for now, technical performance is only one part of the conversation. CFOs want to understand lifetime capital exposure, sustainability leaders are looking at regulatory resilience, and boards are increasingly evaluating digital sovereignty alongside operational capability. Infrastructure decisions have become business decisions, which means engineering excellence alone is no longer enough.

Infrastructure has become a strategic business investment, yet we continue to evaluate it using assumptions inherited from a far more predictable technological era.

Perhaps that is understandable. Industries rarely recognize structural change while they are living through it. We naturally extend familiar planning models into unfamiliar environments because those models have served us well in the past. The difficulty is that AI is not changing only the technology, it’s changing the assumptions that sit underneath every infrastructure investment we make.

Across Europe, organizations are trying to reconcile two timelines that were never designed to coexist.

On one side sits the business. AI strategies are measured in months, and competitive advantage is measured in quarters. Boards want to know when new capabilities will be available, customers expect rapid deployment and internal roadmaps assume computing capacity can expand almost as quickly as demand itself.

On the other side sits physical infrastructure. Substations cannot be accelerated by ambition. High-voltage equipment cannot be manufactured because a board has approved additional budget. Planning approvals, utility connections, transformers, and switchgear all move according to timelines measured in years rather than quarters. In many European markets, lead times for critical electrical infrastructure now extend well beyond eighteen months. The gap between those two timelines is becoming one of the defining challenges of the AI economy.

Yet even that is not what concerns me most. Long lead times are frustrating, but they are visible. They appear in project plans, procurement schedules, and board reports. Organizations can plan around them.

The real challenge is what happens while those projects are being delivered. Technology keeps moving. The assumptions made on day one does not remain frozen simply because construction has started. By the time a facility is commissioned, technology has often moved on. New GPU generations have emerged, power requirements have increased, cooling strategies have evolved and workloads have changed. A design that looked forward-looking when the investment was approved may already appear conservative by the time it enters operation. The infrastructure has not fallen behind; the pace of technological change has simply accelerated. And that fundamentally changes the investment equation.

After more than two decades working with critical infrastructure, I have learned that technology rarely creates the biggest surprises; our assumptions do.

One project, in particular, has stayed with me because it illustrates this better than any market forecast or analyst report. A financial services organization invested heavily in AI infrastructure. The engineering was sound, the design process was rigorous, and the business case had been carefully developed. By every conventional measure, it was exactly the kind of project our industry would describe as successful. Then the technology moved.

The GPU platform the organization ultimately needed demanded capabilities that had not been considered when the project was originally specified. Yet nothing had actually failed.

The infrastructure performed exactly as designed, and every engineering objective had been met. The infrastructure itself had not changed but the investment looked very different.

Suddenly, the organization faced decisions no leadership team wants to make after committing substantial capital. Should the project be retrofitted before completion, accepting additional cost and delay? Should performance ambitions be scaled back to match the existing infrastructure? Or should strategic AI programmes already promised to customers be postponed until the next investment cycle? None of those options represented success.

What stayed with me was not that the infrastructure had failed. It hadn’t. The assumptions behind the investment had.

We often talk about stranded assets when discussing the energy transition. Increasingly, I think we should also talk about stranded assumptions. Many of the facilities currently under construction will perform exactly as designed. They will achieve their availability targets. They will deliver excellent operational resilience and impressive efficiency.

The more important question is whether they will continue creating strategic value at the same pace that AI technology continues to evolve. Those are two very different measures of success.

For decades, our industry has been driven by optimisation: better efficiency, higher utilization, stronger performance, and lower cost per kilowatt. Those disciplines remain essential, but they are built on one assumption that the destination is relatively stable. AI has changed that.

When the destination itself keeps moving, adaptability becomes more valuable than optimisation. That is why I believe we spend too much time debating individual technologies and not enough time discussing capital exposure.

Liquid cooling, higher rack densities, and power quality all matter. But these are not the questions boards ask when approving investments measured in hundreds of millions of euros. They ask something much simpler.

It is remarkable how rarely our industry answers that question directly.
Instead, we often present certainty where uncertainty would be more honest. We explain today’s specifications in great detail while quietly recognizing that nobody can predict the computing requirements of the next hardware generation with confidence.

Uncertainty is not the problem. Every major infrastructure investment involves uncertainty. The problem is behaving as though uncertainty disappears once a specification has been approved. If AI has taught us anything over the past few years, it is that technology is no longer waiting for investment cycles to catch up – it moves according to its own rhythm. Infrastructure has no choice but to adapt.

For many years, the relationship between customer and supplier has been largely transactional. Requirements are defined, solutions proposed, contracts signed and facilities delivered, with success measured against specifications agreed months or sometimes years earlier.

That model worked when technology evolved at roughly the same pace as the projects themselves. I am not convinced it works anymore.

Today, the most valuable partner is not necessarily the one offering the shortest delivery schedule or the lowest upfront cost. It is the one willing to challenge assumptions before they become expensive.

