Author
Abstract
This chapter explores the labor market transformation in the media industry, with a focus on the rise of Artificial Intelligence (AI)-based automation. The rapid development of AI, especially since the introduction of Generative AI (GenAI) applications, such as ChatGPT, Midjourney, or Adobe Firefly, represents a significant change in the media sector. Here, the integration of AI impacts several fields in the value chain, with corresponding transformative effects on the related workforce and occupational fields. The focus of our analysis is on the interaction between AI as a technological innovation and its workforce transformation. AI-based automation will impact two fundamental directions: On the one hand, productivity increases are to be expected. On the other hand, AI automation can lead to job replacements. By integrating theoretical models and literature-based insights, this chapter structures frameworks for analyzing the effects of AI-based automation on the media workforce. In addition, this chapter comprises an empirical contribution in the form of a pre-study conducted among media professionals in Germany. This quantitative survey provides first-hand insights into how AI-based automation is currently perceived, adopted, and expected to affect employment and productivity across various segments of the media value chain. The findings from the pre-study underscore that AI tools such as ChatGPT and other generative technologies are already widely in use and are perceived to significantly impact both, the media industry structures and workforce. Respondents see a high potential for task substitution and time savings, while simultaneously anticipating a shift toward Human-AI Collaboration (HAIC), particularly in creative and editorial roles. The analysis identifies key trends in productivity gains, task displacement, and skill transformation, while outlining avenues for future research and practical implications for workforce planning in the media sector.
Suggested Citation
Handle:
RePEc:spr:fuobcp:978-3-032-07605-2_12
DOI: 10.1007/978-3-032-07605-2_12
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