Optimizing Consultancy Recruitment Through AI Analytics

In the competitive consultancy sector, recruitment effectiveness is a critical determinant of organizational agility and growth. Traditional recruitment funnels, however, remain fragmented, administratively burdensome, and prone to bias, leading to suboptimal talent fitment and extended hiring cycles. To address these systemic inefficiencies, this conceptual paper introduces the Modular Requirement Funnel (MRF), an AI-driven and analytics-powered framework designed to optimize talent acquisition processes. Grounded in the Resource-Based View (RBV), Dynamic Capabilities, and High-Performance Work Systems (HPWS), the study reconceptualizes recruitment as a strategic organizational capability rather than a purely operational function. The proposed MRF framework integrates three core components: a centralized data repository to ensure transparency, AI-powered automation to enable objective and efficient screening, and a continuous learning feedback loop to enhance predictive accuracy. The paper further develops testable theoretical propositions linking Human Capital Analytics (HCA) maturity and ethical algorithmic governance to strategic outcomes, including quality of hire and sustained competitive advantage.