Earlier this year, Amazon announced its ‘Titus’ initiative, taking its $50-billion investment in U.S. data centers to a new level and aiming to build the infrastructure in under 35 weeks. Titus joins projects like Meta’s Hyperion and the Oracle- and OpenAI-backed Project Jupiter in a race with aggressively condensed timelines—a race the construction industry cannot win with outdated planning, estimating and bidding methods.
PRESSURE ON CONSTRUCTION TEAMS
U.S. data centers are expected to generate 4.7 million temporary construction jobs. The industry does not have 4.7 million workers available. In fact, 92% of construction firms are having a hard time finding workers to hire, leaving the industry with a critical labor shortage.
With a race to compress timelines, contractors are navigating fragmented workflows and costly inefficiencies while being expected to complete technically complex builds like data centers in one-third the typical time. To start a project on the right foot, estimators need to spend their hours on strategy and planning—not manual data entries.
A NEWER, SMARTER WORKFLOW
AI data centers are far from standard commercial projects. These campuses can span thousands of acres and require a mix of standard commercial construction workers and highly skilled technical trade workers like high-voltage electricians, HVAC technicians and plumbers. All systems in data center builds are interdependent, meaning one estimating error at the start doesn’t remain contained—it ripples through the entire project.
Preconstruction is critical for accurate planning before work even begins. With so many parts of a project moving at once, coordination and planning must be exact. If estimators don’t capture down-to-the-decimal details during this phase, even the tiniest error can throw the build off schedule—a delay that a 35-week timeline cannot absorb.
AUTOMATION AND THE EVOLUTION OF ESTIMATING
As timelines tighten, the pressure on estimators to plan quickly and accurately has never been greater. Historically, manual takeoff, siloed Excel sheets and disconnected workflows have caused vague scopes, missed permits or inaccurate bids. Slower processes may have worked when project timelines allowed for delays, but not with the data center demand. AI-accelerated workflows are changing what preconstruction teams can deliver in a 35-week window. Instead of measuring drawings line by line, estimators can use AI-powered takeoff to identify materials, quantities and scope gaps in a fraction of the time, freeing them to focus on strategy and trade coordination.
The speed these builds require will come from modernizing preconstruction itself—automated takeoff, connected project data and AI-assisted estimating that puts an estimator’s time into judgment instead of measurement. The contractors who win data center work won’t be the ones who learn to work faster by hand. They’ll be the ones who stopped doing this work by hand at all.
SEE ALSO: WHEN CONSTRUCTION BACKLOG THINS, FAST PROJECT STARTS WIN
