Digital Twins: A Strategic Decision-Making Catalyst for Business Leaders

Introduction

NASA once created physical replicas of its spacecraft to troubleshoot Apollo missions in real- time. Today, this innovative idea has blossomed into what we now call digital twins: dynamic, virtual counterparts of physical objects, processes, or environments that reflect reality through live data. These models are built on real-time data, accurately representing real-world systems. Building on this idea, managers from various industries are utilizing digital twins as strategic tools. JPMorgan Chase uses them to simulate market scenarios, Google uses them to optimize data centers, and Amazon uses them to optimize warehouse operations. By seamlessly integrating real-time data with analytics, digital twins enable organizations to simulate various scenarios, forecast outcomes, and optimize decisions before implementing changes in the real world. This new capability is transforming the way managers develop strategies and optimize operations, enabling companies to stay competitive in an ever-evolving business landscape. By creating a "virtual mirror" of everything, from individual teams to entire organizations, digital twins provide senior managers with unprecedented foresight. Leaders can test "what-if" scenarios, such as how a supply chain might respond to a disruption or how a new service rollout could impact customer experience, all in a risk-free virtual setting. The insights from these experiments support data-driven strategic insights and continuous optimization of business strategies.

Evolution of Digital Twin Technology

The concept of digital twins originated in the 1960s when NASA engineers built identical physical models of spacecraft on Earth to troubleshoot issues during Apollo missions. The advent of Building Information Modelling (BIM) in the early 2000s allowed for the creation of 3D digital representations of physical structures and enhanced data-rich simulations in architecture and engineering. As sensor and networking technologies advanced, these models were integrated with the Internet of Things (IoT), enabling real-time data transfer between physical assets and their digital counterparts. By the 2010s, increased computing power and cloud platforms empowered digital twins to oversee complex, dynamic systems rather than solely static designs. Today's digital twins are notably more advanced, supported by artificial intelligence (AI) and machine learning.

Three generations of digital twins are currently in use: the first, known as the "knowledge twin," is a static model for research and training, essentially serving as an extensive informational model of an asset or process. The second, the "operational twin," incorporates real-time data for the ongoing monitoring and optimization of live systems, particularly in manufacturing and logistics. The most sophisticated type is the "intelligent twin," which utilizes AI and analytics to provide predictive and prescriptive insights, often operating autonomously to adjust. This evolution illustrates how digital twins have transitioned from merely descriptive to predictive and prescriptive systems over time.

Digital twins have gradually evolved into an organizational capability. Organizations now deploy them across a wide range of functions, from design and operations to supply chain and customer experience. As digital twins evolve in their capabilities, the business impetus to adopt them has also grown significantly. McKinsey estimates that the global market for digital twin technology is poised to grow by about 60% annually and is likely to reach roughly $73.5 billion by 2027. Longer-term forecasts are even more striking, predicting that the market could expand by 30–40% each year and get $125–150 billion by 2032. According to recent surveys, approximately 70–75% of large enterprises are exploring or investing in digital twins as part of their digital transformation strategy. In other words, what was once a niche engineering tool has become a mainstream strategic asset that guides strategic decision-making.

Strategic Value: Data-Driven Foresight and Decision Support

Digital twins primarily attract strategists due to their ability to shift decision-making from a reactive to a proactive approach. By integrating real-time operational data with sophisticated simulations, a digital twin acts as a virtual testing ground for strategic decisions. With it, managers can analyse comprehensive workflows or an entire organization within the twin, adjusting variables and monitoring the outcomes of different strategic alternatives. This setup enables risk-free experimentation with innovative concepts. For instance, a company might predict how modifying a manufacturing process could affect output, evaluate how a supply chain would handle an unexpected demand spike, or assess how changes in branch designs could enhance customer service at a bank. Rather than depending on intuition or outdated reports, executives receive quantitative, evidence-based insights before executing changes in the real world.

Digital twins thus add comprehensive, data-driven foresight to a manager's toolkit. They enable scenario analysis that accounts for complex interdependencies in business systems. These simulations incorporate real-world constraints and behaviours, offering more reliable predictions than traditional spreadsheet models. Importantly, because the twin continuously updates with the latest data, the insights are current and relevant to the evolving context. Leaders can essentially peer into multiple potential futures, see the likely outcomes, and steer strategy in the optimal direction with greater confidence.

