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The query revolves around the expense associated with implementing Artificial Intelligence in the culminating stages of a selection process. This involves the financial investment required for software licenses, hardware infrastructure (if needed), model training, data storage, integration, maintenance, and personnel required to operate and manage the system. For example, a large enterprise might incur significantly higher expenditure than a smaller organization due to the scale of its recruitment needs and data volume.

Understanding this financial commitment is crucial for budgeting and assessing the return on investment. Effective use of AI in this context can streamline hiring, reduce bias, improve candidate experience, and ultimately lead to better hiring decisions, potentially saving significant costs in the long run by minimizing employee turnover and improving overall workforce quality. The initial costs need to be balanced against these potential long-term efficiency gains and strategic advantages. These solutions initially gained traction in sectors with high-volume recruitment, later expanding to roles with specialized skill sets, driven by advancements in AI and machine learning capabilities.

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