Multimodal Content for AI Search: Images, Video, and Audio

Most GEO advice is still written for a text-only world: get cited in a paragraph, get quoted in an AI Overview, get pulled into a chatbot's answer. But a growing share of what AI systems retrieve and reason over isn't plain text — it's an image on a product page, a video walkthrough, a podcast episode with a transcript buried in a CMS field nobody optimized. This piece covers what's actually documented about preparing that content for AI consumption, and — because this subtopic is genuinely younger and less studied than text-based GEO — where the honest answer is "we don't have proof yet."

How multimodal AI models actually see your content

It helps to be precise about what "AI reads your image" means mechanically, because it's not what most marketers assume. Multimodal large language models generally use a three-part architecture: a vision encoder (commonly a Vision Transformer variant like CLIP) that turns pixels into numerical representations, a projector that maps those representations into a space the language model can use, and the language model itself, which reasons over the combined signal. This pattern shows up across model families — LLaVA pairs a CLIP encoder with an MLP projector, BLIP uses a Q-Former, Flamingo uses gated cross-attention — as documented in the multimodal LLM survey literature (Springer Nature Visual Intelligence survey; MDPI literature review).

The practical implication: a capable multimodal model can, in principle, look directly at your image or video frames and extract meaning without any text description at all. But most AI search products don't run every retrieved image through a full vision model at query time — that's expensive. In practice, retrieval still leans heavily on the text signals attached to media: alt text, captions, surrounding copy, transcripts, and structured data. Which is exactly why those text signals remain the highest-leverage thing to get right, even in a "multimodal" world. Think of the image or video as evidence a model could verify visually, but the text around it is what gets that evidence found and summarized in the first place.

Alt text: the one thing that's genuinely proven to help

Direct answer: Alt text is the least speculative recommendation in this entire article. Google's own image SEO documentation states plainly that alt text is the most important attribute for providing metadata about an image, and that Google uses alt text together with computer vision and page context to understand what an image depicts — meaning explicit alt text takes priority over what Google's algorithms infer visually (Google Search Central: Image SEO best practices). Google also warns against embedding important text (headings, menu items) inside images, since neither crawlers nor translation tools nor most AI systems can reliably read it.

The W3C's Web Accessibility Initiative gives the sharpest practical framework, sorted by image purpose (W3C WAI: Images Tutorial):

• Informative images (photos, illustrations) need alt text that conveys the essential information the image communicates — not a literal description of pixels.
• Decorative images should get an empty alt="" so assistive tech (and, plausibly, retrieval systems) skip them rather than treating filler as signal.
• Functional images (an image used as a button or link) need alt text describing the action, not the icon.
• Complex images (charts, diagrams) often need a short alt plus a longer adjacent text description, since alt text alone can't carry that much information.

This is worth stating directly: alt text guidance built for screen-reader accessibility and alt text guidance for AI-readability are, at present, the same guidance. There's no separate "AI alt text" best practice documented anywhere credible — writing for a blind user who can't see the image and writing for a model that's deciding whether to trust the image as evidence turn out to be the same discipline: describe what the image means, not what it looks like.

Image captions and context

Direct answer: Google's guidance also lists caption text, surrounding paragraphs, and page context as ranking/understanding signals for images, alongside file names and alt text (Google Search Central: Image SEO best practices). Schema.org's ImageObject type has a caption property, and per the type's implementation guidance, if a dedicated caption field isn't present, systems commonly fall back to the alt attribute — reinforcing that these two fields are doing overlapping work, not separate jobs (schema.org/ImageObject; Yoast: ImageObject schema piece).

What ImageObject actually is — verified fields

Direct answer: ImageObject is schema.org's type for representing an image with structured metadata; it's a subtype of MediaObject/CreativeWork (schema.org/ImageObject). Verified, documented properties include:

The important honest point: ImageObject markup is not a substitute for alt text, and Google's documentation doesn't claim it improves general ranking or AI-answer inclusion — its confirmed, documented purpose is enabling image licensing metadata display and helping Google identify a page's "preferred" image via the related primaryImageOfPage property. Treat it as metadata plumbing, not a magic AI-visibility lever.

Video: transcripts are the real signal, auto-captions are not enough

Direct answer: Google's crawlers cannot watch or listen to a video; text is how they (and most retrieval-based AI systems) know what's in it. This is precisely why transcripts matter more for video than almost any other single asset.

