- Rise of semantic twins further signifies an exciting chapter, blending AI’s cognitive capabilities with digital models to facilitate intelligent, data-informed decision-making.
- As adoption accelerates globally, digital twins will increasingly become indispensable tools in achieving efficiency, sustainability, and competitive advantage across sectors.
Digital twins, virtual replicas of physical assets, systems, or processes, are increasingly reshaping industries such as manufacturing, healthcare, and aerospace.
By offering solutions to optimise operations, improve efficiency, and enable predictive analytics, digital twins represent a critical advancement in industrial and technological transformation.
The global market for digital twins is projected to witness exponential growth, expanding from $5 billion in 2019 to an estimated $154 billion by 2030, at a compound annual growth rate (CAGR) of 35.6 per cent, according to GlobalData, a leading data and analytics company.
The remarkable growth trajectory underscores the pivotal role digital twins play in advancing industry capabilities.
Widespread adoption
Several factors drive this burgeoning market. The widespread adoption of low-cost sensors embedded in Internet of Things (IoT) devices facilitates the real-time acquisition of data critical to the creation and refinement of digital twins.
Concurrently, a reduction in the cost of high-performance computing (HPC) and enhanced accessibility to cloud infrastructure have made the deployment and scale-up of digital twin technologies more feasible than ever before.
Furthermore, advances in data analytics and artificial intelligence (AI) are pivotal in elevating the capabilities of digital twins, enabling them to deliver more precise insights and predictive results.
Historically, the concept of digital twins is not new. One of the earliest applications was in NASA’s Apollo 13 mission in 1970, where virtual models were used to monitor and troubleshoot spacecraft systems.
More interactive understanding
Despite their longstanding conceptual presence, digital twins have only recently begun to achieve widespread adoption due to advancements in technology and the increasing availability of data.
Today, Aisha U-K Umaru, Strategic Intelligence Analyst at GlobalData, said that digital twins find application across diverse sectors including oil and gas, power generation, sports, and government, offering benefits that range from improving factory efficiency to enhancing spectator experiences in sports events.
A noteworthy evolution in the domain of digital twins is the emergence of “semantic twins,” a hybrid technology that integrates artificial intelligence to yield deeper and more interactive understanding.
Semantic twins leverage large language models (LLMs) to allow users to pose nuanced questions about a twin and its components, thereby enabling informed decision-making based on both data-driven insights and broader contextual knowledge.
For example, a semantic twin representation of a city may answer complex queries related to urban planning and traffic management by comparing it with similar cities worldwide. This integration of generative AI enhances digital twins with advanced predictive analytics, dynamic information retention, and adaptive learning capabilities.
The influence of AI on digital twin technology extends beyond enhanced analytics; it fundamentally enriches the depth of interpretation and interaction users can have with their digital counterparts.
As AI permeates industry after industry, Umaru said that semantic twins represent the next frontier, empowering users to extract meaningful insights and optimize system performance with unprecedented precision and ease.