AI shopping agents are changing how consumers discover and buy products. Instead of searching across marketplaces, comparing listings and visiting brand websites directly, shoppers can increasingly ask AI-powered tools to find the best option, summarise the choices and, in some cases, complete the purchase for them.
That shift creates a new risk for brands. Not because new counterfeit sources are emerging, but because existing threats are being surfaced, trusted and acted on at unprecedented speed. For consumers, it’s simpler and more convenient shopping. But for brands, it creates a new visibility challenge. If AI agents are pulling from existing ecommerce platforms, marketplaces and websites, they can also surface counterfeit listings, scam sites or unauthorised sellers. In the UK, The National Trading Standards has already warned that criminals are poisoning AI models such as ChatGPT and other LLMs to increase the likelihood that scam websites are recommended to shoppers.
It raises important questions about how brands protect themselves as AI-powered commerce and consumer shopping habits evolve, but there is a risk that organisations are focusing on the wrong problem.
Much of the discussion today centres on where infringement is appearing, the important question, though, is where it actually starts. AI shopping agents haven’t created a new source of infringement. They are exposing and amplifying information that already exists elsewhere, changing how quickly it is discovered.
That distinction matters. If brands focus only on where counterfeit content is appearing, they risk chasing the same fake across multiple AI-driven surfaces. Effective enforcement still depends on identifying and removing infringement at its source, before it can be amplified across new shopping channels.
Exploiting trust
The amplification of counterfeit content becomes far more dangerous as consumers place increasing trust in AI-generated recommendations. The convenience AI shopping agents offer is being matched by growing consumer confidence, with Accenture’s Consumer Pulse survey finding that 74% consumers would trust an AI agent more than their best friend to make a purchase on their behalf.
Similarly, data from BCG shows that 60% of consumers have high trust in generative AI results, while assistants and chat tools are ranked as the second most influential touchpoint in the purchase journey. Brands are increasingly investing in ensuring their genuine products appear in AI-generated summaries too, so recommendations made by these tools carry growing authority with consumers.
That trust creates an opportunity for fraudsters. Rather than simply creating counterfeit listings, they are influencing the recommendations themselves by using fake reviews, cloned listings, manipulated product information and scam websites that are designed to appear credible to both AI models and shoppers. As AI-generated answers become more persuasive and personalised, identifying the difference between legitimate and fraudulent recommendations becomes increasingly difficult.
The consequences extend well beyond the immediate fraudulent sale. Counterfeit products divert revenue from legitimate retailers, undermine pricing integrity and erode trust. Consumers rarely distinguish between a poor AI shopping experience and the brand they intended to buy. If an AI assistant directs a customer to a counterfeit seller or scam website, the experience is often associated with the genuine brand, regardless of where the infringement originated.
Surface versus source
AI has fundamentally changed the speed and scale at which infringement spreads. A single counterfeit listing can now be surfaced across AI-generated search experiences, shopping agents, marketplaces and other discovery channels within hours, extending its visibility far beyond the platform where it was originally published. What appears to be multiple instances of infringement is often the same source repeatedly appearing across different interfaces.
In AI-driven commerce, a single counterfeit listing can effectively be replicated through recommendations and summaries, creating the appearance of many threats when the root cause remains one.
That means speed has become just as important as coverage. The longer a counterfeit listing remains live, the more opportunity AI has to discover, index and recommend it, making enforcement increasingly reactive.
The most effective way to stop counterfeit products appearing across multiple discovery channels is still to remove them at the source. Rather than chasing every AI platform or search surface where infringement appears, brands need the visibility to identify where it originated and act quickly to eliminate the root cause before it is amplified.AI, however, is not just changing the threat landscape. Used effectively, it can also become part of the solution. The same technologies accelerating the spread of counterfeit listings can help brands detect infringement earlier, identify patterns across marketplaces, ecommerce websites, and social media and automate enforcement at a scale that manual processes cannot achieve.
When combined with human analysis, AI becomes a highly effective way to scale online enforcement, surfacing the highest-risk activity while allowing experienced analysts to validate findings, avoid false positives and ensure legitimate sellers are not caught up in enforcement action. At scale, the challenge is no longer what brands can see. It is what they can act on with confidence. The risk is not just missing threats, but acting incorrectly, removing legitimate sellers, or allowing high-risk activity to persist. This is where decision quality becomes the defining factor in effective brand protection.
This will become even more important as AI agents continue to learn from the identification of counterfeit and fraudulent content. Over time, they could become better at filtering fake sources from recommendations. But that depends on accurate detection and enforcement.
The new era of brand protection
AI shopping agents are not creating a new infringement problem. They are exposing the limitations of the current model. In a landscape defined by speed, scale and automation, more detection alone is not enough. The organisations that succeed will be those that make better decisions, act with confidence, and remove threats in a way that holds.
That means identifying infringement early, understanding where it originates and stopping threats before they spread. The organisations that protect trust most effectively won’t be those chasing counterfeit listings wherever they appear, but those removing them before they have the opportunity to influence the next purchase. Because in environments where trust drives conversion, protection is not defined by how much brands see or how quickly they react. It is defined by the quality of decisions made and the outcomes those decisions deliver.
