Five Takeaways from the Built for the AI Boom: Data Center Webinar

The AI boom is driving unprecedented demand for data center capacity. For the teams building that capacity, the challenge isn’t just volume. Projects are getting larger, schedules are compressing, and the consequences of a missed milestone are significant.

In our recent webinar, Built for the AI Boom: Data Center Construction Scheduling, Planera Solutions Engineer Sam Durkin and Customer Success Manager Emma Forcier shared what they’re seeing across mission-critical projects and how leading data center builders are adapting their scheduling practices.

Here are five takeaways.

1. The schedule needs to operate at the speed of the project

Data center schedules don’t have the luxury of standing still for a week.

Yet on many projects, the master schedule is still owned by a small number of P6 experts, sometimes working off-site. Updates happen weekly or biweekly, while field teams manage the work through spreadsheets, whiteboards, and disconnected micro-schedules.

The result is a widening gap between what the schedule says and what is actually happening in the field.

As data center projects scale across multiple data halls, buildings, and campuses, that model becomes increasingly difficult to sustain. The CPM schedule needs to become a day-to-day planning tool, not simply a record of what happened during the last update cycle.

That starts by making it accessible to more of the project team and easier to update as conditions change.

2. More schedule visibility means better decisions

Complexity isn’t unique to data centers, but the number of concurrent milestones and driving paths can make these projects particularly difficult to understand through a Gantt chart alone.

During the webinar, Sam demonstrated how teams can use Planera’s visual CPM Canvas and Inspect Path to isolate the driving logic behind individual milestones and see how an upstream change affects downstream work.

That visibility matters beyond the scheduling team.

Superintendents, project managers, trade partners, owners, and schedulers need a shared understanding of what is driving the project. When more people can understand the logic behind the schedule, teams can identify problems earlier and make decisions with the same information.

One superintendent working in Planera described the Canvas as the first time he had been able to truly understand the driving paths through his schedule and the downstream effects of resequencing work.

The goal isn’t to replace the Gantt chart. It’s to make the logic behind it easier for the project team to understand and act on.

3. Schedule quality is risk management

A schedule can calculate successfully and still contain structural problems.

Open-ended logic, unnecessary constraints, and incorrectly connected procurement activities can hide risk until a delay exposes it. On a mission-critical project with compressed timelines, discovering those issues after something slips is too late.

That’s why schedule quality needs to be continuous.

Planera runs a customizable DCMA 14-point-plus quality check whenever the schedule is calculated, identifying issues such as missing logic directly inside the schedule. Instead of exporting files into separate validation tools or manually working through logs, teams can identify an issue, navigate directly to the affected activities, correct it, and immediately see the result.

There’s also a portfolio benefit. Applying consistent quality standards across projects helps contractors avoid having one project with rigorous scheduling practices and another operating very differently.

Better schedule hygiene isn’t administrative work. It makes the risk already sitting inside the schedule easier to see.

4. Delay documentation needs to start when the delay happens

On fast-moving data center projects, today’s problem can quickly become tomorrow’s claim.

The first requirement is documentation.

During the webinar, Sam demonstrated a simple RFI impact that added work to a steel sequence. Once the activity was added and the schedule recalculated, the team could immediately see that the change added four calendar days, shifted the critical path, and affected the project completion date.

For broader impacts, AI can reduce the administrative burden. In another example, Manny, Planera’s AI Scheduling Assistant, identified 62 activities affected by a weather day and added documentation across all of them.

The value becomes particularly clear when a delay turns into a claim.

Emma shared the example of a general contractor that used Planera to document added activities, illustrate changes to the critical path and total float, and clearly communicate the impact as part of a claims package. The result was four and a half months of additional time and more than $5 million approved, without schedule-related negotiation with the owner’s legal team.

Clear claims start with contemporaneous documentation. The easier it is for the project team to capture impacts as they happen, the stronger that record becomes.

5. AI is most useful when it helps teams evaluate decisions faster

Data centers are highly repetitive. The same sequences may appear across dozens of areas, multiple data halls, or several buildings.

That repetition creates an ideal use case for scheduling AI.

During the webinar, Sam showed Manny identifying the same sequence in 42 locations, modeling a proposed logic change, calculating its schedule impact, and then applying the approved change across the schedule.

In another scenario, the team evaluated a lower-cost fire protection bid that required 20% longer durations. Manny analyzed 130 affected activities and found that the overall project finish date would not move. The team could then examine where float had been consumed and decide where additional mitigation might be warranted.

This changes the economics of what-if analysis.

When testing a scenario requires hours of manual schedule manipulation, teams naturally test fewer scenarios. When they can evaluate multiple options in seconds, they can ask better questions before committing to a decision.

The important distinction is that AI isn’t making the decision. The scheduler and project team remain in control. AI handles the analysis and repetitive schedule work so they can evaluate more possibilities, faster.

The takeaway: Data center scheduling has to evolve with data center construction

The data center boom is putting enormous pressure on project teams to deliver more capacity, on tighter timelines, across increasingly complex programs.

The CPM fundamentals haven’t changed. The way teams interact with the schedule needs to.

The strongest scheduling processes bring the field and master schedule closer together, make logic understandable to more people, continuously surface schedule risk, document impacts as they happen, and give teams the ability to evaluate changes before they become problems.

That’s ultimately what modern data center scheduling should enable: more eyes on the schedule, earlier visibility into risk, and faster decisions when the plan changes.