AI Agents Are Changing How People Shop. Here’s What That Means for Brands.

With the advent of gen AI agents that assist consumers in their purchasing journeys, the balance of power will likely shift away from retailers and towards brands and AI-agents. That’s because AI agents will search for consumer goods more broadly, swiftly, and comprehensively than humans. With the advent of gen AI agents that assist consumers in their purchasing journeys, the balance of power will likely shift away from retailers and towards brands and AI-agents. That’s because AI agents will search for consumer goods more broadly, swiftly, and comprehensively than humans. To compete in this new environment, brands and retailers will have to embrace AI agent optimization (AAO), ensuring that their unique strengths—whether it’s product quality, innovation, or customer service—are clearly measurable and easily recognizable by these AI systems, and optimizing for the sources AI-agents will rely on.

AI agents—algorithms empowered to take action on a user’s behalf—are starting to fundamentally reshape the business and consumer landscapes. Case in point: Enough consumers are skipping Google and searching using ChatGPT, an AI agent that interprets questions and synthesizes search results, that some experts estimate it could replace Google in four years. For others, AI-agents are creating a new source of customer leads, independent of traditional SEO. No doubt the dominant players will fight back, but the rapid adoption of AI agents has much bigger implications for who captures value—particularly who owns the end customer, the holy grail of the digital age.

For example, consider product search. In the past, a would-be customer might start by querying on search engines such as Google. They’d click through product reviews to decide what to buy, then search retailers offering the best deal, and finally navigate the multi-step process to purchase.

AI agents are already transforming this process. Today, you can ask an AI portal, such as Perplexity, for the best alternative to Tesla, receive suggestions of which cars to buy, a summary of pros and cons drawn from legitimate product reviews, and links to the best places and prices. It is a tiny step for Perplexity to complete the transaction, thereby almost entirely removing the influence of gatekeepers (e.g., Google, Amazon) or influencers (e.g., brands, Instagram personalities). Perplexity is halfway there, launching AI agents for multi-application tasks like booking trips or planning events.

This is the leading-edge of a potentially radical shift. AI agents are already integrated into apps—and all the major AI players (e.g., OpenAI, Claude, Google, etc.) have introduced agents—but they’re mostly able to complete simple tasks. Agents are able to field questions like “Can you help me choose an insurance policy?” or “Can you find the best shipping to get parts to the customer?” It is a short step for AI agents to then complete the purchase and optimize the logistics. Perplexity is reportedly already completing purchases.

The shift to AI agents will change how work gets done and who holds power, since they control access to end customers. Although many industries will be affected, the change will start where activities/products are simpler or more standardized, such as in consumer goods. These companies often deal with relatively simple products but complex streams of information, and AI agents could quickly change the role of retailers and brands, as well as how customers evaluate, decide, and buy products. Here’s how—and what companies can do to prepare.

To understand how AI agents could disrupt the value chain in the B2C world, let’s quickly remember how business was done in the pre-internet era. The balance of information between retailers and brands was relatively even. Retailers had access to receipt-level data—sales data that, although not linked to individual customers, provided insights into what was being sold, in what combinations, and at what frequency. Brands typically lacked access to this granular data but could gather “sell-through” or “sell-out” information across the broader market. Only by collaborating could retailers and brands glean insights that were vital for developing new products or smarter marketing strategies. For example, brands might conduct customer research at retailers to develop new products or retailers might share order history, stock levels, or point-of-sale data to brands to optimize sales or promotions.

With the rise of e-commerce, however, the balance of power began to shift. Retailers, especially the new generation of e-commerce retailers, went from having transaction data at the register level to possessing customer-level data. Brands, meanwhile, typically didn’t gain access to significantly more data. In a digital era where data and access to customers reigns supreme, the power—and the profits—shifted to retailers who control this data. In the past decade, we have seen large retailers like Amazon, Alibaba, and Zalando leverage customer data into significant revenue streams (from advertising, for example) and increasing margins. Their troves of customer insights give them a clear advantage over brands or retailers without end-customer ownership. This trend isn’t limited to the big tech companies. More traditional retailers are also leveraging this power to improve their margins.

