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 [Energy & Infrastructure](/categories/energy-infrastructure)

 # The Next Competitive Advantage in AI Infrastructure Is Predictive Logistics

 


 

 Oct 15, 2026 9:00 AM ET

  Campaign:  [Sustainable Operations ](/news/campaign/sustainable-operations-0)  ![Inside of a Data Center](https://3blcdn.com/styles/carousel_2x/s3/2026-09/data-center-rawpixel-creative-commons_1790703369.jpg)

 

The data center industry has little debate about the value of predictive analytics.

The challenge is putting it into practice.

According to [new research](https://www.dpworld.com/en/insights/data-center-supply-chains-and-ai-insfrastructure-report) from DP World and Supply Chain Dive's Studio by Informa TechTarget, 92% of surveyed executives believe technology is extremely or very important for demand forecasting, while another 92% say predictive maintenance is equally important. Nearly nine in 10 (89%) place similar importance on predicting supply chain disruptions before they occur.

Yet despite this broad agreement, adoption remains surprisingly limited. In its report titled [The Infrastructure Behind AI: Why Data Center Supply Chains Are the New Critical Path](https://www.dpworld.com/en/insights/data-center-supply-chains-and-ai-insfrastructure-report), DP World found that only 29% of respondents currently use predictive analytics in their supply chain operations, although 40% plan to implement the technology within the next 12 to 24 months.

The gap highlights one of the industry's biggest opportunities.

As AI infrastructure grows larger and more complex, organizations are increasingly looking beyond reactive supply chain management toward systems capable of identifying disruptions before they impact projects. Predictive logistics promises earlier visibility into shipment delays, supplier constraints, customs bottlenecks and equipment issues, enabling organizations to respond before downtime occurs.

That capability carries growing financial importance. Delayed shipments can postpone AI deployment, idle expensive infrastructure and tie up significant capital investments. Predicting problems before they materialize is becoming as valuable as resolving them after the fact.

"When there is downtime, that's a cost impact to the hyperscaler," said Glen Clark, CEO of DP World in the U.S. and Mexico and Regional Head of Contract Logistics. "They're looking to get ahead of any delays or failures, and that means having predictive capabilities."

So why has adoption lagged?

The report points to three primary barriers. Forty-seven percent of respondents cite regulatory concerns, 46% point to cybersecurity risks and 43% identify budget constraints. Export controls, customs requirements, evolving AI regulations and concerns about introducing additional cyber risk all contribute to a cautious approach.

Despite these obstacles, the direction appears clear. The report argues that predictive capability will define the next generation of competitive advantage because it enables organizations to anticipate disruption rather than simply react to it.

End-to-end visibility is a key part of that strategy. Real-time tracking, advanced analytics and integrated logistics platforms can provide earlier warning of congestion, customs inspections, labor disruptions and other risks that threaten project schedules.

As AI infrastructure continues expanding, organizations that can see around corners may gain a measurable advantage over those still relying on reactive operations.

The next phase of AI competition may not be decided solely by access to chips or power. It may be determined by which organizations develop the predictive supply chains capable of keeping infrastructure projects moving on schedule – even when disruption becomes inevitable.

Download [The Infrastructure Behind AI: Why Data Center Supply Chains Are the New Critical Path](https://www.dpworld.com/en/insights/data-center-supply-chains-and-ai-insfrastructure-report) on the DP World website.



 

 

 

 

 

 

 

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