Why digital twins fail without a scanning strategy

Over the last decade, digital twins have gone from being a tool used by a few to becoming flexible, integrated systems that have been adopted widely and serve many industries. 

However, many organizations still struggle to make their digital twin initiatives work effectively. There is no universal standard and security concerns around shared data and poor performance metrics have stymied more widespread adoption. Complex industrial environments also make it challenging to keep virtual models fully aligned with physical changes.

Why a scanning strategy is needed

The digital twin market is expected to grow from $24 billion in 2025 to $259 billion by 20321. However, digital twin investment lacks results without a proper strategy in place.

Digital twins need a systematic approach to data management. They quickly become outdated as physical assets evolve. With the need to centralize information coming from multiple sources, data integration is a big challenge for many companies.

One of the most critical mistakes happens right at the beginning when teams jump into scanning without clear objectives. Teams often work on CAD models instead of capturing as-built reality first. This can lead to major discrepancies with the physical environment down the line.

Before scanning, organizations must:

• Define the ultimate purpose of the digital twin project
• Identify which stakeholders will use the data
• Determine the accuracy level needed for intended applications

Starting with reality capture creates a more accurate baseline than a model-first approach. By incorporating scan data into decision-making, the digital twin becomes a reliable source of information for asset tagging and display and can highlight greater operational efficiencies.

Digital twin projects need these strategic foundations. Without them, they risk failing to deliver measurable value.

Choosing the right tools for the job

Scanning tool choices are important for digital twin projects. Many companies miss the crucial fit between scanning technology and what their project actually needs. This mismatch wastes resources and leads to failed implementation. The quality of the data produced matters most during design validation, which means it isn't always recognized from the outset.

Terrestrial laser scanners capture detailed, accurate data in static environments while vehicle or backpack-mounted mobile lidar are perfect for moving around large, complex industrial facilities. Drone photogrammetry excels at covering rooftops and overhead elements.

Pocket lidar scanners and 360-degree cameras are easy to use and readily available. They may not be as accurate as larger, more robust scanners but brought together with other scan data can be powerful for visibility and understanding.

The human factor

The success or failure of digital twin projects also relies on implementation, rather than merely the strategy and technology. Teams face hurdles from skill gaps, misunderstanding about the technology and disconnected work processes.

Specialised training and technical expertise are must-haves to use reality capture devices properly. Running advanced scanning tools takes more than just knowing the equipment - operators need deep knowledge of data capture and processing basics.

A lack of collaboration between field crews gathering the data and office staff processing it wastes time and resources. Field teams might miss what matters for modelling, while office teams struggle to align data from different sources. This gap leads to do-overs, delays, and team frustration. Sharing data in real-time and keeping project information up-to-date and in one place will get the best results.

Fixing the link: Aligning scanning with digital twin success

Digital twins work best when scanning methods match operational goals. Organisations can create valuable digital replicas by focusing on essential implementation factors that bridge data gaps.

The Digital Twin Consortium recommends matching sync frequency to how fast real-life entities change. If changes are happening constantly, frequent scanning is necessary to ensure up-to-date and accurate data. 

Robots can add value for routine data collection, avoiding the need to schedule resource for manual reality capture. Robots can perform static scanning to produce detailed data and continuous scanning for faster coverage.

Cloud-based platforms make digital twin technologies more accessible and integrated. Bi-directional data flows between physical assets and their digital replicas, enabling monitoring and remote control. 

Measure ROI to drive adoption and scale

Recent surveys show 92% of companies tracking ROI from digital twins report returns exceeding 10%, and half see returns above 20%2. 

Successful implementation needs:

• Performance measures before deployment
• Specific KPIs that align with business goals
• A full picture of before-and-after results

Start with a specific objective such as reducing maintenance costs or improving asset use and use the learnings to expand digital twin projects and ensure success.

Setting yourself up for success

Digital twins still face major implementation challenges despite ongoing technological advances. Scanning should have a foundational role to play in delivering successful digital twin outcomes. 

Organisations should avoid rushing into projects without proper scanning foresight. Consider what data is needed, the precision level and who it will be shared with in order that all stakeholders can make reliable decisions. Teams often pick the wrong tools for their specific environments, such as using stationary scanners in dynamic settings or non-expert devices where millimeter precision really matters.

Human factors can be a substantial barrier too. Teams lack proper training and hold misconceptions about what automation can do. Field and office teams don't necessarily work well together without being fully equipped with the right collaboration training or devices. 

These issues explain why many digital twin projects fail to deliver real value. Success depends on how well scanning approaches match operational goals. Each organization needs its own capture frequency based on specific needs rather than using one approach for everything. Cloud platforms are a great way to achieve real-time synchronization. Robotic scanning solutions deliver consistency that manual methods can't necessarily match, especially in ever-changing environments that need regular updates.

ROI measurement plays a vital role in driving adoption. Companies that track their digital twin ROI report good returns. Digital twins become valuable business assets when teams implement them properly with clear goals, the right technology, trained staff, and performance metrics.

Digital twins offer huge potential and scanning is just one part of a complete implementation plan. Real value is gleaned when organizations connect their technological capabilities with clear business goals and systematic execution.

References:

(1) Hardware & Software IT Services / Digital Twin Market, July 21, 2025, Fortune Business Insights

(2) Charted: The return on investment of digital twin, Visual Capitalist, April 9, 2025