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Subhash Chopra, Head of Global COE: Product Sustenance & Services (PSS) at Capgemini, discusses how Dark NOC leverages semantic modeling, digital twins, and Agentic AI to move beyond automation toward smarter, proactive, and transparent network operations
Subhash Chopra
Automation is not autonomy: shining a light on Dark NOC’s misconceptions
When people hear the term ‘Dark NOC’, the mental image is often one of silence and vacancy, an empty Network Operations Center (NOC) where the lights are off and humans are gone. It sounds like a sci-fi scenario: machines in control, decisions made in ‘black-boxes’ and people sidelined.
But this is wrong. In reality, ‘Dark’ refers to the invisible orchestration of intelligence, not the absence of it. It’s about moving from manual oversight to machine-augmented decision-making, where humans remain in the loop, but at a higher, more strategic level.
This shift moves us away from simply monitoring everything to understanding what is important, when, why and what to do next. With the right context, the NOC becomes better equipped to anticipate issues, explain their impacts and respond with precision.
The majority of what has been achieved in operation automation has been domain-specific, rules-based and tightly scoped.
Consider Self Organizing Networks (SON) - a technology that was expected to bring autonomy. Today, SONs also operate in siloes, often relying on trial-and-error methods and rule-based triggers for automated tasks. This illusion, that we have already automated the NOC, has held back deeper transformation.
But automation is not autonomy.
The Dark NOC challenges such illusions, by going beyond automation and introducing the potential for full autonomy.
The shift from a traditional NOC, characterized by rows of screens, 24/7 human monitoring, and reactive incident handling, to autonomous (Dark) operations demands change. It requires updates to tools, people, and processes - redefining network operations to become more autonomous systems that can:
To enable autonomy in operations, Dark NOCs needs a robust kernel, a foundational layer of intelligence, built on advanced digital technologies like digital twins, semantic modeling, and Agentic AI. This kernel goes beyond automation, serving as the cognitive core that drives autonomous decision-making and adaptive responses.
However, true autonomy cannot be achieved with Gen AI alone, it needs a structured understanding as a base. This is why so many Gen AI initiatives struggle in operations. The models have no idea what a router, cell site, or fiber path actually means, let alone how they interrelate.
Helping the models to understand all of this requires a fundamental shift in how data is managed.
Today, most operational data is trapped in silos, performance metrics, inventory records, alarms, and tickets—each confined to its own system, platform, or data partition, with no shared understanding between them. While structured, this data is meaningless when viewed in isolation.
Success is impossible without structured data that has been semantically enriched and made interoperable across the estate. This is where semantic modeling comes in.
By applying ontologies and knowledge graphs, structured data is transformed into a connected, AI-ready representation of the network and its services. These semantic models enable reasoning across domains, infer dependencies, and provide a foundation for Generative AI to make decisions with transparency.
This semantic foundation gives rise to the digital twin, a high-fidelity virtual replica of the physical environment, enriched with these semantic models. It makes system understanding explicit by simulating real-time behavior, allowing AI to test hypotheses, predict outcomes, and validate decisions before execution.
Building upon Gen AI's ability to produce tailored outputs while continuously improving through feedback and advanced algorithms, the Agentic aspect adds further capabilities. Imagine a group of LLM-based agents trained not just on language, but on the operational semantics of the network. They assist with diagnostics, explain network behaviors, highlight hidden insights, and even act, triggering workflows or escalating cases autonomously. Each of these use cases represents a shift from reactive to proactive, from rule-driven to reasoning-driven.
This empowers AI with a reasoning framework, enabling operators to move toward precision, explainability, and scalable actionability. Enabled by semantic understanding, Agentic AI doesn’t merely act; it explains, justifies, and learns. Humans are no longer ‘firefighting’, they are supervising intelligence and continuously refining the system’s understanding over time.
We redefine ‘DARK' as a Digital Twin & Agentic AI-powered Reasoning Kernel, a new architectural paradigm for network operations. This isn’t just a milestone in automation; it’s a transformation in how networks are understood, managed, and evolved.
The Dark NOC is not a black box. It is a glass box: transparent, intelligent and always learning.
It doesn’t replace people, it empowers them. Based on our work implementing Dark NOC for Communication Service Providers (CSPs), we've identified several critical factors for success. Our experience highlights that key asks from clients often center on automation and proactive issue resolution. The primary implementation challenges revolve around data integration and system interoperability. Furthermore, a successful transition hinges on crucial people and process alignments, including upskilling teams and redefining workflows to embrace a more automated, hands-off operational model. We are well-equipped to guide our customers through these challenges, offering a comprehensive approach that addresses technology, people, and process elements to ensure a seamless and effective Dark NOC deployment.
It does not just switch off the lights, it switches on intelligence