That sometimes means asking uncomfortable questions. Is current architecture flexible enough for technologies that do not yet exist? Are we optimizing today’s workload at the expense of tomorrows? Which assumptions are we treating as facts simply because they fit the current business case?

Those conversations are rarely easy. They may increase upfront investment, delay decisions, or even change the direction of a project. But they are far less expensive than discovering the real problem after the investment has already been made.

In my experience, trust has never been built by avoiding uncomfortable conversations. It is built by having them early enough that they still influence the outcome. That is one of the responsibilities our industry carries.

Technical expertise is no longer only about designing resilient electrical infrastructure or selecting the right cooling technology. It is also about translating engineering decisions into business consequences.

Too often, we assume good engineering automatically leads to good investment decisions. It does not. A technically excellent recommendation that cannot be explained in terms of business value is unlikely to influence a board’s decision. Equally, a financially attractive proposal that ignores long-term technical realities simply postpones risk instead of reducing it.

The real challenge is bringing those perspectives together. Infrastructure decisions are no longer owned by engineering alone. While technical teams remain focused on performance and resilience, financial leaders are increasingly evaluating the durability of the investment itself whether it will continue creating value throughout its intended lifetime, how exposed it is to additional capital requirements and how dependent it is on assumptions that may prove short-lived. Sustainability leaders are asking similar questions from a different perspective, examining whether today’s infrastructure can continue meeting tomorrow’s environmental and regulatory expectations. In reality, these are not separate conversations. They are different ways of assessing the same long-term investment.

That may be the biggest change our industry has experienced over the past decade. Data centers are no longer just technical infrastructure. They have become long-term strategic assets.

And strategic assets shouldn’t be judged solely by how efficiently they operate. They should also be judged by how well they retain their value tomorrow.

When I look ahead, I have little doubt about where technology is heading. AI models will continue demanding more power, higher densities, and increasingly sophisticated infrastructure, while new hardware platforms and cooling technologies will continue reshaping what “AI-ready” really means. That’s the easy prediction.

The more difficult question is how we will judge the investment decisions being made today. Every generation eventually discovers that it underestimated the future. We look back and wonder why demand was misread, why infrastructure became a constraint or why assets were optimised for assumptions that proved surprisingly short-lived.

I suspect our generation will have its own blind spot. Not because we underestimated AI, but because we underestimated how quickly it would challenge the assumptions behind the infrastructure built to support it. Many of the facilities commissioned this decade will still be operating well into the 2040s. They will remain reliable, continue supporting critical workloads and, from an engineering perspective, be considered highly successful. The more important question is whether they will still be delivering the strategic value their investors expected when construction began. Technical success and long-term investment success are not necessarily the same thing.

For years, our industry has asked whether infrastructure is ready for AI. Perhaps the more important question is whether our investment thinking is. The organisations that succeed over the next decade will not necessarily be those that build the largest AI infrastructure, but those that preserve optionality: recognising flexibility not simply as an engineering feature, but as a strategic capability. In an environment where technology refuses to stand still, the greatest competitive advantage is not certainty – it’s adaptability.

Every few years another benchmark became the industry’s obsession. First availability, then modularity, then energy efficiency. Power Usage Effectiveness became the language we all spoke because it gave us something measurable to improve.

Those improvements created the modern digital economy. Without them there would be no hyperscale cloud providers, no global streaming platforms and certainly no artificial intelligence revolution unfolding today.

While we were becoming better at building data centers, the world around them was changing much faster than many of us realised. Our industry still behaves as if solving design and financing means the project will get built. That used to be true. It isn’t anymore. We still spend a great deal of time discussing layouts, PUE, cooling strategies and construction speed. Meanwhile, the real challenges have moved somewhere else.

In many key markets, the defining question is no longer simply whether we can design the next data center, but whether we can secure sufficient power to operate it. That may sound like an obvious distinction, but I believe it changes almost everything. For many years a successful project followed a relatively predictable path. Secure the investment, identify the right location, complete the design, obtain the necessary approvals and build. Engineering excellence was the differentiator because engineering was usually the most complex part of the process.

Increasingly, that is no longer true. Projects are delayed because electrical infrastructure is unavailable. Grid connection timelines stretch far beyond construction schedules. Planning approvals become politically sensitive. Local communities ask questions that were rarely asked a decade ago. Governments are forced to balance industrial growth against national energy security, climate targets and competing infrastructure priorities. The bottleneck has quietly moved.

Ironically, the industry that exists to enable digital transformation is becoming increasingly dependent on challenges that have very little to do with digital technology. Electricity has become the defining strategic resource. That reality forces us to think differently about what a data center actually is.

For decades, energy was simply something the data centers consumed. As long as power was available, the conversation focused on how efficiently we used it. Today, energy itself has become the defining limitation. It determines where new capacity can be built, how quickly it can scale and whether future AI infrastructure is possible at all. That is why I believe we need to rethink what a data center actually is. It is no longer just digital infrastructure; it’s becoming critical energy infrastructure.

