Introduction
As modern organisations grapple with unprecedented volatility and operational complexity, traditional methods of management are quickly reaching their limits. Digital twins are becoming an increasingly important way for organisations to understand and manage complexity.
In simple terms, a digital twin is a dynamic digital representation of a real-world system (such as an asset, process, organisation, or network) that is continuously updated using data from the real world. Unlike traditional models or reports, digital twins are designed to reflect what is actually happening now, not just what was planned or previously observed. This makes them particularly valuable in environments where conditions change quickly and decisions carry real operational or strategic risk.
For our purposes as business leaders, the significance of digital twins lies less in the technology itself and more in the decision capability that they enable. Digital twins allow organisations to explore ‘what if’ scenarios, anticipate emerging issues, and test potential interventions before acting in the real world. Rather than relying solely on retrospective performance data or intuition, leaders can use digital twins to shift towards more predictive, evidence-informed decision-making
Moving Beyond Simple Automation
It is a common misconception that implementing a digital twin is merely a technological upgrade focused on operational automation. In this sense, digital twins are not primarily about automation, but about augmenting managerial judgement under uncertainty. This strategic shift can lead to significant productivity gains and cost-saving benefits.
Within the context of AI, digital twins often become more powerful when combined with advanced analytics and machine learning. AI techniques can help identify patterns, forecast outcomes, and recommend actions within a digital twin environment. However, it is important to recognise that a digital twin is not automatically an ‘AI system’; rather, it is a broader organisational capability that integrates data, models, and governance choices.
Many digital systems look almost like digital twins, yet fall short because key capabilities are not connected. Individually, dashboards, simulations, AI models, and automation can be powerful, but on their own, they rarely change how decisions are made or how work is organised. By joining up these systems, the potential impact of digital twins can be unleashed.
Moving Beyond Simple Automation
Digital twins combine multiple digital technologies to create a living, continuously updated representation of a real-world operation. Rather than being a single tool or system, a digital twin is an integrated capability that connects the physical world to a digital environment in (near) real time. This allows organisations to observe how an operation is actually performing, explore how it might behave under different conditions, and, in some cases, intervene automatically to improve outcomes.
At a practical level, digital twins are built on four interdependent building blocks:
Sensors: These capture the required data from the physical environment. The physical operation generates real-time data via sensors embedded in machines, systems, or processes.
Cloud infrastructure: This provides the backbone to store and process the massive amounts of data generated.
AI: That data flows into an AI and machine-learning layer, where it is analysed to identify patterns and generate predictive insights about future performance or potential issues. These insights are then used to maintain a virtual replica (the digital twin) which mirrors how the operation is behaving now and how it is likely to behave next.
Automation: Crucially, the flow does not stop at analysis: Predictive signals can be sent back to the physical operation to trigger automation, adjusting how the system runs in real time.
The Brain of the System: AI's Role in Anticipatory Control
While sensors, cloud infrastructure, and automation are essential components of a digital twin, it is AI that gives the twin its intelligence. AI functions as the brain of the digital twin: It processes the continuous stream of data collected by sensors, learns from historical and real-time patterns, and translates raw signals into predictions, insights, and recommended actions.
Without AI, a digital twin remains largely descriptive: It is able to show what is happening, but not to reason about what is likely to happen next or how the system should respond. In practice, AI enables the digital twin to move from monitoring to anticipatory control. Machine learning models compare current operating conditions against expected performance, detect anomalies, and forecast future states of the system.
These predictions can then be used to guide human decision-making or, in more advanced configurations, to instruct automated systems directly. In this sense, automation does not ‘think’ for itself, but instead executes decisions shaped upstream by AI. The digital twin therefore operates as a closed-loop system in which sensing, intelligence, and action are tightly coupled.
Real-World Impact: The Enerjisa Case Study
A useful illustration of digital twins comes from the energy sector, where a large electricity generation company called Enerjisa developed digital twin capability to remotely monitor and operate geographically dispersed power plants. Thousands of real-time data points (including temperature, vibration, and operational performance) were fed into AI models trained on years of historical plant data.
These models continuously compared actual conditions against expected performance, identifying early signs of deviation or risk. When anomalies were detected, the AI system generated predictive insights, such as the likelihood of equipment failure or efficiency loss, allowing operators to intervene before problems escalated. In some cases, these insights directly informed automated actions within the physical plants, effectively closing the loop between prediction and execution.
Building on this foundation, the organisation later introduced OnePact AI, a generative AI layer designed to make the digital twin’s intelligence more accessible to human decision-makers. Rather than requiring operators to interpret complex dashboards or raw analytics, OnePact AI allowed users to interact with the system conversationally, asking questions about plant performance, potential risks, or recommended actions. Behind the scenes, the AI drew on the digital twin’s data, predictive models, and embedded operational expertise to generate responses and guidance.
Crucially, OnePact AI did not replace human oversight; instead, it augmented managerial judgement by translating the digital twin’s insights into understandable, decision-relevant advice. For our purposes, this highlights a critical insight: The strategic value of digital twins increasingly depends on how effectively AI is embedded as the system’s decision-making core, not simply on the sophistication of sensors or automation.
