Abstract
The adoption of industrial Artificial-Intelligence (AI) systems in production environments has increased considerably in recent years, enabling new forms of process automation, engineering support, and data-driven decision making. However, practitioner-oriented insights regarding their practical development, configuration, and operation remain limited. To address this gap, we conducted a four-round Delphi study with 26 practitioners involved in industrial AI systems in production-related environments. Using iterative expert assessments, thematic analysis, and consensus-oriented evaluation, the study synthesizes benefits, issues, risks, and mitigation strategies associated with industrial AI systems.
The findings indicate that industrial AI systems are increasingly evolving from isolated analytical models toward integrated and highly configurable production ecosystems. While experts associated AI systems with operational and economic benefits, they simultaneously emphasized challenges related to integration complexity, configurability, dependency management, traceability, lifecycle management, and governance. In particular, dependency hell, combinatorial explosion, and configuration mismatches emerged as recurring practical concerns.
Overall, the study suggests that many practical challenges of industrial AI systems no longer primarily emerge from model development itself, but from their integration and operation within heterogeneous production environments. We argue that our findings provide practice-oriented insights into the development, configuration, and operation of industrial AI systems.
Publication Date: 2026-06-23