AI integration in business excellence award assessment: assessors’ perspective

Purpose This study explores assessors’ and jury members’ perceptions of integrating artificial intelligence (AI) in Business Excellence (BE) award assessments. While AI can enhance efficiency, consistency and the quality of feedback, stakeholder acceptance remains underexplored. Understanding these perceptions is crucial, as trust is essential for effective AI implementation. Design/methodology/approach A sequential mixed-methods design was employed. First, survey data were collected from 248 assessors across different BE contexts to identify patterns of AI acceptance and use. This quantitative phase was followed by in-depth interviews with 24 assessors and jury members. The interview data were analyzed using thematic coding to explore participants’ concerns, expectations and preferences regarding the integration of AI into BE assessment processes. Findings Results show cautious optimism toward AI adoption. Participants recognized its potential to automate tasks and support decisions but expressed concerns about transparency, fairness and overreliance. Maintaining human oversight was viewed as critical to preserving the credibility of the assessment. Practical implications The study highlights key enablers and barriers to AI adoption and recommends hybrid models combining AI with human expertise. Findings support policymakers and practitioners in implementing AI responsibly within excellence frameworks. Originality/value This study contributes to AI and quality management literature by focusing on stakeholder perceptions rather than technical aspects, offering empirical insights into human acceptance in BE award evaluations.

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Publication Details

Journal
Measuring Business Excellence
Published
2026-09-16
DOI
https://doi.org/10.1108/mbe-04-2026-0104
Primary Topic
Ethics and Social Impacts of AI
Type
article
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article

AI integration in business excellence award assessment: assessors’ perspective

Mian M. Ajmal, Rassel Kassem, Hamoud Almahmoud
Measuring Business Excellence
Ethics and Social Impacts of AI
article

AI integration in business excellence award assessment: assessors’ perspective

Mian M. Ajmal, Rassel Kassem, Hamoud Almahmoud
article en

Abstract

Purpose This study explores assessors’ and jury members’ perceptions of integrating artificial intelligence (AI) in Business Excellence (BE) award assessments. While AI can enhance efficiency, consistency and the quality of feedback, stakeholder acceptance remains underexplored. Understanding these perceptions is crucial, as trust is essential for effective AI implementation. Design/methodology/approach A sequential mixed-methods design was employed. First, survey data were collected from 248 assessors across different BE contexts to identify patterns of AI acceptance and use. This quantitative phase was followed by in-depth interviews with 24 assessors and jury members. The interview data were analyzed using thematic coding to explore participants’ concerns, expectations and preferences regarding the integration of AI into BE assessment processes. Findings Results show cautious optimism toward AI adoption. Participants recognized its potential to automate tasks and support decisions but expressed concerns about transparency, fairness and overreliance. Maintaining human oversight was viewed as critical to preserving the credibility of the assessment. Practical implications The study highlights key enablers and barriers to AI adoption and recommends hybrid models combining AI with human expertise. Findings support policymakers and practitioners in implementing AI responsibly within excellence frameworks. Originality/value This study contributes to AI and quality management literature by focusing on stakeholder perceptions rather than technical aspects, offering empirical insights into human acceptance in BE award evaluations.

Measuring Business Excellence
Abu Dhabi University (AE), University of Sharjah (AE)
Openalex Percentile: Top 7%
Ethics and Social Impacts of AI
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AI integration in business excellence award assessment: assessors’ perspective — Mian M. Ajmal, Rassel Kassem, et al. · Measuring Business Excellence (2026) | TGRS Research Map | TGRS