As we look toward the future of retail, it’s clear that generative AI (gen AI) could fundamentally transform the landscape, much like e-commerce did before it. Historically, the success of retailers has been tied to a combination of formula, price, and location. In the past, a store’s physical location was crucial—being in the right spot meant visibility and accessibility for consumers. Then came the rise of e-commerce, which disrupted the retail environment, shifting the focus away from location toward transactional efficiency. E-commerce platforms began to excel by offering competitive pricing, fast delivery, and leveraging brand equity to build trust with consumers, as we’ve seen with companies like Amazon.

With the advent of gen AI agents that assist consumers in their purchasing journeys, we anticipate a shift in the balance of power away from retailers and towards brands and AI-agents. That’s because AI agents will search for consumer goods more broadly, swiftly, and comprehensively than humans. While most consumers shop primarily at a narrow set of retailers—it’s simply too overwhelming to search everywhere, manage an ever growing number of accounts, and evaluate the trustworthiness of every e-commerce retailer—AI agents can do this and optimize on key factors that humans sometimes miss but still value. AI agents will sweep data people find less biased by company influence, like Reddit, to surface the most relevant data and suggest options based on a broader scope of data on key metrics such as:

• Price: Which retailer offers the lowest price?
• Availability: Is the product in stock? Can multiple variations be shipped and unwanted ones easily returned?
• Reliability: Does the retailer have a consistent track record of on-time deliveries?
• Service: Does the retailer provide reasonable and easy returns or assistance?
• Partnerships: Does the retailer collaborate with reputable payment gateways and delivery services?

The power of getting all the available data on these objective criteria will flatten the retail landscape, as the consumer’s AI agent will prioritize these pragmatic factors over brand loyalty. For example, whereas in the past consumers wanting something like a French chore coat might rely on retailers they trust (e.g., Amazon, Zalando, Uniqlo) because it simply takes too much time to search everywhere for everything and make accurate comparisons, an AI agent can scour this data. An AI agent might suggest a small, less known designer like Paynter Jackets because of the groundswell of enthusiasts who love their French chore jacket. Thus, in this future, power will shift away from retailers and towards those with the ability to provide the best service at the lowest cost.

In this new world, we foresee clear winners and losers. Retailers like Amazon, with their razor-thin margins, extensive delivery network, and flexible return policies, are well-positioned to thrive. Their ability to meet the high standards of a gen AI agent—who will filter options based on criteria such as price, delivery speed, and customer service—will cement their dominance.

On the other hand, “middle-of-the-road” retailers, such as department stores, that neither excel in price nor in service quality are likely to struggle unless they can leverage brick-and-mortar to get an edge. These companies will be squeezed between low-cost, high-efficiency players like Amazon and premium retailers offering unparalleled service or exclusive goods. The future of retail won’t be about a race to the bottom in pricing alone; high-priced retailers can still win, but only if they provide a superior service package. This includes excellent delivery, easy returns, and strong customer support, creating a seamless and delightful shopping experience.

Local retailers that offer unique experiences may still hold an important place in the evolving retail environment, if they can be surfaced by AI agents recognizing the value of shopping locally—something that will depend on their ability to to get noticed. In sum, in a world where data becomes very more transparent, experiences will be unique differentiators for capturing customers.

Taken together, all of this suggests the potential rise of a new domain—AI agent optimization (AAO)—to help retailers and brands will stand out not just to consumers, but also to AI agents. Just as SEO helps retailers stand out in an e-commerce world, so too will AAO likely become an important future discipline.

AI agents will likely be programmed to evaluate a product based on factors like quality, features, and reviews. Therefore, brands must ensure that their unique strengths—whether it’s product quality, innovation, or customer service—are clearly measurable and easily recognizable by these AI systems. Moreover, brands and retailers will need to optimize for the sources AI agents will rely on, for example, increasing the importance of resources like product reviews or customer reviews, and their content even more. Whereas in the past the number of reviews on Amazon was a major driver of purchase, it will be ever easier for AI agents to synthesize and act based on the aggregated content of these reviews.

For brands that rely heavily on traditional brand values without offering much extra in terms of product quality or differentiation, this shift could pose a significant challenge. The premium these brands have historically commanded based on their name alone may suffer if AI agents view them as offering no significant advantage over cheaper alternatives.

