Технический анализ решений на основе искусственного интеллекта для согласования кадровой политики с удовлетворенностью сотрудников

DOI:

Employee satisfaction is a cornerstone of organizational productivity and retention. However, traditional HR policies often fail to meet the evolving needs of a diverse workforce, leading to dissatisfaction and disengagement. The advent of advanced technologies, particularly AI, offers transformative solutions to bridge this gap. This paper examines the technological effects and AI approaches that can align HR policies with employee satisfaction, ensuring a more engaged and productive workforce, follow Figure 1. Employee satisfaction is a critical driver of organizational success, yet traditional HR policies often fail to meet the diverse and evolving needs of employees [1]. Artificial Intelligence (AI) and Machine Learning (ML) offer transformative capabilities to bridge this gap by enabling personalized, data-driven HR practices. This technical analysis explores the underlying technologies, algorithms, and systems that power AIdriven HR solutions, focusing on their potential to improve employee satisfaction [2].

Employee satisfaction is a cornerstone of organizational success, directly influencing productivity, retention, and overall workplace morale. However, a persistent challenge for many organizations is the misalignment between HR policies and the actual needs and expectations of employees [3]. Traditional HR policies, often rigid and generic, fail to account for the diverse and evolving dynamics of the modern workforce. This disconnect can lead to dissatisfaction, disengagement, and ultimately, higher turnover rates. In an era where employees increasingly seek personalized experiences and meaningful engagement, organizations must adopt innovative approaches to ensure their HR strategies resonate with their workforce [4].

Enhanced by AI

Figure 1. Artificial Intelligence approach HR Functions.

The rapid advancement of technology, particularly Artificial Intelligence (AI), offers a transformative solution to this challenge. AI and ML enable organizations to move beyond one-size-fits-all policies and embrace data-driven, adaptive HR practices [5]. By leveraging predictive analytics, sentiment analysis, and personalized recommendation systems, AI can help organizations design and implement HR policies that are not only aligned with employee needs but also responsive to changing workplace dynamics [6]. These technologies empower HR teams to make informed decisions, foster a culture of continuous feedback, and create a more inclusive and engaging work environment (Figure 2).

At the heart of AI-driven HR solutions is the ability to collect, analyze, and act on vast amounts of data. Employee feedback, performance metrics, and even real-time communication patterns can be processed to uncover insights that were previously inaccessible. For instance, predictive analytics can identify early signs of employee disengagement or turnover risks, enabling proactive interventions [7]. Sentiment analysis, powered by Natural Language Processing (NLP), can decode the emotions and concerns expressed in employee surveys or communication channels, providing HR teams with a deeper understanding of workforce sentiment [8]. Meanwhile, personalized recommendation systems can tailor learning and development opportunities, benefits, and career paths to individual employees, enhancing their sense of value and belonging.

Figure 2. Flow of AI in HRM.

However, the integration of AI into HR practices is not without its challenges. Ethical considerations, such as data privacy and algorithmic bias, must be carefully addressed to ensure that AI solutions are fair, transparent, and trustworthy [9]. Organizations must also invest in the necessary infrastructure and expertise to implement these technologies effectively. To simplify the processes of hiring, onboarding, development, performance management, and retention, AI automates the many stages of the employee lifecycle by providing data-driven insights, improving decision-making, and customizing experiences, in Figure 2. Despite these hurdles, the potential benefits of AI-driven HR solutions are immense, offering a pathway to more agile, employee-centric policies that drive satisfaction and organizational success [10].

This paper explores the role of technology and AI in addressing the discrepancy between HR policies and employee satisfaction. It examines the technical foundations of AI-driven HR solutions, including predictive analytics, sentiment analysis, and personalization engines, and highlights their applications in real-world scenarios. Additionally, the paper discusses the challenges and ethical considerations associated with implementing AI in HR, providing a roadmap for organizations seeking to harness these technologies effectively. By embracing AI, organizations can not only bridge the gap between policies and employee satisfaction but also pave the way for a more engaged, productive, and future-ready workforce.

