2.10 How Can AI Improve the Quality of Work?
AI is often presented as important and effective mean for improving the quality of human performance. To what extent and how AI related technologies can improve the quality of our work?
Kreinsen and Ittermann (2019) claim that automation and increasing flexibility of production (where AI contributes) help to optimize values chains and to develop business models based on highly intensive involvement of customers. It can lead to improvement of work quality, better opportunities for human-oriented shaping of work organization, as well as better fit between the work and private life.
Upgrading of work in the all occupational fields can occur through spread of intellectual skills and theoretical understanding of the developing and new work processes together with the growth of the complexity of work. Gradual upgrading of technologies and work organization including implementation of the AI permits smooth and gradual upgrading of skills and competence, through involving decentralization and reintegration of the functions of planning, execution and control of work. This scenario also forecasts development of the lean work organisation based on the networked and project work. The AI in such scenario becomes a factor of technological change, which enables and empowers improved and more productive (effective) execution of work. This leads to the emerging of the phenomenon of augmented worker or augmented operator:
AI increases the performativity, quality and productivity of work, thus reducing the quantitative demand of new workers and specialists, but in the same time the requirements and needs raise for the skills and competencies in such fields as maintenance, monitoring and repair. These competence needs concern technical and physical characteristics of the equipment, digital working and other fields. There can be expected increase of demand for knowledge and skills needed to identify specific complex and difficult to detect faults in the systems driven by the AI, what would also require in-depth and systemic understanding of technologies and work process knowledge related to the networked production systems. In general the demand of vocational skills and competence may decrease, what would require to revise and shorten the volume of VET curricula and duration of training. Increasing demand of competencies and qualifications in the planning, monitoring and maintenance of digitally networked production systems cannot be satisfied with the current VET curricula.
AI helps to make work in the whole process chain more transparent through higher transparency of data, but in the same time increasingly complex and requiring higher responsibility. It requires wide work process knowledge, customer orientation, project work skills and communication skills. Although AI facilitates general improvement of the work productivity and the quality of work in the different sectors of activities, there can be expected some specificities of this development which depend on the characteristics of enterprises. For example, automation and digitalization of the “craft” work processes in the SME’s can lead to more supplementary application of the digital technologies, including AI, in the production process, rather than to the substitution of human work by AI (Checcucci, 2019). Specificity of economy, like the domination of the SME’s can create rather specific conditions for the implementation of the digital innovations and technological solutions, and would require to find the ways on how to make digital technologies, including AI, more accessible for the SME’s in terms of costs, demand of investments and skill requirements (Alleva, 2017).
This audio discloses the implications of the AI for the content of work and related skills requirements.
This video provides interesting insights about the potential of usage AI in the quality control of manufacturing.