It nearly appears to be magic, yet predictive network technology is everything but.

Predictive network technology, which employs artificial intelligence (AI) and machine language (ML) mathematical models and algorithms, notifies an organisation to network concerns as early as feasible and gives problem-solving solutions. “Through predictive analytics, the technology enables networks to learn from previous instances utilising large volumes of data,” argues Titus M, a senior analyst with technology and business research firm Everest Group. “It collects network telemetry data, analyses trends, forecasts network challenges that may negatively impact user experience, and proposes alternative solutions.”

According to Sam Halabi, technology consulting competency leader at business consultancy firm EY, predictive network technology can also provide network remediation solutions for automatic or manual adoption, depending on the use case.

The benefit of predictive network technology is that it enables network operations to switch from a reactive to a proactive strategy when dealing with anticipated problems. Network issues can arise for a variety of reasons, according to Halabi, including deterioration in the transport network, bandwidth congestion/traffic loss, ineffective routing, network outages, and more. “Such issues can have a significant negative financial impact when they arise and are quite disruptive to the business.”

Predictive network technology is a strong and useful tool, but it also has some significant risks. The fact that the algorithm can only choose from the possibilities that are provided raises several questions. According to Chuck Everette, head of cybersecurity advocacy at cybersecurity technology business Deep Instinct, “the system might not be able to respond effectively if you haven’t planned for it or haven’t educated it for particular eventualities.” “Automated decisions were happening at such a pace you couldn’t make heads or tails of the root cause owing to the continual changes in the adaption of the network trying to cure or heal itself,” Everette claims he has seen such instances.

Additionally, prospective users should be aware that predictive network technology only works well when it is implemented by businesses and their service providers. According to Halabi, “predictive network technology must operate end-to-end to predict and repair problems.” “Having a service provider who doesn’t use the technology may hamper enterprises that adopt the technology.” He suggests that prospective adopters consult with their service providers to see if there could be any compatibility problems. The decision of whether to turn their network operation over to AI software presents another difficulty for new adopters. Human managers and operators will still be held responsible for any poor judgments made on their behalf when predictive network technology is in charge.

Halabi suggests approaching adoption from the centre of the road. According to him, operations staff will presumably try to use [service] recommendations provided by [predictive network] systems to the best of their abilities but won’t likely switch over immediately to automatic remediation. The combination of effective predictive technology applications and knowledgeable, experienced IT and operational teams will pave the way for success.

Predictive network technology can be used effectively by choosing a sound use case — a problem or other crucial business requirement — and then conducting a trial to evaluate the results. Michael Haynes, principal client engagement leader, worldwide telecoms industry at IBM, advises, “Plan it like you would any Agile project.” This is a good strategy, he says, because insight often needs to be verified or prioritised by a person before it is automated. People cannot scale, but technology can. Starting small gives teams a better chance to demonstrate incremental improvement, pick up new skills along the way, and drive the plan’s next logical step, according to Haynes.

Large businesses and telcos are putting themselves in position to be early adopters of predictive network technology. Once the technology is shown to work dependably, Haynes anticipates that medium, small, and micro-enterprises will follow suit.

The automatic remediation provided by predictive network technology promises to lessen management strain while improving performance and reducing service failures as networks change and become more sophisticated. Although a lot of network technologies, like SD-WAN, have already started to optimise traffic routing based on network and application performance and visibility, such solutions still tend to be somewhat reactive. The adoption of predictive network technologies, according to Halabi, “changes the game since they handle both problem avoidance and problem treatment.” The software-defined and application-aware network is an emerging technology that is ideally positioned to establish a baseline.

Predictive network technology is important to keep in mind, but it doesn’t replace the necessity for human supervision and monitoring. This kind of technology cannot be “set-and-forget,” according to Everette. It must be constantly watched over and tuned in order to function at its best.