2025 Data Center Energy Storage Industry Insights Report

How many employees are in your organization, including all locations?

In what region(s) are you located?

How many megawatts of total load do your data centers use across all campuses?

What is your typical deployment UPS size?

If you use centralized UPS, what are your UPS battery backup run times?

If you use server rack level battery backup (BBU), what are your backup run times?

• 5G and Telecommunications Integration 
• Adopting new tools/approaches 
• AI
• AI
• AI
• AI Infrastructure
• AI, cloud, and IoT are driving unprecedented power demands. 
• Artificial intelligence 
• As users demand faster access to data and applications, data centers need to minimize latency.  
• Availability of power for market expansion
• Balancing upfront capital expenditures with operational expenses 
• blockchain 
• Budgets 
• Capacity range
• Carbon Footprint
• Carbon Footprint
• Carbon Footprint Reduction
• Challenges with power and cooling as adoption of energy efficient hardware is still slow 
• Changing/Evolving ITE Load Profiles 
• Chip performance  
• Companies will need to balance cost efficiency with sustainability and performance demands. 
• Cooling
• Cooling
• Cooling
• Cooling and Heat Management
• Cooling and Thermal management
• Cooling Efficiency
• Cooling Fluids and Environmentally 
• Friendly Practices
• Cybersecurity
• Cybersecurity & Data Privacy Risks
• Cybersecurity Issues
• Cybersecurity Privacy
• Cybersecurity threats
• Data compression
• Data Explosion
• Data Privacy and Security
• Data Privacy and Security
• Data Security
• Data Security
• Data Security
• Data Security & Privacy
• Disaster Recovery and Business Continuity
• Edge Computing Growth
• Emerging or disruptive technology
• Energy availability
• energy consumption
• energy consumption
• Energy Consumption and Sustainability
• Energy Consumption and Sustainability
• Energy consumption growth
• Energy consumption in data centers is a challenge, and the need to adopt renewable energy is key 
• Energy creation and grid access 
• Energy efficiency 
• Energy Efficiency and Sustainability 
• Energy Efficiency and Sustainability 
• Energy Efficiency and Sustainability 
• Energy Efficiency and Sustainability 
• Energy Sustainability 
• Ensuring cost-effective procurement of hardware and software.  
• Ensuring low-latency connections across vast geographic areas.
• Ensuring the agility and flexibility of IT resources 
• Growing Utilization of Energy 
• I think there will be an increased market competition 
• Highly qualified staff will be needed due to the growing complexity of data center operations. 
• I think an increased competition in the data center market will pressure pricing and service offerings. 
• I think there will be skill shortages because in recent times, there have been a growing need for skilled professionals in the data center sector. 
• I think with the rapid growth of cloud computing and the Internet of Things (loT), data centers will need to be more scalable and flexible to accommodate fluctuating demands. 
• Increase cyber threats, ransomware attacks

• Increased energy consumption 
• Increasing energy 
• Increasing energy demands and sustainability concerns 
• Infrastructure Limitation 
• Infrastructure Limitation 
• Infrastructure Limitation 
• LA SEGURIDAD DE LOS DATOS  
• labor 
• Latency and growth of edge  
• Limited availability of renewable energy sources 
• Liquid cooling 
• Maintenance 
• Managing global supply chain issues for hardware components  
• Managing hardware lifecycle 
• Modular data centers 
• One challenge could be Energy Efficiency. As demand for data centers grows, improving energy efficiency will be crucial to reduce operational costs and environmental impact. 
• Power 
• Power 
• Power 
• Power availability  
• Power availability  
• Power availability  
• Power availability from grid  
• Power Costs 
• Power equality 
• Power requirements 
• Power Supply & Reliability 
• Prevention of data breaches  
• Procurement / Supply Chain 
• procuring plant and equipment 
• Protecting sensitive data 
• Regulatory and Compliance Pressures 
• Regulatory Compliance 
• Reliability 
• resilience  
• Rise of edge computing the require for a little localized data centers near end users will rise as IoT. 
• Rising energy consumption 
• Rising operational cost 
• Rising power demands will challenge sustainability efforts. 
• Security and Data 
• Security is the big one. Hackers are getting smarter and are making money out of data so it is become more and more of a target when data in consolidated in our data centers 
• Security threats 
• shortage of skilled workers 
• Specialized hardware will be necessary for 
• Striking a balance between scalability and energy consumption. 
• Supply chain disruptions 
• Sustainability 
• Sustainability 
• Sustainability and Energy consumption 
• Tackling the vulnerabilities introduced by IoT devices connected to data centers.
• Technology 
• The complexity of handling hybrid-cloud environments. 
• The continuous increase of cybersecurity threats  
• The growth of AI and GenAI 
• the move to liquid cooling 
• The rollout of 5G will impact data center 
• The rollout of 5G will impact data center 
• The rollout of 5G will impact data center design and operations. 
• Thermal cooling 
• Tough cooling requirements 
• Transmission Lines 
• Use AI-powered detection of threats and negotiate around tight regulations. 
• utility power 
• Viruses / Malware 
• Well, I think a global supply chain issues can affect hardware availability and lead times, impacting data center operations. 

