Harnessing generative AI for evidence-informed content creation: a GenAI-human hybrid content creation framework

Purpose This study examines the integration of generative AI (GenAI) into content creation workflows within a small-to-medium enterprise (SME). The research explores how AI–human hybrid processes can enhance efficiency while maintaining brand authenticity and evidence-informed messaging. Design/methodology/approach An exploratory case study methodology was employed to investigate the role of GenAI in creating social media content for an education SME. The study mapped AI-assisted workflows across seven key activities: (1) evidence synthesis, (2) thematic classification, (3) credibility assessment, (4) script generation, (5) content refinement, (6) media production and (7) evaluation. Data were collected through practitioner interviews and workflow analysis. Findings The case study findings indicate that GenAI improves efficiency in research synthesis, content ideation, and language refinement. Human oversight remains essential for credibility verification, brand alignment, and audience engagement when creating evidence-informed content for social media. The study also highlights the need for hybrid workflows, in which AI automates low-risk tasks while practitioners maintain control over high-risk elements, such as fact-checking, strategic alignment, and brand alignment, as well as final endorsement. Practical implications The research provides a framework for SMEs to leverage GenAI for content marketing while maintaining brand authenticity and audience engagement while mitigating risks associated with copyright concerns. It offers actionable insights and a practical checklist for enabling a hybrid workflow for marketers utilising GenAI for content creation. Originality/value This study advances the literature on AI-assisted marketing by introducing a novel, structured hybrid content creation framework for SMEs. Unlike prior work, it empirically demonstrates how GenAI can be systematically integrated across the content lifecycle, specifying which tasks benefit from automation and which require human-led execution at each stage.