In 2013 the Centre of Applied Labour Market Research (Kenniscentrum Arbeid, KCA) has developed a method for data collection to get an insight in employer’s future demand for staff. The method is developed to contribute to solve an action problem in the Eemsdelta region. Despite indications of a threat of shortage of technicians in that region, none of the regional actors undertakes action. They miss detailed information about the employers’ future demand for staff. To be able to take tailor-made measures, the actors must have a proper idea of the labour market problems which can be traced back to company level. For each job opening must be clear to which profession it is related and to which educational specialism and educational level. These information appears to be not available. For employers it is, understandable, difficult to estimate their future demand for staff, because a lot of uncertain factors influence that need. Especially SME’s who often don’t have a HR-officer are missing the knowledge and time or money to invest in making a future picture of their need for staff. And data from existing labour market information sources can’t be translated well at regional or local level, never mind at company level. Without detailed information about the future employer’s demand for staff, possible problems stay latent. There is no sense of urgency for the employers to take action and the regional policy makers are missing information to develop specific educational and labour market policy. To get the needed detailed information, it has to be obtained from the employers themselves, at company level. During a research pilot in 2013 KCA has designed a method for data collection and practiced it with nine companies in the Eemsdelta region. The results indicate that the method works. In a relatively labour-extensive way the needed information can be obtained. At company level it gives the employer insight in his actual and future staff requirements and makes him aware of possible problems. As regards to the policy makers, the pilot was too small for a complete regional picture, but it demonstrates that the anonymised data of the individual companies can be merged to one umbrella data-file. From that file analyses can be made to find trends and possible problems at the labour market, both at regional and sectoral level and to obtain input for developing effective policy. The successful results of the pilot offers good reasons for a follow-up study with much more companies and to develop the method into a complete labour market monitor, by broadening the method with data about the labour supply and data of new employers.
In 2013 the Centre of Applied Labour Market Research (Kenniscentrum Arbeid, KCA) has developed a method for data collection to get an insight in employer’s future demand for staff. The method is developed to contribute to solve an action problem in the Eemsdelta region. Despite indications of a threat of shortage of technicians in that region, none of the regional actors undertakes action. They miss detailed information about the employers’ future demand for staff. To be able to take tailor-made measures, the actors must have a proper idea of the labour market problems which can be traced back to company level. For each job opening must be clear to which profession it is related and to which educational specialism and educational level. These information appears to be not available. For employers it is, understandable, difficult to estimate their future demand for staff, because a lot of uncertain factors influence that need. Especially SME’s who often don’t have a HR-officer are missing the knowledge and time or money to invest in making a future picture of their need for staff. And data from existing labour market information sources can’t be translated well at regional or local level, never mind at company level. Without detailed information about the future employer’s demand for staff, possible problems stay latent. There is no sense of urgency for the employers to take action and the regional policy makers are missing information to develop specific educational and labour market policy. To get the needed detailed information, it has to be obtained from the employers themselves, at company level. During a research pilot in 2013 KCA has designed a method for data collection and practiced it with nine companies in the Eemsdelta region. The results indicate that the method works. In a relatively labour-extensive way the needed information can be obtained. At company level it gives the employer insight in his actual and future staff requirements and makes him aware of possible problems. As regards to the policy makers, the pilot was too small for a complete regional picture, but it demonstrates that the anonymised data of the individual companies can be merged to one umbrella data-file. From that file analyses can be made to find trends and possible problems at the labour market, both at regional and sectoral level and to obtain input for developing effective policy. The successful results of the pilot offers good reasons for a follow-up study with much more companies and to develop the method into a complete labour market monitor, by broadening the method with data about the labour supply and data of new employers.
This study provides a comprehensive analysis of the AI-related skills and roles needed to bridge the AI skills gap in Europe. Using a mixed-method research approach, this study investigated the most in-demand AI expertise areas and roles by surveying 409 organizations in Europe, analyzing 2,563 AI-related job advertisements, and conducting 24 focus group sessions with 145 industry and policy experts. The findings underscore the importance of both general technical skills in AI related to big data, machine learning and deep learning, cyber and data security, large language models as well as AI soft skills such as problemsolving and effective communication. This study sets the foundation for future research directions, emphasizing the importance of upskilling initiatives and the evolving nature of AI skills demand, contributing to an EU-wide strategy for future AI skills development.
MULTIFILE
