How AI Energy Usage is Shaping Your Industry

It seems like everyone else in your industry is adopting AI into their operations, but this new technology comes with increased energy usage. You start to worry: can your facility handle the amount of power needed to optimize workflow with AI?

According to the International Energy Agency (IEA), accelerated servers built specifically for AI are expected to use 30% more energy each year while regular servers will use 9% more energy annually. You’re likely already seeing different AI tools being adopted in your workflow such as generative AI, chatbots, AI-enabled signal processing, AI appliances, and more. These tools are slowly straining your physical infrastructure. Think your industry is the exception? This is unlikely. All mission-critical industries are seeing major changes to adapt to AI, especially the following:

• Data Centers
• Healthcare
• Manufacturing
• Education

As a supplier of power, cooling, and infrastructure solutions, we help these and many other mission-critical industries adapt to growing AI energy consumption with the right products and services. Learn just how much AI is impacting your industry and what you can do about it below:

Data Centers

Data centers are one of the facility types most affected by AI usage. In fact, there are even AI data centers now that work solely to provide AI applications and services to the rest of the world. Both kinds of data centers require a great deal of electricity to keep operations running smoothly though, and this need isn’t expected to slow down anytime soon. According to the International Energy Agency, data centers are expected to account for nearly half of electricity demand growth between 2025 and 2030 in the United States. This is largely due to the number of AI servers now being used in these mission-critical locations. The sheer number combined with the excessive amount of AI energy used to keep them running can cause serious complications such as:

• Overheating: AI generates a large amount of heat that can quickly overwhelm a space filled with other servers and equipment. As a result, high-density rear door heat exchangers and direct-to-chip liquid cooling solutions are becoming more common to prevent overheating where air-cooling alone could not.
• Not enough power: Traditional data centers were not built to handle AI energy needs. That’s why traditional and AI data centers are switching to high-density power distribution equipment. These devices help deliver power to AI servers without overloading the rest of the data center’s systems, helping prevent downtime.

Preparing a data center for AI infrastructure isn’t just about having the right equipment though. AI data centers must be structurally redesigned to provide enough space between servers to manage heat. AI’s higher rack densities are pushing hot/cold aisle containment from a best practice into a necessity. Ultimately, redesigning a data center to implement AI infrastructure requires an expert like the ones at Common Framework to make sure everything is installed, laid out, and integrated correctly. Data centers aren’t the only buildings that need upgrades to continue their operations in light of the AI boom though.

Healthcare

Neither private practices nor large hospitals were built to handle AI. And yet, AI is becoming an integral part of healthcare to provide faster care to patients by helping with:

• Imaging
• Diagnostics
• Data analytics
• Large language models
• Answering patient questions via chatbots
• Improving administrative responsiveness

These benefits greatly improve productivity by assisting patients and helping professionals find answers faster. However, this is only the case if the hospital or clinic’s infrastructure is ready for AI implementation. Hospitals and clinics are starting to purchase access to AI products such as cloud-hosted SaaS and specialized on-site hardware that can handle AI workloads. Every one of these tools must meet HIPAA’s requirements for safeguarding patient data, which places added weight on how securely that AI infrastructure is built and monitored.

Healthcare facilities are seeing a surge in software and hardware that must run simultaneously without interruption. Equipment like KVM switches is then needed to help users control multiple computers from a single console to make sure AI products are running effectively. More specifically, monitoring solutions help healthcare facilities know exactly how much power capacity they have to run AI software. With this knowledge, you can upgrade your equipment without overloading your system.

Manufacturing

Productivity is perhaps the biggest benefit of AI. This is especially true in the manufacturing industry. According to NAM’s Q1 2026 Manufacturers’ Outlook Survey, 44.68% of manufacturers cite attracting and retaining quality workers as one of their biggest business challenges. To combat this, many manufacturers are turning to technology like AI to keep operations running. For example, AI can run automations to keep equipment on the shop floor running 24/7 without the need for human involvement. And to help make sure this is always the case, AI helps managers perform predictive maintenance. AI software does this by analyzing data from sensors on all machinery in the facility. This data reveals signs of equipment inefficiencies that could lead to unexpected downtime if not addressed quickly.

Many manufacturers are looking to purchase AI software to optimize predictive maintenance and shop floor workflow. However, many are finding they do not have the AI infrastructure needed to implement these systems. With these AI systems come sensors and data collection software that need to run simultaneously to provide accurate data AI can use to make maintenance recommendations. This often pushes older facilities to their limits and requires upgrades to their power distribution and IT management systems. If these pieces of equipment are overlooked, your facility runs the risk of a total system failure, bringing production to a halt, affecting the technology and people running your facility. As AI reshapes even hands-on, labor-driven operations, it’s changing the technology, system layouts, and infrastructure priorities of the modern shop floor.

