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To deliver on time and on budget, contractors are updating legacy processes

This article originally appeared in IEC Insights in September 2026. Follow IEC to stay up-to-date on the trends shaping the industry for electrical contractors.
Data center construction has exploded, with U.S. spending reaching $41.1 billion last year—up from $1.8 billion in 2014. The work is there for electrical contractors ready to take it on, but whether a firm can capitalize increasingly depends on that firm’s ability to operate efficiently under intense time pressure with narrower margins for error.
Data center builds are, on average, larger, more tightly scheduled, and more complex than traditional commercial and residential work. When a firm goes looking for the bottleneck that threatens margin, the instinct is to look first at the field, where labor costs are highest and delays are most visible.
The field offers real ways to gain efficiency, yet it subjects a contractor to forces beyond their control. Weather, government actions, and site conditions will set a job back no matter how well it’s run, and trade sequencing routinely stalls the crews waiting downstream through no fault of their own. What a contractor can control comes down to crew speed and productivity, but the hardware meant to lift those, like layout robots and exoskeletons, must be deployed, learned, and maintained one jobsite at a time. Gains spread slowly across an organization even as data center demand climbs.
The back office carries the same level of risk to the schedule, but more of that risk is under a contractor’s control. A single change to how the business runs affects every active project at once, making office-level bottlenecks higher-leverage opportunities for efficiency gains.
A data center build makes it clear which of those bottlenecks needs to be addressed most.
A data center packs in more electrical gear than traditional commercial and industrial jobs while carrying lead times two to four times their pre-pandemic norms:
In response to the growing demand, hyperscalers like AWS, Google, and Microsoft are now buying in bulk, which concentrates production capacity in a handful of large buyers and leaves everyone else waiting longer. Most data center work is colocation, enterprise, and regional builds, and those jobs are less likely to be owner-furnished than hyperscale campuses—putting sourcing pressure for long-lead equipment directly on contractors. A quoting or ordering error that once cost a shop a day can now cost it a place in an extremely tight and expensive manufacturing queue.
Long-lead gear is only part of the problem. A data center is built to hit a fixed power-on date, and the schedule around it is sequenced so tightly that even routine materials can derail it. Conduit, wire, and fittings carry little lead-time risk, but a crew scheduled to install them on a set day has no slack for a wrong count or a missed delivery.
Naming material as a leading cause of delay puts the spotlight on the work of buying, tracking, and managing it. For most contractors, that work is still done by hand. Purchasing teams and project managers juggle dozens of suppliers across multiple document formats and communication channels. Every quote, confirmation, and update must be reviewed, consolidated, and keyed by hand into a spreadsheet or accounting system to keep information centralized and up to date. One mistyped number or a missed shipment delay update that never reaches the right person can throw off a job. Across dozens of suppliers on every project, those small errors compound into stockouts, overorders, and work stoppages.
For a data center project, that process meets higher volumes and tighter tolerances, so mistakes become more inevitable and intolerable.
Historically, construction has been harder to digitize than most industries. The work is project-specific, the players operate in separate systems, and the information moving between them is messy by nature. AI is the first technology suited to that reality. AI can work with unstructured information, and it can absorb the coordination and handoffs between teams and systems that defeated earlier software. The McKinsey Global Institute estimates AI and automation could add roughly $228 billion in annual value to the U.S. architecture, engineering, and construction industry by 2030, and that AI could automate 39% of the sector’s nonphysical work, with invoicing and data entry among the areas expected to change most, precisely the manual, error-prone core of procurement.
Applying AI to procurement in practice looks like addressing manual points of failure with new solutions that:
The contractors best positioned for the data center boom are the ones treating procurement as operating infrastructure, built to move material accurately and at speed. Handled that way, procurement protects the schedule, defends the margin, and creates the capacity to take on the next project. As the build-out accelerates, procurement speed, operational visibility, and back-office efficiency are what enable firms to meet the moment and scale with confidence.

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