8 common pitfalls from using digital twins

Digital twins offer significant potential. But like any transformative approach, they also come with challenges. Across industries, companies are moving from experimentation to implementation and along the way, some consistent lessons are emerging. Understanding these early can make the difference between a successful initiative and one that struggles to deliver value.

1. There is no single definition

One of the first challenges is surprisingly simple:

What exactly is a digital twin?

Depending on context, it can refer to either a simulation model, an emulated system connected to real control logic, or a fully integrated virtual representation of a physical asset. Without alignment, teams risk working toward different goals under the same label.

Lesson:
Define clearly what a digital twin means in your organisation, and what you expect it to do.

2. Aiming for perfection from day one

A common misconception is that a digital twin must be a complete and highly detailed replica of reality. In practice, this approach often slows progress.

Many successful implementations focus on:

• early prototypes
• simplified models
• targeted use cases

The goal is to create value quickly, not to model everything.

Lesson:
Start simple, fail fast and evolve the model over time.

3. Not everything can be simulated

Even the most advanced models have limitations. Certain aspects, especially those related to physical behaviour, can be difficult to replicate accurately in a digital environment. This means that some validation will always need to happen in the real world. Overestimating what a digital twin can do can lead to misplaced confidence.

Lesson:
Use digital twins to reduce uncertainty, not eliminate it completely.

4. The value depends on how you use it

A digital twin is not valuable by default. We’ve seen multiple times that the impact of a digital twin is curbed, for instance because it is built once and not maintained, disconnected from real work processes, or accessible only to a few specialists. On the other hand, when used actively for testing, iteration and decision-making, it becomes a powerful capability.

Lesson:
Focus as much on usage and integration as on building the model itself – the digital twin should live in the complete equipment lifecycle.

5. Organisational setup matters

Digital twins are not just a technical initiative, and they require the correct organisational support. Challenges often arise when ownership is unclear, expertise is too distributed, or knowledge is not shared across teams. However, many organisations find value in establishing a central role that supports multiple projects and ensures consistency.

Lesson:
Plan for how digital twin capabilities are managed, not just how they are developed.

6. Collaboration is essential

Digital twins often sit at the intersection of multiple stakeholders:

• end users
• machine builders
• integrators
• software and hardware providers

Without collaboration, it becomes difficult to build models that reflect reality and deliver value across the full system. At the same time, collaboration introduces questions around data sharing and intellectual property.

Lesson:
Treat digital twin initiatives as collaborative efforts from the start and address data ownership and IP early.

7. Technology is not the biggest barrier

It is tempting to focus on tools and platforms, but in many cases, the real challenge lies elsewhere. Shifting from a traditional, sequential way of working to a more iterative, virtual-first approach requires new ways of thinking, a willingness to experiment, and an acceptance that not everything is fully defined upfront.

Lesson:
Adoption is as much about mindset as it is about technology, and change management is key to succeed.

8. Be clear about the objective

Digital twins can support many different use cases, but trying to do everything at once rarely works.

Whether the goal is reducing commissioning time, improving system quality, enabling training, or optimising production the approach should be tailored accordingly.

Lesson:
Start with a specific problem and build from there. There will always be ancillary benefits around the twin and around the main objective.

In conclusion

Digital twins are not a plug-and-play solution. They require:

• clear objectives
• realistic expectations
• and the right organisational setup

But when implemented thoughtfully, they can become a powerful way to reduce risk, increase production, improve quality, and enable better decisions across the entire lifecycle of a system.

Want to talk about your needs and how a digital twin can support your business? Reach out to Lotte Høeg Jul Jensen, Head of Digital Twin & AI at ProjectBinder: