How AI-driven Networks Can Ramp Up Operational Efficiencies

Automation represents perhaps the clearest embodiment of Benjamin Franklin’s legendary “time is money” aphorism — and artificial intelligence (AI)-driven networks are one area where it’s relatively easy to see the near-term benefits that give new meaning to Franklin’s simple phrase.

Network automation simplifies operations for network teams and reduces configuration errors. So, it stands to reason that greater automation through AI will deliver a more predictable and reliable network that seemingly can speed up time while saving lots of money. We turned to the CIO Experts Network of IT professionals and industry analysts to collect their views on AI-driven networks and how the technology is likely to change the lives of network teams.

“I think of an AI-driven network as one that can be prepared in advance of a catastrophe or breach by capturing and saving critical data prior to a network outage or cyber event,” says Scott Schober (@ScottBVS), President/CEO at Berkeley Varitronics Systems, Inc. “When this is an integrated part of the network, troubleshooting time is reduced delivering improved efficiencies for network teams.

Still, like all things AI, it’s necessary to sort what’s real from what’s hype, experts say.

Hyped up AI technologies are often rolled out as the solution to all problems, observes Nicki Doble, Chief Transformation Officer AIA Philippines. “I don’t buy into the hype,” she says. “However, I absolutely agree that an AI-driven network helps in detecting new and never seen before threats. Its biggest advantage is that it helps us to baseline our networks to learn about normal behaviour to detect abnormal behaviour anytime.”

Simply making the case for AI

Alex Farr (@AlexFarr_IT), Chief Technology Officer at Christie Group, offers a relatively simple-to-understand explanation of how this can play out: “An AI-driven network is made of up system(s) that learn and evolve to understand potential risks or problems that could impact your network. Over time these systems learn how to resolve or prevent these issues from occurring or provide information about the events that lead up to a problem that occurred.”

“This in turn creates time-saving efficiencies for network teams,” Farr continues. “Rather than [the team] sifting through event logs or alerts, the AI-driven systems can focus the team on specific areas and in turn speed up troubleshooting, driving down resolution times.”

Networks may be one area to where the benefits of AI will most apparent. “While the power of AI for extracting complicated business insights is obvious, it’s power in analyzing subtleties of workflow across networks is even more impactful to the enterprise,” says Frank Cutitta (@fcutitta), CEO & Founder HealthTech Decisions Lab. “More important the ability to use AI and RPA to relieve constituents of mundane tasks and redirect their efforts to mission-critical tasks is a major factor in competitive advantage as a result of efficiencies in an era of workforce talent shortages.”

Peter Nichol (@PeterBNichol), Chief Technology Officer at OROCA Innovations, points out that “network teams are buried with work from installing routers, fixing load balancers, tuning QoS, performing maintenance, and hot fixing security configurations. The work is endless and thankless. No one cares when the network is up; [but] when it is down, it’s a disaster.”

Imagine, Nichol asks, “if your network engineers only had to respond to 5% of urgent security hotfixes. Automation can take care of 95% of all requests. This frees network engineers to focus on value-added activities such as experimenting with new networking tools or modeling future security configurations.”

Already over-achieving?

Indeed, networks may be one area to point out where AI reality may be over-achieving against the hype that’s built up in the past couple of decades.

“AI is playing an ever-increasing role helping create efficiencies for network teams,” says Kieran Gilmurray (@KieranGilmurray), CEO at Digital Automation and Robotics Limited. “For example, companies are increasingly using AI to create autonomous networks that can analyse and repair themselves when problems arise. This helps secure and improve business networks, leaving IT and business teams free to concentrate on value-add tasks that drive their businesses forwards.”

Isaac Sacolick (@nyike), President of StarCIO and author of Digital Trailblazer, points out that although eachorganization’s operating model differs from its competitors, “the business requirements concerning network performance, security, and reliability are similar.”

“Network teams know their zero tolerance to network issues, but a static or rules-driven approach to managing them is ill-suited to today’s highly dynamic usage patterns,” Sacolick adds. “AI-driven networks combine monitoring, predictive ML modeling, and automation so that networks are more resilient to shifting demands and threats.”

Echoing Sacolick’s thoughts and driving deeper, Ramprakash Ramamoorthy, Director of AI Research with Zoho Corporation, says that an “AI-driven network is far more autonomous than basic or traditional legacy LAN and WAN systems, and AI plays a crucial role in maintaining and flagging issues regarding network connectivity. This frees up IT professionals to focus on other tasks at hand.”

“In an AI-driven network it is easier to predict incidents well before they happen, taking advantage of the wealth of past behavioral data,” Ramamoorthy says. “An outage is one of the biggest overheads for a network team and proactively identifying and mitigating a potential outage could be a game changer. Even in case of an inevitable outage, it will be easier to pinpoint the root cause of the outage by looking at past data in an AI-driven network.”

Don’t forget security

With AI powering anomaly detection, organizations will experience efficiencies in security, says Will Kelly (@willkelly), a content and product marketing manager focused on the cloud and DevOps, “When AI steps in to automate certain network management and security tasks while improving data collection and reporting it can save time for AI-savvy network teams who are strategic in implementing an AI driven network,” Kelly asserts.

That essentially the definition of AIOps, which Emily Gray-Fow (@Emily_Gray_Fow), a B2B tech and engineering content writer, points out “is an increasingly essential part of any enterprise network.”

“Collecting data and synthesising reports and recommendations at the depth and scale now possible can put organizations that use it ahead of competitors using legacy network monitoring systems,” says Gray-Fow. “Add to this the time saved every step of a typical user journey when AI and automation are involved, from initial setup and onboarding through to establishing user access requirements and monitoring usage and security.”

For more insights factoring AI into your network’s time and money equation, visit.