Before moving forward with any solution, first gain a strong understanding of your network needs. Assess current network infrastructure, understand challenges and requirements and identify areas where AI might be most beneficial.
Gartner suggests starting small and performing proof of concept (PoC) tests before moving tools into production. In sandboxing environments, act on a model’s recommendations and predictions and test their accuracy, then rely on automation as systems prove themselves (or don’t) and human trust increases.
Identify what type of AI networking system — NaaS, AIOps platforms, vendors, multi-vendors or managed service providers — is best for your business based on existing resources and needs, Gartner advises. Determine whether a vendor’s operational model is MNS, DIY and single or multi-vendor, and require them to provide specific details and breakdowns of their tools, what they can and cannot do, and offer clear timelines from implementation to a few years out.
NaaS company Nile urges enterprise leaders to invest in systems that efficiently collect and process data and are routinely retrained. To help keep up with cyber threats, ensure that solutions adhere to security standards and compliance requirements, the company advises. Also, understand — and be prepared for — the cost of implementation, which can vary from vendor to vendor.
Just as importantly, give IT teams the tools they need to act on AI recommendations and assess how network operations may (fundamentally) change, Gartner emphasizes. Finally, justify adoption by calculating cost savings and benefits around network availability and performance and resource efficiency.
The wrap on AI networking
AI networking has the potential to transform and modernize IT networks. There are many opportunities — and important considerations and challenges, too. As with any technology, it is critical to take a strategic approach, iterate carefully and educate, train and constantly upskill to gain the greatest benefits.
