With the rise of generative AI, AI has taken over the business world. But don’t take the adoption rate as a sign of endorsement. It wasn’t until March 2022 that Pew Research reported that most people tend to be cautious when it comes to AI.
Of the 10,260 U.S. adults surveyed, about 37% said they were more concerned than excited about AI, and 45% said they were just as concerned as excited. They reported fear of unemployment and the potential for AI to surpass human skills as their top concerns about AI. Network professionals have similar fears about AI and are hesitant to use it despite its use case in network management.
Most IT professionals believe AI-driven network management enhances business success. But network teams have struggled to use the technology, and enthusiasm has waned in recent years, said Shamus McGillicuddy, vice president of research at Enterprise Management Associates, based on his EMA research report. said in a webinar.
The study surveyed 250 IT professionals with some involvement in AI about their AI-driven networks. As a result, the network expert turned out to be one of the most cynical users of AI in network management. McGillicuddy provided his best practices to dispel some of this skepticism.
AI strategies in network management
Respondents told EMA that they are using several strategies to introduce AI into network management. McGillicudi says most IT professionals follow a two-pronged approach. Typically, you first select your network management tools AI, machine learning, and AIOps products from your vendor, then combine them with your network infrastructure vendor’s AI and ML offerings.
Regardless of the approach taken, McGillicudi said those who made the use of AI in IT management a top priority experienced greater success with AI-driven networks. Those who paid less attention to AI tended to have a harder time.
“This shows that as CIOs steer toward AI-driven operations, people in networking silos looking to transform their network management with AI will have more fun,” said McGillicuddy. rice field.
Use Cases and Benefits of AI-Driven Network Management
Nearly 57% of organizations say optimizing EMA networks is a use case for AI, and those in successful organizations are more likely to say so. Vendor management, network digital twins, and anomaly detection are also use cases for successful organizations.
AI-driven network management empowers network teams to modernize how businesses build, maintain, and deliver digital services to their users, end users, employees, and customers.
Seamus McGillicudiEMA Research Vice President
About 47% of respondents also said that network optimization is a benefit of AI-driven network management. According to McGillicuddy, many respondents told EMA that, among other benefits, AI can help network teams modernize their application infrastructure.
“AI-driven network management enables network teams to help companies modernize how they build, maintain, and deliver digital services to their users, end-users, employees, and customers,” he said. Stated.
While most IT professionals believe that AI-driven networking will bring benefits, opinions on how effective AI is will vary among various IT team members. More than two-thirds of his respondents said AI-driven networking had improved the end-user experience, and almost one-quarter said the improvement was significant. But his IT professionals who have had the most success with AI are likely to report greater improvements, McGillicuddy said.
Additionally, 92% of respondents say AI-driven networking improves business outcomes, up slightly from 90% in 2021. However, the number of respondents who strongly agree with this opinion has dropped from 56% to 50% in 2021. In 2023.
Network professionals struggle with AI in network management
The survey found that most network professionals struggle to assess the success of AI in network management. McGillicuddy says the challenge has less to do with the types of strategies network teams use, but more with how teams evaluate strategies.
Tool evaluation
About 60% of respondents told EMA that their ability to measure AI success in network management was marginally effective or worse. McGillicuddy said IT professionals have different perspectives on how to evaluate tools.
Network engineers and NetOps staff in particular were more pessimistic than other IT teams and were less likely to appreciate their ability to evaluate tools. The percentage of successful AI users increased from 30% to 36% he compared to 2021, but networking professionals were still less likely to say they were successful than other IT team members. is.
The problem of AI-driven networking
Respondents reported both business and technical issues with AI in networking. Business issues experienced by respondents included security and compliance risks, skills gaps, and budget issues.
And about 20% of respondents said they lack confidence in AI, and network engineers are twice as likely to say so, McGillicudi said. This resistance, he said, stems from fears that AI can replicate human skills.
Network complexity ranks as the top AI technical issue in network management, with a quarter of respondents saying so, and NetOps professionals twice as likely to cite it. bottom. Respondents told EMA they found AI had trouble understanding different parts of complex networks and systems.
Boosting AI optimism for network professionals
Much of the cynicism about AI stems from pessimism and mistrust. The network expert has long worried that automation could replace human staff, but research predicts that AI will assist humans in their roles rather than take them away. I’m here.
To reduce anxiety, organizations should encourage network professionals to develop skills and interact with colleagues, analysts and vendors to understand how technology is used, McGillicuddy said.
He added that network professionals should consider using chatbots such as ChatGPT for network management. Some companies are already starting to take advantage of technology. For example, McGillicuddy said one network engineer of his said he completed a full day’s work in less than 10 minutes using ChatGPT.
Most of the network professionals who told EMA that their AI tools were successful, pledged to use the technology across their networks whenever possible. AI in network management helps organizations create and manage agile and modern networks. As enterprises continue to adopt AI, networking professionals must take advantage of this technology that makes AI an essential tool for business.