Another key strategic benefit is continuous optimization. Whereas strategic planning used to be periodic, digital twins support an ongoing cycle of improvement. As one set of changes is implemented, the twin can immediately reflect new data and identify further refinements, creating a loop of iteration and learning. This fosters agility. With it, decisions become part of a learning system where the twin helps refine operations and strategy in near-real-time. McKinsey notes that top companies use digital twins to significantly speed up  decision-making by having this real-time simulation capability.

Critically, digital twins also elevate risk management and resilience as strategic priorities. By stress-testing systems virtually, organizations can identify vulnerabilities and failure points in advance, facilitating proactive mitigation. For strategists, digital twins provide the tools to bolster that resilience with hard data, creating "self-monitoring and self-healing" systems that adjust to disruptions automatically. The strategic upshot is an organization that not only plans for success but is also prepared for disruption, with playbooks tested and refined digitally.

Digital Twins in Action: Examples Across Industries

Manufacturing and automotive companies were pioneers in adopting digital twins to enhance production lines and product development. For instance, Tesla utilizes AI-optimized digital twins of its manufacturing process to analyse actual production data and progressively determine the most efficient factory layouts. This smart twin learns from every vehicle produced, pinpointing adjustments that boost throughput and automatically modifies workflows on the factory floor. BMW's "smart factories" use AI-powered digital twins to coordinate various robotic and human teams and can swiftly reconfigure the assembly line whenever a new car model is launched or a bottleneck occurs. The twin serves as the brain of the factory, managing real-time adjustments to reduce inefficiencies.

In supply chain and logistics, digital twins have become vital for achieving comprehensive visibility and optimization. E-commerce leader Amazon, for instance, employs several AI agents within a digital twin to explore various picking and packing scenarios, enhancing the routing of orders to workers or robots for fulfilment. The digital twin can mimic increases in order volume or adjustments in inventory locations, identifying the most effective responses, which are then applied on the floor to accelerate deliveries.

In the realm of healthcare, digital twins enable hospital administrators to model patient flow, staff distribution, and room configurations, facilitating the identification of bottlenecks in scanning services. For example, Mater Private Hospital in Dublin, along with Siemens Healthineers, developed a digital twin of its radiology department, resulting in a 13-minute decrease in CT scan wait times and a 25-minute reduction for MRIs, which significantly enhanced patient throughput within weeks. Ultimately, this led to a 26-32% uptick in the utilization of costly MRI and CT equipment, along with a reduction of nearly 50 minutes in daily overtime for technicians.

Banks and insurers are experimenting with a digital twin of the organization that mirrors their complete set of processes, from customer onboarding and transactions to IT systems, to understand how a new regulatory requirement would influence operations, or alter customer behaviour, or affect service loads and staffing needs. JPMorgan Chase, for instance, used digital twins to forecast market trends and customer behavior patterns, essentially stress-testing strategies against a range of economic scenarios. Such uses of digital twins enable managers to move beyond traditional operational tweaks to tackling complex strategic uncertainties like market entry decisions or M&A integrations.

Essentially, any business that can be quantitatively modelled can gain from a digital twin-whether that's improving a BPO call centre's workflow or refining service design in a telecom network through user demand simulation. These examples across various industries highlight a common result: organizations that integrate digital twins into their strategic toolkit become more agile, informed, and prepared for the future.

Challenges and Limitations

While the potential of digital twins is immense, managers must acknowledge the obstacles to adopting this technology. A significant challenge is the need for high-quality data. Digital twins rely on comprehensive and reliable data streams to function correctly. Any missing, outdated, or inconsistent data can lead to inaccurate or misleading insights. Fragmented information, caused by data residing in isolated systems or departments, can impede the development of integrated, real-time data, which often results in a limited understanding of an organization's interconnections. Without diligent data governance, a digital twin initiative could falter, resulting in erroneous conclusions for managers. Thus, it is crucial for businesses to establish a robust data infrastructure to fully leverage the benefits of digital twins.

Additionally, creating and managing digital twins necessitates a combination of domain expertise, data science, software engineering, and systems thinking. Many organizations may lack personnel with expertise in all these areas. Furthermore, the technology can be intricate, involving IoT architecture, simulation modelling, AI, and cloud computing. Companies might need to enhance their employees' skills or hire specialists to successfully implement twins. Therefore, business leaders should view twin initiatives not merely as technological projects but as strategic endeavours that synchronize people and processes towards a new, data-driven decision-making approach.

Technological obstacles underlying the adoption of digital twins can be overwhelming. As digital twins typically combine various technologies and need to connect with current enterprise systems, facilitating communication between isolated systems can present substantial technical challenges and incur significant costs.

