From CAD Models to Open, Interoperable Digital Twins

KEYWORDS: Industrial Grade AI, Manufacturing Operations, Data Governance, Engineering, Process, Trust, Digital Twin

Overview

Discussions at the ARC European Industry Leadership Forum 2026 revolved around the adoption and implementation of digital twins. Digital twin adoption is advancing, but it remains uneven across industrial organizations. According to ARC’s AI and Robotics Survey, 33 percent of participating companies have already implemented digital twin and simulation technologies, with an additional 36 percent planning implementation within the next three years. In contrast, enabling technologies such as IoT are more mature, with 50 percent already deployed and 29 percent planning near-term adoption.

End users clearly articulated that future digital twin solutions must move beyond visualization toward operational value.
 

This gap highlights a key reality: the technical foundation for digital twins largely exists, but organizations are still working to integrate these capabilities into scalable, operational systems. Digital twins are not emerging as standalone technologies but as an extension of connected data environments, requiring alignment across engineering, operations, and enterprise systems. End users consistently emphasized that the challenge is no longer defining the digital twins conceptually but operationalizing them in a way that delivers measurable value across the asset lifecycle.

The Second Phase of Digital Twin Adoption

Industrial organizations are entering a second, more demanding phase of digital twin adoption. The first phase was largely focused on creating models, visualizations, and representations of assets. The current phase shifts attention toward making digital twins operational - connected to real data, embedded in workflows, and capable of supporting decisions at scale. However, structural gaps continue to limit progress. The connection between engineering and operations remains weak, preventing organizations from fully leveraging lifecycle insights. At the same time, interoperability across systems and platforms is still limited, creating fragmented environments where digital twins cannot easily scale.

Digital Twin: Beyond CAD and Visualization

A central takeaway from the discussion is the need to clearly distinguish between CAD models and digital twins. CAD models provide structured descriptions of assets, capturing geometry and design intent, but they remain static and disconnected from operational reality.
Digital twins, by contrast, are dynamic systems. They are continuously updated with real-time data and enable simulation, analysis, and decision making. Their value lies not in representing assets visually, but in reflecting current conditions and supporting action.

In practice, particularly in brownfield environments, this distinction becomes less clear. Many organizations do not have accurate or up-to-date engineering models and instead rely on reality capture technologies such as laser scanning and point clouds. These approaches generate data-driven representations of assets that are often more relevant for operational use. As a result, the definition of a digital twin is evolving. From a visual model to a functional system designed to answer operational questions.

Engineering vs. Operations Digital Twins

Another important insight is the distinction between engineering and operations digital twins. Engineering twins capture design intent and provide the foundation for asset creation, while operations twins reflect how systems perform in real time.

Although these two domains should be tightly connected, this is rarely the case in practice. Operational data is often not fed back into engineering processes in a structured way, limiting opportunities to improve future designs. This represents a significant gap. Digital twins should not be static artifacts tied to a specific lifecycle phase, but part of a continuous loop in which operational experience informs engineering improvements.
 

ARC Advisory Group clients can view the complete report at the ARC Client Portal.

Contact Us if you would like to speak with the author.

Obtain more ARC In-depth Research Market Analysis.