Drivers of automation and consequences for jobs in engineering services: an agent-based modelling approach

This paper studies the uptake of AI-driven automation and its impact on employment, using a dynamic agent-based model (ABM). It simulates the adoption of automation software as well as job destruction and job creation in its wake. There are two types of agents: manufacturing firms and engineering services firms. The agents choose between two business models: consulting or automated software. From the engineering firms' point of view, the model exhibits static economies of scale in the software model and dynamic (learning by doing) economies of scale in the consultancy model. From the manufacturing firms' point of view, switching to the software model requires restructuring of production and there are network effects in switching. The ABM matches engineering and manufacturing agents and derives employment of engineers and the tasks they perform, i.e. consultancy, software development, software maintenance, or employment in manufacturing. Policy parameters influencing the results are occupational licensing and protection of intellectual property rights. We find that the uptake of software is gradual; slow in the first few years and then accelerates. Software is fully adopted after about 18 years in the base line run. The adoption rate is slower the higher the license fee for software, while the adoption rate is faster the higher the mark-up rate of consultancy. Employment of engineers shifts from consultancy to software development and to new jobs in manufacturing. Spells of unemployment may occur, if skilled jobs creation in manufacturing is slow. Finally, the model generates boom and bust cycles in the software sector.