But "add a transcript" and "add an accurate transcript" are different claims, and the gap between them is documented, not speculative. Word Error Rate (WER) is the standard metric for caption accuracy, and research shows wide variance: under ideal audio conditions automatic captions can approach 98% accuracy, but a 2023 industry accuracy report found even the best automatic speech recognition engines topped out around 93% on ordinary, non-specialized audio — and accuracy can fall as low as 57.5% when background noise, overlapping speakers, or accents are present (Interprefy: Understanding AI caption accuracy). Research has also documented that auto-caption accuracy is not evenly distributed — a Consumer Reports study with Northeastern University and Pomona College researchers found more errors when transcribing non-native English speakers (arXiv: Evaluating usability of automatically generated captions). The W3C's own accessibility guidance states plainly that automatic captions are not sufficient unless confirmed fully accurate, and that they usually require significant human editing.

The practical takeaway: publish a real, human-reviewed transcript — not a raw export from automatic speech recognition. A garbled transcript doesn't just fail accessibility; it actively misinforms any retrieval system or AI summarizer that treats the transcript as ground truth for "what this video says."

VideoObject — verified required and recommended fields

Direct answer: Google's structured data documentation is specific about what's required for a video to be eligible for video-rich treatment in Search: name, description, thumbnailUrl, and uploadDate, plus at least one of contentUrl (direct file link) or embedUrl (embeddable player link), with both recommended together (Google Search Central: Video structured data; schema.org/VideoObject). Recommended-but-optional fields include duration, caption, regionsAllowed, and expires. Google also documents nested types for advanced features: Clip for manually marked segments and SeekToAction for auto-detected "key moments" timestamps in search results.

Note what's not in this list: there's no documented transcript property on VideoObject itself that Google confirms as a ranking or rich-result input. The transcript's value is as on-page, crawlable text — publish it in the page body or description, not only as invisible schema payload.

Audio and podcasts

Direct answer: For podcast content, schema.org provides PodcastSeries and PodcastEpisode types, with PodcastEpisode built on top of AudioObject (schema.org/PodcastEpisode). AudioObject carries a documented transcript property, which is the correct place to attach transcript text at the schema level — though, as with video, the on-page visible transcript text is what most retrieval systems will actually index and use, not the schema payload alone.

Text-only retrieval vs. genuine multimodal consumption — and what's still unsettled

Here's the honest state of the field: different AI answer engines use meaningfully different retrieval mechanics. Google's AI Overviews run on the Gemini model family, which is natively multimodal (text, images, audio) and shares Google's existing web index and ranking infrastructure, so multimodal signals plausibly reach it through the same crawling pipeline as everything else Google indexes. Perplexity, by contrast, crawls the live web and synthesizes cited answers with inline numbered citations by default (Search Engine Land: How different AI engines generate and cite answers). ChatGPT's web-search mode cites sources only when browsing is explicitly invoked. None of the public documentation from these vendors specifies how much weight, if any, is given to image alt text vs. video transcripts vs. schema markup in citation selection — that's proprietary ranking logic, and any specific percentage you see claiming otherwise in SEO marketing content should be treated skeptically unless it links to a named, reproducible study.

So here's the honest split:

Reasonably well-supported, backed by primary documentation:

• Write real, purpose-driven alt text (informative vs. decorative vs. functional).
• Publish accurate, human-reviewed transcripts for video and audio, not raw ASR output.
• Implement ImageObject and VideoObject with the documented required fields — it's low-cost and has a confirmed, if narrow, purpose (rich results, licensing metadata).
• Keep important text out of images; keep it in HTML.

Genuinely speculative, not yet backed by public, verifiable research:

• Whether schema markup itself increases AI-answer citation likelihood (versus simply enabling traditional rich results).
• The relative weighting AI answer engines give to image/video/audio evidence versus surrounding text.
• Whether a transcript property on AudioObject/VideoObject schema is actually consumed by any AI search system, versus only the visible on-page transcript.

Anyone telling you the second list is settled science is overstating the evidence. Treat multimodal GEO the way you'd treat any frontier practice: apply the well-documented accessibility and structured-data fundamentals now, because they're low-cost and correct regardless of AI outcomes, and stay skeptical of specific ROI claims until someone publishes a methodology you can check.

Sources:

• schema.org/ImageObject
• schema.org/VideoObject
• schema.org/PodcastEpisode
• Google Search Central: Image SEO best practices
• Google Search Central: Image licensing metadata
• Google Search Central: Video (VideoObject) structured data
• W3C WAI: Images Tutorial
• Yoast Developer Portal: ImageObject schema piece
• Springer Nature Visual Intelligence: Efficient multimodal large language models survey
• MDPI: From Large Language Models to Large Multimodal Models literature review
• Interprefy: Understanding the accuracy of AI captions
• arXiv: Evaluating the Usability of Automatically Generated Captions for Deaf/Hard of Hearing Users
• Search Engine Land: How different AI engines generate and cite answers