Multimodal AI
AI systems that can understand and generate multiple types of content including text, images, audio, and video.
Definition
Multimodal AI refers to artificial intelligence systems that can process and generate multiple types of content—text, images, audio, video, and more—within a single model. Unlike single-mode models that only handle text, multimodal models can understand images, answer questions about visuals, generate images from text, and work across media types.
Examples include GPT-4V (vision), Google Gemini, and Claude 3 which can analyze images alongside text. Image generators like DALL-E 3 and Midjourney are text-to-image multimodal systems.
Why It Matters
Multimodal AI opens new possibilities for marketing—analyzing visual content, generating images, creating video scripts from images, and building integrated campaigns across media types all within AI workflows.
As multimodal capabilities improve, marketers can handle increasingly complex creative tasks with AI assistance.
Examples in Practice
A marketer uploads competitor ads to GPT-4V and asks it to analyze visual design patterns, identify messaging themes, and suggest differentiation strategies.
A content team uses DALL-E 3 to generate custom blog images by describing the concept, then refines with specific visual direction through text prompts.