Mixed-Methods Analysis of AI Technology Usage in German Engineering: Quantitative and Qualitative Perspectives from the Viewpoint of Employees

Artificial intelligence (AI) technology is believed to significantly alter the work and employment landscape, prompting inquiries into future working practices, often depicted in media narratives with dystopian visions (Frey & Osborne, Technological Forecasting and Social Change 114:254–280, 2017; Humm et al., TATuP—Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 30(3):Article 3, 2021; Susskind and Susskind, The Future of the Professions: How Technology Will Transform the Work of Human Experts, 2015), emphasizing AI’s potential to automate processes and address engineering challenges (Humpert, Wäschle, et al., Procedia CIRP 119:693–698, 2023). Nevertheless, discussions of AI’s potential drawbacks often lack input from engineering employees, who could provide nuanced perspectives on its benefits, as previous research (Giering, Zeitschrift für Arbeitswissenschaft 76:1–15, 2022; Jung & Garrel, TATuP—Zeitschrift für Technikfolgenabschätzung in Theorie und Praxis 30(3):Article 3, 2021) has largely overlooked the employee viewpoint, which could offer valuable insights into suitable areas for AI integration within the workforce. In addition, the empirical data used to gain more insight into employee appraisals is usually either qualitative or quantitative, which always affects the generalizability of the results. In order to close this gap this paper aims to explore the potentials regarding the implementation of AI in German engineering using a mixed-methods approach by summarizing the perspectives of 11 employees in the field and their attitudes toward AI. In addition, a quantitative analysis of over 1300 respondents in German engineering companies was conducted. Rather than focusing solely on the technical aspects of AI, this mixed methods approach emphasizes employees’ preferences and needs in their work environment. Despite the employees sharing similar technical backgrounds, their perspectives, evaluations, and envisioned applications of AI vary considerably. Moreover, the quantitative data shows how the actual situation regarding the diffusion of AI in these companies. The findings stress the importance of individually assessing employees’ viewpoints when integrating AI into the workplace, ensuring that technology is designed to support their needs. Employees should therefore be seen and treated as contributors, as experts of their own workplace and have a say in the integration process from the outset in order to make AI a sustainable part of their work (Herrmann & Pfeiffer, AI & Society 38:1523–1542, 2023).