The application of AI in the field of human resource management may only bring about an improvement in efficiency, and the next step will inevitably promote profound changes in the underlying logic such as organizational structure, decision-making mechanism and corporate culture, and promote the transformation of enterprises from traditional human-driven to digital and intelligent organizational forms.

Text: Guo Yajun (Director, MBA Education Center, Northwest University)

In the wave of digitalization, AI is becoming a key force in reshaping human resource management, driving the efficiency transformation of the entire process from recruitment to performance management. As management guru Peter Dulac said, "Efficiency is about doing things right, and efficiency is about doing things right". AI technology not only optimizes the cumbersome process of traditional human resource management, but also provides unprecedented decision-making insights for enterprises in a data-driven way, truly opening a new chapter in human resource management.

How does AI realize the digital reconstruction of human resource management?

First, the intelligent recruitment system: from "audition" to accurate matching

Traditional recruitment is like looking for a needle in a haystack or even a blind man's elephant, recruiters need to manually screen a large number of resumes, not only subjectively screen the truth of the information but also strive to identify the characteristics of candidates, which is inefficient and easy to miss potential talents. The intervention of AI has completely changed this situation. Microsoft CEO Satya Nadella pointed out that "the goal of AI is not to replace humans, but to empower them." With the help of natural language processing and machine learning technology, AI can automatically parse keywords in resumes, accurately extract key information such as candidates' work history, skills and expertise, and educational background, and build a comprehensive and detailed talent portrait based on this.

This process greatly improves the accuracy of job matching. For example, after a headhunting company introduced an AI recruitment system, the matching degree of people and jobs soared from 82% to 0%, and the recruitment cycle for a single position was shortened by 0 times. The system can not only identify the superficial keywords in the resume, but also deeply determine whether the candidate's communication style and thinking mode are suitable for the team culture of the target position through semantic analysis, so as to ensure that new employees can quickly integrate into the team and improve work efficiency and teamwork. Based on micro-expression recognition and voice intonation analysis technology, the AI "interviewer" can capture the candidate's subtle expression changes and voice characteristics, gain insight into his emotional state, self-confidence and true attitude towards work, and can assist the interviewer to accurately judge the authenticity of the candidate's on-site statement and even the career stability in the next 0 years, with an accuracy rate of up to 0%. The improvement of intelligent recruitment methods can screen out more loyal and stable talents for enterprises, and effectively reduce the recruitment, training costs and business risks caused by personnel turnover.

The low efficiency and high cost of manual "audition" and the shortcomings of "halo effect", "proximate effect" and "like-me effect" can all be solved by the intelligent recruitment system, but it must also be recognized that the intelligent recruitment system is not a panacea, nor should it completely replace professional interviewers. The premise of accurate matching of intelligent recruitment system is that the identification technology has good reliability and validity, the authenticity of the candidate's resume information and the standardization of the job description.

Second, the automated training system: from "one-size-fits-all" to personalized empowerment

Employee training is an important part of enterprises to enhance the competitiveness of talents, but the traditional "one-size-fits-all" training model often cannot meet the individual needs of employees, resulting in poor training results. The application of AI technology has transformed the training system from extensive to refined and personalized.

AI can build a capability map of employees based on multi-dimensional data such as job requirements, skill levels, past training records, and job performance, and then dynamically generate personalized learning paths. Taking a financial institution as an example, after the introduction of the intelligent mentor system, the training cycle of new products has been reduced by 82%, and the knowledge retention rate has been increased to 0%. For example, for financial product marketers, the system may recommend courses such as "Financial Market Analysis" and "Customer Relationship Management Skills", and adjust the learning plan in real time according to the learning progress and feedback of employees to ensure that the training content is closely integrated with the actual needs of employees.

Digital employee "mentors" provide employees with more intimate training services through intelligent interaction. It can communicate with employees in real time, understand the problems and confusions encountered by employees at work, and then recommend relevant courses such as "Pyramid Collaboration Principles" to help employees solve practical work problems. As management scientist Tom Peters said, "The essence of a learning organization is the ability to continuously create value." At the same time, the digital employee "mentor" can also track the learning effect of employees, collect feedback from employees, form a closed loop of "learning-practice-feedback", continuously optimize the training content and methods, improve the learning experience and training effect of employees, and help employees grow rapidly in their careers.

