Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.

For more than a year now, organizations have struggled to hold onto talent. According to the U.S. Bureau of Labor Statistics, 4.2 million people voluntarily quit their jobs in August 2022. At the same time, there were 10.1 million job openings. Between the Great Resignation and more recent trends like "quiet quitting," traditional approaches for winning talented workers haven't always cut it in this fiercely competitive market. An emerging wave of AI tools for talent management have the potential to help organizations find better job candidates faster, provide more impactful employee development, and promote retention through more effective employee engagement. But while AI might enable leaders to address talent management pain points by making processes faster and more efficient, AI implementation comes with a unique set of challenges that warrant significant attention.

Many modern technologies address the issues involved in developing such systematic automated information support solutions, but AI has been rarely applied for enhancing practices in employment management (Vrontis et al., 2022). Capabilities of AI are viewed in existing cases of studies for the construction of interventions in employment prospective, but various disruptive innovations to enhance the current frameworks of talent systems are not holistically studied in the past recent years. The advancement of general-purpose AI technology is of paramount task to revolutionizing workforce management (Agrawal et al., 2018). While creating new AI oriented applications for employment management, a number of obstacles such as dehumanization, biased algorithms and fairness in requirement have identified, so it is imperative to conduct precise design research (Tambe et al., 2019). A recent industry survey identified at least 300 HR technology start-ups developing AI tools for people management, with roughly 60 of these companies achieving traction in terms of clients and venture investment (Bailie & Butler, 2018). Furthermore, an AI-powered talent intelligence platform that aids in attracting, developing, and retaining outstanding employees, has just raised $220 million and is now valued at over $2 billion (Charlwood & Guenole, 2022).Many organizations have started with their massive investment in AI for workforce management.

Talent management's many challenges in keeping employees engaged are helping to define the future of work. Every organization is struggling to meet its need for experts who bring new skills, made more difficult by high attrition rates and a competitive job market. Chief human resources officers (CHROs) and the organizations they lead are looking to build the expertise they need by upskilling talent. Add to those challenges getting internal mobility right, providing employees with learning and growth opportunities, coaching managers to be talent champions, achieving less bias in hiring decisions and the future of work's growing challenges become clear. A data-driven approach to solving these challenges using AI delivers results, as the interviews and presentations at the Eightfold Cultivate 22 Summit showed.

Artificial intelligence and human resources collaborate to help save money, enhance planning, and, most significantly, transform companies. The collaboration between artificial intelligence and human resources increases employee performance and expertise and lowers costs. AI technology in HR aids organizations in gaining a complete understanding of their staff's behaviors and inclinations. This data may be used to improve employee happiness by enhancing the job experience. AI is also used to assist human resources professionals in various areas of their profession, from early applicant shortlisting through performance evaluation.

AI & Machine Learning Applications in the Real World According to the latest trends of AI-based solutions, there is hardly any decisive sector or industry that does not rely on smart algorithms and automation to perform highly advanced tasks that would be impossible for most humans. Many companies use Machine Learning and Artificial Intelligence to identify and sort through the best possible candidates for a position. With a few Machine Learning courses that are specially designed for regular people, without advanced technical knowledge, it's easy to understand why there are so many applications of advanced technologies in the real world. Luckily, this situation can now be avoided by training machine learning algorithms to take over the task. According to a case study performed at Canada's largest bookstore chain (Indigo), the use of AI and machine learning algorithms to screen job candidates and decide who to hire has led to an increase in overall productivity.

Technology and data-driven processes continue to transform every aspect of today's HR operations. Whether it's automating laborious tasks or analyzing a mass of HR analytics, talent management with AI has emerged as the most-suited tool for modern HRs. Artificial Intelligence is the leading technology being used in HR operations today and is heavily relied upon to automate processes that earlier required human intervention. The integration of AI in talent management has resulted in higher retention rates, improved productivity, and diverse and inclusive work culture and has earned it the title of "Talent Intelligence." These use cases are just the tip of the iceberg; AI has a much deeper presence in talent management.

The use of artificial intelligence (AI) and AI methods in the workplace holds both great opportunities as well as risks to occupational safety and discrimination. In addition to legal regulation, technical standards will play a key role in mitigating such risk by defining technical requirements for development and testing of AI systems. This paper provides an overview and assessment of existing international, European and German standards as well as those currently under development. The paper is part of the research project "ExamAI - Testing and Auditing of AI systems" and focusses on the use of AI in an industrial production environment as well as in the realm of human resource management (HR).

Recently the pandemic has pushed digital transformation to the front of the line. While collaborative tools allowed us to work from home and maintain close contact with our co-workers, the next step is just around the corner, thanks to artificial intelligence and machine learning. In every element of the company, the pandemic is driving a move towards a hybrid work paradigm, changing people's management and the way we work. Enterprises are on the verge of digital transformation and the use of artificial intelligence in HR departments will accelerate this process. Digital transformation improves the customer experience while also unlocking new value.