The Data Center Gold Rush: How AI Can Help Construction Firms Win Big

The data center boom presents a generational test for construction: can the industry finally fix its technology adoption gaps using the same AI fueling the rush?

Construction is a low-margin, high-volume industry that is generally averse to innovation and slow to adopt technology. It’s also a sector facing significant challenges: an ongoing labor shortage, poor collaboration, and high suicide and overdose rates.

Meanwhile, external pressures are growing. By 2030, data center projects are expected to attract investments of nearly $6.7 trillion worldwide. Construction firms sit at the center of facilitating these massive investments, presenting a multitude of opportunities for construction firms, but are they ready to deal with all the associated pressures? The firms that tap into opportunities and overcome major industry challenges will be the winners by managing risks better and smarter. To do so requires using the same AI driving the data center boom and stepping up as partners to hyperscalers and utilities.

The three core challenges

Racing against a ticking clock

Hyperscalers (think Silicon Valley giants) urgently want their data centers operational. Thanks to tools like ChatGPT, there’s an ever-increasing demand for computing power. In construction, that means completing projects with maximum efficiency and minimal time constraints. Any delays translate into enormous expenses and setbacks for the competing tech firms.

Struggling with labor shortages, suicides, and poor tech adoption

Each hyperscale project calls for plenty of boots on the ground, meaning 1,500 on-site workers with a need for best-in-class EHS programs. This is a major challenge for an industry already contending with a severe labor shortage.

The labor shortage is compounded by a deeper crisis: the U.S. construction sector now has one of the highest suicide rates of any major industry. Layered with the data center boom and relatively outdated methodologies, construction firms are being asked to do more, and faster, with a workforce already at breaking point. Supporting AI-fueled growth while dealing with foundational challenges and largely relying on manual processes is untenable.

Navigating an overstrained and outdated power grid

Electricity demand for data centers is expected to double in the next four years. Meanwhile, the grid’s ‘check engine light’ is on. Power generation capacity already fails to meet electricity demand, and permitting delays to upgrade infrastructure are inflating costs. These problems pose a significant threat to energy security that impacts all involved stakeholders, including the construction industry.

To bypass these issues, hyperscalers are moving off the grid in a ‘bring your own power’ approach. These forces have cascading effects on the construction companies that serve hyperscalers and the utility industry. Construction firms are expected to execute projects with better coordination, project management, and virtually no schedule slippage.

The high-impact areas of AI

AI and real-time analytics have a powerful role in enabling construction firms to more effectively manage financial, operational, supply chain, and EHS risks. Embedded AI tools and real-time analytics in project management not only allow project managers to track current expenses and bottlenecks, but also prospective costs and threats months in advance. In hundred-million-dollar projects, flagging critical details before they snowball has significant financial implications.

Moreover, AI helps humanize construction by protecting the people on the ground. AI systems can track patterns like long shifts, excessive travel, and risky job-site behavior so managers can proactively protect employees before a crisis occurs.

However, the industry must overcome widespread reluctance to adopt AI tools. A recent ASCE survey found that only 27% of the sector is using AI in their operations. Manual-dependent operations mean key details and risks slip through the cracks, weakening auditability, traceability, and accountability. It also prevents construction companies from stepping up as partners that can keep pace with the breakneck demands of hyperscalers.

The same survey reveals the positive impact of AI: 94% of the 27% already using AI plan to increase their usage. That conviction from early adopters is an industry-wide signal that AI is already delivering measurable value to improve EHS, project management, cash flow, and supply chains.

Evolving from contractor to partner

In the regulated utility industry, legacy contract structures often overlook reward outcomes and performance, and time-and-materials for contractors’ work can extend timelines and increase invoices. This creates an embedded motive to stretch projects as part of a system that rewards inefficiency over savings and strong performance. That not only ramps up utility bills for ratepayers but also slows technology adoption in finance and operations from the construction side. Applying this model to a billion-dollar data center project means a few months of schedule drift in a time-and-materials contract translates into tens of millions in additional costs for owners and financiers.

Instead, a co-developed model is needed, where hyperscalers, construction companies, and utilities collaborate to design, finance, and operate the required infrastructure for these projects.

Moreover, a symbiotic reward system emerges. Hyperscalers provide the capital, utilities bring the infrastructural know-how, and construction gets the data centers off the ground. High financial and project outcomes are recognized as key metrics to evaluate contractor performance, rewarding technology-savvy construction firms that step up as innovation partners.

The data center gold rush presents risk but also undeniable opportunities for construction firms willing to embrace technological innovation. A digital-first approach coupled with a co-development model will separate the winners from those left behind in one of the fastest-growing segments of the U.S. economy.