It is foreseeable that the AI-powered automated training system can accurately identify the training needs of the training object, and whether it can be personalized and empowered also depends on the supply capacity that matches the demand, and the supply-side problems such as training content, training methods, and training forms.

Third, data-driven performance management: from subjective evaluation to intelligent insight

Performance management is a key and difficult point for all companies. Traditional performance management methods mostly rely on subjective evaluation, which is easily affected by factors such as personal bias and subjective cognition of evaluators, resulting in insufficient objective and fair evaluation results. How to build strategy-oriented performance management? How to improve the objectivity and accuracy of performance appraisal? The application of AI technology has brought a new data-driven perspective to performance management, which can be based on massive data inside and outside the industry and accurate portraits of companies, and can be used to establish a strategy-based performance management system with strategic management tools such as BLM (Business Leadership Model) and BSC (Balanced Scorecard). From the perspective of performance appraisal, AI can integrate data from multiple dimensions, such as employees' work task completion, project results, teamwork performance, customer feedback, etc., to build a comprehensive talent value evaluation model. Through in-depth analysis of multi-dimensional data, data-based AI models can more accurately tap the potential capabilities and contributions of employees, avoiding the dilemma of "partial generalization" and "formality" of indicators common in traditional assessments.

Compensation management, which is closely related to performance appraisal results, can also be optimized and improved with the help of AI compensation assistant. Data-driven performance management can avoid the phenomenon of "lifeless without performance awards" and "endless quarrels with performance awards" that may be brought about by the "head-patting" decisions of performance pay and year-end performance awards. Data-driven salary adjustment plans with incentive effects can be generated in a very short period of time according to many parameters such as market conditions, industry salary levels, employee performance, and working years, so as to enhance employees' satisfaction and trust in the company's performance management and compensation system, and reduce the turnover rate with loyalty.

How does AI reshape the underlying logic of human resource management?

The application of AI in the field of human resource management may only bring about an improvement in efficiency, and the next step will inevitably promote profound changes in the underlying logic such as organizational structure, decision-making mechanism and corporate culture, and promote the transformation of enterprises from traditional human-driven to digital and intelligent organizational forms. Peter Dulac predicted that "the organization of the future will no longer be hierarchical, but a network of knowledge workers".

First, the deconstruction and reorganization of the organizational structure

The in-depth application of AI technology has forced the traditional human resource management structure to undergo a radical change. Deloitte China Consulting Director Yun Peng predicts that the three-pillar HR model proposed by David Ulrich (COE, HRBP, SSC) will be reconstructed into DP (Digital Partner), DI (Digital Agent), and DR (Digital Relationship). This change indicates that enterprises need to rethink their organizational structure to adapt to the needs of the AI era. Under this trend of change, Neusoft has actively explored and launched the TalentBase digital intelligent human capital management product, which realizes the automation and real-time management of the whole life cycle of employees by introducing digital employees, covering the four major scenarios of recruitment, personnel, growth, and decision-making. The digital employee "assistant (HR partner)" can automatically process employees' leave requests, from receiving the request, reviewing the data, to triggering the leave process, without manual intervention, greatly improving the efficiency of the human resources department. In terms of employee career development, the digital employee "mentor (growth partner)" can accurately analyze and provide targeted ability improvement suggestions and course recommendations according to employees' needs, such as job transfer intentions, to help employees achieve their career goals, and also cultivate more adaptable and competitive talents for enterprises. This innovative organizational structure model breaks the hierarchical restrictions of traditional human resource management, makes information transmission more direct and efficient, and lays the foundation for agile decision-making and rapid response to market changes for enterprises.

Organizational inertia will be the biggest obstacle to organizational structure and reorganization. In particular, when the application of AI technology has a negative effect due to the "lagging" of the old organizational structure, enterprise leaders often question the maturity and adaptability of AI technology, rather than promoting the reorganization of the organizational structure and returning to the original work activity mode and habits.

