Digital twins are evolving from static simulations into real-time operational tools. As organizations explore how to deploy them across complex environments, integrating data, infrastructure and analytics is becoming the key to turning digital insight into real-world action.
The Benefits of Real-time Digital Twins
The benefits of digital twins can run the gamut, finding as many uses as there are products, processes or enterprises. The palatial SoFi Stadium in Los Angeles uses a digital twin to monitor and optimize management and operations, including how it shares real-time football data during games. Meanwhile, Anheuser-Busch InBev keeps track of any bottlenecks in its vast beverage production lines and supply chains. The U.S. Space Force is building a digital twin of space itself, including simulations of extraterrestrial bodies and satellites.
In basic terms, the benefits of digital twins include:
- Improved operational visibility. By integrating data from sensors, enterprise applications and infrastructure platforms, digital twins provide a more complete view of complex systems.
- Faster scenario testing. Organizations can test a greater variety of operational changes and conditions before taking anything live.
- Reduced risk. As has always been true of digital twins, they enable organizations to test and evaluate impacts without disrupting live systems.
- Better decision support. With their use of real-time data, leaders can make timely decisions based on current data rather than historical reports.
- Greater resilience. Digital twins allow organizations to model disruptions, and then evaluate accurate scenarios of response and recovery actions, speeding up response times should a real emergency hit.
Some of the most impressive benefits, however, may come as they are applied to emerging technologies and new processes.
The National Science Foundation, for example, is funding projects to further push the digital twin envelope. The NSF Center for Digital Twins in Manufacturing brings together students, faculty and industry to study how to make digital twins easier to build, while also addressing reskilling needs among the workforce. NSF also funds the AI Institute in Dynamic Systems, which integrates artificial intelligence capabilities into digital twins replicating critical systems, including the nuclear energy infrastructure. And the agency also is funding research into quantum networks, using digital twins as test beds for designing architecture components and devices.
Digital Twins, AI and the Next Frontier
AI is fast becoming a vital component related to digital twins, whether it involves a digital twin helping to manage an AI deployment or making use of AI itself. Digital twins that address processes or even full enterprise operations will necessarily include AI, since generative AI has become so entwined in organizations day-to-day operations. Large language models (LLMs) in GenAI platforms, for example, create a wide range of content, including images, text, audio, code, simulations and videos. Even content from unapproved apps (shadow AI) can work its way into the process because people make such frequent use of the tools.
Even without shadow AI, however, the ubiquitous use of GenAI is a integral part of processes, For example, an LLM could draw data from a smart city plan for more efficient traffic systems and public utilities to look for patterns and potential improvements in systems over time, as McKinsey pointed out. A digital twin, designed to make use of large amounts of data, could do a lot with that information—as long as it can handle the amount of data being provided.
Ultimately, digital twins and AI can work together to their mutual benefit. Gen AI, which is always hungry for data, could pull real-time data from digital twins to add to its knowledge within the context of the digital twin’s scenarios and make its own insights even more current. Those insights, of course, would also go back to the digital twin, creating an ongoing process where they keep each other in-the-loop with real-time updates and improvements. A big advantage is that a digital twin provides a controlled environment where an LLM can test its ideas without impacting the enterprise.
GenAI also could shorten the window required to create digital twins, which can be time- and resource-consuming. By being able to write code for the digital twin, an LLM can accelerate the process.
The New Reality: Visualization and Operational Awareness
One of the most valuable aspects of digital twins is their ability to make complex systems easier to understand. Across industries, the goal is consistent: create a digital environment that reflects real-world systems closely enough to support smarter and faster decisions.
Platforms designed for operational digital twins increasingly bring data from many different sources together into unified environments that connect data, analytics and visualization tools. Technologies such as those developed by Edge TI integrate multiple data sources and software applications into a single digital twin environment so organizations can monitor operations, explore scenarios and coordinate responses across teams.
By presenting complex data within a visual environment that mirrors the physical world, digital twins can help organizations identify emerging issues, evaluate operational performance and coordinate responses across distributed teams.
No longer limited by a static model, digital twins provide a framework for understanding systems, testing potential responses and improving decision-making under changing conditions. For organizations responsible for infrastructure, logistics networks and mission-critical operations, the digital twin is rapidly evolving from a once-experimental technology into an essential operational capability.
Digital Twins Move From Concept to Operations: What It Takes to Deploy Them
As digital twins move from concept to operational tool, organizations are discovering that building and sustaining them requires more than simply connecting data streams. Successful deployments often depend on integrating infrastructure, enterprise applications, sensors, analytics platforms and visualization environments into a single operational view of the enterprise.
That integration challenge is where partner ecosystems can play an important role. Platforms such as those developed by Edge TI can help organizations bring together disparate systems and data sources into a unified operational environment where digital twins can continuously update, simulate scenarios and provide actionable insights across complex operations.
“Digital twins become truly powerful when they move beyond visualization and begin informing real operational decisions,” said Jim Barrett, CEO of Edge TI. “The goal is not just to mirror the real world digitally, but to give organizations a dynamic environment where they can understand what’s happening, test options and coordinate responses across teams in real time.”
Working with solution partners such as SD3IT and Edge TI can help organizations accelerate that transition. By aligning infrastructure planning, data integration and operational workflows, organizations can deploy digital twin environments that support real-world mission requirements rather than remaining limited to static models or isolated simulations.
“In many cases, the hardest part of deploying a digital twin is bringing together all the systems and data sources required to make it meaningful,” Barrett added. “When organizations approach the problem holistically, combining the right platform technology with experienced integration partners, digital twins can quickly evolve into a core capability for operational awareness and decision support.”
Together, that combination of platform capability and integration expertise allows organizations to move digital twins beyond experimentation and into day-to-day operations where they can continuously inform planning, coordination and response across complex environments.
To explore more insights on innovation, technology trends and issues shaping the IT landscape today, visit the Inside the Mission with SD3IT blog pages where we regularly share practical perspectives from the field. As these challenges grow more complex and timelines continue to tighten, organizations should take time to reassess and prioritize their most mission-critical needs. To learn more about SD3IT and how we help organizations plan and act decisively in uncertain conditions, visit our website or reach out and contact us to start the conversation.
