Digital twins adoption lags in data centers despite 60% faster project delivery
Data centers are under pressure to handle AI workloads, sustainability targets, and infrastructure investments, yet only 42% of decision-makers are using digital twins—a tool McKinsey says can cut costs by 15% and speed up projects by 60%. The gap points to systemic challenges, including talent shortages and siloed IT and operational technology (OT) systems.
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TABLE OF CONTENTS
KEY HIGHLIGHTS
- Only 42% of data center decision-makers currently use digital twins, despite their potential to accelerate project setup by up to 60% and reduce costs by 15%.
- Digital twins expose interdependencies between IT and operational technology (OT) systems, reducing risks of catastrophic failures.
- 58% of UK data center operators struggle to fill roles critical for digital twin adoption, highlighting a talent shortage.
- Poor system integration has led to only 25% of AI implementations achieving expected ROI, underscoring the need for better planning tools.
Why digital twins are gaining attention
The data center industry is grappling with rapid growth in AI workloads, stricter sustainability requirements, and rising infrastructure investments. Traditional planning tools—spreadsheets, static models, and manual processes—are failing to keep up. Digital twins, already used in manufacturing, aerospace, and energy, offer a dynamic alternative by creating precise virtual replicas of physical data centers.
These replicas allow operators to simulate scenarios like cooling adjustments, power reconfigurations, or workload migrations without risking production systems. For example, failure scenarios such as power outages or cooling failures can be tested to identify gaps in disaster recovery procedures, which is critical for meeting Service Level Agreements (SLAs).
Adoption gaps and ROI challenges
Despite their potential, only 42% of data center decision-makers currently use digital twins. McKinsey estimates that implementation can accelerate project creation and setup by up to 60% and reduce costs by around 15%. However, only 25% of AI implementations have achieved expected ROI, a shortfall attributed to poor system integration and ineffective data management strategies.
A key barrier is the separation of IT and OT systems. Digital twins expose hidden interdependencies between these systems, reducing the risk of catastrophic failures under stress. Yet, building and maintaining these models requires deep integration, sophisticated analytics, and specialized talent—resources many operators lack.
Talent shortages slow progress
In the UK, 58% of data center operators report difficulty filling critical roles related to digital twin technologies. Another 40% struggle with retaining their core teams. The talent shortage compounds the challenge of adopting digital twins, particularly for smaller operators who may not have the resources to develop in-house expertise.
This gap presents an opportunity for startups to provide managed services, training, or pre-built digital twin templates tailored to data center operations.
How digital twins compare to legacy tools
Unlike spreadsheets or static models, digital twins enable real-time simulation of changes before they are implemented in physical infrastructure. This capability is critical for optimizing performance during capacity expansions, testing disaster recovery plans, and negotiating SLAs with customers.
The technology also helps operators avoid blind spots created by siloed IT and OT systems. By revealing interdependencies, digital twins reduce the likelihood of failures that could lead to costly downtime.
What this means
Lazyfounder analysis — our interpretation, not reported fact.
Digital twins are not new, but their application in data centers is still emerging. The 42% adoption rate suggests that while the tool is powerful, its hefty requirements—integration, talent, and investment—are holding back wider use. For operators, this creates a split market: those who can afford digital twins gain a strategic advantage in cost, speed, and resilience, while others risk falling behind.
The talent shortage is a clear signal for startups. Solutions that simplify digital twin deployment—such as turnkey platforms, training programs, or partnerships with system integrators—could address a major bottleneck. Similarly, operators struggling with AI integration may find that digital twins offer a way to improve ROI by surfacing inefficiencies before they become costly problems.
Key takeaways
- Data center operators face growing complexity from AI workloads, sustainability demands, and infrastructure investments—digital twins offer a way to simulate and optimize responses to these challenges.
- The 15% cost reduction and 60% faster project delivery reported by McKinsey make digital twins a competitive differentiator for operators scaling AI or high-availability infrastructure.
- Integrating IT and OT systems through digital twins reveals blind spots that legacy planning tools like spreadsheets cannot address, directly impacting uptime and disaster recovery.
- The talent gap in digital twin technologies suggests opportunities for startups to offer managed services, training, or pre-built digital twin templates for smaller operators.
Related on Lazyfounder
Sources
- TechRadar · 2026-11-04
Digital twins will create a new blueprint for data center design
This story is an original summary drafted with AI by Lazyfounder from the reporting listed above and checked by automated validation. Facts are attributed to their original publishers; sections marked as analysis are Lazyfounder's. Where a source is in another language, facts were machine-translated and quotations are reported, not reproduced. Read the original coverage via the links, and see our AI policy and corrections policy.
About the author
Editor, Lazyfounder
Tarun Mottlia edits LazyFounders, covering Indian startups, funding rounds, AI and product launches. Every story on the site is AI-assisted and checked against its cited sources before publication.
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