Why Infra Teams in DC Projects Miss Cooling Risk Until It’s Too Late

The High Stakes of Data Center Cooling

Data centers run on power, but they survive on cooling. If servers overheat, they fail – it’s that simple. Yet cooling issues often don’t get the urgent attention they deserve during design. This is risky, because cooling failures are a major cause of outages and financial losses. In fact, recent analysis shows cooling failures account for roughly 13–19% of all data center outages (fossforce.com) (www.linkedin.com), a share that is growing as high-density racks become the norm. And when cooling does fail, the consequences are expensive: over half of data center operators say a single outage cost them $100,000 or more (www.linkedin.com), with some incidents exceeding $1 million in damages.

Why is cooling risk on the rise? One reason is the explosion in server power density. AI and high-performance computing racks pulling 30–50kW each are pushing thermal loads to extremes (www.linkedin.com). Traditional air conditioning struggles to keep up. Even big industry players have felt the pain: a recent CME Group outage in Chicago was caused by a cooling system failure amid heavy workloads (www.reuters.com). As servers generate more heat, data center cooling shifts from a background utility to a mission-critical system. Up to 40% of a data center’s energy now goes into cooling alone (semiengineering.com). Clearly, effective thermal management isn’t just about comfort – it’s about uptime, safety, and cost control.

Despite these high stakes, many infrastructure teams overlook cooling risks during design. They assume if the power and floor space specs are met, cooling will somehow sort itself out. Unfortunately, that’s far from true. Cooling needs to be baked into the design from day one. If it isn’t, hidden problems can lurk unnoticed until the data center is built and running – at which point it’s too late (or extremely costly) to fix them. In the next sections, we’ll explore why these cooling risks slip through the cracks and how to catch them early with a more integrated approach.

An Overlooked Risk in Design

It’s ironic that something as critical as cooling is often treated as an afterthought in design. Proper cooling is one of the most crucial yet often overlooked aspects of data center design (danacloud.com). As the project races forward, teams focus on server capacity, power distribution, and floor layout – assuming the mechanical engineers will “handle the cooling.” The result? Thermal issues don’t surface until late in the game, during final commissioning or even production use. By then, resolving them might require expensive retrofits or operational workarounds.

What kind of issues are we talking about? A common one is simply underestimating the heat load. Modern servers and GPUs run hotter than ever. If the design doesn’t account for the real power density and future growth, the cooling system could be undersized from the start. Failing to plan for sufficient cooling capacity will inevitably lead to overheating, performance throttling, or equipment damage as the data hall fills up (danacloud.com). Another problem is poor airflow management. Without careful layout and containment, cold air may not reach every rack evenly. Inefficient cooling layouts create hot spots – areas where temperature spikes above safe limits (danacloud.com). Hot spots not only risk hardware failures, they also waste energy (you end up overcooling other areas to compensate) and undermine reliability.

Overlooking cooling needs early on can also cause subtle design flaws. For example, imagine a server row jam-packed with high-density racks but with insufficient air return spacing or misdirected airflow. The cooling units might keep the overall room temperature in check, but that one row could run 10°C hotter – a recipe for intermittent failures. These kinds of issues often aren’t obvious in CAD drawings or simple spreadsheets; they require thermal analysis to identify. If a project skips detailed cooling modeling in the early stages, it’s essentially flying blind on whether the design can handle peak heat loads.

Then there are redundancy and failure scenarios. A design may work under normal conditions, but what if a CRAC unit (computer room air conditioner) goes down, or an extreme heat wave hits? (One of Twitter’s data centers in California shut down during a 2022 heat wave because the cooling systems couldn’t cope (www.axios.com).) If the team hasn’t modeled these contingencies, they may discover the hard way that losing one cooling unit causes temperatures to spiral out of control. All too often, teams assume that if they meet the nominal cooling requirements on paper, things will be fine – forgetting to stress-test the design against what-if situations.