We often describe AI as a software revolution. From where I stand, it is equally an infrastructure revolution. According to the IEA, global data center electricity consumption reached approximately 415 TWh in 2024 and is projected to increase to around 945 TWh by 2030 under the base-case scenario.

Artificial Intelligence is changing what data centers fundamentally are. They are evolving from digital infrastructure that consumes energy into energy infrastructure that enables digital economies. Previous generations of enterprise computing evolved gradually. Every few years servers became more powerful, workloads increased and infrastructure adapted accordingly. Engineers had time to optimise designs, operators learned from experience and technologies matured at a manageable pace. AI has compressed that entire process.

The increase in computing density has happened so rapidly that many of the assumptions underpinning today’s infrastructure are beginning to reach their natural limits. What was considered high density only a few years ago is now becoming standard practice. The pace of change is unprecedented. Conventional enterprise racks have traditionally operated at 10-20kW, whereas today’s AI deployments increasingly require 100-150 kW per rack, with next-generation platforms expected to exceed 250 kW.

Facilities were originally designed for conventional enterprise workloads but they are now suddenly expected to support AI clusters requiring several times the electrical capacity and thermal performance. That changes the engineering discussion completely.

There is a tendency in every industry to assume that existing technologies can simply be optimised a little further. We become comfortable with incremental progress because it has worked before.

Air cooling illustrates this perfectly. For decades it has been one of the great success stories of our industry. Continuous improvements in airflow management, containment strategies and equipment design have allowed operators to support workloads that previous generations would have considered impossible; it has been an extraordinary engineering achievement.

But every technology eventually encounters limits that cannot be overcome simply through optimisation. At some point the conversation stops being about engineering creativity and starts becoming about physics. But physics does not negotiate; heat must go somewhere and energy must be supplied.

Power losses cannot simply be wished away because project schedules are ambitious or budgets are limited. This is why I increasingly view liquid cooling as rapidly moving from an optional solution to a foundational requirement for high-density AI and HPC environments.

The technology is not the challenge; the mindset is. The challenge is whether organisations are prepared to redesign the way they think about infrastructure before external circumstances force them to do so. That, in my experience, is always the more difficult transition. Often technology changes faster than organisations do.

Many of the projects currently being designed will operate for twenty years or more. The decisions made today will shape infrastructure that is expected to operate for decades. Many of those facilities will support workloads that don’t even exist yet.

The biggest risk is not that we build the wrong infrastructure – it’s that we keep optimising for a world that is already disappearing. Too often we evaluate projects according to present-day requirements because those requirements are measurable. Future demand is uncertain, so it becomes easier to postpone difficult decisions. That may reduce capital expenditure today but it often increases strategic risk tomorrow.

The organisations that succeed over the next decade will not necessarily be those that minimise today’s investment; they will be those that minimise tomorrow’s challenges. And increasingly, those challenges have very little to do with servers themselves. They have everything to do with the energy ecosystem that surrounds them.

One consequence of this shift is that it challenges some of the assumptions our industry has relied on for years. One of the clearest examples is how we measure success.

For a long time, Power Usage Effectiveness has been the benchmark by which data centers judge themselves. It has undoubtedly helped improve efficiency across the industry and encouraged operators to reduce unnecessary energy consumption. I would never argue that PUE is irrelevant. It remains a valuable operational metric, but it increasingly answers yesterday’s question rather than tomorrow’s challenge. When a single number becomes the dominant measure of success, there is always a danger that we optimise for the number rather than for the outcome it was originally intended to represent.

AI exposes that limitation. A facility with an excellent PUE may still struggle to support high-density compute efficiently but another facility with a slightly higher PUE may deliver significantly more usable computational capacity because its electrical architecture, cooling strategy and operational flexibility were designed around entirely different priorities.

That is why I increasingly find myself thinking less about how efficiently energy enters a building and more about how effectively it is converted into reliable computing capability.

Perhaps then the more important question is no longer, “what is your PUE?” but “how much reliable AI compute can you sustain for every megawatt available?” Those are two very different conversations where one focuses on the efficiency of the facility and the other focuses on the effectiveness of the infrastructure.

As AI continues to reshape demand, I believe that distinction will become increasingly important. This is also changing the way we think about engineering itself.

For decades we designed power, cooling and IT as separate systems. AI no longer allows us to do that. Every decision in one area now affects performance somewhere else. The challenge is no longer optimizing individual systems. It is to understand how the entire infrastructure behaves as one integrated system.

That may sound like a subtle distinction, but I believe it represents one of the biggest mindset shifts our industry must make over the coming years. Too often we still optimize individual systems while assuming the whole will automatically become optimal as well. While small changes in one part of the system will affect on the result, we need to observe every decision from multiple angles; not only from the most obvious one.

