Digital twins, emerging within organizations of all types, are ripe for a technological makeover. These digital representations of physical facilities or objects on which users can simulate outcomes and experiment with new ideas have been especially prominent within the manufacturing sector, and have even been referred to as the “industrial metaverse.” 

Now, digital twins are receiving a boost from artificial intelligence (AI), promising even greater predictive intelligence, ease of use, and opening possibilities to a broad range of industries. In addition, emerging interfaces such as extended reality (XR), virtual reality (VR), and augmented reality (AR) augur even deeper exploration into the systems that power enterprises.

Generative AI (GenAI), in particular, now offers text-based and conversational AI to digital twin efforts, helping users assemble and deploy these technologies within weeks or days, a 2024 McKinsey report states.

 “Many organizations are separately implementing digital twins and generative AI — two technologies with distinct value propositions and tremendous promise — to support a wide range of use cases,” observe the McKinsey co-authors, led by Alex Cosmas and Guilherme Cruz. 

AI accelerates the process of designing and building digital twins. “Building a digital twin, especially for highly specialized applications — such as multimachine production scheduling or vehicle routing — can be time-consuming and resource-intensive,” the McKinsey analysts note. “The effort often entails designing and developing new digital-twin models, a process that can take six months or longer and incur substantial labor, computing, and server costs.”

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The inclusion of GenAI with digital twins enables the rapid creation of code and also opens up vast stores of information, Pascal Brosset, global lead for production and operations at Accenture Industry X, told ZDNET. “It allows the twin to tap into the vast repositories of unstructured data available in companies’ intranets, and give people access to the resulting structured information in natural language.”  Conversely, digital twins of AI systems can also be constructed, enabling users to fine-tune their models. 

Putting together the pieces

The integration of AI models is the latest boost to the power of digital twins. But digital twins require a multi-layer foundation that extends from actual physical objects, wired to deliver data, to data stores to end-user interfaces. The foundation of digital twin implementations incorporates the following basic components.

1. Physical assets 

Physical assets consist of the environments or components that will be digitally replicated. These could range from entire facilities to individual machine parts.  

2. Data assets

The data layer is where data is sourced, stored, and transformed for delivery to the digital twin application. It’s important that such a data foundation is built on “the most relevant and high-quality data possible,” Naveen Rao, vice president of AI for Databricks, told ZDNET. “This data could take many forms, so it’s important that your data platform supports many types of data — structured, unstructured, real-time, and batch. Ideally, this data also lives in a highly performant format, so it can be efficiently used downstream to both train models and be used for analysis.”  

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This includes “a model that represents some physical system at its core,” said Rao. “These models enable predictive analytics, help optimize systems, and influence important business decisions. To keep these models as accurate as possible, organizations need to track their model experimentation from raw data to deployment and through every stage of their model evolution.”  

Importantly, before the selection of technologies for a digital twin, “organizations have to have the right philosophy in place for their entire suite of data,” Simon Bennett, director of innovation and incubation at Aveva, told ZDNET. 

“What data do they store? How much of this is essential for running the business? Is the data consistently representing the real world? Who owns the data? Is intellectual property locked away in the data? Is it up to date? Is it managed and cared for? A well-funded and clear strategy for the management of data is the primer needed for starting the journey to a digital twin. Arguably, the selection of technologies is considerably easier to organize.” – Simon Bennet

3. The Internet of Things (IoT)

Digital twins typically represent complex environments and therefore depend on data collected, preferably in real time, from all key points and processes. These can include essential IoT technologies such as “sensors, 5G, cobots, augmented reality, virtual reality, simulation and GenAI reporting analytics,” Ryan Hamze, principal consultant with ISG, told ZDNET. 

IoT sensors “are the foundation of digital twins,” agrees Logan Mallory, vice president of marketing at Motivosity. “These sensors collect real-time data from actual assets, providing the necessary information for precise digital reproductions. For example, in a manufacturing setting, IoT sensors on machinery monitor temperature, vibration, and their operational status, sending continuous data to the digital twin.”

