Capabilities of GenAI in Content Creation
In today's working environment, GenAI has become a key supporting tool in digital content production, particularly in tasks related to data management and content preparation. These include processing and summarizing large volumes of information, structuring content systematically, and generating initial drafts of articles, images, or audio materials to serve as starting points for creative work. These capabilities stem from GenAI models being trained on large-scale datasets, enabling them to predict language patterns, select appropriate presentation approaches, and adapt content to specific contexts or target audiences to a certain extent. According to Forbes, the primary role of GenAI in content-related work is being an assistant rather than a replacement, helping to reduce repetitive tasks, technical workloads, and time-consuming activities such as initial drafting. At the same time, GenAI's ability to operate quickly and continuously has led many organizations to adopt it as a means of improving efficiency, reducing costs, and managing increasing workloads. These factors have contributed significantly to the widespread adoption of the technology.
Limitations and Challenges of GenAI
Despite its high potential, several studies indicate that generative artificial intelligence still faces limitations across multiple dimensions in content creation. One major challenge lies in producing content that is both exciting and usable at the same time, which can lead to inaccuracies or content that does not align with reality (Mukherjee & Chang, 2023). In addition, GenAI faces issues related to neutrality and ethics, as the content it generates often reflects biases present in its training data (Karagoz, 2024). When compared to humans, GenAI also shows gaps in creativity, particularly in visual and artistic work that requires deep perception and cultural context (Rondini et al., 2025). Furthermore, when audiences are aware that content has been generated by GenAI, their perceived value of and emotional response to the work may decrease. This highlights limitations in consumer acceptance of AI-generated content (Raj et al., 2023).
The Future Collaborative Role of GenAI and Humans
Considering both its capabilities and limitations, the question of whether GenAI will replace humans may no longer be the most relevant issue. Instead, a more meaningful question is how humans can work effectively alongside GenAI. In practice, GenAI is not replacing humans entirely in the content creation process. Rather, it is reshaping human roles, from managing every detail of production to focusing on defining creative direction, designing concepts, ensuring quality, and adding value through meaning, context, and deeper perspectives. These areas remain strengths that GenAI cannot fully replicate. Content creators who can integrate their expertise, critical thinking, and knowledge with GenAI's ability to process data and support technical tasks are likely to produce higher-quality outcomes. Such collaboration can enhance speed, efficiency, and overall content value compared to relying solely on either humans or GenAI.
Lastly, the changes brought about by GenAI extend beyond the adoption of new tools. They represent a shift in mindset, work practices, and expectations regarding the role of humans in the digital era. Developing a comprehensive understanding of both the advantages and limitations of GenAI. Using it responsibly and appropriately will be essential in enabling humans and technology to grow together and create sustainable shared value.
Reference
Karagoz, A. (2024). Ethics and technical aspects of generative AI models in digital content creation. https://arxiv.org/abs/2412.16389
Mukherjee, A., & Chang, H. (2023). The creative frontier of generative AI: Managing the novelty-usefulness tradeoff. https://arxiv.org/abs/2306.03601
Raj, M., Berg, J., & Seamans, R. (2023). Art-ificial intelligence: The effect of AI disclosure on evaluations of creative content. https://arxiv.org/abs/2303.06217
Rondini, S., Manovich, L., & colleagues. (2025). Stable diffusion models reveal a persisting human and AI gap in visual creativity. https://arxiv.org/abs/2511.16814
Shepherd, I. (2024, April 26). Human vs. machine: Will AI replace content creators? Forbes. https://www.forbes.com/sites/ianshepherd/2024/04/26/human-vs-machine-will-ai-replace-content-creators/