Beyond the Blueprint: How AI and Digital Twins Are Building Smarter Systems in 2025

By Joseph Callahan, CEO — Ciright

Working with technology and advanced systems is complicated. Success requires knowing how each component works, not only on its own but also in relation to any others. And as technology becomes more advanced, so do the elements that make up systems, creating an even bigger headache for those who manage these systems. That being said, there is an innovative new solution that promises to make systems management easier and more intuitive than ever. 

Enter the digital twin, a revolution in spatial computing technology that recreates a real-life asset in a digital space. The goal of a digital twin is to create a fully realistic replica of the physical object or machine so that viewing it or interacting with it in the metaverse is as realistic and immersive as possible, to the point of allowing it to rival or even exceed the experience of interacting with the real-life object.

However, the true power of this technology is when multiple digital twins are connected. By creating digital twins of multiple machines and linking them together, one can create a replica of their entire system. This capacity of digital twin technology can be particularly useful for simulating complex systems such as data networks.

Digital twins for real-time monitoring and predictive analytics

Real-time monitoring is one of the most powerful use cases of digital twin technology. Often, the real-life counterpart of a digital twin is affixed with sensors that collect data on the machine. These sensors then feed this real-time information to the digital twin, which can then be accessed from a variety of platforms, even including a mobile phone. This ability can be helpful in situations such as malfunctions, where a system can issue an alert to the user’s phone, allowing them to jump into action quickly.

These same capabilities are also pivotal in allowing users to perform predictive maintenance. Indeed, those who take advantage of the power of digital twins can gain a better understanding of their systems, potentially even to the point that they never malfunction in the first place. When the sensors identify a particular “red flag,” such as a drop in performance efficiency, the user can be alerted, letting them know that it is time to perform maintenance and restore the machine to ideal operating condition before any downtime is caused or costly repairs are required.

Additionally, by combining digital twin technology with the power of advanced artificial intelligence, businesses can use their digital twins to test scenarios in real time. If a malfunction arises, a user can quickly run simulations of different repair and isolation scenarios to determine which approach will be most effective and minimize system downtime and damage. In doing so, artificial intelligence helps facilitate better real-time decision-making for business leaders, allowing them to make more informed, data-driven choices.

Similarly, users can run simulation testing before real-world deployment. For example, if a business is considering adding a new machine to their stack, it can create a digital twin of that machine and integrate it into their metaverse system. This will allow them to ensure compatibility and optimize the integration process, minimizing downtime and reducing the expense of machine integration errors, such as configuration or synchronization issues.

Digital twins for system optimization

Artificial intelligence can also be used to help business leaders optimize the performance of their systems. Since digital twins supply a steady stream of data, and AI models are great at analyzing data, the AI model can be trained to identify weaknesses in the system. 

For instance, if one component or machine is consistently underperforming compared to the rest of the system, that link can be singled out for repair or replacement. These models can also identify other areas of concern, such as energy consumption, distribution of bandwidth, and other factors that can be optimized.

However, the capabilities of digital twin technology have evolved in such a way that businesses can now use this capability to optimize their system design even before it is built. Physical prototyping is an expensive process; if a machine or component does not work, it can be costly to return to square one and restart the process. Meanwhile, if one works in a digital space, it’s easy to make (or potentially reverse) adjustments before bringing them into the real world.

Indeed, using digital twins to simulate technologically complex systems will give businesses a more effective view, both at the higher level and at an incredibly precise level of detail. Empowering everything from real-time monitoring to system optimization, digital twins are the future of systems management.

Joseph Callahan is a serial entrepreneur, perpetual innovator, and award-winning business leader with over 30 years of experience in technology development. As CEO of Ciright, he has pioneered groundbreaking solutions across industries, holding multiple patents in digital media, IoT, fintech, and the metaverse. Callahan is a proud Drexel University graduate and has been recognized with numerous accolades, including EY’s Entrepreneur of the Year – Technology.