Digital twin maturity model | IBM

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.