• 5G
• Addressing bottlenecks in cross-data-center connectivity.
• advocate for stronger efficiency standards and emission-free regulations. 
• AI
• AI
• AI
• AI & Automation Adoption 
• AI & Automation integration 
• AI and high-speed computing demand huge quantities of processing power, necessitating more effective server structures. 
• AI Complexity 
• AI development 
• AI training models require massive computational power, increasing the strain on data center resources. 
• Algorithm calculations 
• Another challenge I could think of would be security threats. Data centers must invest heavily in security to protect their data.  
• Automating data center expansion and upgrades. 
• Available energy 
• Blockchain and Data Integrity 
• bugs due to technology advancement 
• Carbon emission control 
• Chain supplier 
• Challenges pertaining to supply chain of essential equipment 
• Cloud Provider Dominance 
• Confidential Computing 
• Cooling costs 
• Cooling Technology 
• Cooling technology adoption 
• Cost management will be a challenge too because managing costs effectively while maintaining service quality will be a constant challenge. 
• Cost-effective energy storage solutions to ensure round-the-clock operations. 
• Cost-effective scaling of server and storage capabilities 
• Cyber security with new technology 
• Cybersecurity
• Cybersecurity
• Cybersecurity Threats
• Cybersecurity threats due to growing data volumes and sophistication of attacks 
• Data centers consume massive amount of electricity. 
• data centers to be highly scalable 
• Data Privacy 
• Data Privacy 
• Data Security and Compliance 
• Data security and cyber threat 
• Data Security and Cyber Threats 
• Data Security and Privacy 
• Data Sovereignty 
• Data Sovereignty 
• DE LA DISPONIBILIDAD DE LOS MISMOS 
• Edge Computing 
• Edge Computing & Decentralization 
• Edge Data Centers and Localized Computing 
• Efficiency 
• Efficiency 
• Efficiency and energy usage. These data centers are massive and use tons of energy. larger centers are harder to operate  
• Efficient heat management will become increasingly difficult. 
• Energy consumption and power grid 
• Energy costs 
• Energy draw 
• Ensuring reliable network connections will be 
• Environmental impact 
• Environmental issues 
• Equipment availability 
• eradication of diesel generators 
• Government 
• Government sustainability regulations 
• Green Energy Transition 
• Grid energy availability 
• Hacking 
• Hacking risks will grow with evolving attack strategies. 
• Hardware Advances and Obsolescence 

• Hiring employees
• Hybrid Cloud Architecture 
• Increasing sophistication of cyberattacks 
• Infrastructure complexity 
• Infrastructure Scalability 
• Interconnection and Network Performance 
• Keeping up with rapid technological changes such as advancements in hardware and software and infrastructural management, will be essential. 
• Keeping up with rapid technological changes, such as Al and machine learning integration, will require ongoing investment and adaptation. 
• Labor 
• Labor Shortages and Skilled Workforce 
• Lack of training for new workers 
• Land 
• Land Management 
• Latency and Network Performance 
• Latency-Sensitive Applications 
• Local building restrictions 
• long delivery times 
• Maintaining affordable pricing for customers amid growing demand. 
• Managing data sovereignty and cross-border data transfer restrictions.
• Managing the cost of data transfers, especially in multi-cloud environments. 
• Managing the increasing complexity of data center architecture. 
• Meeting environmental regulations and certifications 
• Meeting growing customer demands for speed, reliability, and flexibility will be challenging. 
• mismatch of internet services to skillsets in the marketplace 
• Need for better UPS batteries and grid-independent storage. 
• network security 
• Our operations are growing very fast and we are experiencing challenges with data center scalability 
• Power 
• Power 
• Power 
• Power 
• power load 
• Power reliability 
• Power requirements 
• Predictive maintenance for infrastructure and equipment 
• Price 
• procuring labor to build and run 
• Protecting data centers from cyber threats 
• Quantum computing disruption 
• Regulation 
• Regulations
• Regulations
• reliability 
• Renewable energy integration 
• Scalability & Growth 
• Scalability and Adaptability 
• Scalability and Edge Computing 
• Schedule performance 
• Securing AI and machine learning-based infrastructure 
• Security and Privacy 
• Security breach 
• site location opposition 
• Sustainability 
• talent shortage 
• The demand for energy in data centers is also 
• There may be Regulatory Compliance which certainly means adapting operations and ensuring that data handling practices meet legal requirements.  
• There will be a need for new cooling and infrastructure as specialist AI and machine learning hardware becomes more prevalent. 
• Threats to Cybersecurity 
• Training staff members to change & learn the new tools 
• Transitioning to renewable energy sources
• Utilities to support all coming on line 
• With rapid advancements in technology, data centers need to keep up with emerging trends like edge computing, AI and machine learning.

