AI Data Centers Are Forcing Logistics to Run at Compute Speed

AI data center construction is accelerating at a pace that existing supply chains were not built to handle. Hyperscalers have committed hundreds of billions of dollars toward new capacity, with more than 10 gigawatts of additional infrastructure expected to come online in 2025 alone.

At the same time, these projects are encountering delays tied not only to power and permitting, but to the ability to source, move, and stage equipment at the right time. Nearly half of planned U.S. data center projects for 2026 are already facing delays or cancellations due to a combination of supply chain constraints, energy limits, and material shortages.

Traditional infrastructure timelines have not disappeared. Large data center builds still take years from planning to completion. What has changed is the speed of execution within those timelines. Equipment deployment, staging, and activation now operate on compressed schedules measured in weeks.

Andreas Podwojewski, Managing Director, North America and Brazil at Arvato, says to AIM Media House, “In the AI and data center space, 24 months is a lifetime… you have to be ready within weeks.”

Podwojewski said this shift is forcing logistics systems, traditionally designed for efficiency and predictability, to operate on timelines measured in weeks and delivery windows measured in hours.

From Multi-Year Planning to Weeks-Long Deployment Cycles

Traditional third-party logistics (3PL) operations are built around long onboarding cycles. A new warehouse or distribution setup can take months to years, particularly in automated environments. That model does not hold in AI infrastructure, according to Podwojewski.

To meet compressed timelines, operators are standardizing deployment. Podwojewski described a predefined playbook covering site setup, logistics infrastructure, IT systems, and power requirements, allowing teams to move from approval to operational readiness in weeks.

Data center construction now runs on tightly compressed execution windows, where delays in any component propagate across the entire project. In large-scale builds, missed deliveries can idle crews, disrupt sequencing, and push back commissioning timelines. Research on infrastructure projects shows that delays in complex systems cascade across dependencies.

In practice, this has placed logistics on the critical path of deployment. If components do not arrive on time, the data center does not go live.

“If one piece of your supply chain is delayed, then your whole project can’t deliver,” one industry report noted.

The scale of these projects amplifies the impact. Hyperscale facilities can require thousands of coordinated deliveries per day, including server racks, cooling systems, and electrical infrastructure. Each component must arrive in sequence and be ready for installation without delay.

Podwojewski characterized the operating model as a “sprint,” with teams expected to stand up logistics infrastructure within weeks of receiving approval.

He also noted that inventory at these sites can be worth billions, with any interruption to operations considered “absolutely catastrophic,” requiring both speed and strict security controls.

The result is a system where logistics execution directly determines how quickly compute capacity can be deployed.

The End of Centralized Logistics

The shift in timelines is also reshaping the physical structure of supply chains.

Traditional logistics networks rely on centralized distribution. A small number of large warehouses serve broad regions, with delivery windows measured in days. That model depends on predictability and scale efficiency.

AI infrastructure breaks those assumptions.

Data centers are increasingly distributed across multiple regions, often in locations determined by power availability and latency requirements. This creates a need for logistics networks that are physically closer to deployment sites and capable of responding within hours, not days.

“We need to be very close to the data centers,” Podwojewski said.

Instead of one or two regional hubs, operators are building localized inventory points near major data center clusters. According to Podwojewski, these metro hubs are designed to support rapid fulfillment and just-in-time delivery of critical components.

He added that, in some cases, delivery windows are measured in hours, with certain operations designed to move inventory “within a few hours, even down to two hours” to meet deployment requirements.

According to Podwojewski, this requirement changes how inventory is positioned and how facilities are staffed, with components staged close to deployment sites for immediate movement as construction progresses.

He also pointed out that logistics providers are now involved earlier in the build process, often at initial construction stages, to begin staging infrastructure and equipment before facilities are fully built.

In large AI data center builds, equipment must arrive in a precise sequence aligned with construction phases. Deliveries that arrive too early can create site congestion and storage risks, while late deliveries can halt progress.

Developers are already encountering the consequences when this coordination fails. Supply chain disruptions, combined with labor shortages and power constraints, are now among the primary reasons projects are delayed.

The centralized warehouse model is being replaced by a distributed network designed for proximity and precise execution.

Why Hyperscalers Still Depend on Logistics Partners

Despite the strategic importance of logistics, hyperscalers are not moving to internalize these operations at scale.

Their primary constraints lie elsewhere. GPU availability, component lead times, and infrastructure scaling dominate execution. Constraints in hardware supply and deployment timelines limit how much of the supply chain can be brought in-house.

“They have very different problems to tackle,” Podwojewski said.

According to Podwojewski, hyperscalers continue to rely on third-party logistics providers to handle baseline supply chain operations, allowing them to focus on scaling compute infrastructure.

These providers are expanding beyond transport and warehousing into operational roles tied directly to deployment.

Podwojewski noted that the scope now includes on-site technical work such as cabling, patching, and equipment installation, which can require thousands of technician hours in large facilities.

He also pointed to staffing as an ongoing constraint at the technical level, with roles requiring engineering and specialized expertise still difficult to fill despite broader labor market improvements.

Logistics providers are also taking on lifecycle responsibilities, including decommissioning and returns management, as hardware cycles shorten and infrastructure is upgraded more frequently.

From a market perspective, the United States accounts for the majority of global data center capacity, with continued investment across large-scale projects.

Podwojewski indicated that emerging markets such as India and Malaysia are beginning to see increased activity, particularly in transport and early-stage logistics support, though warehousing infrastructure is still developing.

He added that investments in regions such as the Middle East are likely to continue despite geopolitical instability, given existing capital commitments.

Across these markets, AI infrastructure continues to expand, with deployment timelines increasingly tied to the ability to execute across a tightly coordinated supply chain.