Article by Francisco Pareja, Technical Director at Metricell
Artificial Intelligence has the potential to deliver significant benefits to the telecommunications industry by increasing operational efficiency and improving customer experience. However, while the promise of AI is widely acknowledged, many telecom operators are not yet in a position to implement these tools at scale. The reality is that without the right foundations, particularly in terms of data quality, infrastructure, and the right balance with deterministic systems, AI initiatives are likely to underperform.
The integration of AI into telecom operations opens a wide range of opportunities. Predictive fault detection and resolution can reduce downtime and improve service reliability, while traffic optimisation and dynamic capacity planning will enable networks to adapt in real time to user demand. For customer service teams, personalised customer support and intelligent self-service tools can dramatically reduce support costs while enhancing customer satisfaction. AI also has the potential to support strategic decision-making in areas such as network planning and rollout strategies, ensuring investments are targeted and data driven.
Yet it is important to recognise that AI alone is not a silver bullet. Telecoms have long relied on deterministic and rules-based logic to deliver predictable, reliable, and safe operations, from compliance checks to escalation paths and service assurance. These systems provide consistency and transparency, attributes that are essential in regulated environments. AI, by contrast, is probabilistic; it excels at extracting information, identifying patterns, anomalies, and opportunities; but its outputs are not always predictable. The most productive solutions are those that combine both approaches.
A key issue in telecoms is the overreliance on prediction models that offer limited visibility into actual, real-time network performance. Without live insights into how the network is functioning, AI tools can only work with approximations, limiting their value. This challenge is compounded by the widespread use of legacy tools and platforms, and even when valuable data is available, it is often siloed across the organisation preventing a unified view of network performance and customer experience.
When AI recommendations are consistently inaccurate or unhelpful, both internal teams and end users become frustrated and resistant, slowing down future AI adoption and potentially derailing investments. Implementing AI without a clear strategy, the necessary operational readiness, or clear guardrails from deterministic systems risks wasting resources and creating disillusionment, undermining future adoption efforts.
Successful implementation of AI in telecoms does not happen overnight. The businesses best positioned to benefit from AI are those approaching it strategically by starting with scalable use cases that deliver operational value while laying the foundation for more advanced capabilities. These organisations are also the teams who are investing into improving the overall quality of their data, integrating OSS/BSS systems, and building the internal structures needed to support AI in the long term.
Metricell has worked with several telecom businesses to support this journey and help teams integrate AI tools into their workflows in a targeted and sustainable way. By focusing on real-world challenges and aligning AI solutions with operational goals, these projects help to improve efficiency today while preparing businesses for the broader opportunities AI can offer. Crucially, success comes not from replacing deterministic systems, but from combining their reliability with AI’s adaptive intelligence.
To find out more, contact a member of the Metricell team today or read more about our experience with implementing AI here, here, here and here.
