Digital twins: From one twin to the enterprise metaverse

Companies can begin the journey by starting with just one digital twin that has a data product at its core, evolving it over time to provide increasingly powerful predictive capabilities. They can then move on to interconnecting multiple digital twins to unlock even more use cases and, finally, layer on the additional technologies required to transform this network of digital twins into the enterprise metaverse.

What is a digital twin?

There are many different definitions of digital twins, so it’s important to start by answering this question. We see a digital twin as a virtual representation of a physical asset, person, or process. The twin comprises data collected from multiple sources, a layer of behavioral insights derived from the data, and visualizations. But a simple 3-D visualization or stand-alone simulation would not be considered a digital twin. Multiple AI use cases, “what if” simulations, and additional visualizations can be built on top of it.

As an example, a digital twin could provide a 360-degree view of customers, including all the details that a company’s business units and systems collect about them—for example, online and in-store purchasing behavior, demographic information, payment methods, and interactions with customer service. It would also generate insights derived from the data, such as the average length of a customer service call. AI use cases leveraging the twin could include customer churn propensity models or a basket of the next products a customer would be likely to buy.

Alternatively, the twin might replicate the operation of real-world assets or processes (such as an entire factory production line or critical pieces of machinery) and generate information on average equipment downtimes or the average time for completing a product assembly. AI use cases it supports could include predictive maintenance and process automation and optimization.

Digital twins speed the time to market (and value) of many applications and use cases because development teams don’t have to spend time cleaning and restructuring raw data every time they build an application. As a result, we often find that digital twins reduce the time needed to deploy new AI-driven capabilities by up to 60 percent and capital expenditures and operating expenditures by up to 15 percent. They can improve commercial efficiency by about 10 percent.

CEOs are increasingly recognizing the importance and power of digital twins and even mention them with growing frequency on earnings calls.¹ Our research indicates that 70 percent of C-suite technology executives at large enterprises are exploring and investing in digital twins. This interest, combined with rapidly advancing supportive technologies, is driving market estimates for digital-twin investments of more than $48 billion by 2026—a 58 percent compound annual growth rate.² Already, we’re seeing some advanced implementations.

Transforming the digital-twin network into the enterprise metaverse

As organizations begin to connect these multiple twins of different business domains, functions, and operational ecosystems, the enterprise metaverse could start to take shape. A retailer, for instance, could connect the digital twin of its retail store to digital twins of its warehouses, supply chain, call center, and more until every part of the organization was replicated, sharing insights, simulating scenarios, and enabling automation and AI use cases.

On top of the digital-twin foundation, companies could then build a layer that stitches together all the digital applications and analytics they’ve created on the back of the twins. APIs will integrate the system.

Finally, companies can add a unified consumption layer to give employees and customers integrated, immersive experiences that leverage augmented and virtual reality.³Although AR and VR have become synonymous with the commercial metaverse, in some cases they won’t be essential in the enterprise metaverse. For example, AR and VR technologies will be necessary to enable employees to “enter” digital experiences such as maintenance staff physically interacting with virtual replicas of manufacturing equipment to gain repair experience right from the desk. However, AR and VR may not be needed for replicating, say, end-to-end customer service processes.

As implementations mature, leaders will want to shift from simply replicating what exists today to digitally reengineering entire processes and experiences, saving precious time and resources in the end. Say, for example, that customers don’t enjoy the checkout experience or that engineers struggle with existing product design processes. The enterprise metaverse offers an opportunity to reinvent these experiences and processes in a digital context to achieve even better outcomes rather than replicate the existing experiences and processes.