LITERATURE REVIEW

The integration of technology and AI into HR practices has emerged as a promising solution to address the misalignment between HR policies and employee satisfaction. Predictive analytics, sentiment analysis, and personalization engines have revolutionized how organizations approach employee satisfaction by analyzing historical and real-time data [11]. ML algorithms like Random Forest and Gradient Boosting have been employed to predict employee turnover with high accuracy, enabling proactive interventions and fostering a more engaged workforce [12]. Sentiment analysis, powered by Natural Language Processing (NLP), has gained traction as a tool for understanding employee feedback. Transformer-based models like BERT and GPT have demonstrated remarkable capabilities in decoding emotions and concerns expressed in employee surveys, emails, and chat logs [13]. Companies like Google have used sentiment analysis to identify dissatisfaction trends and implement targeted interventions, resulting in improved employee morale. Personalization has also made significant strides with collaborative filtering and reinforcement learning algorithms, creating personalized recommendation systems for learning and development, benefits, and career paths [14]. However, the implementation of AI in HR faces ethical concerns, such as data privacy and algorithmic bias, and technical challenges, requiring significant investment in infrastructure and expertise [15]. The transformative potential of technology and AI in aligning HR policies with employee satisfaction is evident, but successful implementation requires addressing ethical and technical challenges [16]. AIdriven HR solutions will play an increasingly critical role in fostering a more engaged and satisfied workforce [17]. The alignment of HR policies with employee satisfaction has gained renewed attention in the post-2020 era, driven by the rapid evolution of workplace dynamics and the increasing adoption of advanced technologies [18]. The COVID-19 pandemic accelerated the shift toward remote work, hybrid models, and digital transformation, highlighting the need for more flexible and employee-centric HR practices. Recent literature emphasizes the role of AI and ML in addressing these challenges, offering innovative solutions to enhance employee satisfaction and engagement [19]. One of the key areas of focus in recent research is the use of predictive analytics to anticipate employee needs and behaviors [20]. Studies have shown that AI-driven predictive models can effectively identify turnover risks and engagement trends by analyzing large datasets, including performance metrics, attendance records, and employee feedback [21]. For example, ML algorithms such as XGBoost and Random Forest have been employed to predict employee attrition with high accuracy, enabling organizations to implement targeted retention strategies [22]. These tools allow HR teams to move from reactive to proactive decision-making, fostering a more engaged and satisfied workforce. Sentiment analysis, powered by Natural Language Processing (NLP), has also emerged as a critical tool for understanding employee feedback [23]. Recent advancements in transformerbased models like BERT and GPT-4 have significantly improved the accuracy of sentiment analysis, enabling organizations to decode emotions and concerns expressed in employee surveys, emails, and chat logs [24]. For instance, companies like Microsoft have leveraged sentiment analysis to monitor employee well-being during the pandemic, identifying stress points and implementing supportive measures [25]. This real-time feedback mechanism helps organizations address dissatisfaction before it escalates, improving overall morale [26]. Another significant development is the use of personalization engines to tailor HR policies and benefits to individual employee needs. Collaborative filtering and reinforcement learning algorithms have been widely adopted to create personalized learning and development programs, career paths, and wellness initiatives [27]. For example, IBM’s AI-driven HR platform uses personalized recommendations to align employee growth with organizational goals, resulting in higher engagement and retention rates [28]. These systems enhance employee satisfaction by addressing individual preferences and fostering a sense of value and belonging. Despite these advancements, the implementation of AI in HR is not without challenges. Recent literature highlights ethical concerns, such as algorithmic bias and data privacy, as critical barriers to adoption [29]. Ensuring fairness and transparency in AI-driven HR practices is essential to maintaining employee trust. Additionally, integrating AI tools with existing HR systems requires significant investment in infrastructure and expertise, posing technical and financial challenges [30]. In post-2020 literature underscores the transformative potential of AI and technology in aligning HR policies with employee satisfaction. Predictive analytics, sentiment analysis, and personalization engines offer powerful tools for creating dynamic, responsive HR practices. However, successful implementation requires addressing ethical and technical challenges, ensuring that AI solutions are fair, transparent, and aligned with organizational values. As organizations continue to navigate the complexities of the modern workforce, AI-driven HR solutions will play an increasingly critical role in fostering a more engaged and satisfied workforce.

