AI could reshape digital twins, but we have a way to go before it might be reliable enough
Digital twins in telecommunications are exactly what they sound like — virtual replicas of network infrastructure that mirror both the physical hardware and the logic running through it. What makes them interesting is the so-called “digital thread” they create — a bidirectional connection that keeps virtual models continuously synchronized with real-time data flowing from actual network operations. The technology works across the board, from fixed-line and broadband to mobile environments spanning 2G through 5G and eventually, 6G.
The appeal makes sense. Operators get what amounts to a risk-free sandbox for network experimentation, where they can simulate deployments, stress-test configurations, and predict outages without touching live systems or disrupting users. It’s also an area that could see massive disruption from AI — but while AI systems are still being refined, operators need to be able to test them in a risk-free environment.
The shift to AI-driven digital twins
Artificial intelligence is fundamentally reshaping what digital twins can do. According to Mark Fenton, Product Engineering Director at Cadence Design Systems, “AI allows Digital Twins to move from reactive and manually intensive systems to be proactive and highly intelligent.” That’s not incremental improvement. It changes the entire value proposition of virtual network models. Cadence Design Systems builds digital twins for data centers.
Steve Zisk, Principal Data Strategist at Redpoint Global, puts the evolution in practical terms: “The first iterations of the ‘Digital Twin’ used to be a snapshot, but now with AI it has become a living model that learns and updates as new data is introduced.” Previous systems could replay historical events and extrapolate from past patterns. AI-enabled twins can imagine potential futures and test them in real time.
The operational impact could be significant. AI can simulate all kinds of real-world situations, creating telemetry data and processing it in ways that allow for automated, intelligent, and near-instantaneous decision-making. Instead of dumping raw data on operators and expecting human interpretation, AI-enhanced twins surface actionable insights directly. They predict problems, identify patterns, and recommend fixes, moving well beyond what simple historical analysis could deliver.
Advanced simulation
Traditional simulation hit a hard constraint: the manual effort needed to define rules, set conditions, and execute individual tests capped how many scenarios operators could realistically explore. Engineers had to specify parameters for every simulation, which made comprehensive what-if analysis impractical for most organizations.
AI breaks through this bottleneck using surrogate models trained ahead of time. As Fenton explains, “With the use of AI and surrogate models, simulations can be done ahead of time to train the model. Then, when the user comes to ask questions, AI can infer an almost instant result.” This unlocks the automatic evaluation of thousands of scenarios — including things like natural disaster responses, traffic spikes from major sporting events, and weather disruptions.
Zisk highlights the exploratory potential. Once twins internalize a system’s patterns, they can investigate thousands of variations without waiting for human direction. Networks can be stress-tested in hours rather than weeks, with the AI modeling disruptions and surges that would take human teams far longer to even conceptualize. More importantly, each scenario could probe possibilities that humans might not have time to consider or might never think to test. That said, this capability demands careful oversight. AI-generated scenarios need to be realistic, and insights need to translate meaningfully to real-world conditions.
Generative AI and natural language
Generative AI is changing how engineers actually interact with digital twins, just like it’s changing all kinds of other industries. Workflow-heavy interfaces are giving way to conversational dialogue. Rather than navigating dashboards and mastering specialized tooling, operators can increasingly ask questions in plain language and get intelligent answers back.
Fenton frames this as a major accessibility win: “Whether a request such as ‘Where is the best place to house a new 120kW rack in my data center?’ to ‘What happens to my data center performance if I lost mains utility power?’, users can now get incredible insight without being an expert.”
But Zisk raises an essential warning: “The biggest problem with a conversational layer on top of bad data is that the model won’t recognize the bad quality data and create mistakes while sounding confident.” Clean, current data remains the foundation everything else depends on. Without proper guardrails, like context awareness, audit trails, and confidence checks, natural language interfaces can produce guidance that sounds authoritative but is fundamentally wrong. Eventually, engineers may interact with these systems through speech as naturally as they use command lines today. But Zisk is clear that this will enhance rather than replace engineering work, improving human ability to understand network conditions.
ROI
The business case for digital twins is obvious. Industry data points to potential savings of up to 20% on operational costs, with energy consumption dropping roughly 15% through better network planning and maintenance strategies, according to the Digital Twin Consortium. These numbers represent significant potential returns, though actual outcomes depend on implementation quality and organizational factors.
Optimization benefits span multiple areas. Digital twins let operators fine-tune backbone traffic routing, validate antenna placement before physical deployment, and allocate spectrum more efficiently. Simulating capacity needs and translating validated designs into real-world configurations cuts down on the costly trial-and-error that happens in live environments.
Beyond direct optimization, digital twins create a path toward autonomous network operations. Training and testing AI algorithms in safe sandbox environments allows operators to develop self-configuration, self-healing, and self-optimizing capabilities that would be far too risky to experiment with on production systems. Predictive maintenance is another major value driver as it catches emerging issues before they affect end users. Achieving these benefits, though, requires substantial upfront investment and genuine organizational commitment to data quality and process transformation.
Challenges
Data quality may be the single biggest barrier to making digital twins work. Success hinges on accurate, synchronized real-time data, yet many telecom operators are still wrestling with legacy systems built around rigid procedures and scattered, low-quality datasets. Fragmented sources and inconsistent collection practices can undermine even the most sophisticated simulations, turning integration into a labor-intensive prerequisite before implementation can even begin.
Upfront investment and implementation timelines add further complexity. Building real-time digital twins demands specialized, scalable software architecture that can analyze streaming data continuously. Organizations need to coordinate data acquisition technologies, modeling platforms, and connectivity infrastructure — a substantial undertaking requiring significant resources and expertise. Integration platforms, message brokers, and API management systems all become necessary parts of the technical stack.
Security concerns make things more complicated still. Real-time data streams create potential privacy and security exposures that demand careful architectural planning. The same bidirectional connectivity that makes digital twins valuable also opens new attack surfaces requiring protection. And organizations face a learning curve in adopting AI-driven decision-making. Shifting from traditional manual processes to autonomous optimization isn’t just a technical challenge. It requires cultural readiness to trust and act on algorithmically generated insights.
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