Ultimately, I believe the future is not about building better individual products. It is about designing better integrated infrastructure. The greatest opportunities will come from understanding how power, cooling, and IT interact as a single system rather than treating them as isolated technologies.

Some of the most valuable lessons I have learned throughout my career came not from catastrophic failures but from situations where, technically speaking, nothing actually failed. Every component was functioning exactly as designed, redundancy existed as planned and all the calculations were correct. Yet the system behaved differently under real operating conditions than anyone expected.

Partial loading, unexpected control behaviour, delayed responses between interconnected systems; individually these issues appeared manageable. Together they created operating conditions that increased temperatures faster than the infrastructure could stabilize itself.

Nothing had failed and nothing was broken but the system wasn’t truly resilient. That experience fundamentally changed the questions I ask during infrastructure discussions; I no longer begin by asking whether a design is redundant – I ask how it behaves when reality refuses to follow the design assumptions.

Everything should work during normal operation; that is the expectation for any technical product. True resilience reveals itself during uncertainty. As workloads become denser and businesses grow increasingly dependent on continuous computing, the difference between redundancy and resilience becomes more significant. A minor cooling issue is no longer merely a facility concern: it can quickly become a business continuity issue, directly affecting revenue, customer trust, regulatory compliance, and the AI-driven processes that organizations increasingly rely on.

Resilience is therefore not achieved simply by adding redundant capacity, but by ensuring that the infrastructure responds predictably to dynamic operating conditions. Control-loop delays, thermal ramp rates, partial-load cooling behavior, and rapidly changing AI workloads all influence whether the system can absorb disturbances without compromising compute performance or service availability.

Infrastructure decisions therefore carry strategic consequences far beyond the data center itself and that’s also why I believe our commercial relationships need to evolve. Too many infrastructure decisions are still rewarded for being delivered on time rather than performing over the next twenty years. I believe that needs to change.

The value of infrastructure is increasingly determined after commissioning rather than before it. We find ourselves asking questions such as “can the facility adapt to changing workloads” or “can capacity increase without major redesign”. Will the performance remain stable under operating conditions that were impossible to predict when construction began? Those questions define long-term value far more than whether delivery occurred exactly on schedule.

I believe we should begin moving towards outcome-based partnerships that reward measurable operational performance rather than simply successful equipment delivery. Infrastructure should not merely be installed; it should continuously prove its value throughout its operational life.

Perhaps the greatest misconception I still encounter is the idea that sustainability and performance somehow compete with one another. I see the opposite. The most sustainable infrastructure is often also the highest-performing infrastructure. Reducing unnecessary power conversion stages improves both efficiency and reliability. When we are designing thermal systems that support higher operating temperatures, we reduce waste while increasing operational flexibility. If we understand the workload behaviour, we’ll enable better energy utilisation and greater computing output.

Performance and sustainability only appear to conflict when we measure the wrong things. Unfortunately, our industry sometimes spends too much time discussing sustainability as a reporting exercise rather than an engineering discipline.

Publishing ambitious targets is relatively easy but measuring operational reality is considerably harder. Still, the real progress begins with honest metrics. Those metrics should reflect how infrastructure actually performs rather than how we would like it to perform.

The Next Decade Will Belong to Integrated Infrastructure
If the past decade was about building larger data centers, I believe the coming decade will be about building smarter infrastructure. But not smarter because individual technologies become more sophisticated; smarter because we finally begin understanding the relationships between them.

Power, cooling, software, operational intelligence and energy management are converging into a single ecosystem. The organisations that recognise this earliest will operate more efficient facilities and adaptable businesses.

Looking ahead, I believe our industry will eventually look back on this period as a turning point. We may wonder why we spent so much time optimising decimal improvements in familiar metrics while the real competitive landscape was changing around us.

Every industry experiences moments when established assumptions quietly lose their relevance and I believe the data center industry is living through one of those moments now. Artificial intelligence hasn’t only increased demand for digital infrastructure – it has fundamentally changed what that infrastructure needs to be.

Artificial intelligence has changed more than workload requirements. It has changed what a data center fundamentally is and the organisations that succeed over the next decade will not simply build better facilities; they will make better decisions about energy, resilience, adaptability and long-term risk.

For years we have thought of data centers as digital infrastructure that consumes energy. I believe the opposite is increasingly true. Data centers are becoming critical energy infrastructure that enables the digital economy. Once we begin looking at data centers through that lens, many of the decisions facing our industry today become much clearer.

When Saudi Arabia launched its National Data Centre Strategy in June 2025, targeting 1.5 gigawatts of capacity by 2030, telecom operators across the Kingdom faced immediate pressure to scale. For one major provider, the priority was Dammam’s Eastern Province. But building data centre capacity in Dammam is no straightforward task. Extreme temperatures reach up to 50°C, fine desert dust is constant, and seasonal humidity pushes cooling systems to their limits. A traditional build, easily eighteen months or more from start to finish, was simply too slow for what the market demanded.