Education

Students of all ages use AI, and teachers are adopting it in classrooms and daily routines. Yet many schools and campuses are falling behind when it comes to supporting this technology with the right AI infrastructure. Today, AI on campus isn’t just about students asking a chatbot questions. It’s baked into learning management systems, tutoring platforms, and administrative tools. All these tools draw on the same compute and network resources institutions built for a pre-AI world, and that infrastructure is struggling to keep up.

Most AI tools used in the education industry run in the cloud rather than on dedicated on-site AI servers. This means that education leaders need to make sure the following components of their school are solid:

• Connection to the cloud is stable and able to handle high-traffic moments when multiple devices connect to the network at once.
• On-site equipment like servers, switches, and networking gear is able to route and manage all traffic.
• Student- and teacher-facing devices have enough power to run AI-assisted applications smoothly.

Some districts do keep limited on-premises servers for things like local data storage, student information systems, or hybrid setups where sensitive data can’t leave the building. In these cases, the equipment needs the same protections any data center relies on. This includes making sure underlying physical systems can perform without interruption. As a result, campuses must make sure the following systems are up to date to be able to handle AI workloads:

• UPS protection keeps servers and network equipment running through power blips, so a brief outage doesn’t knock out access to AI tools mid-lesson or corrupt data mid-save.
• Resilient power distribution ensures on-site equipment can handle sustained, growing demand as more classrooms and departments adopt AI tools simultaneously.
• Precision cooling protects servers and network hardware from overheating as equipment works harder to keep up with constant, always-on cloud connections.
• Monitoring and management systems give IT staff real-time visibility into equipment health, so problems get caught and resolved before they turn into a schoolwide outage.
• Racks and containment provide a clean, organized, and protected home for whatever on-site equipment a district does maintain, whether that’s a single server closet or a larger data room.

While AI infrastructure may remain in the data centers where the tools are sourced from, the physical infrastructure in schools must be ready to handle the increase in network usage resulting from AI.

Learn More About What Your Industry Needs to Implement AI Effectively

AI is changing the way everyone does things, and that includes how you run your operations. However, in order to implement AI effectively, you first need to make sure physical infrastructure can handle it. Sign up for our newsletter to learn more about the latest cooling, power, and monitoring technologies and how they can help you with AI deployment.

Mission-Critical Industries FAQs

What is AI infrastructure, and how is it different from traditional IT infrastructure?

AI infrastructure refers to the power, cooling, networking, and physical facility systems needed to support AI workloads. This includes GPU-based servers, high-density racks, and the distribution equipment that keeps AI running. Unlike traditional IT setups, AI infrastructure must handle much higher power draw and heat output per rack, which often requires liquid cooling, upgraded power distribution, and redesigned server layouts.

Why does AI energy consumption keep increasing?

AI energy consumption is rising because accelerated servers built for AI training and inference use far more electricity per unit than conventional servers, and organizations across every industry are adopting AI tools faster than their facilities can absorb the added load.

What is an AI data center?

An AI data center is a facility built or retrofitted specifically to run AI applications, model training, and inference at scale. These facilities typically require higher-density power distribution, advanced cooling systems, and structural changes like hot and cold aisle configurations to manage the heat and electricity demands that come with AI-specific hardware.

What infrastructure upgrades do data centers need to support AI?

Data centers adapting to AI infrastructure typically need high-density power distribution equipment, liquid cooling or rear door heat exchangers, and physical redesigns that create adequate spacing between servers to manage heat buildup effectively.

Is AI infrastructure only a concern for large hospital systems, or does it affect smaller practices too?

AI infrastructure needs apply to healthcare facilities of any size. Even smaller practices adopting AI-driven scheduling, diagnostics, or patient communication tools need adequate power and cooling support, since the underlying AI energy demands don’t scale down just because the facility is smaller.

How is AI infrastructure changing the manufacturing industry?

In manufacturing, AI supports automated monitoring, predictive maintenance, and 24/7 production systems that reduce dependence on manual oversight.

How is AI energy demand affecting schools and universities?

Educational institutions are seeing AI energy demands grow as students and teachers use AI in learning management systems, tutoring platforms, and administrative tools. Because most campus and district infrastructure was built before AI adoption accelerated, aging devices, cloud dependency, and cybersecurity gaps are becoming more pronounced under the added load.

How can a business determine if its infrastructure is ready for AI?

The best way to assess AI readiness is through a professional facility evaluation that reviews current power capacity, cooling performance, and distribution equipment against the demands of planned AI deployments. An experienced infrastructure partner can identify gaps before they cause operational disruptions.

How is AI energy demand affecting data centers specifically?

Data centers are seeing some of the sharpest AI energy increases of any industry, since they host the servers running AI workloads directly. The IEA projects that data centers will account for nearly half of U.S. electricity demand growth through 2030, driven largely by the number of AI servers now in operation.

Can traditional data centers be retrofitted for AI, or do they need to be rebuilt?

Many traditional data centers can be retrofitted for AI infrastructure rather than rebuilt entirely, but it usually requires significant upgrades to power distribution, cooling capacity, and rack layout. Working with an experienced infrastructure partner helps determine whether a facility’s existing systems can be adapted or need a more substantial redesign.