Why do these oversights happen? Here are a few common reasons:

• Siloed Teams and Tools: Data center design involves multiple disciplines – architecture, electrical, mechanical, IT engineering – each using their own tools. The cooling strategy might live in a mechanical engineer’s Revit model or a CFD simulation, while the IT load sits in an Excel sheet, and the layout in a CAD drawing. If these remain disconnected, critical information falls through the cracks. It’s easy to miss that adding equipment in one system (say, an asset list or DCIM tool) requires a cooling upgrade in another. Poor integration of complex systems is a known contributor to data center failures discovered during late stages (journal.uptimeinstitute.com).
• Late Changes in Requirements: Data center projects are moving targets. Perhaps late in design, the client decides to add a new cluster of servers or increase rack densities for future AI needs. If the cooling design isn’t revisited, you end up with a layout that no longer matches the reality. Many teams have learned too late that a seemingly minor change (like going from 5kW to 10kW per rack in one zone) overwhelms the cooling in that area. Without an always-updated model of your cooling capacity vs. load, these creeping changes can introduce risk unnoticed.
• Over-Focus on Electrical and Floor Planning: Power and space are often seen as the hard limits in data centers – if you have enough UPS capacity and floor area, you’re good to go. Cooling, by comparison, has historically been more forgiving: if a room gets too warm, you often have minutes (or more) to react, unlike an instantaneous power loss (fossforce.com). This has led to a complacency where teams assume cooling problems will give advance warning. But as power densities climb, that cushion is shrinking (fossforce.com). The old approach of simply over-provisioning air conditioning is no longer efficient or sufficient. Cooling needs equal priority with power from the start.
• Lack of Early Thermal Analysis: Some projects delay detailed cooling simulations (like CFD airflow modeling) until late, or skip them entirely due to time and cost. The result is that thermal behavior isn’t fully understood during design. By using CFD and thermal simulations early in the process, engineers can spot where heat will concentrate and how effective their cooling strategy really is (semiengineering.com). Without these insights, teams rely on generalized rules of thumb that might not hold in a particular layout. It’s a bit like building a plane without ever testing it in a wind tunnel – you’re hoping it will fly as expected.
• Underestimating Future Loads: A design that meets today’s cooling needs might falter tomorrow. One of the top mistakes in data center design is not planning for future scalability (danacloud.com). If your initial deployment runs cool but you’ve left no headroom for adding servers or higher-density equipment, you're embedding a ticking time bomb. The infra team might pat themselves on the back at launch, only to discover two years later that new IT demands are straining the cooling beyond design limits. Future-proofing with modular, scalable cooling (and monitoring capacity) is essential, yet it’s often skipped when fast-tracking a project.

In summary, cooling risk is frequently missed because it hides between disciplines and reveals itself only under stress. No single engineer intentionally ignores it – it’s the gaps between electrical, mechanical, and IT planning that allow thermal issues to slip in. So how can we bridge those gaps? The answer lies in a more unified, proactive design approach.

Bridging the Gap with a Unified Approach

To avoid unpleasant cooling surprises, data center teams need to break down the silos and get everyone working from the same playbook. This starts with establishing a single source of truth for design data. In the building industry, the concept of a common data environment (CDE) has gained traction – a central hub where all models, files, and data are maintained together for easy access (mobile.engineering.com). The goal is that architects, engineers, BIM managers, and operators are all looking at one integrated model of the project, not separate disconnected versions. When cooling information (like heat loads or HVAC specs) lives in the same digital space as the IT equipment layout and power plan, it’s much harder to overlook a conflict or constraint.

BIM managers play a critical role here. They are often the custodians of the master model and can champion the use of a connected data environment. Instead of having an architectural Revit model, a separate mechanical model, and an Excel equipment list that never quite match, the team should link these together. Modern BIM collaboration platforms and AI-driven design tools make it possible to tie everything into a live model. For example, a change in server rack count could automatically update cooling load calculations and alert the mechanical team of needed adjustments. The idea is continuous synchronization – whenever a change happens in one domain, the implications ripples through to all others in real time.

Hand-in-hand with data integration is continuous analysis. Rather than doing one-off thermal studies, teams can leverage simulation throughout the design. Tools for CFD and thermal modeling can be integrated into the BIM workflow (or at least used iteratively as the design evolves) to validate cooling performance at each stage. Leading data center designers now routinely use computational fluid dynamics early in the design process to optimize cooling layouts (semiengineering.com). This proactive modeling helps right-size cooling infrastructure (avoiding both under-cooling and over-engineering) and catches hot spots on the virtual model before they become real. In essence, treat your data center design like a digital twin of the eventual facility – simulate how it will behave under various loads and failure scenarios. A robust digital twin can predict hotspots or equipment failures and let you test “what-if” scenarios easily (medium.com) (medium.com). For instance, you can simulate a CRAC unit failure in the model and see if temperatures stay within safe limits; if not, you know to add redundancy or improve airflow now, not during the chaos of an outage.