So how do you deploy a data centre at least a year faster than a conventional build, engineer it to operate reliably in extreme heat and dust, and make sure it can scale on demand?

After assessing the client’s requirements, Delta and IPT PowerTech recommended a prefabricated modular approach. Rather than committing to a single large facility built from the ground up, Delta designed a data centre assembled from self-contained, factory-built modules that can be deployed rapidly and expanded over time.


The client started with six modules, enough for current demand. As traffic grows, a second data centre can be added on the same site. Each unit can evolve independently and expand vertically into a full two-storey structure. And if a land lease changes or strategic priorities shift, the modules can be relocated entirely.


Design is only part of the challenge. Each module still needs to be engineered, fitted out, and proven before it reaches the site.

The speed advantage of prefabricated modular construction comes from one fundamental principle: engineering, integration, fitting, and testing all happen in a controlled factory environment, while site preparation takes place in parallel.


At Delta’s production facility in Europe, specialist engineers pre-fitted each module with high-efficiency UPS and 48VDC power systems delivering up to 96% energy conversion efficiency, giving customers flexibility across power architectures. Each module also received power distribution units, batteries, and precision cooling. Every component was installed, integrated, and rigorously tested under controlled conditions, built to European manufacturing and quality standards. By the time a module left the factory, it had been fully validated as a complete data centre unit, ready for deployment.


The result is a consistent, quality-assured product delivered under a single point of accountability, with none of the delays that come from coordinating multiple contractors on a live construction site.


Delta’s own data confirms the impact. Modular design and turnkey delivery reduce overall costs by up to 30% compared to traditional data centre construction. In this case, the time saving was at least twelve months.

Cooling typically accounts for nearly 40% of a data centre’s total energy consumption. In extreme climates, that figure can climb higher. Getting the cooling design right is not just an engineering requirement. It is a direct determinant of operating cost, energy efficiency, and long-term sustainability.


Delta’s solution centres on a fully contained cold aisle. The server racks are arranged in rows facing each other, with a physical enclosure sealing the corridor between them. Chilled air is delivered directly into this enclosed space, passes through the servers, and exits as warm exhaust on the other side. Because the cold and hot airstreams never mix, every watt of cooling does useful work.


The results speak for themselves. Delta’s contained cold aisle design delivers measurable cooling efficiency gains compared to conventional open-room layouts. In a climate where cooling costs can dominate an operating budget, those gains translate directly into lower total cost of ownership and a reduced carbon footprint.

Delta’s engineering extends beyond the modules themselves. Through the DCIM platform, every aspect of the facility is monitored in real time from the moment it goes live: power, cooling, environmental conditions, and system health. Preventative maintenance keeps systems running at peak performance. Problems are identified before they become outages.


On the ground in Dammam, trusted partner IPT PowerTech prepared the site in parallel with the factory build, so that when the modules arrived from Europe, they were lifted into position, locked together, and connected without delay. IPT PowerTech manages the ongoing on-site lifecycle: installation, rack fitting, and continuous management and maintenance.


From first consultation to a fully operational, continuously managed facility, the project was delivered as one integrated solution under Delta’s technology leadership.

Six modules now stand on the Eastern Province skyline, processing data, serving customers, and proving what smart engineering can deliver in the most demanding conditions. Part of Saudi Arabia’s journey toward a sustainable digital future, with AI and cloud services driving the Kingdom’s next wave of growth.


Whether you are scaling in the Gulf, across Europe, or into new markets in Africa, the challenge is the same. You need a trusted partner that will deliver a data centre at the speed your market demands, built to the highest standards, and designed to grow with you – even in the desert.

Get in touch with our team to explore how prefabricated modular data centre solutions can work for your next project.

As data processing and network demands surge – with AI, 5G, IoT, and cloud services driving unprecedented data volumes – traditional data center builds simply can’t keep pace. Delta’s process begins with a comprehensive pre-sales consultation. Our design experts work closely with you to analyze site-specific requirements and network challenges, crafting customized, scalable layouts that not only meet today’s needs but are flexible enough for future network expansions. This consultative approach ensures every solution is engineered to your exact requirements while addressing cost pressures and competitive challenges highlighted in our white paper.

At the high-tech factory in Croatia, smart engineering takes center stage. Premium-grade steel is meticulously inspected for durability and cut to precise specifications using advanced CNC laser technology. Through machine bending and precision welding – secured with industrial-grade coatings – each module is built to be both robust and long-lasting. This controlled, factory-based construction minimizes on-site disruptions and guarantees consistency, reflecting the advantages of a prefabricated approach that our white paper identifies as key to reducing overall capital costs and enabling a “pay-as-you-grow” strategy.

