Managing the Implementation of Artificial Intelligence in Industrial Processes: An Integrated Governance and Compliance Perspective

The increasing use of artificial intelligence (AI) within industrial operations redefines production systems and organizational routines, yet it also introduces new requirements for governance, accountability, and regulatory compliance. This paper examines how the existing scholarly literature conceptualizes the management of AI implementation in industrial settings, with an emphasis on the mechanisms that ensure responsible and legally aligned adoption. The review covers contributions from management studies, engineering, ethics, and technology regulation, together with relevant international frameworks, including the EU AI Act, ISO/IEC standards, and risk-management guidelines.

The synthesis highlights recurring challenges reported across studies, particularly the difficulty of integrating ethical oversight into technical workflows, the uneven application of documentation and audit procedures, and the limited institutional readiness for sustained human oversight. In response to these issues, the paper proposes an integrated governance model that brings together compliance requirements, operational risk assessment, and organizational coordination. The model is intended to support industrial actors in developing coherent internal structures for monitoring AI systems throughout their lifecycle.

The study aims to contribute to current debates on responsible innovation by offering a structured interpretation of how governance and compliance can be operationalized in industrial environments undergoing digital transformation.