With the development of Artifical Intelligence (AI), digitalisation and automation has become one of the most important issues in social sciences. There is a widely shared consensus between scholars that AI and digitalisation represent a disruptive technology which deeply transform the world of labour and employment. The first wave of the scientific debates around the notion of Industrie 4.0 was mainly about the potential of the new technologies to boost European competitiveness. These optimistic views were then followed by overly pessimistic estimates on the massive job lesses caused by the spread of digital technologies. The second wave of the debates about the social impacts of technological development was based on the recognition that effects of this latter can not be reduced to one single element of job destruction through substituting human labour but it is quite possible that even more workplace will affected by the complementary effects of digital technologies, where AI-based applications will not completely replace human labour but only will complement it. Last but not least a significant amount of new jobs may emerge in the future. It is worth noting that this latter effect is the least forecastable on the basis of our current knowledge. There are tow main scenarios on the potential social impacts of technological development. According to the optimistic one the spread of AI will lead to a general upskilling mechanism (this is the so-called „skill-biased technological change” narrative), according to which machines will replace human labour in the case of jobs with lower value-added and those who become unemployed will soon find a new and higher quality job with the help of an encompassing training policy. According to the pessimistic scenario machines will be able to replace human labour not only in the low-skilled segments of the labour market but at the middle-skilled cognitive activities and routine tasks are also susceptible to digitalisation. This is the so-called routine-biased technological change according to which these potential trends put the whole current social system in danger with a disappearing middle class. Either way, it is indispensable to pay more attention on the social and cultural impacts of technological change as researchers already do that in other countries. One of the most important messages of social sciences for those actively shaping public policies is the decline of technological determinism. Instead the experiences of the past several industrial revolutions show that the same technological development could lead to varying social effects which are mutually shaped by different social, economic and political actors. The whole concept of Industry 5.0 is based on this basic idea.

In order to be able to shape the direction and impacts of technological we need evidence-based public policies. To support such kind of public policy we plan to launch a survey among a relatively high number of Hungarian employees about their job content and skills level in order to map to what extent are their jobs susceptible for substituting and complementing effects of AI, and also to show their readiness to use the newest digital technologies in their working environments. This last point is even more important as this is the single most important precondition to adopt AI-based applications in a critical mass which in turn is essential to boost productivity and competitiveness of Hungarian firms. By doing so, first we intend to make a secondary analysis of the available international survey databases (like PIAAC, European Working Conditions Survey) and also the results of the Hungarian Mikrocenzus carried out in 2016. This exercise will also provide useful inputs for the design of our own employee survey, including the questionnaire. The analysis of these survey results is important to locate the position of Hungary in an international comparison, but their sample size is not big enough to make deeper within the country analyses (e.g. the extent of regional inequalities remains hidden). The second pillar of our research consists of company case studies. Beside quantitative statistical analyses from a macro perspective, it is also essential to get a picture on how AI works on a micro level, what are the facilitating and hindering factors to integrate AI and algorithmic management in the labour process and in the work organisation. We plan to carry out three company case studies: Audi Hungária Zrt. is a flagship company of the automotive industry which plays a key role in the export-driven Hungarian economic development. T-Systems and Nokia are also dominant players in the equally important ICT sector. The aim of the company case studies is to map, to identify it positive and negative impacts on productivity and job quality and to elaborate a special framework able to show the potential impacts, threats and opportunities of the adoptation of algorithmic management in a large company environment. This framework will enable practitioners to assess and measure the most important attributes of this environment which makes possible to examine and to plan the adaptability of algorithmic management in a given labour process.

Keywords: digitalisation, algorithmic management, labour market, digital skills