To complicate matters, brands will need to optimize their offering to multiple AI agents at the same time. This is because AI agent aggregators, like Poe, make it very easy for the user to switch between different agents in their search of products or services. Thus, the retailer will not know whether the customer will use ChatGPT, DeepSeek, Perplexity, or any combination of agents for its queries. Perhaps in the future, marketers will also have to also learn AI agent marketing (AAM) analogous to search engine marketing (SEM).

Brands will need to adapt to a world where AI agents make purchasing decisions for consumers, though they face a different problem: linking their brand to consumer needs with an AI agent optimization (AAO) approach. When asked to recommend a purchase, AI agents will likely not begin their decision-making process by looking at individual products or brands. Instead, they will focus on the consumer’s needs. This is already evident today; on platforms like Google, search queries related to consumer needs (e.g., “best phone charger for travel”) vastly outnumber those for specific products or brands. This means that for a brand to thrive, it must clearly define which customer needs it serves and how it stands out in addressing them.

Brands will need to demonstrate trustworthiness and clearly differentiate their products or services from competitors. This is particularly exciting because it means brands that genuinely offer something unique—whether in product innovation, service quality, or a combination of both—will likely be favored by AI agents over those that offer generic imitations. Rather than working with retailers to push and recommend products, brands will need to amplify their attractive qualities through the channels these agents rely heavily upon, like product reviews.

Generic products are essentially interchangeable goods: items with little to no differentiation apart from the brand name. A great example of this is office lighting. While Signify produces high-quality light bulbs under their old brand Philips, many of the generic brands sell nearly identical products. In fact, many of these bulbs are produced in the same factories, making it difficult for consumers—or AI agents—to justify the price premium for the branded product.

For such brands, the future is challenging. In an e-commerce world, consumers still select the name brand, even on platforms like Amazon that tend to be full of generic products. That’s because evaluating whether the similar product is really as good is a time-consuming challenge. However, as AI agents become more efficient at comparing products—particularly drawing on data like customer and product reviews to demonstrate equivalence—customers will likely lean more towards lower-priced alternatives unless the brand can offer something truly distinct.

This is particularly relevant for products where brand visibility is limited or nonexistent during use, such as light bulbs or other commodity items. Imagine an AI-agent surfacing that two lightbulbs are made in the same factory and recommending (or automatically purchasing) the lower-priced competitor. In these cases, brands may struggle to maintain consumer loyalty against cheaper, often equally effective alternatives.

So, how can brands succeed in this new landscape? The key lies in differentiation in a way that AI agents prioritize, AI agent optimization (AAO). Brands will need to stand out in one or more of the following areas:

1. Price: Offering a compelling price point, or low-cost variant, that will appear in AI agent driven searches as a defense against low-cost generic competitors.
2. Product Innovation: Creating products with superior features, materials, or performance which stand out in AI agent searches, making them hard to compare.
3. Design: Offering aesthetically unique products that appeal to consumers seeking style and quality, once again standing out in agent driven activities
4. Service: Providing exceptional post-purchase services, such as customer support, warranties, and easy returns, but doing so in a way that stands out in the forums AI-agents will source from.

A clear example of this can be seen in the market for wireless phone chargers. On one hand, you have a budget-friendly option like those from Ikea, which appeals to consumers who want functionality at a low price. On the other hand, you have premium brands like Zens, which offer chargers with superior electronics, more charging coils, and faster charging capabilities. Both products serve different market segments, but what will be crucial in the future is how well each brand communicates its unique selling points to the AI agents.

In summary, the brands and retailers that will emerge as winners in this new AI-driven world are those that can clearly differentiate themselves through unique products or services and develop AI-agent optimization (AAO) capabilities to stand out. Whether it’s through innovation, design, or exceptional customer service, these brands will stand out to both consumers and AI agents alike. On the other hand, brands producing generic products that rely solely on brand name recognition without delivering additional value may find it difficult to maintain their market share. The future of brands will be defined by their ability to adapt to a world where AI agents lead the consumer journey—and only those that are truly unique will thrive.