Second, the decision-making mechanism of man-machine coordination

在AI時代，決策不再是單純依靠管理人員的主觀判斷，而是人機協同的過程，通過數據驅動和智慧分析，實現更科學、更高效的決策。某跨國集團在組織架構調整過程中，借助AI技術類比了200種不同的組織架構方案。AI系統綜合考慮了市場環境、業務需求、員工能力分佈等多維度數據，對每種方案的潛在效果進行了精準預測和評估。最終選定的混合式架構，使該集團的決策效率提升了35%，每年節省管理成本超千萬。亞馬遜創始人傑夫・貝佐斯曾說：“決策速度比決策完美更重要”。通過人機協同，企業能夠在複雜多變的市場環境中，快速做出最優決策，提升市場競爭力。

The cooperation between Fosun Tourism and Yilu is also a successful practice of human-machine collaborative decision-making mechanism. The two parties have integrated AI into human resource management modules such as salary and attendance, creating a one-stop human resources digital system. The AI system can collect and analyze employees' work data in real time, such as attendance records, work performance, market salary levels, etc., to provide comprehensive and accurate data support for enterprises. Based on these data, enterprises can formulate more reasonable compensation strategies and attendance systems, and realize the strategic upgrade from "human governance" to "digital governance". This data-driven decision-making mechanism not only improves the scientificity and accuracy of decision-making, but also enhances the transparency and fairness of decision-making, so that employees are more convinced of the company's decision-making, thereby improving employees' work enthusiasm and satisfaction.

Third, the digital reshaping of cultural genes

Corporate culture is the soul of an enterprise, and in the AI era, the shaping and inheritance of corporate culture is also undergoing digital transformation. For example, ByteDance's AI culture diagnosis system can automatically generate cultural health reports through in-depth analysis of multi-source information such as meeting minutes and internal forum data. The system can timely discover potential problems in corporate culture, such as "excessive overtime" and other undesirable phenomena, and provide a strong basis for enterprises to adjust management strategies and optimize corporate culture. As Starbucks CEO Howard Schultz said, "Corporate culture is not a slogan written on the wall, but an employee's experience every day." Through digital means, enterprises can more accurately grasp the ideological dynamics and behavioral habits of employees, so as to carry out targeted cultural construction activities and create a positive and healthy corporate culture atmosphere.

AI can also use natural language processing technology to analyze employee feedback in real time and predict employee turnover risks. Based on these predictions, companies can take measures in advance, such as providing personalized career development plans and improving the work environment, to retain key talent and reduce attrition. This digital cultural management method makes the corporate culture closer to the needs of employees, enhances the sense of belonging and loyalty of employees, and promotes the long-term and stable development of the enterprise.

What are the challenges in the process of change?

At present, AI is a hot topic in all walks of life, AI looks good and sounds beautiful, but it needs to overcome various problems and challenges to really use "great".

First, algorithm ethics and data security

Algorithm ethics and data security have become important issues that cannot be ignored. The decision-making process of AI algorithms is often a "black box", and the logic and basis behind it are difficult to fully understand, which may lead to algorithmic bias and damage to the fairness and rights and interests of employees. At the same time, human resources data contains a large number of employees' personal sensitive information, such as salary, performance, health status, etc., and once these data are leaked, it will have a serious negative impact on employees.

To address these challenges, companies need to establish "explainable AI dashboards" to monitor and evaluate the decision-making process and results of algorithms in real time to ensure the fairness and transparency of algorithms. A financial company introduced an algorithmic bias monitoring system to monitor the algorithms in recruitment, promotion and other links in real time, and successfully controlled the gender-related deviation coefficient below 03.0, effectively avoiding the loss of talent and legal risks caused by algorithmic bias.

The dynamic ethics knowledge base is also an important tool to ensure the ethics of algorithms. It is able to update ethical guidelines and norms in real time, providing dynamic ethical guidance for AI decision-making. When the AI system is processing employee performance evaluation, the dynamic ethics knowledge base can adjust and optimize the evaluation algorithm according to the latest industry standards and corporate values to ensure that the evaluation results are fair and objective.

