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Ask Google AI Is AI stopping enterprises from achieving net-zero? It certainly looks that way. Demand for compute-intensive infrastructure is rising just when organizations are trying to hit ambitious sustainability targets. The irony is hard to miss: a technology businesses are banking on to make them more productive is also spiking their energy bills.
The more AI workloads enterprises deploy, the more electricity their data centers need. The International Energy Agency expects data center electricity consumption to double by 2030.
Growing AI adoption will only make the problem worse – and concern is growing. Just this week, on July 14, the State of New York declared a moratorium on new data center construction over energy-consumption worries.
Yet simply saying AI and sustainability are on a collision course misses a key point. The infrastructure modernization strategies that support AI can also make data centers greener and leaner.
Why AI Is Reshaping Data Center Sustainability
Why does AI consume so much energy?
AI needs a lot more power than traditional enterprise applications because it relies on highly parallel processing and dense GPU clusters to move large volumes of data. These workloads draw a lot of electricity. They also generate heat, which needs to be dissipated. A large data center can consume up to 5 million gallons of water each day.
Adding to its thirst for compute and cooling are the extra demands AI places on broader data center infrastructure.
AI models process billions of calculations simultaneously. Whether training a large language model or running real-time inference, they push processors, memory, storage, and networks to the limit – limits legacy enterprise systems never needed to reach.
GPUs can run thousands of operations in parallel, but supercharged performance comes at a cost. High-density GPU clusters consume considerably more power than traditional CPU-based environments. They also run hot, making cooling an increasingly important part of overall infrastructure design.
AI also places sustained pressure on the entire technology stack. Massive datasets must be stored, moved, and accessed at high speed. Networks must handle greater volumes of traffic with minimal latency. Storage systems need to deliver consistently high performance to keep data-hungry AI workloads fed. When every layer is doing more, everything needs more power.
The Growing Energy Challenge
Enterprises have always had issues with underutilized servers, fragmented tool management, and unused excess capacity. IT inefficiencies are nothing new. What AI does is reprice those shortcomings.
Changing data center economics mean that every new AI initiative has to compete for finite resources: rack space, power availability, cooling capacity, and IT budgets. Simply adding more servers or GPUs, however, is getting harder to justify.
For some firms, electricity availability itself is a limiting factor. For others, the constraint is facilities that can’t support higher-density AI workloads without upgrading power distribution and cooling systems. Those sorts of capex changes don’t come cheap.
At the same time, the push for greater sustainability makes any physical expansion harder. Investors, regulators, and customers expect businesses to continuously cut carbon emissions. In the EU, data center sustainability is being treated as an infrastructure policy.
While AI still promises major productivity gains, those benefits now have to be balanced against the environmental impact of an increasingly energy-intensive IT estate.
What Makes a Data Center Energy Efficient?
How can organizations improve data center energy efficiency?
Organizations can improve data center energy efficiency by increasing infrastructure utilization, consolidating resources, automating routine operations, and adopting integrated platforms that reduce hardware sprawl and make better use of compute capacity.
The goal is twofold: to consume less power – and extract more value from every watt.
Data center growth follows a predictable pattern. When demand increases, organizations add more servers, more storage, and more networking equipment. While this delivers the additional capacity, it also introduces greater complexity and grows the firm’s energy footprint.
AI workloads are now exposing fragmented infrastructure’s hidden costs. Every duplicated management process adds to operational overhead, and every isolated infrastructure silo becomes a drag on overall efficiency.
Five Ways To Build A Leaner Data Center
For data center teams, every new AI project starts with a capacity discussion. Estimate the additional compute needed, price the hardware, then calculate how much more power, cooling, and rack space will be needed to support the new deployment.
Sometimes more kit is the answer. Just as often, the real opportunity lies elsewhere. Current systems may be running well below capacity, fragmented across multiple platforms, or managed inefficiently enough that valuable compute resources remain unused.
Before expanding the physical environment, more enterprises are asking how much more value they can get from the assets they already own.
That shift is what separates a larger data center from a leaner one.
1. Shrink Your Server Footprint
The most sustainable server could be the one you never have to deploy.
Data centers run around the clock, but most operate with low average server utilization. Expensive compute resources lie idle while still consuming power, cooling, rack space, and maintenance time. Server consolidation addresses this imbalance by hosting more workloads on fewer physical machines.
