A Hybrid Generative AI and Predictive Analytics Model for Human Resource and Customer Relationship Management Optimization

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Background As Artificial Intelligence (AI) is more widely adopted, it opens up new possibilities for organizations to optimise their performance with intelligent decision-making and automation. The study presents a Hybrid Generative AI and Predictive Analytics Model (HGPA-PAM) for optimizing Human Resource Management (HRM) and Customer Relationship Management (CRM) functions. Objective To develop and evaluate a hybrid model that combines machine learning algorithms with predictive analytics and Generative Artificial Intelligence (GenAI) to predict organizational outcomes and provide actionable recommendations for HRM and CRM optimization. Materials and Methods The proposed model uses predictive models to determine employee attrition risks, workforce needs, customer attrition, and sales trends. The Generative AI module then translates the predictions into personalized employee retention recommendations, staffing plans, customer engagement strategies, and marketing content. The model was evaluated through simulation studies comparing its performance against traditional analytical methods. Results According to the results obtained by simulation, the proposed model demonstrated higher accuracy in predicting key organizational variables and offered significant improvements in employee retention, customer satisfaction, sales conversion rate, and decision-making efficiency compared with traditional analytical methods. Conclusion The results highlight that predictive intelligence combined with generative capabilities can significantly improve enterprise adaptability, efficiency, and strategy development. The HGPA-PAM framework represents a valuable tool for managing today's dynamic enterprise environment.