Once constructed, the modules move to a dedicated assembly facility where our technicians integrate all essential systems: Uninterruptible Power Supply (UPS) & Power Distribution, Precision Cooling Systems, Server Racks & IT Infrastructure, Security & Access Control, and Monitoring & Management (DCIM & Environmental Sensors). Each component is subject to rigorous Factory Acceptance Testing, ensuring structural integrity and system reliability before shipment. This pre-testing phase not only reduces the installation timeline but also minimizes the risk of on-site delays – an essential factor when deploying networks in fast-paced telecom environments.

After passing all quality checks, the modular units are carefully secured for transport. At the destination, our team orchestrates a seamless reassembly process that delivers a fully operational data center solution up to a year faster than conventional builds. By leveraging standardized, pre-integrated components, Delta’s approach minimizes on-site labor and complexity, allowing network operators to rapidly expand capacity and respond to traffic surges. As our white paper explains, this rapid deployment is a competitive advantage that supports dynamic market demands while keeping capital expenditures in check.

Delta Electronics’ modular data centers exemplify the smart approach to modern infrastructure. Our solutions offer:

• Cost Efficiency: By streamlining construction in a controlled environment, overall costs are reduced and risks associated with traditional builds are mitigated.
• Sustainability: Factory-controlled production minimizes waste and energy consumption, with systems designed for optimal energy efficiency – supporting telecom operators’ sustainability and CO₂ reduction targets.
• Agility and Scalability: Modular design allows operators to add capacity incrementally, aligning infrastructure investments with actual network demand.
• Future-Proofing: Tailored for evolving telecom requirements, our MDCs support advanced services from 5G to edge computing, ensuring your infrastructure adapts as technology evolves.

With a focus on quality, rapid deployment, and energy efficiency, Delta’s modular data centers combine the best of precision Croatian engineering with a smart, scalable approach to meet the challenges of today’s fast-evolving telecom landscape.

Talk to us today to learn how our modular solutions can power your digital future and help you stay ahead in a rapidly evolving market.

Telecoms site backup batteries are a legal requirement in most developed countries – operators need to prove they can keep critical services running during a power outage.

However, compliance comes at a considerable cost. Because the grid is reasonably stable in Europe, you’re typically replacing lead-acid batteries every 7-8 years that are seldom (if ever) used.

So far, so frustrating. But Delta is helping a major European telecoms provider disrupt the status quo with li-ion (lithium-ion) batteries and smart power. And buying grid power so efficiently, its backup batteries come effectively at no cost.

Before we zoom in on our case study, here’s a high-level reminder of the various ways Smart Power can save you money:

• Peak Shaving uses preset or on-demand battery energy during peak demand from the grid to avoid charges related to power consumption above a certain threshold.
• Tariff Control is preset or on-demand use of battery energy stored during high-cost hours and recharged during low-cost hours (or via renewables like solar, if available).
• Demand response is on-demand battery energy use to offload a certain amount of energy in a set time period.

Now, let’s see it in action.

Smart power innovations are already invaluable to this particular telecom client, which manages a fleet of 700 sites.

Take the Delta ORION power controller for instance – the mind behind everything from optimizing rectifiers and managing battery health, to connecting all site hardware to the Internet of Things (IoT).

ORION’s unique feature and the source of its true power is the embedded powerful logic processing engine that allows to optimize the whole power system and the way it manages energy. All of this is easily manageable and configurable both locally and remotely.

But in this project, Delta engineers thought even further outside the box. If ORION could unlock the latent power of site backup batteries to buy grid power in the smartest way possible, the potential would be huge: especially when combatting this particular nation’s complex three daily grid power tariffs.

The first step for our operator was investing in cyclic li-ion batteries across 171 test sites, rather than traditional lead acid. And leveraging time-shift functionality followed – discharging the new batteries at 35% at the highest cost tariff between 9 pm-midnight and recharging at the lowest tariff in the small hours.

But what about safety? Smart monitoring mitigates the risk of not being able to meet mandated power backup requirements – the batteries remain ever-ready to kick in and take over if there’s a power outage.

So what kind of savings can you expect from making the switch to li-ion batteries?

Tariffs vary from nation to nation, so results will fluctuate accordingly.

But nonetheless, it’s a clear success for our client: by saving 12%-15% per site, instead of paying for new batteries every 7-8 years, it gets them for free.

Connecting securely via the cloud to MultisiteMonitor (MSM) puts real-time monitoring and control and your fingertips. A server-based software tool, MSM gathers critical operations data from up to 3000 connected sites and stores it in a database to enable real time alarms, monitoring, and maintenance.

With a real-time status view and the ability to monitor multiple sites from anywhere from a remote PC, MSM empowers you to optimize energy management and battery status, check and modify configurations, distribute firmware updates, and maintain a detailed equipment inventory.

All said, this innovative application simplifies offloading a small portion of energy from the grid, or a larger amount in specific groups of sites.

So what could be the impact of these smart power savings at a national and regional level?

