Digital Twin: What is it, how does it work, and are you ready for them?

Digital Twins are a prominent topic in the AEC industry right now, and they are rapidly moving from intriguing concept to expectation. What they promise is compelling: a true single source of truth that enables better decisions during design and delivery, and more reliable outcomes in operations.  

But in practice, many Digital Twin initiatives struggle to deliver that value. Not because technology or ambitions are lacking, but because the foundations required to make them work are often underestimated. 

This guide brings clarity to what a Digital Twin is, how Digital Twin technology works in practice, and what separates a valuable twin from one that will quickly become outdated or unreliable. 

What is a Digital Twin? 

A Digital Twin is a digital representation of a real-world asset, system, or process that is continuously maintained so it can support real decisions over time.  

Unlike a static model, a Digital Twin only remains useful if it reflects current conditions and can be trusted by the people using it. That means it is not something you create once; it is something you operate and maintain.  

Digital Twins can represent buildings, infrastructure, sites, or entire portfolios. The scope varies, but the principle remains the same: ensuring that information stays usable, connected, and reliable across the building lifecycle. 

Digital Twin vs 3D model 

How does a Digital Twin differ from a 3D model?  

A 3D model represents the geometry of an asset at a specific point in time. It can be detailed and visually rich, but it does not automatically stay aligned with real-world changes as they occur. A Digital Twin may include a 3D model, but it goes beyond visualization by connecting data and workflows so teams can make decisions based on up-to-date information. 

The difference is not just in what is modeled, but in how reliably that information is maintained and used over time. 

Why many Digital Twins fail in practice 

Despite growing investment in Digital Twin technology, many initiatives fail to move beyond pilots or deliver limited long-term value. Why is this happening? The issue is rarely the technology itself. It is the ability to maintain reliable, connected information over time. And often, this crucial aspect is underestimated.  

Common failure patterns include: 

• Information that cannot be trusted due to inconsistent model quality or missing data  

• Fragmented workflows where data is lost or duplicated between design, delivery, and operations  

• Overly complex models that are difficult to maintain 

• Lack of clear ownership for keeping information current  

In these cases, the Digital Twin exists, but teams still need to verify information manually before making decisions. At that point, the twin stops functioning as a reliable source of truth and cannot effectively provide long-term value. 

This is why Digital Twins are not just a technology challenge. They are an organizational maturity challenge, requiring structured workflows, consistent data practices, and connected systems. 

How Digital Twin technology works in simple terms 

Digital Twin technology typically combines three elements: 

• A digital representation of an asset or process (often including 3D models and structured data)  

• A way to keep that information current through workflows, connected systems, or sensors  

• A feedback loop where information is used to make decisions, and those outcomes are reflected in the twin 

In practice, the value of this loop depends on how reliably it is maintained. A Digital Twin does not fail because it lacks detail, but because information cannot be kept consistent and up to date. 

What it takes to make a Digital Twin work in practice 

A Digital Twin is not a deliverable; it is an ongoing system, and its success depends on how well it is set up and maintained in practice. To ensure success, we suggest keeping the following in mind:  

Start with the decisions you need to support 

The value of a Digital Twin depends on its purpose. 

Teams need clarity on: 

• What decisions the twin should support  

• Who will use it  

• What level of detail is actually required  

Without this, Digital Twins often become over-engineered and underused. Define the goals, clarify the purpose, and ensure all relevant stakeholders are aligned on this. 

Build from information you can maintain 

Digital Twins combine multiple layers of information: 

• Models and geometry  

• Structured asset data  

• Documentation, requirements, and history  

A common mistake is prioritizing detail over maintainability. 

In practice, a simpler, structured, and maintainable dataset is far more valuable than a highly detailed model that cannot be kept up to date.  

Keep information current through repeatable workflows 

A Digital Twin only works if updates happen consistently. 

These updates may come from: 

• Project teams during delivery  

• Connected systems (e.g. asset management tools)  

• Sensors and automated data feeds  

Not all Digital Twins require real-time updates. What matters is that updates are predictable and aligned with how the information is used. 