METHODOLOGY

This study employed a mixed-method technique to examine how AI affects employee-pleasing human resource policy. The report examines how Google, Unilever, and IBM are improving HR management using AI. In the case study, researchers examine documents, collect secondary data, and assess the initial implementation's effects. Many AI models are evaluated. Decision trees, SVMs, clustering, and reinforcement learning are examples. An experiment was conducted using sentiment analysis to discover employee satisfaction patterns and emotional components. Past HR data was used to forecast employee turnover and identify effective HR interventions. The models were cross-validated and assessed using organisational standards and satisfaction metrics. Semi-structured interviews with HR analytics specialists and AI system developers were used to examine installation issues, solutions, and expected outcomes. We utilised the Delphi Method to agree on AI-powered HR initiatives' efficacy and feasibility.

HR performance management tools (PMTs) assist in monitoring, analysing, and improving employee metrics. The process includes goal planning, performance statistics, feedback and coaching, skills monitoring, and seamless HR system connection. Performance improvement plans, succession planning, and 360-degree feedback are PMT characteristics. Goal-setting, performance evaluations, feedback and coaching, performance improvement plans, and development planning are PMT practices. In conclusion, HR PMT includes software and methodologies for managing and enhancing employee performance [31].

AI-powered training programs are the only method to bridge skill gaps and promote progress. AI helps firms analyse employee performance data and identify issue areas, ensuring staff get job-relevant training. AI allows lifetime learning so workers may acquire new skills when they choose. Use real-time data to create targeted development strategies and track employee success. The method reveals trends, strengths, and improvement areas. Human resources AI solutions are provided from GPTZero, Lever, Greenhouse, X0PA AI, Pymetrics, Textio, HireVue, and IBM Watson. Organisations must first assess what they need, then whether they are practical, then choose the right ones, and lastly prioritise user experience to maximise AI tool adoption [32]. Using AI in HR requires a pilot program in a single function, honesty with employees, and training for HR experts on how to utilise the technologies, see Figure 1 and Figure 3.

Figure 3. AI using in HRM.

Check key metrics and AI solution efficacy periodically. HR workers must be trained to recognise and avoid biases in AI systems to utilise them effectively. By training HR professionals to use AI, companies can maximise automation and reduce risk.

Employee satisfaction and HR policies: a

CONTRADICTION

HR rules attempt to standardise workplace operations, yet they frequently limit individuality and flexibility. Policies may not be flexible enough to satisfy employee needs or workplace dynamics. Lack of feedback makes workers feel that their thoughts don't matter, which annoys them. Poorly articulated policies may cause confusion and resentment. Politics that strive to please everyone fail. Due to these issues, HR practices must be more flexible and employee-focused. Technological

Impact on HR. Technology has revolutionised HR, making it more efficient and data-driven. Technology advancements include: Freeing up HR workers to work on higher-level initiatives. Data analytics reveal workers' preferences and job satisfaction. Real-time feedback helps management and workers to communicate. Customising perks and rules for each employee. These achievements provide the framework for HR AI. Learning-focused HR encourages employees to constantly learn through formal training programs, one-on-one coaching and mentoring, and technological tools. Organizations must recognize learning as a goal and encourage open communication and criticism. Human resource management includes formal training, on-the-job training, coaching, mentoring, LMS, online resources, knowledge sharing, open communication, and prioritizing learning.

Figure 4. HRM culture (left) and learning method (right).

Socio-cognitive Architectures

Cognitive Architectures

Cross Domain Architectures

Al CAPABILITY DEVELOPMENT

AI methods to raise contentment levels in

THE WORKPLACE

AI offers inventive methods to align HR regulations with employee pleasure. Essential strategies:

• •    Prediction Analysis. AI can analyse previous and current data to predict staff needs. Predictive analytics may identify engagement and turnover trends, enabling proactive policy modifications.

• •    Customised Employee Interactions. AI-powered platforms may customise staff growth programs, learning opportunities, and benefits. Chatbots and virtual assistants boost workplaces by giving personalised support.

