Economic Potential of AI in Industrial Work
Angelika Bullinger-Hoffmann, a member of the Research Advisory Board on Industry 4.0 and a professor of Work Science and Innovation Management at TU Chemnitz, sees significant economic potential in the use of Artificial Intelligence (AI) for industrial work. According to her, AI can enhance both the competitiveness and the quality of industrial jobs.
She identifies four development scenarios regarding the impact of AI on industrial work processes: substitution of human labor, polarization, enrichment of human activities, and innovation. Each of these scenarios presents different opportunities and challenges for the industrial sector.
Scenarios of AI Impact
In the first phase, substitution, AI can replace human labor in tasks such as drafting standardized texts. However, this proportion is relatively low in the industry. Polarization occurs when highly qualified employees train a language model that supports less qualified staff in customer service roles.
The most significant scenario is the enrichment of human activities, particularly in industrial manufacturing. Here, a labor planner can use AI to present various real-time evaluated action options, which they then weigh. Additionally, new job profiles such as data analytics platform specialists or AI roboticists will emerge, shaping interactions between humans, robots, and AI.
Facilitating AI Integration
To ease the integration of AI and improve working conditions, Bullinger-Hoffmann emphasizes the importance of perceived autonomy and the early involvement of future users. She explains that if people feel dominated by AI, they will perceive it as limiting rather than supportive.
Leaders should discuss AI development and deployment with users from the start and collaboratively find solutions – this is their task and requires commitment. Particularly in small and medium-sized enterprises (SMEs), there can quickly be bottlenecks when it comes to further training. Therefore, universities must develop offerings for human-centered AI use, including simple competency concepts for SMEs.
Summary
- Angelika Bullinger-Hoffmann sees enormous potential in AI for industrial work.
- Four development scenarios: substitution, polarization, enrichment, and innovation.
- AI can enhance job quality and competitiveness in the industry.
- Importance of perceived autonomy and early user involvement for successful AI integration.
- Evolving job profiles and the role of training in SMEs are crucial.