AI-Driven Talent Analytics for Modern HR Solutions

  • Authors

    • Dr. Rajesh Kumar Sharma Professor, University of Delhi, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2021PI7X4K

    Published 02-04-2021

  • Artificial Intelligence, Talent Analytics, Human Resource Management, Machine Learning, Predictive Analytics, Workforce Analytics, Ethical AI

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    R. K. Sharma, “AI-Driven Talent Analytics for Modern HR Solutions”, ijmiet, vol. 4, no. 1, pp. 01–13, Feb. 2021, doi: 10.67228/30715628/IJMIET-2021PI7X4K.

  • Abstract

    Artificial Intelligence (AI) stands out as the new reality in Human Resource (HR) management and the way organizations attract, evaluate, improve, and maintain talent. Conventional HR methods, which rely mostly on manual assessment and judgement, are not only subjective, but are also inefficient and can only be scaled according to a finite extent. The increased data volume of the workforce, the data of recruitment and performance reviews, as well as the data of employee engagement, is growing exponentially and creates unexploited opportunities in the field of advanced analytics. Talent analytics AI-based uses machine learning and natural language processing, and predictive modeling to identify actionable insights usable to make data-driven HR decisions out of this dense and high-dimensional data. The given paper includes an in-depth analysis of AI-powered talent analytics as a fundamental element of the updated HR solutions. It discusses the changes in talent analytics between descriptive and diagnostic intelligence to predictive and prescriptive. The article systematically conducts a review of available literature to emphasize current models, methods, and applications, as well as exposing the essential issues, including algorithmic bias, data privacy, ethical issues, and explainability. It suggests a systematic approach that will combine data collection, principle, feature engineering, model creation, validation, and implementation in the enterprise HR ecosystem.Moreover, the paper argues the results of analysis in prominent HR areas such as optimization of recruitments, employee performance prediction, attrition prediction and planning of workforce. The effectiveness of AI-based solutions in comparison to conventional HR analytics can be proven with the help of quantitative performance indicators and comparative analyses. The discussion highlights the strategic maneuvers of AI-based talent analytics, especially in promoting agility, equity, and competitiveness in the organization. The paper ends by stating the direction of future research, which proposes explainable, ethical, and human-focused systems of AI as a way to guarantee the long-distance adoption of AI in HR settings.

  • References

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