Several best practices emerge when bridging these gaps:

• Plan for Peak and Surge Conditions: Don’t design just for the average load. Include extreme scenarios (peak IT load, highest ambient temperature, a cooling unit offline) in your planning. This ensures resiliency. Many operators maintain strict uptime standards and expect that even during a worst-case event the cooling holds steady (www.reuters.com). Your design should meet those expectations on paper before it’s built.
• Implement Airflow Management Early: Incorporate features like hot/cold aisle containment, blanking panels, and strategic vent tile placement in the layout phase. Good airflow management can make a moderate cooling system perform like a high-end one by eliminating hot spots (danacloud.com). Don’t leave containment as an afterthought; it should be part of the initial design narrative when positioning racks and CRAC units.
• Coordinate Across Disciplines Continuously: Set up regular cross-team reviews focusing specifically on cooling and thermal issues. This might mean mechanical engineers, electrical engineers, and IT equipment planners all reviewing the model together. Often, a simple coordination meeting can reveal, for example, that an electrical design decision (like placing power distribution units that generate heat) could impact cooling airflow, or that network cabling plans could block underfloor air paths. Breaking down these silos early saves headaches later.
• Use Smart Monitoring and Forecasting: During design, consider how you will monitor temperatures and loads in the live facility. Designing in extra sensor capacity (for temperature, pressure, etc.) can provide the data for a feedback loop. With a good DCIM system or digital twin, you can continuously compare design vs. reality once operational. More importantly, you can forecast when you’ll hit cooling capacity. For example, trending data might show that adding 10 more servers will push a room to 80% of cooling capacity – information you can use to plan upgrades before an outage occurs.
• Adopt Automation Tools: Humans miss things – especially in complex projects with thousands of components. This is where automation can be a game-changer. Newer platforms can automatically check designs against rule sets and best practices. Imagine a tool that flags: “Rack R23 has a projected intake temperature above recommended limits” or “Room 2’s cooling redundancy will fail Tier III requirements if one more rack is added.” Automated rule-checking and floor plan generation can enforce cooling design standards without relying on someone remembering every detail.

That last point leads to an important development in our industry: AI-powered, cross-stack design platforms. These are emerging solutions that not only integrate data, but also automate the heavy lifting of analysis and design coordination. Let’s look at how this kind of approach can specifically help ensure cooling is never overlooked.

Cross-Stack Automation to the Rescue (ArchiLabs’ Approach)

One example of this new breed of integrated tools is ArchiLabs. ArchiLabs is building an AI operating system for data center design – essentially a platform that connects your entire tech stack (Excel sheets, DCIM software, CAD/BIM platforms like Revit, thermal analysis tools, databases, custom apps… you name it) into a single, always-in-sync source of truth. Instead of juggling multiple files and manually updating one system after a change in another, your team works on a unified canvas of data. Everything stays aligned: if a rack layout is tweaked in the CAD model, the power and cooling calculations in your spreadsheets or BIM parameters update automatically. All stakeholders see the latest information, eliminating version mismatches and nasty surprises.

On top of this unified data layer, ArchiLabs adds powerful automation. It doesn’t just hold your data – it can do work for you. Repetitive planning tasks that used to take hours can be done in minutes (or automatically in the background). For example, tasks like rack and row layout optimization, cable pathway planning, or equipment placement can be handled by the AI. You could let the system generate an initial rack layout that balances power and cooling loads evenly across the floor, or have it route hundreds of cable runs while respecting airflow clearances and avoiding obstructions. By automating these tedious tasks, teams can quickly iterate and refine designs, exploring alternatives that might have been too time-consuming to manually evaluate.

