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Digital twins in telecom: a 6-point success strategy

August 19, 2025

Digital twins in telecom: a 6-point success strategy

Digital twins establish the connected, trusted enterprise data foundation that telecom companies need—and that accelerates their AI adoption.

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Sometimes you really can have too much of a good thing. Telecom companies have terabytes of valuable enterprise data, but much of it is siloed in hard-to-access formats—and largely untapped. Digital twins offer a win-win solution: Not only do telecoms garner the big benefits of virtual representation of key functions, but they also tackle the long overdue task of connecting their enterprise data. What’s more, the resulting unified data foundation serves as the layer of connected trusted data that’s needed to accelerate their AI efforts. Successfully establishing a data foundation, however, requires careful execution. With our proposed six-step strategy, telecom companies can roll out a digital twin initiative and a data foundation that’s ready to deliver benefits across the entire organization. <h4>Telecoms’ unique challenges</h4> If your telecom company has launched and then halted a digital twin initiative, you’re not alone. When it comes to creating real-time, virtual versions of physical things, telecoms face several hurdles unique to their industry. One is sheer size; creating a mirror of their complex functions and infrastructure is no easy task. Another is the expanse of siloed legacy applications, which make integration difficult. Data management can be overwhelming, especially when network, CRM and OSS/BSS systems run on disparate processes and applications. Efforts that look promising in small pilots and platform marketing product guides often fail to scale. But the benefits of digital twins and the data foundation they build on are compelling. For most companies the biggest payoff is in simplified, efficient operations—qualities critical to customer retention and market share.  Here are some ways telecoms can benefit from digital twins: <ul> <li>Customer 360: With a dynamic digital twin of a customer base, telecom companies can perform a variety of analyses. They simulate and analyze customer behaviors and segment audiences more precisely. They can also deliver hyper-personalized offers. By integrating real-time data across the marketing tech stack, they can optimize campaign performance to enhance targeting, engagement and ROI through predictive insights and dynamic decision-making.</li> </ul> <ul> <li>Supply chain: When leveraging digital twins for real-time scenario planning, organizations can optimize everything from inventory, logistics and manufacturing to supplier collaboration and demand forecasting. They also gain end-to-end visibility and predictive insights to make faster, smarter decisions across the supply chain.  </li> <li>Network analytics and AI operations: Digital twins can be used to model network architecture and simulate disaster recovery scenarios. As a result, telecoms can identify failure points and self-heal outages, reroute traffic, dispatch field technicians or add a human in the loop for more advanced troubleshooting.  </li> <li>Cash flow optimization: Companies unlock major benefits by linking real-time financial modeling to network build milestones and equipment inventory. When data moves smoothly across departments, organizations can simulate cash flow, forecast revenue, assess risk and fine-tune budgets and working capital—all of which boost financial agility and support smarter strategic planning.</li> </ul> <h4>Why a data foundation is the boost AI efforts need</h4> Getting the data foundation right is a key step for a digital twin initiative. Data is collected, cleaned and integrated into a centralized platform that feeds the twin. It’s a major challenge, and AI can help tackle it: the same data layer that powers digital twins will help telecom companies accelerate their AI efforts, nudging them out of pilots and into production. While AI models and large language models (LLMs) are great at providing context for publicly available data, they often stumble on enterprise data that’s in disparate sources and formats. A unified data foundation enriched by the tribal knowledge of those closest to the customer provides the base AI needs. When a semantic layer is added on top, it acts as a translator and deciphers insights into plain English, providing a language that AI models can read, interpret and act on. Linking a digital twin to AI and automation use cases provides big benefits. “<a href="https://www.lightreading.com/ai-machine-learning/-ask-at-t-gives-network-management-a-genai-facelift" target="_blank" rel="noopener noreferrer">Ask AT&T</a>” is a great example; the company tapped partners and employees alike to develop its AI technologies. The result is a flexible tool now used by 100,000 AT&T employees for a wide range of tasks, such as responding faster to customer queries and writing, fixing and explaining software code.  <h4>A six-point strategy to execution</h4> With a comprehensive strategy, telecoms can implement a successful digital twin initiative and the data foundation that’s ready to benefit the entire company. 1.    Identify the business value before establishing architecture diagrams and picking platforms and tools. Development of a digital twin should be driven by clear business outcomes, whether that means optimizing operations, improving customer experience or enabling predictive maintenance. In addition, the effort’s champions should be operational leaders who understand the nuances of the physical systems being mirrored. IT plays a critical role, but success hinges on collaboration with those closest to the data and processes. This ensures the digital twin reflects real-world conditions and delivers actionable insights.

