Volume 10,Issue 9
Rationale and Governance Framework for the Prudent Adoption of Artificial Intelligence in Traditional Manufacturing Enterprises
Artificial intelligence (AI) is permeating traditional manufacturing through applications such as production scheduling, quality inspection, equipment monitoring, supply chain collaboration, and knowledge retrieval. This paper analyzes the practical drivers behind manufacturers’ adoption of AI, proposes a governance framework for prudent AI adoption built on three mechanisms: a risk-grading and determination process, a mandatory human-intervention protocol, and a soft-fault protection layer design. These three mechanisms correspond respectively to task selection (which tasks are suitable for AI), operational oversight (who retains oversight during operation), and failure response (how to respond when AI fails), forming a closed loop that spans pre-deployment assessment, in-operation supervision, and post-failure response. The role of AI in manufacturing should follow two principles: boundary limitation (AI undertakes critical tasks only when boundaries are clear, performance validated, and independent safety layers exist) and role restriction (in high-risk scenarios, AI serves as an assistive tool rather than an exclusive controller).
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