LAUSR

Purpose This paper aims to develop an artificial intelligence (AI) audit tool for auditing text-based evidence and determine its efficiency and effectiveness. Design/methodology/approach A manual audit checklist and an AI audit tool are developed with fuzzy front-end (FFE) from Innovation Management System Standard (IMSS) as the audit scope, First, a manual audit of five organisations is conducted to determine their compliance scores. The transcripts of the audit are recorded which are used by the AI audit tool to assign compliance scores for the same organisations. The effectiveness and efficiency of the AI audit tool are determined by comparing their results with the manual audit. Findings This paper demonstrates the development of the FFE AI audit tool which led to 92% improved efficiency while being 95% effective compared to a human auditor. Practical implications The publication of new financial and non-financial standards (such as ISO56002: IMSS) have implications for internal auditing (IA). The scope of IA must expand to include new standards while remaining efficient. Emerging technologies, such as AI help achieve this. Even though the use of AI in financial auditing is widely studied, it has not received similar attention in non-financial auditing. This paper develops a non-financial AI audit tool to audit an essential component of the IMSS, the FFE of innovation and determine its efficiency and effectiveness. Originality/value The study develops an FFE AI audit tool for the first time. The methodology used has practical and academic implications for the use of AI in non-financial auditing.