What does this research mean for the field?

Implementing an AI-driven Decision Support System and NLP-based outreach module for talent acquisition reduces candidate processing time by over 90%, yields approximately $700 in savings per vacancy, and increases candidate reply rates by 40-60%. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

May 1, 2026Open Access

The Economic Efficiency of AI-Driven Talent Acquisition

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Abstract

Investigated the critical inefficiency of manual recruitment in the technology sector, where HR professionals spend over 50% of their working time on routine document processing, causing recruiter fatigue and hiring errors costing up to six months of a developer's salary. Developed an automated Decision Support System (DSS) built on FastAPI, PyMuPDF, and the Nemotron-3 large language model, transforming unstructured PDF resumes into a structured Candidate Scorecard with a Match Score, Skill Gap Analysis, and targeted interview questions. Proposed a Speed-Dating Bot module that increases candidate reply rates by 40–60% through NLP-driven personalized outreach. Processing a vacancy with 50 candidates decreases from 25. 5 to 2 hours, yielding approximately 700 in savings per vacancy at near-zero operational cost. Confirmed full GDPR compliance through Docker containerization with in-memory-only data processing.

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Cite This Study

Artemenko et al. (Fri,) studied this question.

synapsesocial.com/papers/6a088137ab15ea61dee8e4cb https://doi.org/https://doi.org/10.36074/grail-of-science.01.05.2026.033

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