The integration of Artificial Intelligence (AI) is fundamental to the structural transformation of workforce acquisition framework operations. Modern enterprises increasingly depend on automated architectures to handle expansive applicant volumes, optimize workflow efficiencies, and fortify talent assessment strategies. This empirical review scrutinizes the functional implementation of algorithmic mechanisms across talent discovery, candidate parsing, fit-score prediction, conversational interfaces, interview logistics, candidate assessments, and predictive recruitment analytics. Furthermore, this study contrasts the pragmatic operational benefits against persistent structural limitations through the dual perspectives of corporate recruiters and prospective candidates. Drawing upon secondary academic literature, the study evaluates technological advancements, legal compliance risks, and core systemic constraints. Although algorithmic tools substantially elevate procedural velocity, computational scale, and organizational productivity, they do not inherently guarantee procedural equity or impartial decision-making. Pre-existing dataset skews, sub-optimal parameters, and opaque algorithmic logic can perpetuate institutional bias. Concerns regarding data privacy, computational explainability, candidate engagement, and governance further complicate implementation. Consequently, effective talent acquisition relies on hybrid socio-technical models where automated processing assists—rather than supplants—expert human evaluation. Organizations must balance algorithmic execution with active oversight, context-aware screening, structural bias testing, rigorous data governance, and regulatory transparency.

Sakshi Krushna Dhurjad, Prof. Kushal Dharmik, "Dynamics of Artificial Intelligence in Talent Acquisition: A Critical Appraisal of Contemporary Human Resource Protocols" International Journal of Scientific Research and Engineering Development, V9(5): Page(1533-1540) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.