Why AI Still Recommends Nike and Coca-Cola

One of the more surprising moments during Cahoot’s Ugly Talk: Selling in a World Run by Algorithms panel in New York came when the discussion turned to a common assumption about artificial intelligence and ecommerce.

Many people believe that AI-powered shopping assistants will level the playing field for smaller brands. If customers stop typing short keywords into search engines and instead ask conversational questions, the thinking goes, algorithms might focus more on product relevance than brand recognition.

In theory, that would make it easier for lesser-known brands to compete with global incumbents.

But as the panelists discussed how AI discovery systems actually behave today, a different pattern began to emerge. “Structured product data matters, but the product itself matters just as much. When we look at AI search results today, top brands still appear at the top most of the time.” — YiQi Wu, Aimerce

Even when shoppers ask open-ended questions, the same familiar names often appear in recommendations. Brands like Nike or Coca-Cola show up repeatedly, even in situations where the question itself does not mention them. “Even if someone copied Nike’s website exactly, ten different versions wouldn’t outrank Nike. Brand authority still plays a huge role.” — YiQi Wu

This observation raised an interesting question during the discussion: if AI is supposed to change ecommerce discovery, why do the biggest brands still dominate the answers?

AI product recommendations analyze customer data to suggest relevant products based on user behaviors and preferences. Their effectiveness relies on the quality and completeness of the underlying product data. To implement AI-powered product recommendations, an ecommerce business typically needs to collect and store a large amount of data on their customers’ behavior. AI product recommendations can significantly increase customer engagement, average order value, conversion rates, and foster customer loyalty and retention by providing personalized suggestions and improving inventory management.

The answer may lie in how AI systems interpret information in the first place. AI-powered recommendations and AI product recommendation engines are now key technologies in ecommerce platforms and ecommerce business, personalizing shopping experiences and increasing sales by leveraging customer data and machine learning.

This article is part of a series inspired by Ugly Talk: Selling in a World Run by Algorithms, a live panel hosted by Cahoot in New York. The discussion brought together operators and technology leaders including Manish Chowdhary of Cahoot, Nihar Kulkarni of Roswell NYC, Frank Pacheco of Nearly Natural, and YiQi Wu of Aimerce.

Throughout the conversation, the panel explored how artificial intelligence, recommendation systems, and platform algorithms are changing how ecommerce brands compete for visibility and customers.

These ideas are part of a broader framework for understanding how AI is reshaping ecommerce. For a complete breakdown of how discovery systems, product pages, brand authority, behavioral data, and fulfillment infrastructure interact, see The AI Commerce Playbook for Ecommerce Brands.

AI product recommendations matter because they enhance customer engagement, satisfaction, and loyalty by delivering relevant, personalized suggestions at key touchpoints. The effectiveness of these systems depends on data quality, high quality data, and up-to-date data – high-quality structured data and data completeness are essential for accurate and effective AI product recommendations.

Introduction to AI Product Recommendations

AI-powered product recommendations have become a cornerstone of modern ecommerce, transforming the way online shoppers discover and engage with products. By harnessing the power of machine learning algorithms, ecommerce businesses can analyze vast amounts of customer data—including purchase history, browsing behavior, and demographic details—to deliver highly relevant product suggestions tailored to each individual customer. This personalized approach not only enhances the customer experience but also drives sales by encouraging customers to explore more products that match their preferences.

The impact of AI product recommendations extends beyond just suggesting items; it directly contributes to higher average order value and improved customer satisfaction. When customers receive recommendations that align with their interests and needs, they are more likely to add additional items to their cart, increasing the average order and boosting overall revenue for the business. Moreover, by consistently providing relevant product suggestions, ecommerce brands can foster stronger relationships with their customers, leading to greater loyalty and repeat purchases.

In today’s competitive ecommerce landscape, leveraging AI-powered product recommendations is essential for businesses looking to stand out and drive sales. By utilizing machine learning to analyze customer data and deliver personalized recommendations, brands can create a shopping experience that feels uniquely tailored to each shopper—ultimately improving customer satisfaction and increasing average order value.

How AI Algorithms Work

At the heart of effective product recommendations are sophisticated AI algorithms designed to collect and analyze customer data, uncovering patterns that reveal individual preferences and shopping habits. These algorithms draw from a variety of data points, such as browsing history, purchase history, and demographic details, to build a comprehensive profile of each customer’s behavior.

One of the most widely used approaches is collaborative filtering, which identifies patterns in customer behavior by analyzing the actions of similar customers. For example, if a group of shoppers with similar purchase histories and browsing habits frequently buy a particular product, the algorithm will suggest that product to others in the group. This method leverages the collective wisdom of the customer base to suggest products that are likely to resonate with each individual.

Content-based filtering takes a different approach by focusing on the attributes of products a customer has already shown interest in. By analyzing the features and characteristics of previously viewed or purchased items, the algorithm can recommend similar products that align with the customer’s established preferences.

By combining these techniques, AI algorithms can generate highly personalized product recommendations that guide customers toward relevant products, increasing the likelihood of conversion. The ability to identify patterns in customer behavior and suggest products that match their interests not only enhances the shopping experience but also drives sales and encourages repeat purchases. For ecommerce businesses, implementing AI-powered recommendation engines is a powerful way to deliver personalized product recommendations, improve customer engagement, and ultimately boost conversion rates.

Prominent Brands Get Mentioned More Frequently Due to Customer Satisfaction

AI models and recommendation engines do not simply scan product catalogs the way traditional search engines do. Instead, they rely on patterns learned from enormous amounts of data — product descriptions, customer reviews, brand mentions, online articles, and countless other sources of information across the internet. These systems analyze customer behavior, shopper preferences, and customer interactions to generate relevant recommendations tailored to each user.

