Before proceeding with any solution, first gain a strong understanding of your network needs. Assess your current network infrastructure, understand challenges and requirements, and identify areas where AI could be most beneficial.

Gartner suggests starting small and conducting proof-of-concept (PoC) tests before moving tools into production. In sandboxing environments, you act on a model’s recommendations and predictions and test their accuracy, then rely on automation as systems prove (or fail) and human trust grows.

Identify what type of AI network system — NaaSAIOps platforms, vendors, multi-vendors or managed service providers — is best for your business. based on existing resources and needs, Gartner recommends. Determine whether a vendor’s operating model is MNS, DIY and single or multi-vendor and ask them to provide specific details and breakdowns of their tools, what they can and cannot do and provide clear timelines from implementation to several years .

NaaS company Nile urges business leaders to invest in systems that efficiently collect and process data and are routinely retrained. To help you keep up with cyber threats, make sure your solutions meet 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 points out. Finally, justify adoption by calculating cost savings and benefits related to network availability and performance and resource efficiency.

Completion of AI networks

AI networks have the potential to transform and modernize IT networks. There are many opportunities – and important considerations and challenges, too. As with any technology, it is essential to take a strategic approach, iterate carefully, and constantly educate, train, and improve skills to reap the greatest benefits.