What is the digital twin maturity model?
The digital twin maturity model is a framework that helps organizations assess their digital twin initiatives and progress toward more advanced, value-generating implementations.
As a maturity assessment tool, the model enables enterprises to evaluate their use of digital twin technology to represent, monitor and optimize physical assets and systems. It is also a structured digital twin implementation roadmap that guides organizations from basic digital representations through dynamic models and toward predictive, interconnected and autonomous systems.
The stages of the model represent increasing levels of data integration, system complexity and analytical prowess. As enterprises progress through different levels of maturity, their digital twin models evolve from static visualizations to intelligent systems that can simulate outcomes, inform strategic decisions and act autonomously.
Many organizations formalize this progression by using internal frameworks or external benchmarks outlined in digital twin white papers focused on sustainability, asset lifecycle optimization and operational resilience. For enterprise leaders, digital twin maturity can engender improved operational resilience, more accurate capital planning and greater visibility into asset performance.
What is a digital twin?
A digital twin is a virtual representation of a physical asset, system or environment. It uses data from Internet of Things (IoT) sensors and other sources to model the asset’s condition, performance and behavior over time. Digital twins enable monitoring, simulation and optimization of their physical counterparts.
Academic research has further formalized digital twins as cyber-physical systems that continuously synchronize physical and virtual states.
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The digital twin maturity model features five stages of maturity through which organizations can self-assess their progress along the digital twin roadmap. The maturity model begins with low-level static models based on historical data and charts advancements through high-level autonomous models powered by machine learning.
Each stage builds on the previous one, increasing in data fidelity, system integration and decision-making capability.
Several academic and industry frameworks outline progressive stages of digital twin maturity, from static visualization models to fully autonomous systems. As outlined by Yung Woon Kim, these are the five stages of the digital twin maturity model:
- Look-alike digital twin
- Static digital twin
- Dynamic digital twin
- Interactive digital twin
- Autonomous digital twin
1. Look-alike digital twin
The first stage of digital twin development is a one-time rendering of the physical asset. The digital twin creator models the asset by using computer-aided design (CAD) or building information modeling (BIM) software. The result is a 2D or 3D model of the physical asset, similar in visual appearance.
Digital twins created in this way do not often incorporate sensor data as part of the modeling process and lack real-time simulation functions. Instead, look-alike digital twins are created as a more generalized representation of the physical systems or assets.
2. Static digital twin
A static digital twin is a digital representation of a physical asset based on historical data, snapshots or periodic updates. Unlike more advanced twins, a static digital twin does not maintain a continuous live data connection to its physical counterpart and thus cannot accurately depict its current state.
Static digital twins use predefined process control logic workflows—sets of basic rules or thresholds—to trigger alerts and generate simple responses based on preset conditions. Static twins provide a baseline view of an asset’s structure or condition but do not reflect live operational changes or generate simulations of potential future states for forecasting.
Meanwhile, more advanced digital twins can model complex behaviors and system dynamics to provide insights into how a physical system or object will change over time.
The ideal use case for a static digital twin in enterprise asset management (EAM) is as a smart dashboard, highlighting the live status of a system for more informed human decision-making. Examples include supervisory control and data acquisition (SCADA) and data capture systems (DCS). In many organizations, these systems integrate with a computerized maintenance management system (CMMS) to support maintenance workflows and asset visibility.
3. Dynamic digital twin
A dynamic digital twin is a virtual model of a physical system that, unlike static twins, includes behaviors and dynamics. This process allows dynamic twins to model the operation of a system over time, showcasing how components interact and how the system handles changing conditions. Dynamic twins maintain a synchronous real-time data link to the physical object, powering its simulations and analyses with operational data.
Dynamic digital twins can generate “what-if” simulations, allowing operators to experiment and test the system under different scenarios. Stakeholders can explore failure states, potential optimizations and environmental changes without affecting the real-world system. Similarly, users can analyze cause and effect when disruptions happen.
Dynamic twins excel in situations that require a clear understanding of system behavior and causality for accurate predictions. These capabilities are especially valuable for enterprise decision-makers, enabling scenario planning, risk analysis and operational optimization without disrupting live systems.
Use cases include engineering, healthcare, smart manufacturing and at-scale system modeling where testing, diagnosing and forecasting performance are critical.
4. Interactive digital twins
Interactive digital twins connect multiple digital twins into a federated system, meaning that they operate independently but can interact and share data. In this way, interactive twin systems reflect the interdependencies of real-world systems and operations, such as in supply chain management.
Coordinating the interactions between the twins in the system is a digital thread: a bidirectional communication framework spanning the entire lifecycle of the physical systems. This interface bus integrates data throughout the system, allowing the twins to respond to and influence each other as they experience change.
Interactive twins provide human operators with system-level visibility and understanding into operational metrics and performance and require human intervention for action. Stakeholders use cross-system insights generated by the interactive twins to make data-driven strategic decisions for maximum impact and efficiency.
At scale, interactive digital twin systems provide organization-wide visibility, supporting cross-functional coordination and complex strategic planning.
5. Autonomous digital twins
The potential of digital twins is most fully imagined in the highest level of the maturity model, where automation comes into play. Autonomous twins not only model system behaviors and changes but also decide and act independently based on those conditions. In contrast, all lower levels in the digital twin maturity model need human operators to affect physical systems.
Autonomous twins synchronize in real time with the physical systems that they represent and control by using the incoming data to evaluate operations conditions and decide to maintain optimal system performance. Autonomous digital twins use orchestration to manage entire systems, such as with drone fleets or smart infrastructure.
Machine learning and artificial intelligence (AI) enable autonomous twins to decide and act in place of human operators. Predictive analysis, decision optimization, anomaly detection and adaptive learning are all required for true autonomy. The ability to process data, generate insights and act autonomously in a closed-loop is what sets autonomous digital twins apart from lower maturity levels.
In enterprise environments, autonomous twins can support self-optimizing operations, reduce manual intervention and enable faster responses to changing conditions. Digital twins are also increasingly used to support sustainability initiatives by optimizing energy usage, reducing waste and improving resource efficiency across physical systems.
Recent developments from IBM Research demonstrate the application of autonomous digital twins in real-world environments. In a 2025 case study, IBM researchers developed an AI-powered digital twin for complex industrial systems, such as shipping operations.
Organizations across numerous industries can use digital twin maturity models to guide implementation strategies during an ongoing digital transformation and measure the return of investment (ROI) at each stage of development.
Digital twins streamline production systems and reduce downtime through predictive maintenance and process optimization.
Detailed replicas of aircraft engines and other components can indicate how real-world components will react under stress and changing environmental conditions.
Digital twins are being explored to model patient-specific conditions and hospital operations, with the goal of more personalized care and greater efficiency.
In smart buildings, digital twins often use edge AI to optimize heating, ventilation and air conditioning (HVAC) systems, energy consumption and occupancy-based resource allocation.
City planners can use digital twins to model transportation systems, utilities and environmental conditions, improving resilience and long-term planning.
Digital twins can explore how landscape designs and projects will weather floods, extreme heat and other effects of climate change.