Lastly, the computational demands of AI-powered twins also bring up practical challenges. Running simulations with AI agents requires significant computing resources and may necessitate a scalable cloud computing infrastructure which might make them operationally very expensive.

Crystal Gazing: What is Ahead

Digital twin technology is rapidly evolving through the incorporation of AI, especially generative AI. These advanced AI-powered twins merge the predictive strengths of machine learning with simulations, unlocking exciting new opportunities. "Agentic" AI is increasingly being integrated into digital twins, enabling autonomous agents to monitor system conditions and make real-time decisions without human intervention. These agents are designed to exhibit goal-directed behaviour, perceiving their environment, reasoning through alternatives, and taking actions to optimize outcomes, making them capable of independently managing complex tasks within the twin's operational scope. Consequently, the twin not only replicates reality but also engages with it, allowing for experimentation and adjustments within set parameters.

The emergence of AI-enabled digital twins, however, introduces significant challenges, particularly regarding trust, transparency, and the ethical use of AI. AI-driven twins can often act as "black boxes," obscuring how their recommendations are generated. This opacity can impede adoption, as managers may be reluctant to depend on algorithms they do not understand. AI mistakes, such as a twin making a poor recommendation that disrupts production, can harm both project outcomes and the company's reputation. Therefore, it is essential to guarantee explainability, thorough validation, and human oversight. Managers must insist on auditable and bias-tested AI algorithms, accompanied by clear fail-safes for situations that are unclear.

Another advancement is the use of multi-agent systems in digital twins. Instead of one AI managing the twin, several specialized agents work together to perform various tasks. For example, Amazon's warehouse twin, where one twin oversees inventory, another optimizes robotic paths, and a third forecasts order surges, operates within the integrated twin model.

Conclusion: Adopting Digital Twins for Strategic Advantage

Digital twins are no longer peripheral technologies. They are beginning to shape how strategy itself is formulated and executed. When deployed with intention, they enable firms to model trade-offs, simulate complex systems, and refine decisions with precision and responsiveness that traditional tools cannot match. For senior leaders, this is not about adopting the latest gadget. It is about rethinking how organizations make decisions in environments defined by interdependence and volatility. Companies like Tesla, Amazon, BMW, and Mater Private Hospital offer early evidence of what is possible when digital twins are embedded into the fabric of operations. But the path from pilot to enterprise-wide impact is neither short nor automatic. It requires strategic clarity and disciplined follow-through. Without these, the twin risks are being absorbed into the long list of digital initiatives that promised transformation but delivered little.

The first order of business is to define the problem the twin is meant to solve. Whether it is to reduce downtime, test new service rollouts, anticipate supply chain disruptions, or model alternate investment scenarios, the use case must be tied to a real managerial concern. This clarity should anchor design choices, data architecture, and organizational ownership. Equally important is the structure around the twin. Because it operates across IT, operations, and strategic planning, many firms benefit from a centralized team or centre of excellence to govern its development, while embedding domain specialists who ensure relevance to business needs. And perhaps most difficult is the cultural shift. Encouraging managers to replace instinct with simulation and to trust a model over a hunch takes time. Success stories must be communicated, not as isolated wins, but as proof that the organization is learning to use the twin to see around corners-and act accordingly.

Looking ahead, digital twins are likely to become as integral to the strategic toolkit as spreadsheets did in the 1980s. They integrate real-time data with simulation and AI to support continuous experimentation and learning. In an era where responsiveness and foresight separate leaders from laggards, the ability to model uncertainty, iterate on strategy, and test operational configurations before committing capital will confer a real advantage. Firms that build these capabilities early and embed them into how decisions are made will likely be at an advantage. Those who delay may find the competitive landscape shifting beneath their feet as rivals make better, faster, and more adaptive choices.

[1] McKinsey & Company. What is digital-twin technology? (2023) - McKinsey analysis projects the global market for digital twins to reach $73.5 billion by 2027, growing ~60% annually.

[2] World Economic Forum. Leveraging digital tools in the age of supply chain disruption (2025) - Market analyses indicate 30–40% annual growth in the digital twin market, reaching $125-150 billion by 2032.

[3] Siemens Healthineers & Mater Private Hospital. Digital Twin Radiology Case Study (2022) - Implementing a digital twin for hospital radiology cut patient wait times by 13-25 minutes and increased MRI/CT utilization ~30%, while reducing staff overtime.

[4] Deloitte. New uses for digital twins in the race to navigate an uncertain future (2024) - Discusses how digital twins are being adapted to simulate strategic decisions and notes the importance of addressing uncertainty through twin-driven scenario planning.