• Achieving carbon neutrality in data centers.
• AI and Machine Learning 
• AI Integration 
• AI- powered automation, and improved connectivity solutions 
• AI workloads 
• Data Center Consolidation 
• Automation and Robotics 
• Availability 
• availability of power 
• Balancing between on-premises andcloud-based infrastructures 
• Blockchain and Data Integrity 
• Broadband stability 
• Business continuity 
• Data Security and Cyber Threats 
• CAGR for tech/data center industry 
• Change of Business Models 
• Compliance requirements 
• Conflicting privacy laws must be negotiated by multinational data centers. 
• connectivity 
• construction support 
• Consumer confidence 
• cooling 
• cooling 
• Cooling & heat management 
• Cooling Efficiency and Innovation 
• Cooling Technologies 
• Cooling technology advancements 
• cost 
• cybersecurity 
• Cybersecurity and counter threat mitigation 
• Cybersecurity threat is also one of the challenges that is gonna have an impact on the data center industry in the future 
• Data center construction, energy, and operational costs are steadily increasing, making profitability a challenge. 
• Data center supply lagging demand
• Data Growth and Management 
• Data security & privacy 
• Data Volume 
• DC Operators 
• Decentralization will strain traditional data centers. 
• Developing reliable AI and machine learning models for automation. 
• Difficulty in predicting future data storage requirements accurately. 
• Edge Computing and Network Expansion 
• Edge Computing Growth 
• Education 
• electric power 
• Energy management/ESG 
• Ensure data centers comply with legal and regulatory requirements 
• Ensuring compliance with evolving data protection regulations 
• Ensuring security in edge computing environments 
• Environmental Concerns 
• Equipment Availability 
• ESG
• designing highly scalable physical infrastructure.
• Evolving Technologies 
• finding qualified labor  
• Foreign Agencies 
• Handling the costs of compliance and security certifications.
• Hardware Obsolescence 
• Hackers
• Heat Management and Cooling
• High cost of security labor 
• Hyperscalers may disrupt traditional data center models.  
• In addition, traditional cooling methods may not be sufficient, leading to the need for innovative cooling technologies that can efficiently manage heat in high-density environments. 
• Increased cost pressures due to sustainability requirements. 
• Industry interruptions 
• Infrastructure as Code 
• Infrastructure demand 
• Infrastructure Scalability and Edge Computing 
• Integrating Al for operational efficiency and predictive maintenance will be necessary. 
• Integration of green technologies such as solar and wind. 
• Interoperability issues 
• LA VELOCIDAD CON LA CUAL SE ACCEDERAN 

