Work

AI-Augmented Digital Twins in Large Industrial Companies: From Use Cases to Industrialisation

Year: 2026

Type: conference-paper

Abstract: Digital twins in manufacturing are often presented as a basis for intelligent and autonomous operations. However, we still have limited understanding of how AI helps them move from passive representations to practical decision-support tools. Based on interviews with eleven large manufacturers and software suppliers, we examine how digital twins and AI interact in manufacturing settings. We identify eighteen empirically grounded use cases and industrialisation constraints, grouped into six themes: real-time decision support, operator guidance, simulation-based optimisation, AI-assisted model generation, data governance, and deployment constraints. Many initiatives were still at the idea, pilot, or prototype stage, with respondents highlighting fragmented data governance and limited cloud connectivity as key barriers to deployment. Our findings suggest a two-way relationship: digital twins can provide operational context for AI, while AI can extend the analytical and predictive capabilities of digital twins. By outlining both opportunities and limitations, we support decision-makers in assessing the realistic role of AI-augmented digital twins in digital transformation initiatives.

Source: IFIP advances in information and communication technology

DOI: 10.1007/978-3-032-38606-9_28

Authors: Adrian Sánchez de Ocaña, Jessica Bruch

Institutions: Alfa Laval (Sweden), Mälardalen University

Language: English

Topic: Digital Transformation in Industry

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: Mälardalens högskola