# From Recruitment to Retention: AI Tools for Human Resource Decision-Making
## 7 AI in Retention and Attrition Prediction
### Personalized Retention Strategies  
By analyzing replacement costs and suggesting salary adjustments, AI can optimize effective compensation planning, especially for \"at risk\" personnel. This ensures competitive pay. Research has demonstrated that mentorship programs and career routes help employees grow professionally and personally, which in turn increases job satisfaction and retention [103; 104]. Employees are more satisfied and loyal to their employers when they have opportunities to improve their own well-being, such as more adaptable work schedules and better work-life balance [103; 104]. According to [PERSON] [105] and [PERSON] [106], firms can boost their performance, engage their employees, and decrease turnover by implementing AI-driven tactics. New studies show that predictive analytics powered by AI may successfully identify high-risk employees. For example, [PERSON] [107] highlights how AI-powered firms may predict employee turnover by sifting through data from performance reviews and surveys, among other sources, to identify patterns in behavior. Human resources professionals can improve retention rates by taking a proactive approach and implementing targeted interventions before turnover risks become worse [108]. AI is useful for more than just making predictions; it can also help with things such as tailoring pay and advancement chances to each individual. [PERSON] et al. [109] state that HR practitioners can automate routine operations using AI solutions, freeing them up to focus on strategic issues such as employee engagement and individualized career paths. In order to recruit and retain top people, firms must be able to offer compensation packages that are customized according to individual performance criteria and market trends. By combining AI with emotional intelligence frameworks, a more personalized work environment can be created that supports employee development by making growth possibilities more personalized. With predictive modeling and tactical interventions, AI has demonstrated a great deal of promise in lowering employee turnover rates. For example, by analyzing data from HR information systems (HRIS), machine learning algorithms--specifically, extreme gradient boosting (XGBoost)--have been successfully used to predict employee turnover. This has allowed organizations to implement retention strategies that are specifically targeted at employees based on accurate predictions [110]. Research has indicated that comprehension of factors influencing intentions to leave a workplace, such as job stress and chances of other employment, is crucial. This will help management create policies that improve employee satisfaction and engagement [111]. Expert systems powered by AI can create logical connections between employee behavior and attrition, giving businesses insights to improve their retention tactics [112]. In industries where human connections are essential, such as real estate, the retention of tacit knowledge through AI-driven knowledge management systems is critical to reducing the negative consequences of turnover [113]. AI has emerged as a powerful tool in predicting and addressing employee attrition by leveraging machine learning models and predictive analytics. These technologies enable HR professionals to identify high-risk employees, analyze key factors influencing turnover, and implement targeted retention strategies. By offering personalized compensation plans, career development opportunities, and proactive interventions, AI enhances job satisfaction and reduces turnover. However, balancing these technological advancements with human-centric approaches remains critical for fostering long-term employee loyalty and engagement. Table 2 provides a comprehensive summary of the key AI tools used across the various stages of an employee's career lifecycle, from recruitment to retention. It highlights the specific applications of these AI technologies, summarizes key contributions and findings from the literature, and identifies gaps or limitations in current research.