• •    Evaluation of Public Opinion. AI may analyse employee feedback (e.g., surveys, emails) to identify mood and dissatisfaction. Natural

language processing (NLP) helps HR teams solve difficulties.

• •    Auto-policy modification. AI enables HR rules to be rapidly changed to meet employee needs and corporate goals. Productivity statistics may enable on-demand flexible employment options. Minimising bias Data-driven AI may reduce bias in recruiting, promotion, and performance evaluations. A more equal workplace enhances employee pleasure.

• •    Worker Health. AI-powered systems can analyse stress and work behaviours to monitor worker health.     Vacation or wellness program

suggestions may enhance happiness. Advancement and Education AI-driven learning systems that adapt training may help employees feel valued and master new abilities. Thus, organisational goals guide staff development.

AI-powered HRM solutions require a robust technology foundation to acquire, process, and analyse data. The key sections are: Data Gathering Layer, Employee surveys, KPIs, email and chat histories, wearable tech APIs, site scraping, and IoT devices for real-time data collection. Organisational and unstructured data exist:

• •    Data Cleaning. Data cleaning uses Pandas in Python or Apache Spark to remove noise and abnormalities. Integrating data from several sources into a SQL database is data integration. There are cloud storage services like Amazon S3 and Google Cloud Storage.

• •    The AI and Analytics Layer. Predicting engagement, happiness, and staff turnover via ML models. Use NLP to analyse employee comments for sentiment. Personalisation engines may tailor policies and benefits via collaborative filtering or reinforcement learning.

• •    User Interface Layer. HR professionals may visualise data using Tableau or Power BI. AI-powered conversational interfaces help workers instantly.

ANALYSIS AND RESULTS

Transparency in communication, recognition of efforts, and opportunities for advancement are strongly linked to employee pleasure, according to sentiment analysis results. Clustering algorithms identified three main groups of employees: compensation-orientated, culture-orientated, and career-orientated, each with unique factors influencing job satisfaction. Support vector machines achieved 92% recall and decision trees 89% precision when it came to forecasting staff turnover. The models also pinpointed the main factors that influence turnover, including the quality of management, the amount of work required, and the balance between work and personal life.

Key algorithms and models

AI-driven HR solutions rely on a variety of algorithms and models to analyze data and generate insights. Key examples include:

• a.    Predictive Analytics

• •    Algorithms: Random Forest, Gradient Boosting (XGBoost, LightGBM), and Neural Networks.

• •    Use Case: Predicting employee turnover by analyzing historical data on turnover rates, performance metrics, and engagement scores.

• •    Technical Implementation:

• b.    Sentiment Analysis

• •    Algorithms: Transformer-based models like BERT, GPT, and LSTM for NLP.

• •    Use Case: Analyzing employee feedback to identify dissatisfaction trends.

• •    Technical Implementation:

0.92}]

from transformers import pipeline sentiment_pipeline = pipeline("sentiment-analysis") feedback = "I feel undervalued and overworked." result = sentiment_pipeline(feedback)

print(result) # Output: [{'label': 'NEGATIVE', 'score':

• c.    Personalization Engines

• •    Algorithms: Collaborative Filtering, Matrix Factorization, and Reinforcement Learning.

• •    Use Case: Recommending personalized learning programs or benefits.

• •    Technical Implementation:

from surprise import SVD from surprise import Dataset data = Dataset.load_builtin('ml-100k')

Estimated rating for user 1 on item 100

• d.    Bias Detection and Mitigation

• •    Algorithms: Fairness-aware ML models (e.g., adversarial debiasing).

• •    Use Case: Ensuring fairness in recruitment and promotions.

• •    Technical Implementation:

from aif360.algorithms.preprocessing import Reweighing privileged_groups = [{'gender': 1}]

unprivileged_groups = [{'gender': 0}]

RW =

Reweighing(unprivileged_groups, privileged_groups) dataset_transformed =

RW.fit_transform(dataset)

The conversation provided insight into how AI improved training customisation and significantly cut down on recruitment time, sample model in Figure 5. However, the Delphi panel repeatedly raised concerns about algorithmic fairness and openness. For AI to be useful in HR, the Delphi panel agreed that transparent frameworks, continuous procedures for employee input, and strong data governance rules are necessary. The performance visualization shown in Figure 6 and result presented in Table 1 and 2.