• Latency Concerns
• Liquid cooling, immersion cooling, and heat reuse strategies. 
• Local Power availability 
• Managing cooling systems efficiently to reduce energy waste 
• NA
• Multi-cloud places will keep growing, ensuring an easy switch between on-premises and cloud services. 
• New technology 
• Nuclear energy 
• OEM’s supporting this market 
• Older employees aging out of our industry 
• Operating in multiple countries will require data centers to adapt to diverse regulations and market conditions. 
• Optimization 
• People / Talent 
• Permitting delays 
• Political Stability 
• Post Quantum Encryption & Store Now Decrypt Later (SNDL) 
• Power 
• Public outreach 
• Rapid improvements in software and technology could cause obsolescence to occur more quickly. 
• Regulations & Compliance  
• Regulatory pressure and compliance 
• Renewable energy adoption  
• Rising Operational Cost 
• Rising operational costs due to increasing energy prices 
• Safeguarding infrastructure from insider threats 
• safety concerns 
• Scalability & Infrastructure complexity 
• Scalability and infrastructure management 
• Scalability to meet the rapidly growing demand for data processing power driven by technologies like AI and cloud computing 
• Security 
• Security and Resilience  
• Security Threats and Cyber-Attacks 
• Service and Support 
• Shortage of skilled professionals will also be a challenge. 
• Security 
• Security and Resilience 
• Security Threats and Cyber-Attacks  
• Service and Support 
• Shortage of skilled professionals will also be a challenge.
• Skilled worker shortage 
• Space Constraints 
• Speed to market 
• Staff Training 
• suitable place  
• Suitable place 
• supply chain challenges 
• Supply chain disruptions 
• Supply Chain Issues 
• support distributed networks 
• Sustainability 
• Sustainability as we focus on sustainability in all our business issues, unfortunately, data centers that we use still need improvement in sustainability 
• Takes time to build, can’t be speedily set up to meet market demand 
• Talent availability 
• Technology improvements making data centers obsolete. Need to keep reinventing to improve upon the data center 
• The ability to scale infrastructure quickly to meet growing demand without compromising performance will be a key challenge. 
• The challenge of providing disaster recovery solutions without increasing costs. 
• The high increase in data generation from various sources like loT devices, social media, and more means data centers must find efficient ways to store, process, and analyze massive amounts of information. Managing this growth without compromising performance can be tough. 
• The last challenge I could think of is Sustainability. 
• The rapid iteration of technology requires data centers to continuously invest in new equipment 
• The Shift to Hyperscale and Modular Data Centers 
• Trained manpower 
• Transmission 
• Water resources 
• Water usage concerns 
• zero carbon footprint