What really sets this approach apart is the use of custom agents that orchestrate workflows across all your tools. You’re not stuck with a black-box algorithm; you can teach the system your own processes. For instance, you might create an agent that takes a new equipment list from an Excel file or database, cross-references it with available rack space in the BIM model, writes the equipment into the Revit design, and then triggers a cooling analysis script to verify thermal capacity. The agent could then generate a report or even update a DCIM system with the new layout and cooling load data – all automatically. This kind of end-to-end workflow means once you define the process, adding or moving equipment is no longer a risky manual endeavor; the AI ensures all steps (from CAD updates to analysis to documentation) are executed every time, in sync.

Because ArchiLabs connects across the full stack, it serves as a cross-checking layer for cooling requirements. The platform can pull live data from external sources too – for example, reading temperature sensor data or equipment thermal specs via an API. This means your design model isn’t a static plan; it can be an active, evolving model that compares planned vs actual. If something drifts out of spec (say an equipment vendor update shows higher heat output, or an API reports a higher ambient temperature forecast for the site’s location), the system can flag it and suggest design adjustments. Essentially, it’s like having a diligent digital assistant constantly auditing your design against all relevant parameters and alerting you to cooling risks before they become issues.

Another advantage is multi-step process orchestration. Complex tasks like performing a CFD simulation require performing multiple sub-tasks in different tools (exporting a model, running the sim, importing results, etc.). ArchiLabs agents can handle these multi-step operations across different software. For example, an agent could automatically export your current design to an IFC file, feed it into a cooling simulation tool or cloud service, retrieve the results, and then update the BIM model with color-coded markers showing hot spots. This could be scheduled to run nightly so that every morning the team has up-to-date insight on whether any new hot spot emerged from yesterday’s design changes. With this kind of proactive automation, the phrase “missed cooling risk” can become a thing of the past – you’re effectively always testing your design in the background.

It’s important to note that ArchiLabs is a cross-stack platform for automation and data synchronization, not just a point solution or a plugin for one software. Revit integration is one feature (since BIM models are crucial), but the philosophy is that all your tools – from CAD to spreadsheets to monitoring systems – work off a shared truth. This holistic approach is what allows cooling considerations (and other constraints) to be woven in throughout the design process. You’re not remembering to check a separate cooling model on the side; the cooling intelligence is embedded in the central model and workflow.

For BIM managers, architects, and engineers, this means less time doing manual updates or chasing data, and more time validating and improving the design. The BIM manager can trust that the cable tray layout automated by the system isn’t going to block air vents, because the rules and agents have been set up to prevent that. The mechanical engineer can sleep easier knowing that if an IT guy increases a server spec in an Excel sheet, the change will reflect in the load calculations and model instantly, not months later during commissioning. It’s a safety net and a productivity booster at the same time.

Designing for No Surprises

Ultimately, the goal is to never be blindsided by cooling problems at the eleventh hour. By recognizing that cooling is as fundamental as power and floor space, infra teams can change their approach to data center projects. It means integrating disciplines, continuously analyzing thermal performance, and leveraging automation to handle the complexity. The cost of doing so is far less than the cost of an outage or an emergency retrofit. As we’ve seen, data center outages due to cooling failures can halt operations and incur massive losses (www.linkedin.com). Conversely, robust cooling design and proactive planning enable facilities to handle growth and unexpected stress with ease – no panicked scrambling, no finger-pointing between teams.

New AI-driven platforms like ArchiLabs make this integrated, automated approach feasible. They act as the connective tissue between all the disparate tools and data, ensuring nothing slips through unnoticed. The result is a living design environment where cooling risk is identified and addressed in real-time, throughout the project. BIM managers can coordinate effectively, architects can incorporate cooling needs into layouts, engineers can validate performance at each step, and everyone shares the same up-to-date information. When the design phase wraps up, you can have confidence that you’re not going to discover a hidden thermal flaw during commissioning – because you’ve been checking all along.

In short, avoiding cooling risk in data center projects comes down to breaking the mold of isolated workflows. By uniting data and automating design intelligence across the stack, teams can catch issues early and design with full awareness of thermal constraints. The days of missing cooling issues until it’s too late should be behind us. With the right approach and tools, we can ensure our data centers run cool and reliable from day one – no surprises, no excuses. Integrated design and automation are the keys to keeping our cool (literally) in modern mission-critical projects. The technology and best practices are here today – it’s up to us to use them and build a future of data centers that never break a sweat. (danacloud.com) (semiengineering.com)