As digital transformation accelerates across the built environment, digital twins have emerged as one of the most discussed technologies in infrastructure and asset management. But beyond the buzz, what tangible value do they deliver? Garrett Owens shares insights into the benefits, challenges and future trajectory of digital twins in infrastructure.
Firstly, What Is a Digital Twin?
At its core, a digital twin is a virtual representation of a physical asset or system. It integrates complex datasets into a dynamic digital environment, enabling stakeholders to analyze performance, test scenarios and generate insights without interfering with the physical asset.
A digital twin communicates these insights to owners and operators, supporting more informed operational and maintenance decisions. Better decision-making leads to targeted interventions, cost optimization and measurable performance improvements.
Do Digital Twins Live Up to the Hype?
Digital twins are undeniably a prominent technology trend. However, focusing solely on the hype can obscure their practical value.
Organizations adopting digital twins report benefits including:
- Reduced operational costs
- Increased throughput and production capacity
- Improved asset reliability and performance
- Enhanced resilience against disruption
Digital twins are increasingly applied at multiple scales — from modeling components of the human body to managing entire cities. As organizations mature digitally, applications continue to expand, reinforcing their role as a value-creation tool rather than a speculative innovation.
How Do Digital Twins Improve Infrastructure Delivery and Operations?
In the infrastructure sector, digital twins are designed to enhance efficiency throughout an asset’s lifecycle — from design and construction through operations and maintenance.
Key applications include:
- Advanced simulation and scenario testing
- Real-time performance monitoring
- Remote intervention capabilities
- Predictive maintenance through data analytics
These capabilities allow asset owners and operators to reduce downtime, improve system reliability and optimize resource allocation. Ultimately, digital twins strengthen infrastructure resilience against both short-term disruptions and long-term systemic change.
What Unexpected Challenges Do Organizations Face?
While the value proposition is compelling, implementation is rarely straightforward.
1. Data Complexity and Quality
Data is foundational to any digital twin. However, data must be accessible, structured and compatible with integrated analytical tools. Combining datasets from multiple sources often introduces complexity and quality control challenges.
2. Real-Time Processing Demands
Effective digital twins rely on real-time data ingestion, cleansing and anomaly detection. This requires robust IT infrastructure capable of handling high data volumes with minimal latency — a capability not all organizations possess.
3. Organizational Buy-In
Technological deployment alone does not guarantee success. Leveraging insights effectively requires alignment across leadership, operations and technical teams. Resistance to new decision-making processes can slow adoption and reduce impact.
What Questions Should Organizations Ask Before Implementation?
From an operational standpoint:
- What specific business drivers justify implementing a digital twin?
- Is our current digital ecosystem sufficiently mature?
- What key performance indicators will define success?
- Who will own and manage the digital twin throughout its lifecycle?
From a strategic perspective, expectations must be realistic. Digital twins are not “plug-and-play” solutions. Their value emerges incrementally through process refinement, cultural change and continuous improvement. The success of digital twins in infrastructure depends as much on people and governance as on technology.
What Role Will AI and Machine Learning Play?
As artificial intelligence and machine learning technologies mature, their integration into digital twins will significantly expand capabilities.
Future digital twins are expected to:
- Deliver real-time operational recommendations
- Improve predictive maintenance accuracy
- Adapt dynamically to changing system conditions
- Support autonomous optimization
By enabling proactive problem-solving through advanced analytics, AI-enhanced digital twins will transition from descriptive tools to predictive and prescriptive platforms.
The Bottom Line
Digital twins do live up to the hype — but only when implemented strategically. They are not a standalone solution but a framework for smarter, data-driven infrastructure management. Organizations that approach digital twin adoption as a long-term transformation journey, rather than a quick technological fix, are most likely to unlock sustained value.
