A network looks stable on paper. Capacity is within limits. Latency is under control. No major alarms are triggered. Yet, customers in a specific cluster begin to experience dropped calls and buffering during a live event. By the time the issue is escalated, analysed and addressed, the damage is already done. Not because the network failed. But, because the network reacted too late.
With GSMA Intelligence reporting that global mobile data traffic is expected to grow more than fourfold by 2030, crossing 5,400 exabytes a year,¹ telecom operators are under immense pressure to respond before service quality begins to slip.
Why traditional optimisation models are losing relevance
Telecom networks today are shaped by:
- Unpredictable traffic spikes
- Rapid shifts in user behaviour
- Increasing dependence on real-time services
The issue is not visibility. It is decision latency. In practice, this means operators are often resolving issues after customers experience them, not before. And so, network optimisation turns into damage control rather than performance management.
Most optimisation models still depend on:
- Static thresholds
- Reactive alerts
- Manual escalation
For telecom operators, this gap directly affects service quality, operational cost and customer retention.
What difference AI actually makes in network optimisation
AI does not replace network management. It compresses the time between signal and action.
From detecting congestion to preventing it, AI models analyse traffic patterns continuously and anticipate where congestion is likely to occur. This allows networks to rebalance capacity before performance drops, not after.
from fragmented systems to connected decisions: In many telecom environments, network data, customer data and operational workflows exist in silos. AI connects these layers, allowing decisions to be triggered automatically, whether it is rerouting traffic or reallocating resources. Stronger telecom fraud detection and prevention depends on how quickly operators can identify anomalies and respond before revenue or customer trust is affected.
from manual optimisation to autonomous operations: As networks scale, manual intervention does not scale with them. AI enables semi-autonomous operations where routine optimisation decisions happen without human intervention, allowing teams to focus on critical exceptions.
from cost visibility to cost control: Network optimisation is not only a performance problem. It is a cost problem. With cloud-native architectures and distributed networks, cost leakage becomes harder to track. AI helps align usage, capacity, and cost more precisely, especially when paired with cloud-based spend management solutions that improve visibility across infrastructure and network costs.
Where telecom strategies still fall short
For many operators, AI is already in place. The real challenge is moving it from insight to action.
In many telecom environments:
• AI models operate outside core workflows
• Insights are generated but not acted upon in real time
• Decisions still depend on delayed reporting
Mobile network traffic rose 20% in a year, according to Ericsson’s latest Mobility Report.² The strain is rising in real time, but in many cases, the response still isn’t. Without embedding AI into operational workflows, optimisation remains reactive.
How AI translates into real operational gains
AI creates the most value when it starts influencing live operations. Issues are identified earlier, often before customers notice them. That means faster resolution, less downtime and fewer service disruptions.
Network performance becomes more stable under pressure: Predictive models help operators manage fluctuating demand without compromising service quality. The real advantage lies in using advanced analytics to act on live signals before they become business problems.
better alignment between network and business outcomes: Network performance does not exist in isolation. A slowdown in service quality can quickly affect customer experience, churn, and revenue. AI connects technical signals with commercial outcomes, making it easier to prioritise the decisions that matter most. Stronger use of advanced analytics in telecom helps operators move faster, from tracking metrics to acting on them.
scalable operations without proportional cost growth: As networks become more complex, scaling operations through manual effort alone becomes unsustainable. AI manages higher traffic volumes, more connected devices and greater service demands without matching increases in cost or operational overhead.
The next wave of telecom growth will not be managed through incremental optimisation alone. Operators will need to move towards more autonomous, self-optimising networks that can adapt dynamically to changing demand, service expectations and new AI-driven workloads. The leaders ahead will be the ones building that capability now.
How Infosys BPM can help
For telecom operators, the challenge is no longer whether to adopt AI, but where to embed it for the greatest operational impact. Infosys BPM helps operators integrate AI into network and operational workflows through analytics, automation, and telecom-specific expertise. Teams anticipate demand earlier, respond faster to anomalies, and optimise network performance before service issues affect customers.
Move from reactive to predictive telecom operations with Infosys BPM
