The term Digital Twin has exploded in telecom. As automation and autonomous networks become strategic imperatives, the digital twin is now seen as essential. But the label is being applied to almost anything — from vector databases to data lakes, from legacy inventories to LLM-powered chatbots.
The danger? Choosing the wrong approach isn’t just a technical risk – it’s a cost, time and agility trap:
- Cost to deliver – massive integration projects, endless customization and vendor dependencies.
- Cost to maintain – high OPEX for updates, manual data wrangling and patchwork fixes.
- Time to value – years before anything usable is delivered, only to find accuracy still falls short for automation.
- Lack of agility – rigid models that require custom development for every customer-specific need simply can’t keep pace.
What is quite telling is a recent MIT study which found that 95% of AI initiatives are failing, forcing companies to rethink their strategies.
From vector databases to big data lakes, legacy inventory modernisation projects and LLM-powered dashboards, the market is full of partial solutions marketed as digital twins. The problem? These approaches often hit the same four walls: Cost, Time, Accuracy and Agility.
And when it comes to telecom automation, accuracy is king – because if your “automation” makes wrong decisions, you haven’t automated, you’ve just industrialised mistakes.
Myth 1: Digital Twin Solutions Built Primarily on Vector Databases Will Deliver Automation
The pitch: Store embeddings of network documents and use similarity search to “understand” the network.
The reality: Vector databases excel at unstructured data search, but when they are the primary backbone of a digital twin, they lack semantic modelling of telecom entities, relationships and constraints. Similarity search finds “close matches” in text, but automation requires reasoning over a precise, evolving model of the network.
- Cost trap: Constantly generating and refreshing embeddings for large, dynamic networks is expensive.
- Time trap: Still need a semantic layer to model relationships — meaning more development time.
- Accuracy gap: Similarity ≠ truth. A close text match may still be factually wrong in network terms.
- Agility gap: Adding new domains or technologies requires redevelopment of logic and retraining embeddings.
Myth 2: Digital Twin Solutions Built Primarily on Big Data Lakes Will Deliver Automation
The pitch: Collect all the data in one giant repository and run analytics/AI on top to automate actions.
The reality: Data lakes are great for storage and retrospective analytics, but they are not designed to model a multi-domain telecom network with meaningful, interlinked entities. They store “facts” without semantics, making reasoning, dependency mapping and impact analysis slow and incomplete.
- Cost trap: Building and maintaining massive, real-time data lakes for a full network view is costly.
- Time trap: Long delivery cycles with “value later” promises, often missing target outcomes.
- Accuracy gap: Without semantics, the system can’t infer correct relationships or impacts across domains.
- Agility gap: Changing schemas or integrating new domains requires heavy redevelopment.
Myth 3: Digital Twin Solutions Built Primarily on Relational Database Inventory Modernization Will Deliver Automation
The pitch: Modernize the legacy inventory into a single relational database and you’ll have your digital twin.
The reality: Relational models are rigid. While they can store network records, they lack the flexibility and semantic meaning needed for automation. In telecom, the network evolves faster than a fixed schema can adapt, making real-time automation and reasoning impossible without a semantic overlay.
- Cost trap: Modernization projects are expensive and long-running.
- Time trap: Often take years before first value, by which time the model is already outdated.
- Accuracy gap: Missing semantics means missing context, leading to wrong or incomplete automation outputs.
- Agility gap: Schema changes are slow and costly — bad fit for dynamic, multi-vendor, multi-domain networks.
Myth 4: Digital Twin Solutions Built Primarily on LLMs Connected to Legacy Inventory Will Deliver Automation
The pitch: Put a large language model in front of your old inventory and it can answer anything about the network.
The reality: LLMs are powerful for language tasks, but when paired with non-semantic, rigid data sources, they are limited by the quality and meaning of the underlying data. They can sound confident while producing hallucinations or errors, especially when interpreting network data without a semantic model. Even training the LLM on the legacy data doesn’t fully solve the problem — the model still lacks inherent understanding of telecom entities, relationships and constraints.
- Cost trap: Keeping an LLM updated with real-time, cleaned and structured network data is very expensive.
- Time trap: Much time is spent building middleware to translate between legacy systems and the LLM.
- Accuracy gap: No semantics = no reliable reasoning, increasing hallucinations and wrong impact analysis.
- Agility gap: Adding new data sources or domains requires complex prompt-engineering and retraining.
Why a Semantic Digital Twin Wins
A semantic digital twin models the meaning of the network — entities, relationships, constraints and behaviours — across domains and technologies.
- Accuracy: Understanding is built-in. Queries like “Which customers are impacted by this node failure?” are answered with certainty, not probability.
- Agility: Easily extendable to new domains and operator-specific rules without starting over.
- Cost: Faster to deliver and maintain because it connects to existing data, rather than replacing everything.
- Time: Delivers value in months, not years.
Semantics = Meaning = Use-Case Success
The “semantic” in semantic digital twin is not just a buzzword — it’s the reason it works for automation. Semantic means meaning and meaning only exists when the model reflects the real world accurately for the specific use case it serves.
In telecom, that requires:
- Deep domain understanding — modelling network elements, services and relationships across domains and technologies.
- Collaboration with the customer — capturing operational knowledge, business rules and priorities.
- Integrating that knowledge into the twin — so it becomes a living, context-aware representation of their network, not a generic template.
Relying on rigid, non-semantic models that demand costly development to fit each customer is a dead end. Agility is not optional — it’s the only way to keep up with evolving technology and operational needs.
Bottom Line
For telecom operators, the wrong “digital twin” approach is a slow, expensive path to disappointment. With 95% of AI initiatives failing, according to the referenced MIT study it’s clear that hype alone isn’t enough. The semantic digital twin accelerates time-to-value, reduces total cost of ownership and delivers the agility and accuracy needed for true automation in complex, multi-domain, multi-technology networks — so they don’t end up in the 95% of failed AI initiatives.
