Enterprise digital twins build trust by involving users early

Involving before Evolving: A Vision for Trustworthy Enterprise Digital Twin Engineering

Software EngineeringArtificial IntelligenceHuman-Computer Interaction

Summary

Making digital copies of big companies, called Enterprise Digital Twins, can help organizations make better decisions using data. But creating these copies is hard because many teams don’t share their knowledge and decisions affect the company for a long time. The authors suggest starting by quickly involving people with a working model before making the system more complex and connected. They use new AI language tools to build early versions fast and organize information so systems can later work together. They tested their idea while working with Michelin and saw it helped get people interested and trusting the tool.

What this means in practice

A position paper. It proposes an approach and reports no results.

Authors

Kérian Fiter, Adil Lagrou, Franck Dervault, Bentley Oakes

Abstract

Enterprise Digital Twins (EDTs) promise data-driven decision support at organizational scale, but realizing them requires navigating siloed departments, tacit knowledge, and high-stakes decisions with long-horizon consequences. Existing approaches involve domain experts during model development but focus less on early organizational buy-in in EDTs. We present a vision for trustworthy EDT engineering grounded in an `involving before evolving' paradigm: rapidly involving stakeholders through a working prototype before evolving toward federation and full interoperability. Our three-stage approach combines foundation models for rapid prototyping, an ontological backbone for federated interoperability, and observability tooling for stakeholder trust. We ground our vision in an ongoing collaboration with Michelin, a multinational manufacturer, where an initial prototype has helped support stakeholder buy-in.
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