Cluster analysis divided employees into groups based on their level of satisfaction; HR initiatives met with varying degrees of success with each subset, in Figure 5. Workers with a pay emphasis were more concerned with financial gain, while careerists were more interested in the opportunities for professional development. Prediction models' high rates of accuracy and recall provide strong evidence that AI systems can detect impending staff departures, shown in table 3. Human resources departments may now get detailed feedback in real-time using qualitative sentiment analysis.

Figure 5. AI training model.

Table 1. Importance Scores of Satisfaction Factors.

Satisfaction Factor	Importance Score (%)
Communication	85
Recognition	78
Career Growth	82
Compensation	69
Work-Life Balance	74

Table 2. Predictive model performance.

Model	Precision	Recall
Decision Tree	0.89	0.84
Support Vector Machine	0.87	0.92
Logistic Regression	0.82	0.79
Random Forest	0.91	0.89

Table 3. Clustering results of employee segments.

Cluster Label	Key Characteristics	Primary Satisfaction Driver
Career-Focused	High ambition, value for growth, skill development	Career Growth
Culture-Focused	Emphasis on team dynamics, inclusivity, collaboration	Recognition & Communication
Compensation-Focused	Concerned with pay equity, bonuses, financial benefits	Compensation

Factor

Figure 6. Employee satisfaction factor.

DISCUSSION

This study demonstrates that AI has significant potential for aligning HR policy with employee satisfaction. ML models have shown the ability to derive valuable insights from complex data by predicting factors that influence cluster satisfaction and attrition. Such analysis enables human resources professionals to provide targeted interventions. Ethical aspects, including data protection, transparency, and justice, must be meticulously evaluated prior to the use of AI solutions. In the absence of frequent audits, dividing employees into groups based on satisfaction attributes may reinforce biases and stereotypes. Human oversight is essential in critical HR functions since workers' perceptions of AI-driven decision-making influence their trust and acceptance of these technologies [33]. Data scientists, human resources professionals, and corporate leaders must collaborate across departments to ensure that AI solutions align with organisational goals and values.

Although AI has many advantages, companies still need to deal with some problems. Promoting the safe and ethical handling of employee data. Workers need to know how AI is factored into decisions. Making sure AI systems are trained on varied and impartial datasets is important to address bias in AI models. Finding a Balance Between AI-Generated Insights and Human Emotions and Judgement. Implementation Best Practices Organisations may make better use of AI in HR if they do the following. Get workers involved in creating and implementing AI-driven solutions. Maintain a vigil on the effects of AI technologies on morale by checking in on a regular basis. Assist HR teams in becoming proficient users of AI products by providing them with the necessary training. Check that AI uses don't conflict with company ethics and principles.

Extensive Case Studies:

• •    Google. Utilises AI to interpret employee input and anticipate potential risks of turnover, allowing for preemptive measures. Natural language processing and predictive analytics. Google puts AI to work by analysing employee input and predicting the likelihood of turnover. In order to proactively intervene, HR must first determine which workers are at danger. Staff happiness and retention were both enhanced.

• •    Unilever. Uses AI to enhance applicant experience while reducing prejudice in recruiting. Analysis of video interviews by natural language processing and face recognition. AI evaluates candidates' answers and body language to decrease prejudice in the recruiting process. Better candidate experience and more diverse recruiting as a result.

• •    IBM customises training programs for employees by using AI-powered learning systems.

Despite the promising future of AI-driven HR solutions, there are a number of technological hurdles to overcome before they can be fully implemented: Make sure the data used to train models is clean, accurate, and representative by focussing on data quality. Real-time processing of massive data sets is an example of scalability. Integrating AI technologies with current HR systems (such as an HRMS or an applicant tracking system) is an example of integration. Tackling prejudices in AI models and guaranteeing openness.

CONCLUSION