Cognitive Infrastructure for Complex Environments
The power of digital twins extends across various sectors, with compelling examples found in organisations like Unilever, Enerjisa, smart farming, airport operations, and healthcare (radiology).
For our purposes, these cases and industry analyses reinforce a key theme: digital twins are most powerful not when they replace human decision-making, but when they provide a system-level perspective that enables better, more informed judgement in complex environments. By making complex interactions visible, enabling anticipation rather than reaction, and supporting exploration of ‘what if’ scenarios, digital twins help leaders reason more effectively in environments shaped by uncertainty, interdependence, and human behaviour. In this sense, digital twins function less as automated decision-makers and more as cognitive infrastructure for navigating complex systems.
Redefining the Workforce: The Human Element
Digital twins create value not only by changing how systems are run, but by reshaping how work is organised around those systems. As the cases have shown, digital twins alter where decisions are made, what expertise is required, and how human judgement interacts with data and automation. As a result, some roles become less central, others are significantly redefined, and new forms of expertise become critical.
This does not mean that digital twins simply eliminate jobs. More often, they reallocate expertise. Tasks that previously required physical proximity to assets or processes are increasingly performed remotely, supported by predictive models and real-time data. The organisational challenge is therefore less about replacing people and more about ensuring that existing expertise can be effectively redeployed within digitally mediated decision environments.
Returning to the Enerjisa case, following the introduction of its digital twin capability, turbine engineers were no longer required to be physically located at remote power plants. Instead, many began working from the company’s digital operations headquarters in Istanbul, where they monitored AI-generated predictions and intervened remotely when needed. While their deep engineering knowledge remained essential, the context in which it was applied changed. Engineers needed to develop new skills in interpreting data, understanding model outputs, and working alongside AI systems that increasingly shaped operational decisions. In this sense, digital twins transformed the nature of Enerjisa’s engineering work, rather than removing its importance.
The Emergence of New Roles and Risks
At the same time, digital twin implementation creates entirely new roles and priorities. As physical operations become increasingly dependent on digital representations, cybersecurity and system integrity become central strategic concerns rather than technical afterthoughts. Organisations deploying digital twins must invest in specialist expertise to protect against data breaches, system manipulation, and operational disruption. This introduces new categories of work (and new forms of risk) that did not exist in more locally controlled, analogue environments.
Decisions about reskilling, upskilling, and workforce transition are also shaped by economic considerations. Evidence from large-scale manufacturing transformation shows that organisations must weigh the costs of retraining existing employees against the costs of hiring new skills or restructuring roles. Retaining institutional knowledge, maintaining operational continuity, and preserving trust within the workforce are often as important as immediate cost savings. For executive leaders, the key issue is not whether digital twins will change work (because they definitely will), but rather how deliberately and responsibly those changes are managed.
Conclusion
Digital twins represent a profound organisational transformation rather than a singular technical initiative. Digital twins help organisations move from reactive to anticipatory decision-making, coordinate activity across distributed systems, and make complex interactions visible. Importantly, they are shaped by organisational choices, not just by technical capability.
Value emerges through improved operational efficiency, through the creation of new digital services and business models, and through changes in how work and expertise are organised. They tend to reshape work rather than eliminate it, shifting expertise away from physical proximity and towards interpretation, coordination, and system-level oversight. Together, these insights set the foundation for thinking about digital twins not just as technical initiatives, but as organisational transformations that require careful leadership and change management.
Additional References
To see how global brands are creatively deploying this technology beyond traditional industrial use cases, check out this example of Unilever Digital Twin Product Photography with NVIDIA Omnivers. This video highlights how large enterprises leverage digital twins to drive content consistency, reduce costs, and accelerate their marketing supply chains.
FAQs
1. What exactly is a digital twin?
In simple terms, a digital twin is a dynamic digital representation of a real-world system (such as an asset, process, organisation, or network) that is continuously updated using data from the real world. Unlike traditional models or reports, digital twins are designed to reflect what is actually happening now, not just what was planned or previously observed.
2. Does having a digital twin mean our operations are fully automated by AI?
No. Digital twins are not primarily about automation, but about augmenting managerial judgement under uncertainty. A digital twin is not automatically an ‘AI system’; rather, it is a broader organisational capability that integrates data, models, and governance choices. In this sense, automation does not ‘think’ for itself, but instead executes decisions shaped upstream by AI.
3. What are the core building blocks of a digital twin?
At a practical level, digital twins are built on four interdependent building blocks:
- Sensors: These capture data from the physical operation.
Cloud infrastructure: The technological backbone required to support the systems.
- AI: Artificial intelligence and machine learning layers that provide predictive analysis.
- Automation: The capability to act upon the insights within the physical environment.
4. Will implementing a digital twin eliminate jobs in my organisation?
This does not mean that digital twins simply eliminate jobs. More often, they reallocate expertise. Digital twins tend to reshape work rather than eliminate it, shifting expertise away from physical proximity and towards interpretation, coordination, and system-level oversight.
5. Why is AI necessary for a digital twin?
While sensors, cloud infrastructure, and automation are essential components of a digital twin, it is AI that gives the twin its intelligence. Without AI, a digital twin remains largely descriptive: It is able to show what is happening, but not to reason about what is likely to happen next or how the system should respond.
