Generative AI is rapidly transforming the marketing landscape, with a recent survey revealing its impact across various use cases. The study, conducted by MartechTribe, analyzed responses from 283 marketing professionals to understand the adoption patterns and importance of GenAI applications. Content-related applications dominate the top 20 use cases, with marketers heavily relying on GenAI for ideation, production, and optimization.
Copy ideation (50.7%) and production (43.9%) are extensively used to streamline workflows and enhance personalization. Content optimization and testing (28.6%) also play a significant role in refining performance. Data-related applications are prominent, with knowledge management, competitor research, and documentation being key areas.
GenAI’s ability to analyze vast data and extract actionable insights drives its popularity in this domain. However, the adoption of GenAI in advertising remains low, with factors such as outsourcing, embedded AI, and creative control contributing to this gap.
Adoption patterns in generative AI
Social and management use cases show mixed adoption trends, with analytics and scheduling tools widely used, while community engagement and influencer management face resistance. The survey reveals that GenAI typically supports strategic planning and specialized tasks on a monthly basis, whereas operational tasks are handled daily or weekly. Image/video ideation (24.3%), knowledge and documentation (22.5%), and competitor research (21.4%) are among the top monthly-used applications.
Daily and weekly use cases focus on content creation and management, including copy ideation (50.7%), copy production (43.9%), and transcription, notes, and summaries (43.2%). The ‘wishlist’ data highlights use cases marketers have yet to try, categorized into high, moderate, and low-interest levels. Audio/podcast production (51.4%), compliance and risk management (46.4%), and social media management (43.2%) are among the high-interest areas.
As GenAI continues to evolve, its varied adoption rates across different marketing functions highlight distinct opportunities and challenges. Marketers need to balance innovation with practical integration, ensuring they leverage AI’s strengths while maintaining essential human elements in strategy and execution.
