AI is Not the Starting Point
Telecom operators are under growing pressure to use AI to improve efficiency and deliver better experiences for customers.
Yet the biggest obstacle is often not the AI technology itself. Over time, operators have accumulated large and complex technology estates. Network performance data, planning information, customer interactions, business systems and operational workflows are often siloed within different departments and platforms.
Some information remains trapped in spreadsheets or non-queryable formats. Different teams may use incompatible tools, while valuable datasets are often fragmented across departments and platforms.
In many cases, operators already possess the information needed to improve decisions. The challenge is connecting it across the organisation so that its full value can be realised.
One Dataset, Multiple Use Cases
Consider a customer-facing coverage checker.
It may have been designed primarily to help a customer understand the service available at their location. However, a sudden increase in service checks within one area could also provide network operations teams with an early warning of a site outage or localised service issue.
The same principle applies in reverse. Real-world network performance and crowdsourced data used by radio planning teams could support customer care teams, enabling systems to proactively identify and diagnose an issue before the customer contacts the support centre.
The value of AI will not come from creating another isolated software platform. It comes from allowing information to flow across network planning, operations, customer care and commercial teams to deliver greater value.
Creating the Environment for Agentic AI
For nearly two decades, Metricell has developed telecoms software and services that bring together information from performance management tools, network OSS, crowd-sourced measurements, planning systems, business platforms, third-party sources and Minimisation of Drive Test data.
This experience has focused on solving a fundamental operator problem: making complex and fragmented data accessible, correlated and usable.
That foundation is now essential for AI.
Before operators can deploy effective AI agents, they need a trusted layer of consolidated network and business information. AI must be able to understand not only individual datasets, but also the relationships between assets, performance, customers, faults and operational processes.
Without that foundation, adding AI may simply amplify existing complexity.
The First Step Towards AI-Native Operations
The journey towards AI-native operations therefore begins with three priorities:
- Connect the data: Information must be made available across organisational and technology boundaries.
- Create shared context: Data should be centralised and contextualised to allow AI to understand what is happening and why.
- Make insight relevant: The same information must be presented differently depending on whether the user is an engineer, customer care agent, operations manager or business leader to ensure it is relevant.
AI is not the starting point for AI-native operations. A model cannot deliver meaningful results if the information it relies on remains fragmented, inaccessible or disconnected from operational context.
Building a connected data foundation is what allows AI to understand the network and turn existing information into practical action.
Contact us: marketing@metricell.com
