For two decades, search meant typing into a blank box. Now customers speak, snap photos, and expect AI assistants to understand context, tone, and visuals instantly. 

I was recently waiting in line to get coffee at a local coffee shop, when I overheard a group of girls admiring another customer’s shoes. Based on the level of admiration they had for those shoes, I was expecting one of them to go up and ask the lady where she got them from, but to my surprise she pulled out her phone, snapped a photo, opened Google Lens, and in a second had the exact model, price, nearby stores, and a buy button. No typing. No brand name. Just sight-to-result.

Search is no longer text. It is a camera, a microphone, and an AI system that bridges all three. With tools like Google’s Search Live and Microsoft’s multimodal Bing, discovery now happens through sight and sound as much as through words. For marketers, this shift is redefining how visibility, ranking, and customer intent work in the AI era.

Multimodal Search Is Here: If You Lack Alt Text, Captions, and Schema, You Disappear

The text-only era of search is ending. Google, Microsoft, and TikTok now all use multimodal AI that processes speech, visuals, and written queries at once. Google Lens alone now handles more than 12 billion visual searches per month, while reports estimate that up to 50% of all searches occur via voice or image. [1][2]

This transformation changes how information is discovered. Instead of typing “red sneakers,” users can point their phone at a photo or say “find these shoes under $100.” Google’s Search Generative Experience (SGE), Pinterest Lens, and TikTok’s visual search all train users to expect instant, context-aware results. Content that lacks the right signals, audio transcripts, alt text, structured schema, simply disappears from AI-driven discovery.

For marketers, this means SEO is no longer a keyword race. It is a multimodal strategy that ensures every asset, text, audio, and visual, can be interpreted and indexed by AI.

Voice Queries Are Full Sentences. Win by Answering Natural Questions and Snippets

Voice search is now part of daily behavior. There are an estimated 8.4 billion voice assistants in use globally, and 20.5% of internet users perform voice searches every month. [1][3] Many of those queries are local and commercial, such as “best mortgage advisor near me” or “where can I buy this coffee table today.”

Unlike typed searches, voice queries are conversational and specific. Users no longer search “car insurance quote.” They ask, “What’s the cheapest full-coverage plan for a new driver in California?” This means traditional keyword targeting underperforms because people speak in full questions, not phrases.

To capture this new intent landscape, marketers must optimize content for how humans actually talk:

• Build natural language FAQs. Create pages that answer “who, what, where, when, and how” questions using everyday phrasing. [4]

• Target long-tail, conversational queries. Optimize for full-sentence searches that mirror how people speak, not how they type. [4][5]

• Win featured snippets. Voice assistants often read directly from position-zero snippets, making that placement the most valuable real estate in discovery. [2]

The brands that master natural language optimization will not only surface more often in AI assistants but also sound more human in the process.

Cameras Now Drive Product Discovery: Image Quality + Metadata Decide Placement

Visual search turns every smartphone camera into a discovery engine. A user can point their camera at a product, landmark, or logo and receive instant information or buying options. The visual search market is projected to grow from $5.2 billion in 2023 to $27.8 billion by 2032, at a CAGR of more than 20%. [5] Retailers using visual search already report an average 30% increase in online revenue after implementation. [1]

This shift changes the technical foundation of product visibility. Every image must now serve both the human eye and the machine that interprets it.

Key workflows include:

• Detailed image metadata. Use descriptive filenames, comprehensive ALT text, and accurate EXIF data so search engines understand what an image shows. [2]

• Structured data markup. Add schema for products, recipes, and how-to content. This helps AI systems contextualize the image’s purpose and link it to queries. [2]

• High-quality visuals. Clear, high-resolution images improve AI recognition accuracy and are ranked higher in visual search results. [2]

Visual search isn’t just e-commerce. It’s product discovery through every lens—social media, UGC photos, or even live camera streams. In 2025, roughly 35% of visual search usage comes from retail, and the percentage is rising each quarter. [5]

What This Means for Marketers: From Context to Commerce

For retailers and marketers, this shift isn’t just theoretical, it’s existential. If your products, images, and descriptions aren’t optimized for multimodal discovery, your brand may simply vanish from the digital shelf. As voice and visual search dominate how consumers find products, every retailer must ensure their websites are interpretable not only by humans, but by AI assistants and search engines that now read, see, and listen.