• Advanced containment strategies that prevent cooling failure by directing airflow in a controlled manner, minimizing hot spots.
• Advanced Liquid Cooling with Integrated UPS
• Advanced monitoring systems are crucial for keeping tabs on liquid phases, system temperature, and cooling efficiency.
• AI algorithms that dynamically adjust cooling efforts based on workload demand, ensuring backup systems kick in only when necessary.
• AI cooling demands are driving backup strategies toward more resilient, energy-efficient infrastructure.
• AI-Driven Monitoring for Predictive Maintenance
• AI-powered systems Integrated Fire Suppression and Cooling
• AI-powered systems that not only detect cooling faults but also automatically re-route cooling resources to backup systems.
• AUN DE MOMENTO NO HAY UN PLAN EMERGENTE
• Backup Generators for Cooling Units
• Backup heat exchangers to ensure heat can be effectively transferred even if one system malfunctions.
• Backup Power for Cooling Systems
• Backup storage units that have their own independent cooling solutions to ensure continued operation.
• Building up systems of redundancies
• Certainly, We are committing resources to modular UPS systems and generator supported cooling solutions to guarantee continuous operations in the event of cooling failures.
• Combining air, liquid, and phase-change cooling methods in a hybrid approach to offer multiple layers of backup.
• Consideration of coupling power design for both mechanical and electrical loads
• Considering more prime power applications (e.g., onsite power generation) to meet power demands.
• Considering whether UPS should cover liquid cooling CDU’s–generally yes.
• Conventional backup power systems, such as UPSs, are expanded to incorporate cooling system
• Creating multiple cooling zones with individual backup systems to ensure localized cooling even if one zone fails.
• Definitely- we are really focusing on double downing on the lithium-ion batteries and continuing to expand that footprint as they seem to perform quite well
• Depends on complex and reliability and warranty
• Designing cooling infrastructure that can easily scale up or down with changing demands can help manage costs while ensuring adequate cooling capacity is always available.
• Designing cooling system components to be hot-swappable without requiring full system downtime.
• direct to chip
• Direct to chip Single phase immersion
• Direct-to-chip or immersion cooling systems rely on active pumps, chillers, or dielectric fluid circulation, which require power.
• Dynamic Cooling Allocation
• establishing hybrid cooling systems that, in the instance that liquid cooling fails to function properly, allow airflow to take over.
• Exploring thermal energy storage systems to store excess cooling capacity for use when liquid cooling systems experience high demands or fail.
• Free Air/Water cooling systems Liquid to the Chip Immersion cooling
• Having extra liquid cooling for flexibility.
• For pure HPC loads, enough backup is provided to securely shut down devices. There will need to be considerations for graceful shutdown of CDU and other components of liquid cooling to racks. Just dropping the power will not be sufficient and will cause damages. These will likely be from centralized UPS (for controls, transfer switch to genset for pumps as these are enormous loads that could not be met by a UPS).
• Hybrid Backup Systems (Battery + Flywheel + Generator)
• hybrid cooling system redundancy
• I don’t know
• I think one of the specific approaches should be establishing protocols and training staff on emergency procedures for power and cooling failures.
• I think there should be back up generators in a way that installing diesel or natural gas generators as a secondary power source to support cooling systems during outages.
• Implementing backup cooling solutions like additional in-row cooling or supplementary liquid cooling systems to maintain consistent temperatures during high demand.
• In the event of a system failure, air cooling is used as a backup to liquid cooling.
• Incorporating dual-phase or redundant liquid cooling systems to maintain effective heat dissipation even if one system fails.
• Incorporating fire suppression systems that also aid in cooling during emergencies, providing a dual-purpose backup.
• Leveraging loT and AI-driven analytics to anticipate cooling issues and address them before they lead to system failure.
• Likely. Shift to hybrid UPS systems and/or more modular power systems. Systems must handle both power failure and cooling system failures. 
• Liquid cooling 
• Liquid cooling as a more cost-effective approach 
• Liquid cooling backup solution 
• Liquid Cooling is what all DC’s should be using 
• Liquid Cooling Redundancy Incorporating dual-phase or redundant liquid cooling systems to maintain effective heat dissipation even if one system fails. 
• Liquid cooling technology involves immersing servers in non-conductive liquid and circulating the liquid to dissipate heat 
• Liquid cooling. 
• liquid-cooling and UPS solutions 
• Local power with storage to replace partial generation from diesels 
• Mechanical static UPS Flywheel for mechanical equipment backup support. Continuous cooling water tanks and pumps, with UPS support. 
• Multi-tiered Airflow Cooling 
• not yet but AI and its power requirements will change the data center overall requirements 
• On chip cooling 
• One of the approach I will be exploring is upgrade UPS systems to handle the increase power demand from liquid cooling setup. 
• One of the specific approaches I could think of is to have an advanced energy storage solution. This can help to provide a reliable backup power. 
• One specific approach could be developing integrated systems that combine cooling and power management for greater efficiency. 
• One specific approach is that some are looking into integrating renewable energy sources, like solar or wind, along with battery storage. This can provide an additional layer of backup power while also being more sustainable. 
• Open to learning 
• Optimize the structural design of heat exchangers to improve heat exchange efficiency, thereby reducing the usage and energy consumption of coolant. 
• Optimizing backups with AI driven energy management 
• Optimizing backups with AI driven energy 
• prevents power-hungry AI workloads from consuming all available UPS energy before cooling stabilizes. 
• Redundant cooling system implementing backup liquid cooling systems to ensure continuous operation in case of failures. 