In terms of data security, enterprises need to formulate the AI Colleague Management Charter to clarify the rights and obligations of digital employees in the process of data processing. The charter should stipulate that digital employees can only access and process employee data within the scope of authorization, and must take strict data encryption and protection measures to prevent data breaches. Enterprises should also establish a data access audit mechanism to record and audit the data access behavior of digital employees in real time, and take immediate measures to deal with abnormal access once abnormal access is found to ensure the security of employee data. As Apple CEO Tim Cook said, "Privacy is not a function, it's a basic human right." ”

Second, leadership and talent transformation

Despite the huge potential of AI in human resource management, only 1% of enterprises have reached the stage of AI maturity, highlighting the challenges faced by companies in leadership and talent transformation. The application of AI requires business leaders to have new ways of thinking and management capabilities, and to fully understand and leverage AI technology to promote the digital transformation of enterprises. Creating the role of Chief AI Officer (CAIO) is an important step to address this challenge. CAIO is responsible for formulating and implementing the enterprise's AI strategy, coordinating AI applications across departments, and ensuring that AI technology is closely aligned with the enterprise's business goals. CAIO must not only have a strong technical background, familiar with knowledge and technology in the fields of artificial intelligence, machine learning, data science, etc., but also have strong leadership skills, keen business insight, and innovation capabilities, and be able to lead the team to transform AI technology into real business value.

Implementing AI literacy certification for leadership with the help of AI is also an effective way to improve corporate leadership. Through certification, business leaders can systematically learn the basic principles, application scenarios, and impact of AI technology on enterprise management, helping business leaders overcome "AI phobia", so as to better guide enterprise change in the AI era. Talent transformation is also crucial. The application of AI technology has transformed human resource management from traditional transactional work to strategic and innovative work, which requires human resource practitioners to have higher digital literacy and data analysis capabilities. AI can not only replace tedious transactional work, but also empower humanized management and promote talent assessment from "fuzzy perception" to "accurate measurement". LinkedIn founder Reid Hoffman said: "Talent is the only sustainable competitive advantage of a business." Through learning and applying AI technology, human resources practitioners can mine valuable information from a large amount of data, provide strong support for the talent strategy of enterprises, and realize the transformation from traditional personnel management to digital human resource management.

The future is here: from efficiency to value symbiosis

Looking forward to the future, AI will definitely encounter many obstacles in terms of concept, talent, environment and other aspects, but the trend of AI reshaping human resource management will not change, and AI will also transform from a simple efficiency improvement tool to a strategic partner of value symbiosis. Enterprises need to build an "AI-first" mindset and turn investment in AI technology into tangible organizational capacity improvement. Peter Dulac said, "The best way to predict the future is to create it." BASF, a global chemical giant, has set up the "AI Black Swan Award" to encourage employees to make bold attempts at AI innovation, which has injected new vitality into the development of enterprises in the AI era. Inspired by this award, employees actively explore the application of AI in production optimization, supply chain management, product research and development and other fields, bringing a series of breakthrough results to the enterprise and enhancing the core competitiveness of the enterprise.

In the future, human resource management will focus more on the design of human-machine collaboration systems, and promote the paradigm shift of organizations from "efficiency first" to "value symbiosis". This means that enterprises need to pay more attention to the collaboration and integration between employees and AI, and give full play to the creativity of employees and the powerful computing power of AI, so as to achieve complementary advantages of both parties. Enterprises can establish an "AI mentor" system to equip employees with personalized learning agents, diagnose employees' skill gaps in real time, and provide targeted training and learning resources to help employees improve their ability to collaborate with AI. Enterprises should also encourage employees to actively participate in the optimization and improvement of AI systems, and integrate employees' practical experience and professional knowledge into AI algorithms, so as to achieve human-machine co-evolution and create greater value. Jack Ma, founder of Alibaba Group, said: "The future is not the world of Internet companies, but the world of Internet companies that are used well." In the AI era, only by combining AI technology with human intelligence can we achieve sustainable development and value symbiosis of enterprises.