Beyond lowering energy consumption, consolidation reduces hardware refresh cycles, simplifies maintenance and frees valuable data center capacity for future growth. It also provides the foundation upon which more advanced modernization strategies can be built.
2. Maximize Resource Utilization With Virtualization
Consolidation alone delivers limited benefits unless workloads can be managed dynamically. This is where server virtualization and modern virtualization software become essential.
Virtualization decouples applications from physical hardware, allowing compute resources to be allocated where they are needed most rather than being tied to individual servers. Organizations can provision new workloads more quickly, improve utilization rates, and reduce the amount of excess capacity maintained purely as a safety margin.
In the AI era, where infrastructure demands fluctuate rapidly, higher utilization improves operational efficiency – and makes it more likely that every watt of energy is supporting productive work.
–Learn about Sangfor’ virtualized network stack: aNet Kernel-Native Security; aSEC Cloud Management Center; and Sangfor Cloud Platform (SCP).
3. Simplify Infrastructure with Hyperconverged Architecture
As virtual environments grow, complexity can quickly replace the efficiencies they were intended to create.
Hyperconverged infrastructure (HCI) addresses this by integrating compute, storage, networking, and virtualization into a single software-defined platform. Instead of managing separate infrastructure silos, IT teams gain a unified operating environment that is easier to deploy, scale, and administer.
This integrated architecture reduces hardware sprawl, simplifies day-to-day operations and creates a more efficient foundation for modern enterprise workloads, including AI applications that require flexible, scalable infrastructure.
–Learn about Sangfor’s HCI compute later: aSV hypervisor and aSAN Distributed Software-Defined Storage.
4. Optimize Resources In An Intelligent Way
Buy more, or do more with less? Modern infrastructure uses intelligent workload balancing, predictive capacity planning and AI-assisted infrastructure management to help organizations distribute workloads more efficiently.
The aim is to reduce bottlenecks and avoid unnecessary overprovisioning. Rather than hold unused resources in reserve on a just-in-case basis, infrastructure can adapt dynamically when business requirements change.
The result is higher performance from the same physical footprint and a more efficient use of energy across the entire environment.
5. Automate for Continuous Efficiency
A lean data center is designed for efficiency – and stays that way.
Automation reduces the manual effort required to provision workloads, balance resources, monitor system health, and maintain consistent operations across complex environments. Routine tasks that once depended on constant administrative intervention can be executed automatically, improving both operational consistency and infrastructure utilization.
As AI workloads continue to evolve, automation becomes increasingly important for maintaining performance, reliability and energy efficiency at scale. Instead of continually adding hardware to solve new challenges, organizations can reduce hidden performance waste caused by human misconfiguration, overprovisioning, and black-box management.
Sustainability and Performance Shouldn't Have To Compete
AI is forcing organizations to rethink what infrastructure should deliver. Alongside performance and scalability, resource utilization and energy efficiency have rapidly turned into defining measures of value.
This is one way Sangfor Technologies is reshaping the enterprise infrastructure landscape. Sangfor Hyperconverged Infrastructure (HCI) moves beyond legacy, siloed setups by delivering a complete, unified software-defined architecture that tightly couples compute (aSV), distributed storage (aSAN), and visualized networking (aNet).
Some platforms rely on fragmented, legacy bolt-on security tools. Sangfor integrates kernel-level native security (aSEC) directly into the hypervisor layer to block ransomware at the core and seamlessly enforce micro-segmentation.
To guarantee enterprise-grade business continuity, Sangfor offers and integrated global ecosystem validated by world-leaders in data protection and business resilience: Veeam for advanced backup, recovery, and DR orchestration; and Cohesity for full-stack data management and compliance.
Data center teams get centralized management and intelligent resource orchestration. Utilization improves while hardware sprawl is reduced. Automated workload balancing ensures compute resources are used more effectively, while integrated security reduces the need to tack on more point solutions.
The result is infrastructure that scales efficiently as the company’s AI adoption expands. Organizations can support new workloads, improve operational resilience and make better use of every server, every workload – every watt – without endlessly expanding their physical footprint.
The Data Center Sustainability Challenge in Numbers
945 TWh
The International Energy Agency projects that global data center electricity consumption will reach 945 terawatt-hours by 2030, more than double 2024 levels and just under 3% of global electricity demand. AI is the primary driver of that growth.
