From prompting to professional judgment: Generative AI, metalinguistic competence, and human-centered continuous improvement in advertising agencies
This study examines how generative artificial intelligence reshapes creative work in advertising agencies and considers its implications for professional capability development and continuous improvement. Attention centers on everyday routines, redistribution of expertise and organizational conditions that convert AI-enabled iteration into learning or, conversely, efficiency without capability growth. An exploratory qualitative design draws on open-ended questionnaire responses from 18 advertising professionals, including copywriters, art directors and video designers/makers. Researcher-led thematic analysis was supported by InfraNodus Lab. Text-network outputs served as sensitizing maps of recurrent concepts and semantic connections; final themes resulted from repeated comparison with complete responses. Four themes organize reported experience: generative AI as everyday creative practice, reconfiguration of creative process, time compression and process optimization and skill reconfiguration with deskilling risk. Participants associated AI with ideation, drafting, visual exploration and alternative generation. Their accounts also relocated professional value toward prompting, selection, evaluation, refinement and strategic interpretation. Metalinguistic competence appears as a capability for translating strategic intent into machine-readable instructions. Advertising agencies should integrate generative AI through reflective routines that preserve human judgment, professional learning and junior skill development. Training should combine prompt literacy with brand interpretation, output evaluation and shared review practices, while performance systems should assess capability growth alongside speed and productivity. Context-specific evidence from advertising agencies clarifies how metalinguistic competence links AI-mediated creation with professional judgment. A PDSA-informed model distinguishes reflective AI use, where generated alternatives are studied and converted into organizational learning, from transactional use, where output speed may rise without sustained skill development. Contribution rests on specifying a quality-management mechanism through which AI-supported iteration can become human-centered continuous improvement.