Hello, I am Kakeya, the representative of Scuti.
Our company specializes in services such as offshore development and lab-type development in Vietnam, as well as generative AI consulting. Recently, we have been fortunate to receive numerous requests for system development in collaboration with generative AI.
Do you know how generative AI is being utilized in the HR industry? Many companies are adopting this innovative technology and significantly improving traditional business processes. For example, in the recruitment process, generative AI automates candidate screening and interview processes, enabling faster and fairer selection. It also helps improve employee engagement and create personalized learning plans. By leveraging generative AI, HR teams can focus on strategic tasks, leading to an increase in overall company productivity. This article will provide a detailed introduction to the specific ways generative AI is being used and its effects.
Basic Concepts of Generative AI
Differences Between Generative AI and General AI
Generative AI is an AI technology that can create new content based on data. In contrast, general AI specializes in analyzing existing data to find patterns. This difference makes generative AI more suited to creative tasks.
Specifically, generative AI differs from general AI in the following aspects:
Content Generation Capability: Generative AI creates new content such as text, images, and audio. For example, a generative AI like ChatGPT can generate natural conversations.
Data Dependency: While general AI analyzes large amounts of data to find patterns, generative AI can create new content even from limited data.
Application Scope: Generative AI is widely used in creative fields, marketing, entertainment, and more. On the other hand, general AI is used for tasks like data analysis and prediction.
These differences make generative AI particularly important in the HR industry. Specifically, it is used for automating candidate screening and interviews, as well as creating personalized learning plans. Understanding the differences between generative AI and general AI is crucial for effectively utilizing these technologies.
Technical Foundation and Applications of Generative AI
Generative AI relies on advanced technologies based on neural networks. Specifically, it is supported by the following technical elements:
Deep Learning: Generative AI uses deep learning technology to learn complex patterns from large datasets and generate new content.
Natural Language Processing (NLP): NLP technology is used for tasks such as text generation, translation, and summarization. ChatGPT uses this technology to generate natural conversations.
Reinforcement Learning: Generative AI applies reinforcement learning to find the optimal results through trial and error, such as in creative ad generation.
Generative AI’s application scope is broad, including various areas in the HR industry. Specific applications include:
Automation of the Recruitment Process: Generative AI analyzes candidates’ resumes and screens appropriate candidates. This reduces the burden on recruiters and enables efficient recruitment.
Creation of Personalized Learning Plans: Generative AI automatically generates learning plans tailored to each employee, supporting skill development. It analyzes employees’ skills and learning histories to provide optimal learning content.
Improvement of Employee Engagement: Generative AI analyzes employee feedback in real time and suggests measures to improve engagement, contributing to a better work environment and increased productivity.
In this way, generative AI leverages its technical foundation to bring innovation to the HR industry. The role of generative AI in future HR activities will continue to grow in importance.
Benefits and Challenges of Generative AI
Improvement in Productivity and Efficiency
Generative AI brings significant advantages in reducing time, costs, and manual labor.
Time and Cost Reduction: Generative AI rapidly processes vast amounts of data and automates analysis and report generation. This enables companies to significantly reduce time and costs. For example, in the recruitment process, automating candidate resume screening and initial interviews reduces the time required for hiring and lightens the load on HR personnel.
Reduction of Manual Labor: By replacing simple and repetitive tasks that were traditionally done manually, generative AI helps reduce manual labor. This allows employees to focus on more strategic tasks, thus improving productivity. For instance, the analysis of employee performance evaluations and feedback is automatically performed by generative AI, which suggests individualized improvement measures.
Improvement in Operational Efficiency: Generative AI has the ability to extract valuable insights from large amounts of data. This enables managers and executives to make swift, accurate decisions, improving operational efficiency. Furthermore, as generative AI operates 24/7, it continues to support operations without rest.
Due to these benefits, generative AI plays an important role in the HR industry as well. However, proper data management and security measures are essential when implementing it. Additionally, it is important to constantly check the reliability of the results generated by AI and use it to complement human judgment.
Elimination of Bias and Data Privacy
Generative AI plays an important role in eliminating bias and protecting data privacy in talent selection.
Bias-Free Candidate Selection: In traditional recruitment processes, unconscious human biases can affect decisions. However, generative AI evaluates candidates based on objective data, enabling fair and bias-free selection. This allows diverse talent to be fairly assessed and the best candidates to be chosen. For example, by standardizing resume and interview evaluation criteria, generative AI objectively analyzes candidates’ skills and experiences.
Importance of Data Security: The data processed by generative AI often contains personal and confidential information, making data security critically important. Companies need to implement strict security measures such as data encryption and access controls when introducing generative AI. Furthermore, compliance with data privacy regulations (e.g., GDPR) is required to ensure proper management of personal data.
Through these efforts, generative AI enhances transparency in talent selection and strengthens data security. However, it is also essential to continuously verify the transparency of the algorithms used in generative AI and validate the accuracy of the results it produces.
Bridging the Skill Gap and Talent Development
Generative AI offers significant benefits in skill matching, career path suggestions, and learning and development support.