Two hour battery backup requirements with 10kW of load means operators have to install 20 kWh batteries ( ~400 Ah) per site. If we multiply this by 3000-5000 sites to reach national level, it equates to 60-100 MW energy stored possibly with one operator per country.

When you consider there are 723,450 telecoms towers across Europe, the potential savings are huge. And the benefits are environmental as well as financial – because optimizing energy use rebalances national grids, thus reducing the strain of energy production.

Switching to li-ion batteries harnesses smart power way to tackle a complex tariff system – by leveraging a mandatory resource that usually lies dormant. And for any telecoms operator that’s serious about reducing rising energy costs, it’s a real eye-opener.

And there’s more. Because as well as unlocking significant grid power savings, smart power also prepares you for the flexibility market: unlocking all that latent battery potential can be the first step away from grid reliance and towards a renewable future.

So, the next time you’re due to change your site backup batteries, remember to think smart.

If you’re interested in learning more about the potential cost savings of li-ion technology, we invite you to explore further. Feel free to reach out to us by using the contact form below. Our team is here to provide you with additional information and assist you in getting started on your journey to maximize cost efficiency with li-ion technology.

Despite progress in digital infrastructure across Eastern and Central Europe, there remained a critical need for reliable, purpose-built data centers. This challenge was met by one of our leading telecom data center clients, spearheaded by telecom and tech expert Üllar Jaaksoo. Their solution? The construction of the largest data center in the Baltics, designed to deliver unparalleled reliability and security. Central to this vision was the selection of the right UPS systems, and this is where Delta’s expertise played a pivotal role.

As part of the Three Seas Initiative (3SI) aimed at strengthening EU cohesion, the Tallinn data center is more than just a facility; it symbolizes the powerful intersection of public policy and private enterprise. Spanning 14,500 m² with a 31.5 MW power capacity, this cutting-edge data center is set to ensure reliable digital services across the region.

Each private suite within the facility is equipped with its own dedicated UPS – a distinctive feature that sets this data center apart. This design enhances transparency in power consumption and minimizes risks associated with power disruptions. The challenge lay in finding a provider that could meet these specific needs while also supplying modular UPS units for the entire facility.

After a thorough selection process, our telecom client chose Delta, largely due to our extensive UPS portfolio, which ranges from 1kVA to 500kVA. Delta’s UPS models stood out for their technological superiority and competitive total cost of ownership. A critical moment in this decision-making process was the client’s visit to Delta’s Experience Center in Soest, Germany.

The Delta Experience Center in Soest is more than just a display of our capabilities—it’s a testament to Delta’s commitment to power innovation. Visitors here can witness the immense 3.6 megawatts of raw power, demonstrating the full potential of Delta’s UPS systems. This hands-on experience allowed our telecom client to see firsthand the efficiency, reliability, and durability of our solutions, ultimately solidifying their decision to partner with Delta.

Despite the challenges posed by the pandemic and the complexities of new construction, the project advanced smoothly, supported by Delta’s partner, DC Solutions Estonia. Any minor issues during installation were swiftly resolved by Delta’s support team based in Slovakia, ensuring that the Tallinn systems became fully operational as planned.

The Chief Development Officer of our telecom client, Kert Evert, praised the project’s success and emphasized the crucial role that Delta’s UPS solutions played. Likewise, Margus Sinisaar, CEO of DC Solutions Estonia, and myself, Pertti Lipponen, Sales Manager at Delta Solutions Finland, celebrated the long-standing collaboration that brought this project to fruition. With the Tallinn data center now fully operational, the region is well-positioned to harness the potential of digital advancements, all while benefiting from unmatched power reliability.

The Delta team only fully understood the challenge after visiting Trollstigen.

Skirting roaring waterfalls and bottomless Fjords, this road passes mountains like The King (1614m), The Queen (1544m) and The Bishop (1,462m).

The lofty location and nature reserve status mean ´on-grid` isn’t an option – it’s prohibited by both expense and environmental regulations. And with a near sheer climb of 700m from viewing platform to peak, installing an off-grid base station would be a highly technical game of chess.

In terms of connectivity, only the viewing platform itself had mobile coverage. And accessibility posed problems too, because the road is closed (and site snowbound) each November to April.

So with no grid plug point and head-scratching access challenges, we needed to think way outside the box to pull off this ‘impossible’ project.

Delta’s team of experts got straight to work, designing the unique infrastructure and formulating a plan for the tricky installation.

Installation engineers would only be able to work onsite during summer months, living onsite for several days at a time (this would be no 9-5 shift). Most of the components would need to be flown in by helicopter, with the remainder delivered by hand. And after the team left, the site would have to operate safely, efficiently and completely unmanned – with only limited opportunities each year for maintenance visits.

Back at the drawing board, the Delta team calculated all these complex criteria and developed an extraordinarily smart solution and viable execution plan.

