Publications

The increasing integration of AI into HR analytics is regarded as a significant development, although empirical research into this area is still in its early stages. This study examines how the use of AI influences existing challenges in HR analytics and what implications this has for its design. Using a qualitative research design, 15 semi-structured interviews were conducted with HR analytics experts from large Swiss companies. The results show that whilst AI can simplify existing processes in HR analytics, this simplification simultaneously generates new complexity that crystallises into three fundamental paradoxes: the organisational paradox (autonomy vs. centralisation), the skills paradox (interpretative-advisory skills vs. statistical-technical skills), and the technology paradox (automation vs. augmentation). Each paradox describes two opposing design options, both of which are necessary but cannot be fully realised simultaneously. These tensions cannot be resolved by a one-off decision; rather, they are permanent and recurring: these are ‘both-and’ rather than ‘either-or’ decisions. The study concludes that value-creating use of AI in HR analytics is achieved less through one-off strategic policy decisions and more through an ongoing and company-specific approach. Concrete recommendations for action are derived at the level of the three paradoxes for practical application.

In this qualitative study, Swiss HR Analytics analyses why the expected benefits of HR analytics often fall short of the possibilities in practice. In interviews with 14 Swiss companies, key causes were identified in the areas of skills, work organisation and technology. The results show that although those responsible have analytical skills, which they often bring with them from controlling, they rarely have HR-specific skills. However, effective HR analytics requires an interdisciplinary mix of skills, especially for the creation of strategic use cases and the translation of data into actionable insights. Fragmented processes and unclear responsibilities also make implementation more difficult. Technological potential, for example through AI and automated data integration, often remains unutilised. The study therefore formulates specific recommendations for action to strengthen analytical competences, strategically anchor HR analytics and promote technology in a targeted manner. Five case studies illustrate how companies can successfully implement data-supported HR decisions despite existing challenges.

This exploratory study by Swiss HR Analytics examines the reasons for the limited use of HR analytics in corporate practice to date. Based on a preliminary study, qualitative interviews were conducted with 14 Swiss companies of different sectors and sizes in order to identify key hurdles in the implementation and impact of HR analytics. The results show that the degree of maturity and impact are strongly influenced by the professional background of those responsible, an often fragmented work organisation and technological interface problems. Although artificial intelligence is seen as a potential lever for increasing efficiency, it has hardly been utilised to date. In addition, there is often a lack of clear responsibilities for the implementation of measures and a systematic impact evaluation. Five in-depth case studies illustrate possible success factors and best practices. The study concludes with practical recommendations for organisations that want to make more targeted use of HR analytics and sustainably increase their added value. Key starting points are a broader skills mix, strategically anchored use cases, integrated technologies and a data-driven corporate culture.

This exploratory study by Swiss HR Analytics examines the reasons for the limited use of HR analytics in corporate practice to date. Based on a preliminary study, qualitative interviews were conducted with 14 Swiss companies of different sectors and sizes in order to identify key hurdles in the implementation and impact of HR analytics. The results show that the degree of maturity and impact are strongly influenced by the professional background of those responsible, an often fragmented work organisation and technological interface problems. Although artificial intelligence is seen as a potential lever for increasing efficiency, it has hardly been utilised to date. In addition, there is often a lack of clear responsibilities for the implementation of measures and a systematic impact evaluation. Five in-depth case studies illustrate possible success factors and best practices. The study concludes with practical recommendations for organisations that want to make more targeted use of HR analytics and sustainably increase their added value. Key starting points are a broader skills mix, strategically anchored use cases, integrated technologies and a data-driven corporate culture.

This study by the non-profit organisation Swiss HR Analytics (SHRA), which was conducted in collaboration with the ZHAW School of Management and Law and the FHNW School of Applied Psychology, examines the current status of the use of HR analytics in Swiss companies. The study is based on a quantitative survey of 133 predominantly large companies. The results show that HR analytics is primarily used at a descriptive and diagnostic level. This poses particular challenges in terms of data quality, the integration of different data sources and the implementation of analytical findings. Although the strategic benefits of HR analytics are recognised, there is a clear gap between the intended goals and the actual added value realised. Only around half of the companies derive measures from the analyses, and even fewer systematically evaluate their impact. The study concludes that in order to realise the full potential of HR analytics, the database must be improved, competencies strengthened and a clear link to business-relevant key figures created. On this basis, practice-orientated recommendations for a more effective establishment of HR analytics are derived.