TIM is building automation into 5G Standalone as it shifts to a cloud-native core network architecture and a more data-driven operating model. Alessandro De Vincentis, TIM’s Head of Network Data Layer and Automation, said the operator sees automation as necessary if the business is to scale.
The Italian operator is also treating network slicing as a core part of 5G Standalone monetization. TIM said a slice can turn into an operational commitment to several hundred enterprise customers across its full lifecycle, which is why it wants the automation layer embedded rather than bolted on later.
Cloud-native core and internal simplification
TIM is introducing a cloud-native core network architecture while simplifying internal processes and consolidating tools. The operator is also moving toward more data-driven decision-making to create the environment it needs for AI.
That shift is not presented as a side project. TIM says the way it designs, delivers, and manages the network directly shapes what it can offer to the market and how fast it can offer it.
Network slicing as an operating commitment
TIM’s network slicing pitch is tied to enterprise operations, not just product packaging. The operator says a slice is an operational commitment that has to be managed through its full lifecycle, and that manual handling will not work at the scale it is targeting.
TIM also says new enterprise services need a clear internal accountability model, alongside a clear business plan, revenue line, and cost structure.
AI is moving into operations
TIM says it is introducing AI into network processes to make the operating model more effective. It also says AI is increasingly taking over tasks that are currently performed by humans in operations.
The operator says AI-driven services are changing traffic patterns and customer reliability requirements at the same time. That leaves TIM balancing AI as both a tool for its own processes and a set of services that its customers consume.
What TIM says still slows the shift
TIM identifies data quality and data fragmentation as major challenges for AI adoption. It also says skills and governance remain major issues for using AI in network operations.
De Vincentis said the challenge is not only technological. He said TIM needs trained teams and a governance framework that turns people from executors into supervisors of what AI is doing.
Read for enterprise 5G and network automation teams
TIM is effectively linking enterprise 5G monetization to the operational model underneath it. For operators planning network slicing, the message is that automation, accountability, and data quality are part of the product, not just the support layer.
That is the same logic TIM applies to its AI plans: the operator wants AI in the network, but it also wants the processes, skills, and governance in place before the scale arrives.