Since off-grid power was the only option, we harnessed two of Trollstigen’s most abundant natural resources – wind and solar power. Then combined these elemental forces with lithium-ion batteries to power the site and hydrogen cells as a backup (should wind or sun fail).

Several partner companies collaborated. For instance, other stakeholders designed and delivered the wind turbines and PV panels and hydrogen fuel cells. Our mission was the cornerstone – producing an innovative power solution that actually made the station viable.

Digging a little deeper, the challenge was integrating these energy sources in a low-maintenance solution working year-round in extreme temperatures, without using excess energy to maintain heat. Therefore, the team decided to place the power systems and batteries in outdoor cabinets that could be heated separately, thus greatly reducing energy use and simplifying maintenance.

But how about the power system setup in the cabinet?

The unique configuration uses 8 of the latest, high-efficiency Eltek solar power modules (3.2 Kw each). And a rectiverter module which converts DC to AC (essential because the wind turbines have AC-powered magnetic brakes preventing them going out of control in extreme conditions).

An Eltek Power Monitor connects to the wind turbines and hydrogen fuel cells, to measure and control currents. And the solar inverters connected to the panels? The solution doesn’t require additional equipment when connecting to hydrogen. The turbines produce the correct voltage. Our primary job is to measure the energy produced by the wind and fuel cell sources.

The solar panels are also divided into strings, with one string on the cabin roof and 2-3 on the mast mounted vertically. They have a nano coating that ice or snow slides off and work surprisingly effectively, despite climatic extremes.

From late November to early February, Norway doesn’t get much sun. But from March until the end of September, solar output is quite good. And because Norway is colder, the PV panel efficiency is higher than in warmer regions.

Clever stuff indeed. But how would Telia monitor this remote site during harsh winter months? Not to mention the problem of getting the equipment (and installation engineers) up there to build it.

Way before the site was operational, our team had to ensure it could be controlled and monitored remotely – with pinpoint precision.

Enter the multi-talented Smartpack 2 Touch controller. For starters, this intelligent device automatically decides which power source has priority. Solar is first in the pecking order, so when the PV panels have gathered ample energy, the controller shuts down the fuel cell (if it’s running). Alternatively, when wind can produce more power, it instructs both sources to work together to optimise.

All units (including onsite heating and other components) are controlled by the Smartpack 2 Touch. There’s remote access to the site via Telia’s own network management system, and Delta developed software to communicate directly with the fuel cell controller. Maintaining the optimum temperature is all-important in this setup. There’s also a small, temperature-regulated, panel oven in the cabin. It’s crucial to maintain the perfect temperature inside, because the by-product of hydrogen is water and if the temperature drops below freezing, the water could turn to ice and damage the fuel cell internals.

And thanks to an ongoing service agreement, Delta provides ongoing support. Because getting your friend up the mountain is one thing but leaving them stranded another. Telia are their own first line of support. But (if necessary) Delta personnel will visit the site annually for system testing to ensure optimal operation, as well as working remotely analysing log files.

Plus, should Telia ever want to expand or alter the site, the Delta team are ready to help. For example, Trollstigen is currently a groundbreaking 5G installation, but it’s also scalable for 6G and other emerging tech evolutions.

With all components packed and the installation team ready, it was time to fly in the lion’s share of the kit by helicopter. And the chopper also dropped off a super-lightweight camper unit for Trollstigen’s engineers/mountaineers.

Between summer 2018 and summer 2019, the intrepid team worked in shifts for the 2-3 months when weather permitted. A tough task. But immense job satisfaction and gorgeous Northern Lights views compensated for the challenges.

Telia was delighted once the site was operational. And Delta is immensely proud that the team had conquered mountains other experts avoided.

The technical solution is seriously impressive. But the true foundation for this project is just as rare and powerful: building complete trust within and between organisations.

Telia trust that we will come up with good solutions. But it’s not only about selling the product. It’s also about being there for the customer when things aren’t working as they should.

And the mutual internal trust between Delta colleagues is also vital. Having the support of the organization made this project possible. Our organization is open to innovative ideas and also lets you know what’s not feasible. This trust and support gave us the confidence to be a reliable advisor for the client.

So there it is. A green, off-grid Telecoms site supporting 5G connectivity in a jaw-dropping site.

Where visitors now snap and send stunning selfies instantly from Trollstigen to the world.
Built by trust and technical excellence – from the ground up.
Talk to us today about your impossible project.

While Trollstigen relies heavily on natural resources like wind and solar, it’s worth noting in general how other renewable power systems compare in terms of their reliance on diesel, a commonly used backup energy source.

The matrix below provides an understanding of how introducing varying mixes of wind and/or solar to a power system can impact diesel consumption. The goal? Minimize the refueling visits, especially in hard-to-access locations, while maintaining energy efficiency.

Renewable energy not only helps in cutting down greenhouse gases but can also significantly reduce lifetime costs for such projects. Integrating renewables in power systems demands a careful evaluation, ensuring the balance between cost-effectiveness and reliability.