The Adoption of Artificial Intelligence in Newsrooms in Kenya: a Multi-case Study
The deployment of Artificial Intelligence (AI) in newsrooms is gaining prominence worldwide, with the technology being used to enhance the processes of news gathering, packaging, and distribution. The study was guided by two research questions: what factors drive/hinder(s) the adoption of AI or lack of it in newsrooms in Kenya? Moreover, what opportunities do journalists feel are offered by adopting AI in newsrooms in Kenya? A qualitative research approach and descriptive research design were employed to investigate the adoption of AI in newsrooms in Kenya. British Broadcasting Corporation (BBC-Africa) and Radio Africa Group (RAG) media organizations were the target population. As a research strategy, a multi-case method was employed. The researchers conducted in-depth interviews with newsroom-based participants. A purposive sampling technique was used to select participants for the research. Collected data were analyzed thematically. The paper identified six factors driving the adoption of AI or lack of it: management buy-in, cost, technical skills, clarity of user case, perception, and company structure. Further, the study identified three challenges presented by adopting AI: lack of quality data, ethical concerns, and unpredictability of the technology’s impact. The study concludes that AI offers excellent opportunities for newsrooms in Kenya to explore. Still, some obstacles need to be addressed before they can benefit fully from the technology. The study projects that human and automated journalism will become closely integrated in the future and recommends that newsrooms in Kenya prepare to embrace AI by laying the foundation for its adoption. Media schools should update curricula to prepare journalists to work with emerging technologies such as AI. Further research is needed to identify the specific skill sets required for Kenyan digital journalists to embrace AI fully. Scholars should investigate how AI can shape new business models given shrinking revenues in the media.
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