Agentic AI in Engineering and Manufacturing: Industry Perspectives on Utility, Adoption, Challenges, and Opportunities
Kristen M. Edwards, Maxwell Bauer, Claire Jacquillat, A. John Hart, Faez Ahmed
Why It Matters
What makes this one worth your time
Understanding the barriers and opportunities for agentic AI in industry can guide future research and development efforts, ultimately enhancing productivity and innovation in engineering and manufacturing sectors.
The paper explores the adoption of agentic AI in engineering and manufacturing, revealing key challenges and opportunities.
Summary
This paper provides a comprehensive qualitative analysis of the current state of agentic AI adoption in engineering and manufacturing, highlighting both the immediate benefits and the significant barriers to broader implementation, including data fragmentation and organizational challenges.
Key contributions
- The paper identifies specific barriers to AI adoption in engineering and manufacturing, including data fragmentation and the need for improved verification frameworks.
Notable insights
- Adoption challenges are more related to data and organizational culture than to the capabilities of AI models themselves.
Possible limitations
- The study relies on qualitative interviews, which may introduce bias and limit the generalizability of the findings.
Abstract
arXiv:2604.09633v1 Announce Type: cross Abstract: This work examines how AI, especially agentic systems, is being adopted in engineering and manufacturing workflows, what value it provides today, and what is needed for broader deployment. This is an exploratory and qualitative state-of-practice study grounded in over 30 interviews across four stakeholder groups (large enterprises, small/medium firms, AI developers, and CAD/CAM/CAE vendors). We find that near-term AI gains cluster around structured, repetitive work and data-intensive synthesis, while higher-value agentic gains come from orchestrating multi-step workflows across tools. Adoption is constrained less by model capability than by fragmented and machine-unfriendly data, stringent security and regulatory requirements, and limited API-accessible legacy toolchains. Reliability, verification, and auditability are central requirements for adoption, driving human-in-the-loop frameworks and governance aligned with existing engineering reviews. Beyond technical barriers there are also organizational ones: a persistent AI literacy gap, cultural heterogeneity, and governance structures that have not yet caught up with agentic capabilities. Together, the findings point to a staged progression of AI utility from low-consequence assistance toward higher-order automation, as trust, infrastructure, and verification mature. This highlights key breakthroughs needed, including integration with traditional engineering tools and data types, robust verification frameworks, and improved spatial and physical reasoning.