Balancing the role of domain experts and data scientists in an AI-powered factory
Report, 2020

Artificial intelligence (AI) is starting to make an impact on factories. AI brings a variety of capabilities to a factory: machine learning (ML), computer vision, natural language processing, and robotics. To successfully adopt AI technologies, manufacturing companies are increasingly recruiting data scientists (who have expertise in computer science and AI). On the other hand, this creates confusion on the role of domain experts (who have deep expertise in manufacturing). Some scholars argue that domain experts’ role will soon become irrelevant, and they are at risk of being replaced by data scientists in AI-powered factories. Data scientists often demonstrate that they can build AI solutions with little to no domain expertise in data science competitions (e.g., hosted in conferences, Kaggle), and their skills are transferable to multiple problems across different domains. These skills make data scientists look attractive to companies. On the flip side, these raise domain experts’ anxiety and fear about the relevance of their manufacturing skills and the prospects of employment in an AI-powered factory. Dealing with this type of situation is a consequential matter of ethical responsibility. Manufacturing companies with ambitions to deploy AI systems must find a solution to balance the role of domain experts and data scientists. In this article, I present an ethical solution of how to balance the role of domain experts and data scientists in an AI-powered factory.

Production system

Throughput bottleneck


Artificial Intelligence


Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

DAIMP - Data Analytics in Maintenance Planning

VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.

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