Skill Matching and Career Path Suggestions: Generative AI analyzes employees’ skill sets in detail and suggests the most suitable roles and career paths for each individual. This helps effectively bridge skill gaps and enables optimal talent placement. For example, based on employees’ past performance and skill data, generative AI can propose specific future career paths and required skills.
Learning and Development Support: Generative AI creates personalized learning plans based on employees’ learning needs and provides the most appropriate training content. This allows employees to effectively acquire skills at their own pace and grow. Specifically, generative AI recommends suitable courses and training programs based on employees’ current skill levels and career goals.
Through these efforts, generative AI promotes overall skill improvement across organizations and enhances the efficiency of talent development. Additionally, by providing real-time feedback and continuously monitoring employees’ progress, generative AI offers appropriate support to ensure effective growth.
Specific Applications of Generative AI in HR
Automation of the Recruitment Process
Generative AI plays a wide range of roles in automating the recruitment process, particularly in areas such as resume screening, job description creation, and communication with candidates.
Resume Screening: Generative AI quickly analyzes large volumes of resumes and automatically extracts candidates with the required skills and experience. This enables HR personnel to efficiently find suitable candidates without spending excessive time. For example, by setting specific criteria such as keywords or years of experience, AI filters candidates accordingly.
Job Description Creation: Generative AI automatically generates optimal job descriptions based on job requirements. This allows for the rapid creation of high-quality, consistent job descriptions, streamlining the initial stages of the recruitment process. Specifically, by inputting required skills and job duties, generative AI creates detailed job descriptions.
Automation of Candidate Communication: Generative AI automates initial communication with candidates, such as scheduling interviews and sending follow-up emails, significantly reducing the burden on recruiters. Additionally, it can automatically respond to general questions from candidates, improving communication efficiency.
By implementing generative AI, the recruitment process becomes more efficient, and quicker, and ensures a fair selection. As a result, companies can acquire better talent in a shorter time, improving the quality of recruitment activities.
Enhancing Engagement and Learning Development
Generative AI is used as an important tool to enhance employee engagement and strengthen learning and development processes. Its benefits are particularly evident in developing engagement tools and providing customized learning and development plans.
Development of Engagement Tools: Generative AI develops tools to measure and improve employee engagement. For example, it collects and analyzes employee feedback in real-time to identify areas for improving the work environment. Additionally, generative AI can provide personalized messages and support to boost employee motivation, contributing to improved engagement.
Provision of Customized Learning and Development Plans: Generative AI creates individual learning and development plans based on employees’ skills and career goals. It suggests the best training programs and career paths based on employees’ past performance and skill evaluations. This allows employees to efficiently enhance their skills using the most appropriate learning resources. Specific examples include generative AI recommending online courses, workshops, and arranging mentorships.
These generative AI functions help improve employee engagement and streamline learning and development processes. As a result, overall company productivity improves, and employee satisfaction increases.
Improvement in Performance Management
Generative AI also plays a significant role in improving performance management, particularly in the areas of data analysis for performance evaluation and the automatic generation of individual feedback.
Performance Evaluation Through Data Analysis: Generative AI thoroughly analyzes employees’ performance data and provides objective performance evaluations. This enables fairer and more accurate assessments compared to traditional subjective evaluation methods. Specifically, it integrates various data points, such as work progress, the quality of deliverables, and patterns in working hours, to evaluate employees’ performance. This process helps improve consistency and reliability in performance evaluations.
Automatic Generation of Individual Feedback: Generative AI generates individual feedback for each employee based on their performance data. This allows for timely and practical suggestions for improvement or praise. For example, feedback on progress in performance or skill improvement for specific projects is automatically created and provided to the employees. This personalized feedback not only boosts employee motivation but also leads to specific improvements in behavior.
With these generative AI functions, performance management becomes more effective and efficient. Companies can clearly understand employees’ strengths and areas for improvement and provide the appropriate support and guidance.
Future Outlook for Generative AI
Impact on the Industry and Expansion of Adoption
Generative AI is expected to play an important role in the future of HR. Specifically, the impact on the industry as a whole and the expansion of its adoption can be summarized in the following points:
The Role of Generative AI in Future HR: Generative AI will continue to drive the digital transformation of the HR industry. For example, automating the recruitment process, improving employee engagement, and enhancing performance management are some of the areas where generative AI will become an essential tool. Additionally, as AI provides more advanced decision-making support, the HR department is expected to further strengthen its strategic role, contributing to the overall growth of the company.
The Importance of Sustainable Implementation and Management: As the adoption of generative AI expands, the sustainable management of AI will become increasingly important. Proper data management and security measures are essential, and companies must ensure the protection of data privacy and compliance with regulations. Continuous monitoring and improvement are also required to ensure that generative AI’s algorithms are fair and transparent. Companies need to have the resources and expertise to address these challenges and develop strategies to maximize the effectiveness of generative AI.
These factors will continue to drive transformation in the HR industry and contribute to enhancing the competitiveness of companies. However, implementing and managing generative AI requires a cautious approach, and the technology must be utilized in a sustainable manner.