Powering AI infrastructure is not just a matter of adding up server ratings. Operators must plan for uncertain demand, secure power on a realistic schedule, handle fast-changing loads, and coordinate electrical, thermal, water, and operating systems. These seven mistakes are planning risks to review early—not a substitute for site-specific electrical engineering or applicable codes.
1. Forecasting from today’s average IT demand
A forecast based only on current consumption or an average server load can miss both the scale of future demand and the uncertainty in how quickly it arrives. Keep the distinction clear between total data-center electricity use and the portion attributable to AI: the published forecasts below cover data centers overall, not AI alone.
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| Source and geography | Period | Estimate | How to interpret it |
|---|---|---|---|
| International Energy Agency (IEA), global | 2024 actual estimate | About 415 TWh, or about 1.5% of global electricity consumption | The IEA also reports average annual growth of 12% over the preceding five years. |
| IEA, global | 2030 outlook | About 945 TWh | Base Case scenario, not a guaranteed outcome; the IEA models uncertainty around AI uptake, efficiency improvements, and energy-sector bottlenecks. |
| Lawrence Berkeley National Laboratory (LBNL), United States | 2030 outlook, published in 2026 | 649 TWh reference estimate; compounded uncertainty bounds of 521–843 TWh | LBNL estimates 11.8% of total U.S. electricity use in its reference case, with scenarios from 9.5% to 15.3%. |
The IEA global outlook and LBNL U.S. estimate have different geography and modeling boundaries; they should not be added together or treated as directly comparable forecasts. LBNL’s 2025 update, published in 2026, uses a bottom-up model incorporating planned equipment shipments, device energy use, cooling simulations, facility types, and locations. (Sources: IEA, Energy and AI, 2025; LBNL, U.S. data-center electricity-use update, published 2026.)
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- Build demand scenarios that vary deployment pace, server efficiency, utilization, and facility growth rather than relying on one point estimate.
- Separate IT demand from the supporting facility load in forecasts; a server-only total is not the whole site requirement.
- Record which assumptions drive each scenario and identify what evidence or project milestone would cause the forecast to change.
The IEA estimates that servers account for around 60% of electricity demand on average in modern data centers, with substantial variation by facility type. That average is useful context, not a multiplier for sizing a specific site.
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2. Assuming utility power will arrive when the building does
A facility schedule and a power-delivery schedule are not necessarily aligned. The IEA notes that a data center can become operational in two to three years, while energy infrastructure takes longer to plan and build. Because data-center demand is concentrated geographically, local grid constraints can matter even when the global share of electricity use appears modest.
Questions to put to the utility and project team early
- What capacity is available at the proposed point of interconnection, and what upgrades or studies are needed before it can be delivered?
- What is the expected sequence and schedule for approvals, equipment, construction, testing, and energization?
- Can the connection support the planned load at each deployment phase, or will the facility need a staged or flexible connection?
- What happens if utility capacity, equipment, or construction milestones slip—and which project decisions depend on those dates?
Do not treat onsite generation as an automatic shortcut around grid planning. In the IEA’s 2025 outlook, natural gas and coal together are expected to meet over 40% of additional data-center electricity demand through 2030; that is a scenario-level supply outlook, not a prediction of the mix serving an individual facility. The U.S. Department of Energy’s 2024 discussion identifies onsite generation and storage, grid improvements, demand-side efficiency, and rate structures as possible flexibility responses, not requirements for every project.
3. Designing for average load instead of peaks and swings
AI training and model use can create large, rapid power swings compared with traditional data-center operations, according to the IEA’s 2026 executive summary. A design review that looks only at a smooth average may overlook peak demand, transient behavior, or the consequences of deploying servers in stages.
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The IEA notes that a data center’s actual peak can be uncertain as servers are added progressively, and that operators may initially oversize grid connections. Its 2026 executive summary says an advanced data-center rack could have peak demand equivalent to 65 households by 2027. This is an illustrative comparison in the summary, not a universal rack specification or a basis for calculating a facility’s electrical capacity.
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- Model expected operating profiles, deployment phases, and credible peak conditions—not just average consumption.
- Ask equipment and controls teams how load changes during training, inference, server startup, and workload transitions.
- Review how those changes affect the proposed utility connection, power conversion and distribution equipment, and control strategy.