Actions to take

Form a cross-functional think tank with execution leaders and SMEs. To fill the funnel with ideas on what to solve next and how to remove roadblocks, include representatives from functions such as customer care, network, field operations, support functions (Finance, HR, Legal), IT, sales and marketing.
Conduct workshops with operations, product and customer teams to define use cases.
Appoint business champions to lead the initiative and align KPIs with business goals.
Partner with IT and the Chief Data Office upfront.

2.    Form a dedicated data ingestion team. The heart of any digital twin is the real-time and historical data that feeds it. Because data is drawn from across enterprise sources—sensors, ERP systems and CRM platforms—establishing a dedicated ingestion team is a key step. It helps ensure the data for this strategic initiative is collected, cleaned and integrated into a centralized platform. Make sure the team works in an agile manner and includes members with expertise in APIs, ETL pipelines and edge computing where applicable. Remember, this is not a one-time project; it’s an ongoing investment in data, which will change as business needs evolve.

Actions to take

Inventory all relevant data sources across the enterprise.
Build scalable ingestion pipelines using modern data integration tools.
Establish SLAs for data freshness and reliability.

3.    Set a bold goal of unifying 90%+ enterprise data. Creating a comprehensive and accurate digital twin takes a lot of data, and all of it must be unified, cleansed and enriched. Setting a bold goal of integrating 90%+ of enterprise data ensures the twin isn’t siloed or partial. This goal also drives cultural and technical alignment across the organization.

Actions to take

Define what constitutes the 90%—include structured, unstructured and streaming data.
Prioritize integration based on business impact and data availability.
Use data catalogs and lineage tools to track progress and coverage.

4.    Operationalize data management. Once the data has been ingested, the focus shifts to maintaining data quality, monitoring flows and managing the lifecycle of data assets. This emphasis is critical for a digital twin, which reflects real-time conditions and supports decision-making. Priorities for the data operations team include implementing observability, anomaly detection and automated remediation.

Actions to take

Deploy monitoring tools to track data latency, accuracy and completeness.
Create dashboards for data health and operational metrics.
Establish protocols for data versioning and archiving.

5.    Establish data governance. Digital twins often involve sensitive operational and customer data. Providing robust governance ensures compliance with privacy laws, secures data assets and enables safe reuse. Governance also supports semantic consistency, which is vital for interoperability across systems.

Actions to take

Define governance policies for access control, privacy and compliance (e.g., GDPR, HIPAA).
Implement metadata management and ontology frameworks.
Train teams on data stewardship and ethical data use.

6.    Develop a strategy for your technology stack that meets your defined business outcomes. The platform hosting the digital twin and technology stack must be scalable, interoperable and future-proof. Avoid vendor lock-in to maintain flexibility as needs evolve. Ensure that modular architecture and open standards such as non-proprietary data storage allow integration with IoT devices, analytics tools and AI models. For example, ServiceNow’s <a href="https://cts.businesswire.com/ct/CT?id=smartlink\&url=https%3A%2F%2Fwww.servicenow.com%2Fnow-platform%2Fworkflow-data-fabric.html\&esheet=54141277\&newsitemid=20241023742891\&lan=en-US\&anchor=Workflow\+Data\+Fabric\&index=2\&md5=71e3bd8f76f902e128d3dab6cc6dadc1" target="_blank" rel="noopener noreferrer">Workflow Data Fabric</a> powers workflows and AI agents with real-time access to data from any source, eliminating unnecessary duplication of storage costs and allowing you to maintain ownership of data assets while speeding time to value. In addition, a successful tech stack strategy requires combinatorial innovation; no platform or technology can do everything end to end; working with a trusted advisor that understands and has been battle tested on what tools to use for the job and what you should develop internally is what creates competitive advantage.

Actions to take

Evaluate platforms based on scalability, openness and ecosystem support.
Inquire where use cases have been deployed at scale and the customer benefits gained.
Consider that the best platforms offer more than data ingestion and cleansing tools. They orchestrate multi-agents regardless of where the data resides or what LLMs are used, and they even score which models produce better answers.
Understand and test-drive the capabilities that make the data valuable, such as the ability to enrich the data, build and manage workflows, and develop automation and AI agents.
Remember that AI and other new technologies have changed time-to-value capture. Expect to cut traditional timelines at least in half—what used to take years in a project timeline Gantt chart can now be achieved in days and weeks.

<h4> Digital twins in telecom—the time is now</h4> Digital twins aren’t just a futuristic concept; they’re a powerful catalyst for solving today’s data fragmentation challenges and unlocking tomorrow’s AI capabilities. But the real differentiator isn’t the twin itself—it’s the connected, trusted data foundation that powers it. For telecom companies, that foundation is a competitive imperative. The roadmap is here. By taking a strategic, cross-functional approach to digital twins and enterprise data unification, telecom leaders can gain the clarity, agility and intelligence they need to lead in an AI-driven world.