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Stream/Area:
-
Subject:
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Paper ID:
IJSREM66060
Volume:
10 |
7 |
Jul 2026
Pages:
1-9
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
MBA
H & G H Mansukhani Institute of Management
Organizations across the Mumbai Metropolitan Region (MMR) are rapidly embedding AI-driven talent assessment tools into recruitment, promotion, and continuous performance-evaluation cycles. While these systems promise consistency and efficiency, an emerging body of evidence suggests that heavy reliance on algorithmic decision support may carry an unintended cost: a gradual erosion of employee judgment, sometimes described as skill decay or “cognitive debt.” At the same time, organizations are investing heavily in workforce agility as a strategic capability for navigating volatile, technology-driven markets. This conceptual paper argues that the relationship between AI-driven talent assessment adoption and workforce agility is not direct but is instead conditioned by the degree of alignment across three interdependent actors: the assessment tool itself (its design and transparency), the individual employee (their trust, self-efficacy, and cognitive engagement), and the organization (its governance, feedback culture, and reskilling infrastructure). We term this the Triadic Alignment Model. Where alignment across the tool–individual–organization triad is weak, AI-driven assessment is proposed to accelerate skill decay and, counter-intuitively, undermine the workforce agility it is meant to support; where alignment is strong, the same tools may instead free cognitive capacity for higher-order tasks and reinforce agility. Drawing on task-technology fit theory, person-environment fit theory, and dynamic capabilities theory, this paper develops seven testable propositions and a detailed mixed-methods design for a future multi-sector empirical study spanning IT/ITES, BFSI, manufacturing, and healthcare organizations in the MMR. The paper closes with implications for HR practice and a research agenda for scholars examining human–AI collaboration in talent management.
KEYWORDS
As AI-driven talent assessment becomes a standard feature of HR infrastructure across the Mumbai Metropolitan Region and beyond, organizations risk treating adoption as a simple efficiency upgrade rather than a triadic governance challenge. This paper has argued that the true determinant of whether such tools build or erode workforce capability lies not in the tool alone, but in the alignment achieved among the tool, the individual employee, and the organization around it. The Triadic Alignment Model and its accompanying propositions offer a starting point for the empirical work needed to test this claim, and, if supported, a practical diagnostic that HR leaders can use to steer AI-driven assessment toward its intended purpose: a more agile, not a more dependent, workforce.
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Publication Date
7/14/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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