Failing to optimize your site and product data is a direct business risk. If you don’t surface in search or LLM results, competitors will capture that demand instead. The cost of invisibility compounds fast when customer acquisition costs are already at record highs.

1. Turn Your Site into a Machine-Readable Product Catalog 

Search engines and AI assistants don’t just crawl text anymore, they learn from it. Retail websites must now serve as structured, multimodal data sources that clearly communicate what each product is, looks like, costs, and where it can be bought.

That means:

• Geo-optimization matters. Local intent is exploding in voice search (“Where can I buy these sneakers near me?”). Retailers should optimize Google Business Profiles, local schema, and location pages to ensure assistants return their listings first.

• Product pages must be semantically rich. Every product should have detailed attributes, dimensions, color, materials, availability, reviews, encoded in structured data. The more complete your schema, the more confidently AI systems can recommend your products.

• Descriptive product content is critical. Write product descriptions in natural language, as if answering a customer question aloud (“Does this jacket come in waterproof fabric?”). This improves visibility in voice queries and generative answers.

2. Ship Complete Product Data LLMs Trust 

Every photo, video, and audio file on your site is a discoverability asset, or a blind spot. For visual search engines like Google Lens or Pinterest Lens, missing ALT text, vague filenames, or compressed low-quality images mean invisibility.

Retailers should implement:

• Image-level metadata: Include descriptive ALT tags, product identifiers (SKUs, brand, color), and EXIF data where possible.

• Structured data markup: For products, how-tos, and reviews to help AI understand content purpose.

• Consistent branding across visuals: Ensure your logo and packaging appear clearly in photos to aid recognition in visual search.

3. Treat Every Image as an SEO Object

In a multimodal search world, product imagery isn’t decoration, it’s how assistants recognize and retrieve your catalog.

Disappearing from multimodal discovery pipelines in retail can directly translate into lost revenue. If AI assistants can’t “see” or “hear” your products, they won’t surface them in recommendations or generative summaries. This is the digital equivalent of being left off the shelf in every store that matters.

Multimodal SEO is not optional, it’s an insurance policy for visibility.

• Make images machine-readable: descriptive filenames and precise ALT (model, color, material), plus image sitemaps.

• Tie visuals to products: link each image to “Product/Offer” schema and the exact variant (GTIN/MPN, size, color).

• Maintain recognition quality: high-res, clean framing, consistent backgrounds/lighting; include a lifestyle shot and a clear branded/packaging angle.

• Keep parity across surfaces: ensure site, feeds (Merchant Center), and DAM metadata match, mismatches erode trust and placement.

4. UX and Performance Train Rankings and AI Answers 

Marketers can start with a multimodal audit:

• Review all product pages for metadata completeness and conversational phrasing.

• Add voice-friendly FAQs to category and product pages.

• Optimize all visuals for clarity, resolution, and labeled context.

• Localize listings for “near me” and voice assistant queries.

• Track your “AI visibility share”—how often your brand appears in AI summaries, snippets, or voice responses.

In short, context is the new keyword. The retailers that train AI systems to understand their products through words, images, and data layers will own the next generation of search visibility. Those that don’t risk fading from digital discovery altogether.

Why this matters: your new front door is an AI assistant

For readers, this means your skill set must evolve from SEO to context design—the practice of structuring content so AI assistants can parse and present it accurately. A quick audit of your existing library can reveal massive opportunities. Add captions to videos, descriptive ALT text to images, and conversational headers to blog posts. Every untagged asset is a lost entry point.

For organizations, the implications are more profound. The “front door” to your business is no longer your homepage. It is an AI assistant answering a spoken query in a car, or Google Lens identifying your product from a photo on social media. Missing multimodal optimization is like having a store with no sign or an ad with no address.

The future of search is not about typing, it is about speaking and seeing. The brands that adapt their digital presence to be both heard and seen by AI will define the next era of discoverability.