Maintain continuity across lifecycle phases 

One of the biggest challenges in the built environment is fragmentation between design, construction, and operations. Without continuity, information loses context; teams no longer know what changed, when, or why. A Common Data Environment (CDE) plays a critical role here, not just as storage, but as a structured environment that preserves meaning across phases.  

Turn information into action 

A Digital Twin is only valuable when it supports real work. 

When information is reliable and accessible, it enables: 

• Faster, more confident decision-making  

• Earlier identification of risks and conflicts  

• More reliable handover and operations  

Without trust in the underlying data, even the most advanced Digital Twin technology will not deliver value.  

Digital Twin examples in practice 

Digital Twins can take different forms depending on what they are designed to support. The key difference is not just scale, but how information is used. 

Example 1: Asset Digital Twin (building or infrastructure) 

A common use case is a Digital Twin of a single asset, such as a building or infrastructure project. 

The goal is to combine models and asset data so teams can manage, maintain, and operate the asset effectively after handover. 

Where it works: 

• When asset data is structured and consistent  

• When updates are captured as changes occur  

Where it breaks: 

• When handover data is incomplete or unreliable  

• When updates stop after project delivery  

Example 2: Portfolio or site Digital Twin 

At a larger scale, Digital Twins connect multiple assets across a site or portfolio. 

The value comes from consistency: being able to compare, analyze, and manage assets in a standardized way. 

Where it works: 

• When data is structured consistently across projects  

• When workflows are aligned across teams and locations  

Where it breaks: 

• When each project uses different standards  

• When data cannot be aggregated without manual effort  

Example 3: Process Digital Twin 

Not all Digital Twins are centered on physical assets. Some focus on processes where reliable, up-to-date information is critical. 

A key example in the built environment is handover readiness. Instead of treating handover as a final step, teams track information completeness throughout delivery. 

Where it works: 

• When information quality is monitored continuously  

• When workflows make readiness visible and measurable  

Where it breaks: 

• When validation happens too late  

• When issues are tracked inconsistently across teams  

This type of Digital Twin reflects a shift from static deliverables to continuous information management, and is often where organizations see the fastest impact. 

What all Digital Twins have in common 

Across these examples, the same principle applies: a Digital Twin is only valuable when teams can trust and use the information without revalidating it every time. 

This is why successful Digital Twins are built on strong foundations: 

• Consistent data quality  

• Connected workflows  

• Clear ownership of updates  

Without these, even well-designed Digital Twin initiatives struggle to scale. 

How BIMcollab supports Digital Twin use in practice 

Digital Twin success depends on the ability to maintain trusted, connected information across the lifecycle. 

BIMcollab supports this by strengthening the foundations Digital Twins rely on most: data quality, workflow consistency, and continuity. 

• Model Quality Assurance ensures that model and asset data remain consistent and reliable across projects  

• Model checking helps teams identify and resolve issues early within structured coordination workflows  

• A connected Common Data Environment (CDE) maintains continuity by keeping information accessible, traceable, and connected across phases  

Rather than treating the Digital Twin as a standalone system, this approach focuses on making the underlying information usable and trustworthy in day-to-day workflows. 

Digital Twin maturity is a process 

Digital Twin maturity reflects how well your organization can produce, maintain, and use reliable information across the lifecycle. 

It is not defined by tools alone, but by the combination of: 

• Data quality  

• Workflow consistency  

• System connectivity  

Organizations typically move through several stages as they become more “ready” to execute valuable Digital Twins: from inconsistent and siloed information, to connected but unreliable workflows, to environments where information can be trusted across phases. 

The risk is not being at an early stage. The risk is scaling Digital Twin ambitions without the foundations required to support them. 

Understanding your current level of readiness is the first step toward building a Digital Twin that delivers real value. Once you know where you are, you’ll know exactly what you need to do to strengthen your foundations. 

To help with this, we developed a Digital Twin Readiness Test.