Image SEO for multimodal AI ensures that visual content is properly optimized with descriptive filenames, alt text, metadata, and structured data so AI systems can accurately interpret images. This improves visibility across AI-driven search, voice, and visual discovery platforms, enhancing reach and relevance.

Search Engine Land has published a new guide, ‘Image SEO for multimodal AI’.

Myriam Jessier says, “Images are now parsed like language. OCR, visual context and pixel-level quality shape how AI systems interpret and surface content.

For the past decade, image SEO was largely a matter of technical hygiene:

  • Compressing JPEGs to appease impatient visitors.
  • Writing alt text for accessibility.
  • Implementing lazy loading to keep LCP scores in the green.

While these practices remain foundational to a healthy site, the rise of large, multimodal models such as ChatGPT and Gemini has introduced new possibilities and challenges.

Multimodal search embeds content types into a shared vector space.

We are now optimizing for the “machine gaze.”

Generative search makes most content machine-readable by segmenting media into chunks and extracting text from visuals through optical character recognition (OCR).”

Image SEO for multimodal AI

Search Engine Land