The holiday shopping season in 2025 started with a bang after a record breaking $11.8B in Black Friday sales. And while Black Friday has been the pinnacle of digital holiday shopping for well over a decade now, the online shopping experience looked a little different this holiday season thanks to AI.
Throughout the last year, AI-powered technology has enabled an entirely new experience when it comes to product discovery, comparison, and ultimately, customer decision-making, and that became especially apparent during the holiday shopping season. Nearly a third of all consumers used ChatGPT to complete a purchase during the holiday season and said they would happily do it again, citing speed and convenience as major benefits.
AI has the ability to elevate the shopping experience to new heights, but here’s the catch: when product information is incomplete and inaccurate, the opposite impact will occur at speed and scale, damaging both the reputation and revenue of any brand or retailer.
AI’s Role in the Modern Shopping Journey & When It Fails
From AI shopping agents to AI-powered checkout, AI-generated gift guides and personalized recommendations, it’s clear AI is here to stay.
On the frontlines, AI’s impact is astounding – reducing the friction in product discovery, educating shoppers more quickly, and compressing the path to purchase pipeline. In fact, over a third of consumers have completed a purchase based on AI recommendations, and 84% reported positive experiences with AI-driven purchase. With such strong results, it’s easy to see the positive impact that AI has had on the online user experience.
When it comes to the shopping experience, AI’s impact directly correlates to the product information underneath it. Most consumers will maintain higher expectations with AI search than with traditional search experiences, given the promised capabilities, but AI can’t perform properly/accurately with poor data.
Because AI interprets the data it’s fed to make the most accurate and relevant decisions based on the search, the quality and reliability of the input data is the far and away most important factor. And since AI systems continuously learn and iterate on their own outputs, any data quality issues are amplified over time, quickly turning any microscopic data issues into exponentially larger ones.
AI Is Only as Good as Its Foundation
Success in the AI shopping era is highly dependent on the foundation on which AI tools are deployed. AI models aren’t built to inherently understand products, context or intent, and must rely on structured and governed product data to provide the best and most relevant result. If AI models are tacked on top of weak product information with inconsistencies and informational gaps, it’s bound to result in errors across AI search, chat assistants, and recommendations, impacting sales conversion rates directly, while also having the potential to cause a loss of customer loyalty.
In fact, recent data showed 65% of consumers have switched brands due to unclear or insufficient product information, and one-third definitively say that inaccurate information erodes brand loyalty, showing a big impact when product information falls short. Weak product data can’t hide in AI-driven experiences, and it will become the only information a customer sees, ultimately weakening consumer trust and losing revenue unless data is remedied to deliver strong results.
Building an AI-Ready Foundation to Drive Sales’ Success
Developing a foundation that is AI-ready may sound like a daunting task, but it doesn’t mean fully starting from scratch. By taking just a few steps, brands and retailers can ensure their product information is ready to convert to sales in the age of AI shopping.
The first component is centralizing product data into a single source of truth. Maintaining all data in a singular location not only makes it easier to keep track of everything, but ensures that there is no data spread across multiple platforms that could impact where AI is pulling insights from. Additionally, having a cohesive structure for the data inputted is crucial. As AI thrives on consistent datasets, it’s important to be consistent with the formats of data entered into systems, whether it’s CSV files or Excel spreadsheets. Having one format is key to making data ready for machine consumption.
Beyond keeping all data elements consistent, it should also be as detailed as possible with more than just the basics. When it comes to AI, it’s crucial that product information has more than surface-level information like the color and materials Why? If a customer asks an LLM a deeper question, like “Is this specific paddle attachment compatible with this specific kitchen stand mixer?”, and the data you provide the LLM doesn’t contain the answer, the LLM will try to fill in the blanks with potentially inaccurate information. Adding things like potential use cases, specific dimensions, material breakdown, SKUs, and more can ensure there are no gaps existing within the data, leaving no room for AI to make assumptions and mislead the consumer.
Alongside detailed insights, there should be systems in place that continue to adapt the data with any new updates as they arise. Products inevitably go through various iterations and receive feedback like customer reviews, returns, and common customer questions that will impact the description moving forward. Maintaining consistent feedback loops and product updates will ultimately help AI know what the latest updates are, allowing it to share the most recent insights when generating product recommendations.
By implementing these few elements, brands and retailers are setting themselves up to generate better AI recommendations, more accurate product comparison across third-party sites, and increased customer personalization without sacrificing trust.
AI Is the Engine and Product Information Is the Fuel
This holiday season illustrated what AI-powered commerce can become with the right elements in play, and where it can fall short. AI shopping will continue to reshape the modern shopper’s experience well beyond the holiday peak, and brands and retailers must take what they’ve learned to drive success moving forward.
As AI continues to surge, brands and retailers that invest without ensuring their product information can support it will risk scaling bad experiences faster than ever before. On the flip side, those businesses that prioritize product data quality will be the ones to unlock AI’s potential, driving their brands’ trust, conversion, and long-term growth goals as the landscape continues to evolve.
