Companies are using data operationalization and AI to create digital twins that enable automation and autonomous systems [1].
These virtual models allow organizations to simulate physical environments and industrial processes in real time. By bridging the gap between static data lakes and active physical systems, businesses can predict failures and optimize performance without risking physical assets.
Data operationalization involves the pragmatic deployment of AI use cases to transform raw information into actionable models [1]. This process is essential for the creation of digital twins, which serve as virtual mirrors of physical systems [1]. According to a Forbes report, this movement is fundamentally a shift toward automation and autonomous systems [1].
These systems are being deployed across various environments, including industrial internet of things systems and urban landscapes [2, 3]. In city planning, urban digital twins are being enhanced with synthetic data to create more humane models of community interaction [3]. These tools allow planners to test infrastructure changes in a virtual space before implementing them in concrete.
Control systems also rely heavily on these models to maintain stability. Microsoft said that digital twins are a critical component of modern control systems because they allow for the creation of a model that serves a physical system [2]. This capability is often facilitated by platforms like Microsoft Fabric, which helps organize the massive data streams required to keep a twin synchronized with its physical counterpart [2].
Beyond operational efficiency, the optimization of data infrastructure can lead to significant financial gains. For example, eliminating "zombie servers"—underutilized hardware that consumes power without providing value—could potentially save billions [4].
"Data operationalization, complemented by the pragmatic deployment of AI use cases with said data, is, at its core, a move toward automation and autonomous systems," the Forbes author said [1].
“Digital twins are a critical component of modern control systems”
The integration of AI-driven digital twins marks a transition from descriptive analytics—understanding what happened—to prescriptive autonomy, where systems can self-correct in real time. As these models move from industrial plants into urban planning and city management, the reliance on synthetic data will become a primary lever for balancing technical efficiency with human-centric design.