• Redundant Cooling Systems 
• Scalable cooling solutions that can be added or removed as needed, making backup systems more flexible. 
• Scaling Power Capacity: As AI workloads increase rack power density, battery backups (such as uninterruptible power supplies, or UPS) are scaled to meet higher power demands. This involves upgrading to larger capacity batteries or modular systems that can expand as needed. Redundancy and Scalability: To handle increased densities, redundancy is a critical feature. Many server racks employ N+1 or 2N configurations, ensuring continuous operation even if one UPS unit fails. 
• Secondary HVAC systems dedicated to cooling AI hardware, ensuring uptime if the primary system fails. 
• Site close to generation 
• Systems that automatically recover and re-distribute heat, reducing reliance on external cooling and improving energy efficiency. 
• Thermal Buffering 
• Too big a topic for such a little box. I do think you will see larger more centralized backup strategies (CUP) with larger scale facilities as repetitive/aggregate equipment designs challenge maintenance routines — just too many pieces to service! 
• Two-phase immersion cooling systems that operate with integrated UPS power 
• Under research 
• Unknown at this time. Too many variables for us at this time to know. 
• Use of distributed cooling networks across multiple sites, so a failure at one location doesn’t affect the entire infrastructure. 
• Using AI-driven analytics to optimize cooling and power usage in real time 
• Using AI-driven predictive analytics to detect and prevent cooling system failures before they happen. 
• Using dedicated backup UPS for cooling systems alongside those for the servers themselves, ensuring redundancy for both systems. 
• Using energy-efficient automated systems for quick response to failures, minimizing downtime. Using liquid cooling technology
• We are developing backup techniques to manage AI’s changing cooling demands, including redundant circuits and automated switchover to assure continuous optimal temperature management. 
• We are implementing backup liquid cooling loops to prevent system failure during primary cooling outages. 
• We have already implemented liquid cooling systems in a lot of our power supplies and will continue to improve on making our sources more reliable 
• We haven’t considered these backup strategies yet, impact to be analyzed in future 
• We’re basically trying to keep our computer systems cool and running smoothly while handling super-powerful AI machines that generate tons of heat. 
• Yes – hybrid cooling systems and AI powered cooling orchestration 
• Yes – more Additionally, backups and ways we can tackle ransomware 
• Yes ,Since water conducts heat about 30 times better than air, it is a highly efficient method of data center cooling 
• Yes, as liquid cooling necessitates denser UPS configurations for rapid failover. 
• Yes, evaluate existing equipment, consider costs and operational risks, make a gradual transition, and replace equipment 
• Yes, evolving cooling demands for AI, like liquid cooling, are leading to considerations of different backup strategies. Approaches include integrating UPS systems compatible with liquid-cooled environments, implementing rack-level BBUs to reduce dependency on centralized systems, transitioning to higher-efficiency batteries like LFP or LTO, prioritizing critical AI workloads during outages through dynamic load shedding, and exploring flywheel energy storage for seamless power continuity and reduced heat OU 
• Yes, evolving cooling demands for AI, like liquid cooling, are leading to reconsideration of backup strategies. We are exploring approaches such as modular backup systems, redundant liquid cooling loops, and hybrid cooling setups that combine traditional and advanced cooling to ensure reliability during failures. 
• Yes, evolving cooling demands for aluminum applications, especially in high-performance industries like electronics or manufacturing, are pushing the adoption of advanced cooling. As power densities increase, traditional air cooling might not suffice. 
• Yes, growing cooling demands for AI, particularly liquid cooling, are forcing us to revaluate backup plans. We’re looking into redundant cooling loops, hybrid systems that combine liquid and air cooling, and improving UPS systems to assure stable power for these advanced cooling technologies during outages. The objective is to ensure cooling efficiency and system resilience. 
• Yes, growing cooling demands for AI, particularly liquid cooling, are forcing us to revaluate backup plans. We’re looking into redundant cooling loops, hybrid systems that combine liquid and air cooling, and improving UPS systems to assure stable power for these advanced cooling technologies during outages. The objective is to ensure cooling efficiency and system resilience. 
• Yes, exploring immersion cooling’s impact on battery backup design for AI driven workloads. 
• Yes, I will be exploring an integrated backup solution and modular backup system in such that it allows for flexibility and scalability as cooling demands change. 
• Yes, liquid cooling requires robust redundancy, and we are exploring dual power sources and enhanced battery backups for continuous uptime in critical systems. 
• Yes, modifications in backup strategies are being driven by AI’s cooling requirements. Pumps and chillers are necessary for liquid cooling. cooling can be disrupted more quickly by power outages than by air-cooled systems. Businesses are investigating UPS cooling systems. Additionally, under consideration are automated leak detection and hybrid cooling. 
• Yes, more AI research on cooling demands. We do not disclose the details. 
• Yes, providing UPS power to cooling pumps 
• Yes, the evolving cooling demands for AI, particularly the rise of liquid cooling, are absolutely leading to a reevaluation of backup strategies. 
• Yes, the shift towards liquid cooling for AI workloads is cooling, data center operators to re-evaluate their backup strategies, as liquid cooling systems present unique challenges and require different approaches to ensure redundancy and continued operations in case of failure. 
• Yes, with AI’s growing cooling needs, like liquid cooling, we’re looking into backup strategies that focus on flexible UPS systems and energy storage solutions to keep power stable and support cooling in high-density AI setups. 
• Yes. We are considering liquid cooling, but our strategies are still in the process of development. We also have a lot of concerns with security of AI applications.

• Procurement Manager
• Sales
• Sales 

• N/A
• N/A
• Will very to match standby generation 

• deferred maintenance 
• N/A
• Failure mitigation

• Change in floor area use ratios 
• HPC loads will generally have less UPS run time or no UPS at all.