Pinterest has put visual search firmly on the advertising agenda. The platform has announced Visual Search Ads, a new performance format that will place advertisers in Pinterest Search Results and Pin close-ups. This will allow brands to reach consumers as they search, compare and move towards a purchase decision. The timing is significant. Pinterest says its users conduct more than 80 billion searches every month and the vast majority of these are visual, with more than half having commercial intent. Even Google said visual searches grew 70% globally in 2025, while India has more Google Lens users than any other country, Google Lens is now also handling more than 20 billion visual searches a month. The scale suggests that visual search is no longer simply a feature consumers occasionally try.
For decades, search began with a word or phrase. Voice allowed consumers to speak an intent they might otherwise have typed, while AI-led search has made it possible to turn that query into a conversation. Visual search introduces a different starting point altogether. Another efficient way of expressing intent. But for marketers, the more interesting question is what happens when that intent becomes an advertising signal.
Showing before telling
The appeal of visual search begins with a simple consumer problem: people can often recognise what they want long before they know what to call it. “Sometimes it is easier to show a search engine what you want than explain it. You see a chair, a bottle, or a plant you like. You take a picture and search for it. There is very little effort involved,” says Prashant Puri, CEO and Co-founder, AdLift.
But the role of visual search extends well beyond the familiar illustration of photographing a product and finding something similar. Puri believes that a consumer can also point a camera at an appliance to understand how to repair it, photograph a building to learn about its history, or show an AI a living room and then ask how it could be redesigned. Thus, a source of information can smoothly transform into the beginning of a recommendation.
Pinterest is particularly well placed to make that proposition commercial because its users have always used the platform to discover, save and shape ideas before they have made a purchase. Pinterest’s advertising move is more consequential than simply adding another ad placement. If the image is already being used as an expression of intent, advertising can enter while that intent is still being shaped.
Venu Madhav, General Manager, Boopin, sees Pinterest’s move as a signal that visual behaviour is being treated as commercially valuable rather than as an experimental feature. For advertisers, that creates an incentive to invest in the quality and discoverability of the visual information attached to their products. “Retail and e-commerce stand to benefit most directly, as visual search shortens the path from inspiration to purchase, letting consumers move from ‘I like this’ to ‘where can I buy this’ in a single step,” adds Madhav.
Visual assets may soon start to perform a role that is traditionally associated with keywords. A product photograph uploaded on web can no longer be only a creative material designed to make an item attractive; it needs to have the visual characteristics and accompanying metadata that can decide whether a machine can identify, categorise or surface that product. Madhav points to video-based search, augmented-reality try-ons, in-store camera search and shoppable social content as the formats that could expand the role of visual discovery further.
But there is a problem with treating a visual match as the end of the search journey: a match is not necessarily a sale. A shopper may find something that looks right. But later discovers that it is unavailable, too expensive, it's of the wrong size or made from a material they do not want. In such a scenario, visual search has succeeded at discovery but miserably failed at the harder part of commerce–helping the consumer make the decision for purchase.
Meher Patel, Founder of Hector, a Wondrlab Company, foresees the inconsistency central to the commercial potential of the technology. The strongest use cases, he argues, are not simply those where an image can find a similar product, but those where the image can be combined with the information needed to complete the purchase process. Fashion, beauty, furniture and home décor are natural starting points because consumers often begin with a look, but the real test lies in what happens after the visual matches. “I expect the next step to be image-led conversations. A shopper could show a photo and ask, ‘Can I get this in cotton, under ₹2,000?’ The image expresses the idea; the question turns it into a purchase brief,” explains Patel.
In that context, marketers should ask: if the consumer starts with an image and then adds constraints around price, size, material, reviews or availability, can the brand’s product information keep up with that details? A similar-looking cushion may require little explanation. A sofa may need dimensions, material specifications, delivery information and inventory before the shopper can commit. For brands, therefore, the value of visual search depends on having enough accurate information.
This is where the technology begins to place a different demand on marketers. Being understandable to a machine requires something strongly systematic. Ambika Sharma, Founder and Chief Strategist, Pulp Strategy, that the next phase of search will involve images being combined with text, voice, screenshots, product scans, visual comparisons and even video frames. The consequence for brands is that product information has to travel with the visual signal. “The future is multimodal. A consumer may photograph a product and ask, ‘Find something similar under ₹10,000, available near me, with better reviews.’ The image establishes what the consumer means, language adds the constraints, and AI interprets the context and produces the recommendation,” illustrates Sharma.
In that environment, a webpage alone is unlikely to represent the full footprint of a brand. Sharma points to product imagery, feeds, structured data, specifications, inventory, entity information, reviews and third-party evidence as inputs that can help machines interpret a product correctly. Her point is less about visual search replacing traditional SEO than about the information architecture behind a brand becoming relevant to a wider set of AI-led discovery systems.
But then there’s another situation, what happens when the machine gets the picture wrong? Visual similarity can be persuasive without being accurate. Two products may look almost identical while differing in material, quality, authenticity, safety or price. Martech experts point to poor matches as a direct threat to trust.
Sharma on the other hand, introduces another complication: the rise of AI-generated or materially altered imagery. If the system cannot reliably establish what it is looking at, the convenience that makes visual search attractive can quickly become its weakness. “Trust will ultimately depend on whether the user can distinguish an exact product match, a visually similar result, and a sponsored result. Those are three different things and should not be confused by the search experience,” Sharma elaborates.
The issue takes on greater importance as visual search becomes a monetised channel. Search advertising has always involved a negotiation between relevance and commercial placement, but visual search makes that relationship more immediate: the user is presenting an object and expecting the system to understand it. If the response is perceived to be driven primarily by whoever paid the most, the usefulness of the interface can deteriorate. Puri argues that platforms will therefore have to balance monetisation with the credibility of the match.
There is another layer to that trust question. A camera does not capture only the object someone intends to search. It can also capture a face, a child, a room, a document, a number plate or clues about where the person is. Madhav sees privacy, consent and responsible handling of visual information as conditions for adoption, while Patel argues that users also need transparency around sponsored results and control over how their images are used.
The Road Ahead
So, is visual search the next marketing battleground? Perhaps the more useful question is how marketers will use it as the ecosystem takes shape. Consumers are unlikely to think in terms of separate text, voice and visual channels; they will use whichever input is easiest for the task. A photograph can become a question, the question can become a comparison, and the comparison can lead to a transaction. Sharma highlights the role of AI here, describing it as the layer that brings text, images, voice, location and context together.
For marketers, this makes visual search a consumer journey with no fixed entry point. A consumer could start with a photograph, switch to text or voice, ask AI to narrow down the options and expect an answer that considers everything from price and location to reviews and availability. As the boundaries between discovery, consideration and commerce continue to blur, the bigger question may be whether brands are ready for a world where consumers no longer search in a particular way — they simply expect to be understood.
