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Rayan Salha, Prodcut Marketing Director at Infovista, explains why automation can't scale when intelligence remains fragmented.
Rayan Salha
Trusted automation requires unified intelligence
Operators are investing heavily in AI, automation and closed-loop operations as they move toward autonomous networks. Yet many initiatives remain confined to isolated domains and struggle to scale.
The reason is simple: Automation cannot scale when intelligence remains fragmented.
Across many telecoms environments, network, service and customer intelligence still sit in separate systems. This fragmentation limits the ability of AI and automation to understand context and act reliably across domains.
As a result, the key challenge is creating a shared operational understanding of the network, the services running on it and the customers using them.
Networks have changed rapidly in recent years. What were once relatively static environments are now distributed across physical infrastructure, cloud-native platforms, multiple vendors, public and private clouds, edge locations and increasingly dynamic service models.
At the same time, operators are under constant pressure to:
However, many operational models have not kept pace with this evolution, and this is one reason why autonomous networks have become a major industry focus. TM Forum’s autonomous network maturity framework has helped create a shared direction around automation, closed-loop operations and intent-based management.
But there is an important distinction: Automation alone does not create autonomy.
A network can automate individual tasks and remain operationally fragmented. True autonomy depends on systems being able to understand context across domains and make reliable decisions based on that context.
Without shared intelligence, automation stays isolated.
Many operators still rely on fragmented assurance and observability environments. Different teams operate different tools. Data models are not aligned. Customer experience systems often remain disconnected from network operations.
As networks become more complex, these silos become increasingly difficult to manage.
The resulting operational challenges are familiar:
An issue emerges. Multiple alarms are triggered across systems. Engineers jump between tools trying to determine whether the problem originates in the RAN, transport, core, cloud or application layer, while customers are already impacted.
This slows down troubleshooting, root-cause analysis, remediation, SLA management and automation itself.
The challenge becomes even greater in multi-domain services, such as 5G slicing, fixed wireless access (FWA), IoT,or enterprise connectivity, where service quality depends on consistent performance across domains. This is why 360º observability is becoming a key element of network strategies.
Modern observability goes beyond infrastructure monitoring. It connects telemetry, packet data, service intelligence and customer experience into a unified operational view across:
While this may sound technical, the impact is practical. If a premium enterprise customer experiences degraded service quality, operators must quickly determine whether the issue originates from radio conditions, core latency, transport congestion or the application itself.
Equally important is understanding the business impact: Is a critical SLA at risk? Is an issue localized or widespread? Without this context, automation cannot move isolated actions.
But when operators connect technical performance with service and customer impact, automation become more intelligent and more effective.
For example, in our eBook Is your network assurance built for autonomous operations?, a Tier-1 CSP in the US reduced alarm noise by 30% and shortened investigation times by 25% using machine learning-driven anomaly detection with contextualized workflows. Meanwhile, a European operator accelerated troubleshooting cycles by up to 80%.
Many operations teams still spend significant time reacting to issues after they impact customers. Autonomous operations shift this model.
By unifying network, service and customer data, AI-driven systems can identify patterns earlier and detect degradations before they escalate into incidents. This allows operators to:
This is particularly critical for enterprise services. As operators expand into private networks, slicing and network-as-a-service (NaaS) models, assurance becomes part of the product experience itself.
Enterprise customers increasingly expect predictable performance, faster resolution and real-time visibility. Delivering reliable systems capable of continuously linking network behavior to service and business outcomes is key.
Trust is critical for scaling AI, and agentic AI and intent-based automation are accelerating this transition.
Operators are beginning to deploy systems that can interpret and coordinate actions across domains – for example, protecting priority services during congestion or maintaining SLA performance across services and environments. However, most operators are not ready to fully delegate critical decisions entirely to opaque AI systems and understandably so.
Autonomous networks depend not only on AI capability but also on trust. Operators need confidence in both the data and the actions generated from it. This is why governance, explainability and oversight are as important as the AI models themselves.
Human-in-the-loop and human-on-the-loop approaches are therefore essential. They allow operators to scale automation progressively while still maintaining control, accountability and operational confidence.
This shift is shaping how platforms evolve. The focus is on moving toward unified network intelligence, bringing together network, service and customer insights into a single operations layer that supports end-to-end observability and closed-loop workflows.
Ultimately, operators must move away from fragmented operational views toward systems that can understand relationships across domains, services and customers in real time. That’s what allows automation to become genuinely operational instead of remaining limited to isolated workflows. At Infovista, we call this True Network Intelligence.
Autonomous networks will not emerge from a single transformation effort. Operators making the most progress are taking incremental steps:
In many cases, the differentiator is not who deploys AI the fastest, but who builds the clearest operational understanding of their network. Because once operators can consistently correlate network behavior, service impact and customer experience across domains, automation becomes more trusted, more scalable and significantly more valuable.
