Multimodal AI

Traditional AI systems are specialists: one model reads text, another recognizes images, a third understands speech. This fragmentation creates friction. Marketing teams have to stitch together insights from different tools, losing context and wasting time. Multimodal AI solves this by combining multiple 'senses' in a single system—like hiring someone who can read, see, and listen all at once.

Think of it like customer research. A traditional approach: you read survey responses (text AI), analyze brand photos (image AI), and review customer call recordings (audio AI) separately. A multimodal system can ingest a customer's social media post (text + image), their product review video (video + audio), and their purchase history (structured data) and synthesize one coherent insight: "This customer loves your product's design but struggles with setup."

In marketing tools, multimodal AI shows up in several ways. Content analysis platforms can now evaluate how well your ad creative performs by analyzing the image, headline, and video together—not separately. Social listening tools can flag brand mentions that combine text sentiment with visual context (someone posting a complaint with a photo of a broken product). Generative AI tools like GPT-4V can create marketing copy based on product images, or analyze competitor ads by seeing and reading them simultaneously.

For CMOs, this matters because multimodal AI reduces the number of point solutions you need. Instead of buying separate tools for image analysis, text analysis, and video insights, one multimodal platform can handle all three. It also produces richer insights: understanding that a customer's negative review includes a photo of a defective item is more actionable than just reading the complaint. When evaluating AI vendors, ask what modalities they support and whether they're truly integrated (one model) or just bundled (multiple models in one interface).