4. User interface and user experience

One of the most compelling aspects of digital twin adoption is the use of visualization tools — employing XR or VR — that enable users to enter (virtually) the premises of digital realms to conduct simulations, do fact-finding, and even repair systems.

 “These technologies provide immersive visualizations that help stakeholders interact with digital twins in more intuitive and impactful ways,” Jen Mowery, M.Ed., instructor of agile methodologies and project management at Harrisburg University of Science and Technology, told ZDNET. “In sectors like advanced manufacturing and health futures, XR and VR enable detailed simulations and training applications, while in mobility and advanced agriculture, they facilitate design, planning, and real-time decision-making.”

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However, XR and VR integration into the digital twin world is still in the earliest stages and “only used in very special cases,” said Accenture Industry X’s  Brosset. “The requirement for quality and constantly updated 3D data is a high hurdle to more general adoption.” 

 In the past five years, “we’ve seen increased use of XR and VR, but their high cost contributes to lagging adoption,” Hamze added. “However, they have proven to be much more cost-effective than traditional approaches in the long run in their main areas of use, which are predictive maintenance, training, and simulation within digital environments. We expect to see an uptick in XR, VR, and AR implementations, especially with GenAI being integrated into those solutions.” 

5. Governance and oversight

Governance and lineage are also essential to assuring that digital twin efforts are delivering on their investments. “Organizations need a strong grasp on governing both the data and the AI models training on said data,” Rao said. “A strong governance solution should be able to give fine-grained access across the entire model pipeline, yet be simple enough to manage so that it doesn’t slow down development speed. There also needs to be a framework to correlate the outputs of the model against outputs of the real system.” 

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It’s also important to involve domain experts in the entire life cycle of the digital twin, Manfred Kügel, data scientist and IoT industry advisor to SAS, told ZDNET. “These experts typically have lots of ideas how to build digital twins to increase efficiency. They know how to best implement digital twins for maximum gain. And once a digital twin is running, domain experts recognize when it needs to be retrained. Involving domain experts in building, implementing, and running a digital twin will also help ensure smoother adoption and better results.” 

6. Infrastructure

The technology building blocks for digital twins are now all available through the cloud. Cloud-based services “provide access to cutting-edge hardware like the latest GPUs, networking, and data storage,” said Rao. 

At the same time, “some organizations might still require on-premises technology for specific use cases that demand higher security or have latency concerns,” Mowery observed. “We advocate for a hybrid approach that combines the strengths of both cloud and on-premises solutions to meet diverse needs.” 

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In addition, “fast communications are essential,” Tim Rawlins, senior adviser and director at NCC Group, told ZDNET. “The use of public and private 5G networks is increasingly common, while the move to edge computing and 6G networks should significantly increase data transfer speeds and reduce latency, thereby supporting more effective modeling.” 

Use cases

While manufacturing is seeing strong adoption of digital twin technology, its reach crosses industry boundaries. “Environments as diverse as town planning — where digital twins combine architectural plans, building information management systems, pedestrian and vehicular traffic flows, air quality, energy use, weather patterns, city and emergency services and development visualization — to help engage communities in what the city might become,” said Rawlins.    

In advanced manufacturing, “digital twins enable the simulation and optimization of production processes, leading to increased efficiency and reduced downtime,” Mowery said. “In the mobility sector, digital twins are revolutionizing vehicle design and maintenance through real-time monitoring and predictive analytics. Health futures are also benefiting from digital twins in personalized medicine and patient care, where virtual replicas of organs and systems enhance diagnostics and treatment planning. Additionally, advanced agriculture is leveraging digital twins to optimize crop management and improve sustainability.” 

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Digital twin technology has also proven to be a valuable tool within process manufacturing, such as oil and gas or chemical companies, Brosset said. “Their sophisticated plant control systems are de-facto digital twins, which can be easily extended with AI and machine learning optimization logics. Food and beverage companies, whose continuous processes lend themselves to similar optimizations, are catching up fast because they are under extreme pressure to reduce costs and become more flexible.”