Beyond AI: Why businesses should start paying attention to digital twins

Every few years, the technology world finds a new favourite. Artificial intelligence is enjoying that position today. Before AI, it was blockchain. Before that, cloud computing. Each arrived with promises of transforming businesses, and each eventually found its place in how organisations operate.

While the attention remains firmly on AI, another technology is steadily gaining ground. Digital twins may not be generating the same headlines, but their potential to change how businesses operate deserves considerably more attention.

The concept is simpler than the name suggests. A digital twin is a digital representation of a physical asset, process or system, connected to real-world data and updated as its counterpart changes. Unlike a conventional 3D model or dashboard, it can do more than display information. It can simulate different conditions, test scenarios and help businesses understand the likely consequences of a decision before implementing it.

And that is where things get interesting.

Consider manufacturing. A machine showing signs of wear can mean an unexpected shutdown, delayed production and additional costs. A digital twin, when connected to the right operational data, can help identify patterns that indicate a potential failure. It can also simulate different maintenance schedules and assess their impact on production. Instead of waiting for something to go wrong, businesses get an opportunity to act before it does.

This is already happening at an industrial scale. BMW Group, for instance, has developed digital twins across more than 30 production plants. Working with NVIDIA, the company has been using virtual factory environments to plan layouts, optimise robotics and simulate logistics before implementing changes in physical facilities. Its Debrecen plant in Hungary was planned and validated virtually before production began. This is a practical example of how businesses can reduce the risks and costs associated with making changes in the real world.

The applications extend well beyond manufacturing. Cities can use digital twins to simulate traffic movement and improve infrastructure planning. Healthcare organisations are exploring patient-specific models to support treatment decisions. Logistics companies can simulate supply chains, test disruption scenarios and identify bottlenecks before they affect deliveries.

What makes this technology particularly relevant now is the convergence of the Internet of Things (IoT), cloud computing, data analytics and artificial intelligence. Sensors are becoming more accessible, computing infrastructure is increasingly affordable, and AI is making it possible to extract greater value from operational data. While sophisticated digital twin implementations still require investment and technical expertise, the technology is no longer exclusively the domain of large industrial corporations.

For businesses, this opens up possibilities worth exploring.

Consider retail. Before changing the layout of a store, a retailer could use a digital twin to simulate customer movement, product placement and inventory distribution. Before introducing a major promotional campaign, the business could evaluate different scenarios and understand their likely operational impact. Rather than making changes and waiting to see what happens, management could test different approaches in a virtual environment first.

AI makes these models even more valuable. A digital twin can evolve from reflecting the current state of an asset to helping predict what might happen next. It can identify patterns, recommend interventions and evaluate possible outcomes. The combination of continuously updated data, simulation and intelligent analysis is what gives this technology its real potential.

However, I believe the more significant opportunity lies beyond individual machines, buildings or facilities. What if businesses could create digital twins of their entire operations?

Imagine an organisation with a digital representation of its sales pipeline, customer support, marketing performance, hiring processes and financial operations. Its leadership could simulate what would happen if demand increased by 30 per cent, a new market were entered, prices were changed or customer enquiries doubled.

How would these changes affect revenue, operational capacity, employee requirements and customer experience? Which parts of the business would struggle first? What resources would need to be allocated differently?

Instead of relying entirely on historical reports and conventional forecasts, decision-makers could explore these possibilities through simulations informed by live business data. A change in one part of the organisation could be evaluated against its potential impact on others.

This is where digital twins could take business decision-making to another level. Today, most organisations use technology to understand what has already happened. Dashboards provide performance reports, analytics identify patterns and forecasting tools estimate what might happen next. A business-wide digital twin could bring these capabilities together in a model that allows leaders to experiment with different futures before committing resources.

Of course, this is an evolving possibility, not something that every organisation can implement overnight. There are practical challenges. A digital twin is only as reliable as the data supporting it. Disconnected software systems, inconsistent information and poor integration can significantly limit its effectiveness. Businesses must first understand their own processes and establish a dependable digital infrastructure. Building a sophisticated digital model on top of fragmented information is unlikely to produce meaningful results.

It is also important to distinguish digital twins from conventional dashboards and pilot projects. A dashboard tells you what is happening. A pilot allows you to test a specific idea under defined conditions. A digital twin, when properly developed, maintains a connection with its real-world counterpart and allows multiple scenarios to be simulated as conditions change. Its value lies in bringing monitoring, simulation and continuous optimisation together.

Much of today's AI conversation revolves around chatbots, content generation and coding assistants. These are useful applications, and their impact should not be underestimated. But I believe AI's larger opportunity may lie in how it helps us understand and optimise the physical and operational world around us. Digital twins could become an important part of that transformation. For businesses, the question is no longer simply which new technology to adopt. It is how technology can help them make better decisions, anticipate problems and respond to change. Those willing to explore digital twins today could gain an advantage that goes well beyond operational efficiency.

After all, there is a considerable difference between knowing what happened yesterday, predicting what might happen tomorrow and being able to test what happens if you change something today. Digital twins bring businesses a step closer to the third.

Author: Aravind Menon (CEO of Aventus Informatics), is a technology entrepreneur and business leader with experience in digital transformation, technology consulting and product innovation. He writes about the intersection of technology, business and the opportunities shaping the future.