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Artificial intelligence (AI) is increasing its coverage of human resources and staff-led recommendations. But the staff interpretations of automated recommendations are still important in project-based construction settings. The qualitative study examines how AI-supported recruitment, project implementation, performance assessment and learning in talent management are experienced in the Pakistan construction industry. Covering all HR management, engineering, supervision, skilled trade and digital operations, the study uses organizational equity to interpret staff responses and considers innovative performance as a requirement for interaction with AI-supported decisions on the basis of 18 codes of participant accounts. The thematic analysis identifies five interconnected issues: screening efficiency versus contextual competence; data visibility and procedural fairness; challenges to algorithmic ratings; unequal access to learning; and employee innovation as a source of improvement or resistance. The findings reveal that AI-supported decisions would be more acceptable if data mirrored practical skills, the assessment rules were transparent and the human assessment made available. Conversely, opaque evaluations, unequal digital access, and limited potential for challenging decisions can undermine perceived fairness. The study develops an evidence-informed qualitative framework and implications for governance without claiming statistically tested mediation, moderation or population-level generalizability.
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Muhammad Ehtisham Khan (2026) studied this question.
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