TITLE:
Where Can Artificial Intelligence Support Risk Prioritization in an Integrated Management System of a Testing Laboratory and a Certification Body? A Perspective and Validation Protocol
AUTHORS:
Karina Rafailovna Zagidullina
KEYWORDS:
Artificial Intelligence, Risk Assessment, Risk Prioritization, Testing Laboratory, Certification Body, Integrated Management System, Validation
JOURNAL NAME:
Intelligent Control and Automation,
Vol.17 No.4,
November
6,
2026
ABSTRACT: An organization that combines a testing laboratory and a product certification body must rank risks of different nature in one register. In earlier work, the author proposed a weighted risk priority index and showed by Monte Carlo simulation that its ranking is more sensitive to errors in expert probability and consequence scores than to uncertainty in the weights. This perspective paper asks where artificial intelligence (AI) could reduce that score uncertainty without replacing expert judgment. Three advisory functions are discussed: evidence for probability and consequence scores drawn from nonconformity and audit records, early-warning signals from key performance indicator (KPI) trends, and mapping of changed requirements to risks. Conditions for use are formulated from the literature on AI in risk management, laboratory quality control and AI risk governance. A simulation on the author’s register of 19 risks then quantifies how accurate score support would need to be: halving the nominal share of scores that are off by one point (from 0.10 to 0.05 per direction; realized shares are lower because scores are limited to 1 - 5) raised the mean rank correlation with the reference ranking from 0.88 to 0.94 and the retention of the four leading risks from 37% to 62%, and improving only the laboratory risks helped the leading risks less than improving the impartiality and management risks. A retrospective validation protocol with pre-specified acceptance criteria is proposed. No AI method was implemented; the simulation does not evaluate any AI model, and the functions proposed are untested. The register, the random seed and the simulation script are given in the appendices.