Artificial intelligence (AI) and data centers are inseparable. AI models require vast amounts of data and servers to store all that information, but the relationship can go deeper than that. AI needs data centers, but using AI in the data center itself can yield impressive benefits.

Like any other AI implementation, successfully using these technologies in data centers hinges on a careful, thoughtful approach. Here is how organizations can leverage AI to optimize their data center operations.

Where to Apply AI in the Data Center

The first and one of the most important steps is deciding where to use it. With half of all cloud data centers expected to use advanced AI by 2025, use cases in this area vary widely. Here are some of the best ways to use artificial intelligence in the data center.


Physical and digital security is one of the most crucial considerations for any data center. AI is an excellent tool for addressing these concerns.

Understanding what protections a data center needs starts with understanding the risks it faces. AI can analyze a data center’s physical design and location to assess hazards like flooding, fires or electrical damage to determine what deserves the most attention. They can then recommend steps like using blast-resistant enclosures to protect equipment from physical harm or installing backup generators.

AI can also track physical access to server rooms to improve visibility and accountability. The algorithms can analyze keycard data to see patterns in who enters the room at what times. This insight makes it easier to follow up on potential breaches and reveals if anyone may be abusing their access privileges.

Machine learning algorithms can continuously monitor data center activity to watch for potential breaches. The more network activity they analyze, the better they learn what is typical or unusual. They can then spot suspicious activity, flagging and containing it for further investigation to prevent and mitigate cyberattacks.

This security automation helps detect and respond to potential breaches faster, minimizing costs and downtime. It also reduces the burden on IT staff, which ensures talent shortages do not jeopardize security.

Network Optimization

Another ideal use case for AI in the data center is network optimization. Just as AI algorithms can monitor networks for security threats, they can analyze traffic, workload distribution, usage patterns and similar performance-related factors. They can then balance loads accordingly to ensure more efficient operations and less downtime.

Data center needs fluctuate widely, even throughout the same day. Consequently, increasing capacity or adding additional computing infrastructure is insufficient to keep up with changing demands. Modern networks must adapt moment-by-moment and AI enables that.

With predictive analytics, AI tools can even predict future needs and distribute workloads to prepare for incoming changes. These early responses will help prevent downtime, which costs nearly $9,000 per minute on average.


Similarly, data center AI can help optimize maintenance workflows. Many facilities use an operate-to-failure approach to maintenance, fixing issues as they arise. While simple, this technique makes costly downtime more likely. Regular preventive care is better, but it can mean downtime from unnecessary repairs. AI provides a better way forward.

Predictive analytics can analyze equipment health factors like temperatures and performance to learn when they will need maintenance. These algorithms then alert employees about the issue so they can fix it before it becomes a more significant problem. As a result, they avoid both breakdowns and unnecessary repair-related downtime.

This predictive maintenance approach has already seen widespread use across heavy industries. As data center demands grow, the big data sector would benefit from following suit and implementing AI in this area.

Energy Consumption

Another leading use case for AI in data centers is regulating energy consumption. Data is a dirty industry. Storing roughly 347 terabytes of data – which most businesses do – can generate around 700 tons of carbon dioxide a year. That takes a toll on organizations’ sustainability, but AI can help.

As AI balances workloads to changing demands, it can ensure data center infrastructure only uses as much power as it needs. It can go further by analyzing real-time energy consumption data. This analysis will reveal any inefficiencies or issues, highlighting how data centers could improve to become more energy-efficient.

The longer these systems are in use, the more helpful they will become. Algorithms can produce more accurate and insightful reports with more data, leading to more significant long-term improvements.

Steps to Implement Data Center AI

Once organizations know where to apply AI, they can make more informed decisions about how to do so. While AI‘s benefits are substantial, achieving them is not always easy, so this process should involve considerable research and planning.

Find the Ideal Application and Vendor

While data center AI has many potential use cases, organizations should not apply it everywhere at once. Instead, they should focus on one area where they expect to see the most improvements. Finding that involves looking at modern AI‘s capabilities and comparing them to the company’s most inefficient or error-prone data center processes.

Similarly, organizations should carefully compare vendors to find the ideal AI solution. While it is possible to build a new algorithm in-house, 63% of decision-makers today do not have enough AI-skilled employees. Consequently, it is often better to turn to expert third parties for help. Look for vendors with experience building similar AI solutions with high-security standards and reliable reviews.

Address Data Concerns

As organizations train their data center AI, they must be careful with the data they use to do so. The large volumes of information required to teach a model can introduce data breach and privacy risks, so tight controls are necessary.

Using synthetic data that mimics real-world information but does not contain any can mitigate privacy concerns. Teams should also restrict access to training databases to prevent data poisoning. Cleaning and organizing all the information before feeding it to an AI model will also help it reach its full potential faster.

Start Small and Grow Slowly

Finally, it is vital to recognize applying AI in the data center can be a long and expensive process. Even relatively simple models can cost more than $50,000 to train and while they can produce an impressive return on investment (ROI), that can take time. Companies should account for these expenses by starting small and growing slowly.

Begin applying AI to one specific workflow and document the entire process, including what goes well and what fails to meet expectations. These insights will help inform cost-efficient and effective AI projects in the future. Each new AI application will be more straightforward and may produce a faster ROI.

Make the Most of AI in the Data Center

Artificial intelligence in the data center has too much potential to overlook. However, it takes careful planning to capitalize on that potential fully.

Organizations can form a more effective strategy when they know where and how to apply AI. Following these steps can help data centers make the most of their AI projects, optimizing their operations and preparing for tomorrow’s business landscape.

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