- Revisit peak assumptions as equipment orders and deployment plans become more concrete.
The cited sources do not establish a universal GPU-server or rack sizing method. Facility capacity, equipment ratings, and protection settings require engineering based on the selected equipment and site conditions.
4. Leaving UPS, backup generation, and continuity planning until late
Uninterruptible power supply (UPS) batteries and backup generators are among the systems the IEA identifies as supporting continuity during outages and high data-center reliability requirements. Treating them as late-stage add-ons can leave unresolved space, integration, operating, and project-schedule questions.
Set the continuity requirement before selecting equipment
- Define which loads must remain supported and what interruption, if any, each can tolerate.
- Have the engineering team determine the appropriate UPS arrangement, battery runtime, generator capacity, transfer behavior, and redundancy for the site’s requirements.
- Plan how backup systems will be tested, maintained, and coordinated with utility supply and facility controls.
There is no universal UPS topology, runtime, generator rating, transfer time, or redundancy level established by the cited evidence. Those are site-specific design decisions; a rack-mount UPS category, by itself, does not establish suitability for a facility-scale AI deployment.
Onsite generation also has trade-offs. The IEA’s 2026 executive summary estimates that reliable onsite gas generation for critical and variable data-center load could require overbuilding generation capacity by 30% to 70% relative to demand. That finding applies to the particular supply approach analyzed, not every generator or power system.
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5. Underestimating cooling and thermal-management energy
AI compute density makes thermal planning inseparable from power planning: electricity used by cooling and environmental control adds to the facility’s energy demand. The share varies considerably. The IEA’s 2025 report puts cooling at about 7% of electricity demand in efficient hyperscale data centers and over 30% in less-efficient enterprise data centers.
Those figures are examples of variation, not a universal allowance to apply to a new project. Climate, load density, facility design, and operating conditions affect thermal needs; assuming one cooling percentage can therefore distort both the energy forecast and the infrastructure plan.
Review thermal assumptions alongside the IT plan
- Check that thermal design reflects the planned equipment density, operating profile, and deployment phases.
- Evaluate energy and water use together when comparing cooling approaches.
- Test the design across relevant climate and load-density conditions rather than relying on a single headline efficiency value.
- Coordinate thermal controls and facility operations with electrical planning so changes in one system are visible to the other.
6. Optimizing power, cooling, water, and grid strategy in separate silos
A component can look efficient in isolation yet create a constraint elsewhere—for example, a thermal choice can affect water use, while storage or onsite generation can change how the facility interacts with the grid. Planning these systems independently makes it easier to miss those interactions.
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Use a shared planning review
- Compare grid-supplied, onsite, or co-located supply options against schedule, availability, reliability, and emissions considerations.
- Assess firm and flexible connection options against the facility’s operating needs and ability to shift or manage demand.
- Review power density, thermal efficiency, water use, storage capability, and continuity requirements as connected design choices.
- Assign owners from facilities, IT, utility coordination, cooling, controls, and operations to reconcile assumptions before major commitments.
The IEA’s 2026 summary projects that 20–25 GW of battery storage could be installed in data centers globally by 2030, potentially allowing facilities to serve as grid assets if incentives are appropriate. This is a projection, not current installed capacity or a recommendation that every data center should provide grid services.
7. Skipping commissioning, performance validation, and operating discipline
A design is not proven simply because its equipment has been installed. Commissioning and performance validation are needed to check whether systems work together as intended under relevant operating conditions. The AI Data Center Energy Performance Framework includes commissioning, validation, operations and maintenance, and retrofit as parts of the facility lifecycle.
Make verification part of the project plan
- Define acceptance criteria. Before equipment is commissioned, specify what performance, reliability, and control behavior must be demonstrated for the project.
- Test integrated operation. Verify how power, backup systems, cooling, controls, and monitoring behave together, including relevant transitions and load changes.
- Document measured results. Compare observed operation with design assumptions and keep records that operators can use to identify deviations.
- Establish operating practices. Set responsibility for monitoring, maintenance, periodic review, and updating forecasts as server deployment and workload patterns evolve.
Commissioning methods and acceptance limits depend on the facility design and applicable requirements; the framework is guidance, not a replacement for codes, standards, or engineering judgment.
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