Large Language Models as Marketing Assistants: Productivity Gains and Creativity Trade-offs in Campaign Development

Abstract

This article examines whether large language models can serve as effective marketing assistants in campaign development without undermining creativity and brand authenticity. As generative language models become embedded in everyday marketing workflows, they promise substantial productivity gains in idea generation, copywriting and message adaptation, but may also introduce risks of homogenized content and weakened differentiation. The study conducts a systematic literature review of peer- reviewed research published between 2020 and 2024, identifying and analyzing studies that investigate language- model-based tools in advertising and marketing contexts. The evidence shows that these models reliably accelerate drafting and increase the number of creative options explored, yet collaboration modes that rely heavily on model suggestions tend to reduce novelty and can lower perceived authenticity when AI involvement is disclosed. The article discusses these results through a productivity-creativity lens and highlights contextual factors such as user expertise, disclosure practices and governance. It concludes with a research agenda for designing human-AI workflows that combine efficiency with distinctive, authentic campaigns.