Al4Labour - Restructuring of the Population through AI - ITCL

Al4Labour – Reshaping labour force participation with Artificial Intelligence

European Project


In the last decade, due to the enormous progress in computing power, a new era has been unleashed which is called Industry 4.0.

This revolutionary new progress in manufacturing systems will also create a huge impact on the socio-economic dynamics of society. In addition, the recent pandemic conditions caused by the COVID19 disease demonstrate that the modern workforce is highly sensitive to conditions and subject to change.

Technologies based on data processing are changing not only manufacturing standards, but also people’s daily lives. With studies on the future of work citing the possibility of 30% of global working hours being automated by 2030, it is important to note that Artificial Intelligence (AI) can take over the monotonous and repetitive aspects of jobs performed by humans when some jobs are automated. This may warrant focusing on more strategic or more analytical jobs.

To offer a solution to this problem, this project proposal entitled “Reshaping labor force participation with Artificial Intelligence-AI4LABOUR” aims to predict what kind of new occupations will appear in the near future and what kind of skills will be needed to achieve these new occupations. This can be achieved in this project in cooperation with different disciplines and sectors and thus collaboration between different disciplines and sectors enhances the impact of AI4LABOUR. Soon, in the context of AI4LABOUR, the training needed to obtain these required skills will also be designed and planned.

To achieve this objective, an innovative methodology of modeling and skills development, armed with AI techniques, will be designed for the workforce. This novel methodology will be the building block of a web portal, which will serve as a recommendation tool for all stakeholders (individuals, companies, institutions and policy makers).

Duration time: 48 monts



It is a project funded through the Horizon 2020 call: H2020-MSCA-RISE-2020 (Marie Skłodowska-Curie Research and Innovation Staff Exchange).

Proposal number: 101007961