Digital twins are increasingly transforming industries such as manufacturing, healthcare, and aerospace, offering solutions to optimize operations, improve efficiency, and enable predictive capabilities across various sectors. Against this backdrop, the global digital twins market is expected to grow at a compound annual growth rate (CAGR) of 35.6% from $5 billion in 2019 to $154 billion by 2030, forecasts GlobalData, a leading data and analytics company.
GlobalData’s latest Strategic Intelligence report, “Digital Twins,” reveals that the growth of the global digital twins market will be driven by low-cost sensors used in Internet of Things (IoT) devices, a decline in the cost of high-performance computing (HPC), and cloud accessibility. Advances in data analytics and artificial intelligence (AI) will also drive the growth.
Aisha U-K Umaru, Strategic Intelligence Analyst at GlobalData, comments: “Large companies such as Amazon have tapped into their reach and reputation to partner with firms such as Matterport and Anthropic to enhance their digital twin offerings, and smaller companies such as Aerogility are providing services to specific industries such as aerospace and defense.”
Digital twins: Diverse use cases
Conceptually, digital twins have been around for decades; a forerunner was used in NASA’s Apollo 13 mission to the moon in 1970. While far from ubiquitous today, adoption is increasing across industries.
Umaru continues: “Digital twins are employed in various industries, including oil and gas, power, sport, and government. They serve a wide range of purposes within these fields, from enhancing the efficiency of a factory to providing an enriched viewing experience for sports fans.”
AI’s impact on digital twin industry
Digital twins are increasingly harnessing AI to provide more context to the users. This approach has created a hybrid technology called semantic twins, which can provide a deeper level of understanding by letting users ask large language models (LLMs) questions about a twin and its components. In response to these questions, the LLM can draw from its knowledge of the twin, the twin’s aims and objectives, and its broader understanding of systems and the world. For example, a semantic twin of a city may be asked, “How can I update this twin to be in line with other cities with similar population and transport systems that are managing traffic congestion more effectively?”. Semantic twins also benefit from other features of generative AI, including advanced predictive analytics and information retention.
Umaru concludes: “AI is pervading almost every industry, and it can offer more depth to digital twins. Semantic twins can allow users to draw deeper meaning from their digital twins, using LLMs for support.” GlobalData