In that environment, widely recognized brands possess an inherent advantage. They appear more frequently in conversations, reviews, and media coverage. They have years of accumulated customer feedback. Their products have been discussed, compared, and analyzed across thousands of different contexts.

All of this creates a dense network of signals that AI systems can interpret when generating recommendations. AI algorithms analyze various data points, including browsing habits and past purchases, to deliver tailored product suggestions. Recommendation engines use product attributes and focus on analyzing data to ensure the suggestions are as relevant as possible.

When an AI assistant attempts to answer a question about the best running shoes, or the most comfortable sneakers for standing all day, it is not simply scanning a list of products. It is drawing from patterns it has observed across the data it was trained on. AI-driven product recommendation engines continuously learn and refine their suggestions over time, becoming more accurate as they process more data and customer interactions. AI algorithms also clean and reformat raw data to make it useful for analysis, and continuous optimization is required to deliver highly relevant suggestions. Brands that consistently appear in those patterns naturally become easier for the system to recommend with confidence.

This does not mean the AI is intentionally favoring large companies. Rather, it reflects the reality that well-known brands leave a much larger footprint in the information ecosystem that AI systems rely on.

Established Brands Have Vast Customer Data

During the panel discussion, this point sparked a broader reflection about the relationship between brand authority and algorithmic discovery.

Large brands tend to accumulate advantages over time that extend beyond simple marketing budgets. They generate more reviews, more mentions, and more historical data about how customers interact with their products. Platforms record years of purchasing behavior and engagement metrics associated with those brands, including valuable data on past purchases that AI uses to deliver personalized content and a personalized experience. Media coverage reinforces their visibility, while consumer familiarity strengthens trust.

AI solutions and tailored recommendations further amplify these advantages by fostering customer retention, customer loyalty, and brand loyalty, ultimately leading to higher lifetime value. Personalized product recommendations foster customer loyalty and retention by creating a shopping experience that meets individual preferences. AI-powered product recommendations enhance customer engagement by providing tailored recommendations and personalized experiences that cater to individual preferences. In fact, 76% of consumers get frustrated when they do not receive personalized product recommendations during their shopping experience.

Taken together, these signals form a kind of informational gravity. The more often a brand appears in relevant contexts, the easier it becomes for algorithms — whether search engines, marketplaces, or AI systems — to interpret that brand as a credible recommendation.

AI product recommendations are also boosting sales and increasing sales by presenting customers with relevant products at the right time. AI-powered product recommendations can lead to a 70% increase in the likelihood of a customer making a purchase. Retail giants like Amazon attribute 35% of their total sales to their AI-powered product recommendation engine, demonstrating the significant impact of these technologies on revenue growth.

In that sense, AI discovery may not erase brand advantages as quickly as some observers expect. In fact, early recommendation systems sometimes appear to reinforce them.

For smaller ecommerce brands, this realization can feel discouraging at first. If AI systems rely heavily on existing signals of authority and recognition, does that mean emerging brands will struggle even more to gain visibility, even when they invest in building a direct-to-consumer Shopify website to control their customer data and experience or try to compete directly with marketplaces like Amazon?

The panelists suggested a more nuanced interpretation.

Brands Should Challenge with Consistency to Build Brand Loyalty

While established brands benefit from deeper pools of data, the signals that AI systems rely on are not fixed. Reviews accumulate. Product descriptions evolve. Customer conversations expand across platforms. Over time, the informational footprint of a brand can grow.

Smaller brands that consistently generate clear product data, strong customer experiences, and credible reviews gradually build the signals that algorithms interpret. It is crucial for ecommerce websites and ecommerce businesses to collect data from customer interactions, purchases, and reviews, as this enables AI-driven recommendations and AI solutions to deliver personalized shopping experiences and give brands a competitive edge. AI recommendation systems continuously learn from customer interactions and customer preferences, refining their suggestions over time to better match what customers based on their behaviors and needs are looking for.

AI-powered product recommendation engines also enhance product discovery, helping customers find relevant products more easily. For example, Sapphire, a leading Pakistani fashion retailer, achieved a 12X ROI by using AI-powered product recommendations to improve product discovery. A robust Product Information Management (PIM) system ensures product data is clean and consistent, further improving the quality of recommendations.

To evaluate the performance of AI recommendations, businesses should monitor metrics such as click-through rates and conversion rates. Managing the post-purchase experience with returns management software is also critical, since efficient, customer-friendly returns can significantly influence satisfaction and repeat purchase behavior, and choosing the best returns management software for ecommerce can turn returns into a driver of loyalty rather than a cost center. At the same time, data privacy and transparency are essential when implementing AI product recommendations to maintain customer trust.

By encouraging customers with just that—relevant, timely recommendations—smaller brands can create personalized shopping experiences that drive engagement and help them compete with larger players.

In other words, brand authority in an AI-driven discovery environment may function less like a permanent advantage and more like a signal that compounds over time.

The conversation ultimately returned to a broader theme that ran throughout the Ugly Talk panel. Algorithms are changing the mechanics of discovery, but they do not eliminate the underlying dynamics of trust, reputation, and customer experience.

Consumers still rely on signals that help them evaluate whether a product is credible. Algorithms simply interpret those signals in different ways.

For ecommerce operators, the lesson is not that AI discovery will automatically reward unknown brands or punish established ones. The more important insight is that visibility will increasingly depend on how product information, customer feedback, and brand reputation appear across the broader data environment that algorithms analyze.

In that sense, the emergence of AI-driven discovery does not reset the competitive landscape overnight.

But it does introduce a new layer of interpretation that brands will need to understand as these systems continue to evolve.

Click to continue learning how products that consistently earn positive feedback and customer trust generate signals that compound over time.

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