The impact of generative AI technologies in journalistic processes on organizational performance: a human-in-the-loop approach
Year: 2024
Type: dissertation
Abstract: The diffusion of generative AI has been unprecedentedly rapid due to democratizing user interfaces. The use of its applications has yielded both successes and crises across industries, demonstrating the uncertainty of the potential and justifying the need for human supervision. The thesis examines journalistic processes as a representation of knowledge work and has two primary objectives: to evaluate which journalistic processes have the highest potential for enhancing organizational performance through the utilization of generative AI and to uncover how to determine the criticality of human-in-the-loop. The research methodology is based on a qualitative approach. Nine interviews were conducted with informants working in different journalistic roles to identify a comprehensive list of generative AI applications. Additionally, 14 external expert interviews and two workshops enabled assessment of the applicability of these applications and their potential impacts on the perspectives of internal efficiency, audience engagement, credibility, trust, and quality. The interviews revealed that generative AI adoption in journalistic processes is rapid but still in the early stages. For the time being, the applications in the production process have the highest potential for enhancing organizational performance, since they were perceived as the most applicable and having the most positive impacts on baseline efficiency. In addition, the production process poses the greatest potential to improve audience engagement, credibility, trust, and quality. While generative AI is not applicable for distribution, the potential of the processes of information acquisition and consumption is expected to increase. In particular, the applications in the consumption process augment capabilities, but pose risks to credibility, trust, and quality. Based on the insights, a framework for expected criticality of human-in-the-loop was proposed, enabling the assessment of current and future applications based on their characteristics and role in the news product. Eventually, the collaborative partner of the thesis began to further explore content versioning for audio experiences. Contributing to the existing literature, the thesis systematically compiles generative AI applications across journalistic processes and evaluates their applicability and potential performance impacts from different perspectives. Additionally, it supports the assessment of changes in the human role in knowledge work as generative AI adoption progresses. The insights aid in evaluating generative AI as a technology and its implications for organizational performance management, extending beyond the journalistic context.
Source: Aaltodoc (Aalto University)
Author: Nuotio, Mikael
Language: English
Topic: Journalism and Media Studies
Subfield: Communication
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6982322095