3 Key Approaches to Data Center Automation: RPA vs. AI vs. Intelligent Automation

How Do RPA, AI and Intelligent Automation Differ?

While RPA, AI and intelligent automation are all powerful tools, they offer different capabilities. For state and local agencies looking to dial back the hands-on work needed to keep data centers humming along, it’s important to understand the differences.

RPA is the entry-level approach. It typically involves “using software robots or bots to automate a repetitive task,” says Francisco Ramirez, Red Hat’s chief architect of state and local government.

“RPA can mimic human actions by following rules-based tasks. That would be used to improve organizational efficiency and reduce errors in manual processes — tasks that follow very specific rules and rarely deviate from them,” says Jamia McDonald, principal of the government and public services practice at Deloitte.

AI, by comparison, “can learn and improve and provide net new output,” she says.

This makes AI “a little bit more involved,” Ramirez says. “It’s the development of algorithms that enable the machines to perform tasks that typically require human intelligence. That’ll include things like machine learning and natural language processing.”

Intelligent automation is the next evolution, he says. “It uses automation technologies such as AI and RPA together to streamline and scale decision-making across organizations.”

How Can State and Local Agencies Best Use RPI and AI?

Given the different capabilities of each tool, it’s logical to consider them individually for specific use cases within the broader data center automation effort.

“You would probably incorporate RPA in automated monitoring and alerts. You could also look at routine maintenance tasks — backups, data transfer, system updates — because RPA works well with structured data,” Ramirez says. “When you’re monitoring server health and network performance, that data is normally in a tabular format somewhere, and you can help generate alerts based on that. It’s very structured data.”

To support data center security, RPA could be programmed to look for a known threat.

“It would say: We see people log in to this system and try to get into this system,” McDonald says. In addition, “you might use an RPA to monitor system health if you have a certain server serving a specific program. If there’s a peak season for a government application, RPA can ensure that the servers and system maintain their capability during that peak so there’s no interruption of service.”

With a more sophisticated AI toolset, “you would look at predictive analytics, analyzing historical data to foresee potential issues in the data center,” Ramirez says. “You could do dynamic resource allocation, looking at real-time demand and determining where the most efficient use of computing power storage and network resources will be.”

How Can Intelligent Automation Help Governments?

In a data center, AI monitors system health and safety and identifies patterns. “It can monitor for cyberattacks, and then learn and adapt to how hackers and other people are presenting system threats,” McDonald says.

In addition, she says, “the AI could generate new outputs. It can build a dashboard to say: Here’s a real-time report telling you that these activities have happened, and here are some recommended actions based on our policy.”

In terms of system health, “you can use AI to monitor downtime, system capacity or other things that speak to operational health — before something happens,” McDonald says. “If AI could detect that you are going to have a server load, it can shift the server distribution because it sees that coming.”

With intelligent automation, “you can implement or automate complex end-to-end processes within the data center, from resource provisioning to troubleshooting and resolution,” Ramirez says.

“For example, you would use AI to predict the maintenance requirement, RPA to do the actual patching, and intelligent automation to address the workflow,” he says.

DISCOVER: These counties benefitted from upgrading their on-premesis data centers.

What Are the Pros and Cons of These Tools for Agencies?

Each tool has plusses and minuses, depending on the desired outcome within the data center.

“The pros of RPA are that it’s pretty quick to implement and relatively cost-effective for repetitive tasks,” Ramirez says. “The con is that it has limited capabilities, and it requires structured data and well-defined rules. You need a solid understanding of what’s taking place in the process for the bots to be effective.”

AI, meanwhile, “offers the power to create, and being creative with AI can make service delivery more accessible and more personalized,” McDonald says. “The con is the potential for bias, hallucination and data inaccuracies. It requires both significant computing resources and training time.”

For intelligent automation, “the pros would be that it will integrate with rule-based and cognitive automation. It can address a wide range of tasks,” Ramirez says.

“The con is that it can be complex to implement, and it’s probably going to require a higher initial investment,” he says. “And once you have a system like that in place that is doing many things, you’ll want to make sure it is up to date and maintained.”

LEARN: Here’s how automation delivers economic advantages for governments.

How Do State and Local Governments Determine the Best Option?

To align the tool to the task, “governments should understand the business problem they’re attempting to solve,” McDonald says. “In a data center, that may be improving cybersecurity, issues with scalability or incident response times, or siloed teams. When you know the business problem, the tools become more self-evident.”

Taking a deep dive into the business need is critical to ensure that your agency is leveraging the appropriate technology.

“Assess the processes you’re trying to automate. That’s going to help you determine which is best to use,” Ramirez says. “If it’s a repetitive process, that might be good for RPA. If it requires cognitive capabilities and decision-making, that might be good for AI, and if it requires end-to-end process automation, that’s a good fit for intelligent automation.”

If the process in question uses highly structured data, “then RPA may suffice,” he says. “If it’s unstructured and very complex, that’s where AI and intelligent automation come into play. Then you need to look at budget and resources, because those use cases get incrementally more resource-intensive.”

Taken together, these automation tools promise to have a significant impact as state and local governments look to improve their data center operations and drive efficiencies in the workforce.

“When you think about what an artificial intelligence future might look like in a data center, it’s going to be faster response times, higher efficiency, tighter communication and better predictability,” McDonald says.