What Is a Digital Twin? How They Work & Where They're Used

As artificial intelligence (AI), IoT-connected devices, cloud platforms, and spatial computing become more accessible, we’re seeing interest in digital twin technology grow alongside it. According to Fortune Business Insights’ Digital Twin Market report, the global digital twin market is projected to grow significantly through the end of the decade as organizations increase investments in AI, industrial automation, and smart infrastructure.

Much of that growth is being fueled by AI. While digital twins have existed for years, advances in machine learning, generative AI, and predictive analytics are expanding what organizations can learn from their digital models and how quickly they can act on those insights.

In this guide, we’ll cover what a digital twin is, how AI and related technologies are making digital twins more powerful, where organizations are using them today, and what leaders should know before building digital twins within their own operations.

What Is a Digital Twin?

A digital twin is a virtual representation of a real-world object, system, or environment that continuously receives data from its physical counterpart. Unlike a static model or dashboard, a digital twin evolves over time as new information becomes available.

Most digital twins rely on three foundational components:

A physical asset, process, or environment
A virtual model capable of representing and simulating that asset
A continuous flow of data connecting the two

This ongoing connection is what makes digital twins valuable. Rather than simply displaying information, they can model potential outcomes, test operational changes, and help organizations understand how a system may behave under different conditions.

Digital twins can be created for a wide range of use cases. A manufacturer may create a digital twin of a production line. A healthcare organization may develop a digital representation of a patient population or facility. Cities and infrastructure operators can build digital twins that model transportation networks, utilities, and public spaces.

Digital Twin and AI

The conversation around digital twins increasingly centers on AI. While the concept began with simulation and real-time monitoring, AI is helping organizations unlock predictive and autonomous capabilities that weren’t previously possible.

AI can analyze massive volumes of operational data, identify patterns humans might miss, forecast future conditions, and support decision-making. In modern digital twin environments, machine learning models often help predict equipment failures, optimize workflows, recommend operational changes, and improve forecasting accuracy.

Even more recently, generative AI is creating additional opportunities. Teams can use natural language interfaces to interact with digital twins, making complex operational environments more accessible to business users who may not have deep technical expertise.

AI may be grabbing headlines, but it relies on several other technologies working together.

IoT and sensor networks provide the real-time data that keeps digital twins current. Connected devices, industrial sensors, cameras, and monitoring systems continuously feed information into the model.

Cloud and edge computing support the enormous processing requirements behind modern digital twins. Cloud environments provide scalability for simulation and analytics, while edge computing enables faster decision-making closer to physical assets.

Spatial computing, GIS, and 3D modeling allow organizations to visualize complex environments and understand relationships between assets, locations, and systems. Technologies such as LiDAR, drone imaging, and geospatial AI help create highly accurate digital representations of real-world conditions.

From what we’ve seen, the most successful digital twin initiatives comes from integrating AI, IoT, cloud platforms, spatial data, and analytics into a connected ecosystem that enables better business decisions.

Where Digital Twins Are Making an Impact

From improving patient care to optimizing critical infrastructure, organizations across industries are using digital twins to better understand complex systems, test decisions before implementation, and uncover opportunities for greater efficiency.

Healthcare and Life Sciences

Healthcare organizations are increasingly exploring digital twins to improve patient outcomes, accelerate research, and optimize operations. Digital patient twins can help researchers model treatment responses, while pharmaceutical manufacturers use digital twins to simulate production processes and improve quality control.

According to NVIDIA’s healthcare digital twin research, these technologies have the potential to improve simulation accuracy while reducing the cost and time associated with physical testing and experimentation.

Engineering and Infrastructure

Digital twins are transforming how infrastructure is planned, built, and maintained. GIS-integrated digital twins combine geographic information, asset data, operational metrics, and engineering models into a single environment.

Organizations can:

Test disaster scenarios
Monitor infrastructure performance
Improve maintenance planning
Better manage assets throughout their lifecycle

One frequently cited example is San Francisco International Airport, which uses a GIS-enabled digital twin environment to support operations across hundreds of thousands of assets and facilities.

Manufacturing and Industrial Operations

Manufacturing remains one of the most mature digital twin markets.

Organizations use digital twins to:

Monitor production lines
Improve asset performance
Support predictive maintenance initiatives
Identify operational bottlenecks before they affect output

Digital twins are often paired with AI-driven analytics and edge computing environments that process production data in near real time. This helps manufacturers make faster operational decisions while reducing downtime and maintenance costs.

Energy and Utilities

Energy providers use digital twins to monitor infrastructure, optimize generation assets, and improve grid reliability.

Utilities can:

Model transmission networks
Identify maintenance needs earlier
Evaluate potential impacts of weather events or demand fluctuations before they occur.

AI-driven analysis also helps organizations work with incomplete data, improving visibility across large and geographically dispersed operations.

Financial Services

Digital twins are beginning to emerge in customer experience and risk management strategies.

Financial institutions can create digital representations of customer journeys to:

Better understand behaviors
Identify friction points
Personalize experiences.

Digital twins are also being explored for fraud detection, transaction infrastructure analysis, and operational risk modeling.

CHECK OUT: Why Digital Twins Are The Future of Healthcare/Pharma Innovation

Getting Started Building Digital Twins

Organizations often get excited about digital twin platforms before clearly defining the business problem they want to solve. But successful initiatives usually start with outcomes.

Begin by identifying a specific challenge, whether that’s improving asset reliability, optimizing patient care, reducing maintenance costs, or strengthening operational visibility.

Next, focus on the data foundation. A digital twin is only as effective as the information supporting it. Data quality, governance, integration, and consistency should be established before organizations invest heavily in simulation capabilities.

Leaders should also start with a focused use case rather than attempting to model an entire enterprise at once. Early success creates organizational buy-in and provides a roadmap for future expansion.

Interoperability is another critical consideration. Operational systems, GIS data, IoT platforms, engineering models, and AI tools often exist in separate environments. Bringing them together requires thoughtful planning and architecture.

Finally, organizations need the right expertise. Building digital twins often requires collaboration among AI specialists, cloud architects, data engineers, IoT experts, GIS professionals, and business stakeholders.

Learn more about Insight Global’s AI Services and Tech Services capabilities.

The Bottom Line

Digital twins are now embedded into real-world business operations. As AI, IoT, cloud computing, spatial technologies, and advanced analytics continue to evolve, organizations are finding new ways to model complex environments, predict outcomes, and improve decision-making.

The technology itself is only part of the equation. Success depends on having reliable data, a clear business objective, and the right blend of technical and operational expertise.

Whether you’re exploring what a digital twin could do for your organization or actively building digital twins at scale, Insight Global helps organizations bring together the talent, consulting expertise, and AI capabilities needed to turn ambitious technology initiatives into measurable outcomes. If you’re ready to explore the use of digital twins for your organization, reach out to our experts.