9.3 trillion litres
By 2030, AI-driven data centers could consume 9.3 trillion liters of water annually, according to a UN-backed report, highlighting that cooling is becoming as important a sustainability issue as electricity.
A 26% jump by next year
Gartner forecasts that global data center electricity consumption will increase by 26% this year alone, reaching 565 TWh as AI infrastructure deployment accelerates.
Uncool locations
Approximately 80% of data centers operate in locations that aren't naturally suited to efficient cooling. With AI driving higher rack densities and greater heat output, smarter infrastructure design is becoming essential for reducing AI’s computational heat.
Blocked projects
More than 75 data center projects worth an estimated $130 billion were reportedly delayed or blocked during 2026 because of power and water constraints, demonstrating that access to energy is becoming a strategic limitation on AI expansion.
1 data center = 2 cities
The largest AI data centers currently under construction may consume twice as much electricity as the City of New Orleans, illustrating the unprecedented scale of next-generation AI infrastructure.
2027’s Tipping Point
By next year, AI-optimized servers are expected to consume more electricity than all conventional data center servers combined; a fundamental shift in how enterprise infrastructure uses power.
The New Infrastructure Arms Race: Do More With Less
Every technology boom creates an unexpected bill. The cloud and SaaS revolution drove demand for hyperscale data centers. Streaming transformed broadband networks. AI is reshaping IT infrastructure while upending local electricity markets.
Yet AI isn’t creating inefficiency, it’s uncovering it. The organizations building the biggest data centers may not be able to spend their way to victory. Today’s market favors the leanest.
Recognized by Gartner and trusted by more than 28,000 customers worldwide, Sangfor HCI enables organizations to consolidate compute, storage, networking, built-in security, backup, and disaster recovery within a unified architecture.
Its native capabilities include High Availability (HA), Continuous Data Protection (CDP), Real-Time Virus Prevention and Disaster Recovery Services – plus seamless migration from legacy, virtualized, and cloud environments. Enterprises can improve energy efficiency, simplify management, and modernize AI infrastructure with confidence.
Learn more about Sangfor’s approach to ESG and sustainable infrastructure:
1. How to achieve 2026 Enterprise ESG Goals with Sangfor HCI
2. Earth Day 2026: Our Power, Our Future – Innovating Tech for a Greener Planet
3. AI Capital Reallocation & Social Sustainability: A 2026 Analysis
FAQs
An energy-efficient data center is designed to maximize the value of every unit of compute while minimizing unnecessary power consumption, cooling requirements, and hardware waste. Rather than relying solely on more efficient equipment, organizations improve energy efficiency by increasing server utilization, consolidating workloads, adopting server virtualization, and using hyperconverged infrastructure (HCI) to simplify operations. The result is a data center that supports growing business demands with a smaller physical footprint and lower operational overhead.
AI workloads require significantly more compute power than traditional business applications. AI models rely on high-performance GPUs, fast storage, and low-latency networking, all of which consume more electricity and generate more heat than conventional workloads. As AI adoption grows, organizations need AI infrastructure that delivers higher performance while improving resource utilization and reducing unnecessary energy consumption.
Hyperconverged infrastructure improves energy efficiency by combining compute, storage, networking, and virtualization software into a single software-defined platform. This integrated approach reduces the number of physical servers required, increases resource utilization, simplifies management, and eliminates many of the inefficiencies associated with siloed infrastructure. By doing more with fewer physical resources, HCI helps organizations build a more sustainable and scalable data center.
It can. Server virtualization allows multiple workloads to run on fewer physical servers, increasing utilization while reducing hardware, power consumption, and cooling requirements. For many organizations, virtualization is an important step toward building a green data center and supporting broader ESG and sustainable IT initiatives. It also creates a more flexible infrastructure foundation that can adapt as business and AI requirements evolve.
Yes. Modern hyperconverged infrastructure provides the scalability, flexibility, and centralized management needed to support AI workloads alongside traditional enterprise applications. By integrating compute, storage, networking, and virtualization into a unified platform, HCI simplifies infrastructure management while improving performance, resilience, and energy efficiency. For organizations modernizing their data centers, it provides an AI-ready infrastructure foundation that can support future growth without unnecessary complexity.
