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The frenzy to adopt AI often overshadows the need for an implementation plan and an effective approach
Business executives have always been quick to acquire and deploy the latest technologies in the network to make it faster, more performant — and ultimately autonomous. But adoption of AI at a giddy pace has brought up new challenges ranging from operational disruptions to security break-ins to costly downtimes, leaving operators scrambling.
While there is no denying that AI capabilities can make a network more intelligent and measurably self-driven, it takes a tangible set of steps to effectively integrate AI without causing disruptions. That begins with testing.
“There’s a major journey going on today in our networks. This is a journey towards autonomous networks through the use of AI — and this requires different levels of testing and autonomy,” said Stephen Douglas, head of market strategy at Spirent, during a webinar with RCR Wireless News.
Integration of all AI applications must be guided by careful and routine testing, a process that begins at the design phase, and continues through the lifecycle of the applications.
Spirent puts forth a set of proven test frameworks to unlock network autonomy through various levels of automation.
Level 0 is completely manual. Here humans sift through network analytics and conduct the tests by hand. There is no AI or automation involved, meaning full human intervention and supervision are required.
Level 1 implements low levels of autonomy and use basic machine learning (ML) to accomplish select testing tasks that are repetitive in nature. The rest is still done by humans in this “assisted” autonomy level.
Level 2 is where predictive AI first comes into play. However, its use is very limited, and therefore, only provides partial autonomy through continuous testing in specific sub-domains with static, closed loops.
Level 3 moves up from part-human, part-autonomous testing to a combination of predictive and generative AI capabilities. GenAI performs continuous testing within a given domain in dynamic, closed loops. Required human intervention here is minimal, although supervision is still significant.
Level 4 comes under high-autonomy. Continuous testing is carried out across domains with dynamic, closed loops, requiring very little human supervision.
Level 5 is fully autonomous. Testing capabilities here are self-adapting and operate across domains and third-parties, requiring zero intervention or supervision.
At present, a lot of telco providers are at the lower rungs of autonomy, but the industry is trending towards complete autonomy which will require progressive use of AI-enabled testing to achieve, Doughlas said.
When rigorous testing is embedded in the AI adoption journey, it enables clearer evaluation, validation, and monitoring from design to production — a strategy that is key to building trust and transparency in AI systems.
“Though it is still early days for AI we are already seeing substantial impacts to telecom network infrastructure with every sign pointing to continued high velocity change,” Douglas wrote in a blog.
