Consumers become more comfortable using artificial intelligence (AI) in their shopping experiences. According to Salsify’s “2025 Consumer Research,” 28% of shoppers find AI tools valuable, with 44% of men and 22% of millennials showing the most interest in using them.
Product detail pages (PDPs) are evolving simultaneously. They’re no longer static shopping experiences that offer the bare minimum. With AI-powered PDPs, brands can offer tailored product descriptions, imagery, and more.
Here’s a breakdown of how brands can build and manage scalable, variant-rich PDPs to meet the personalized demands of AI-powered discovery and purchase.
AI’s Role in Personalization
Consumers highly value personalized experiences, McKinsey & Company reports, stating that 71% of shoppers expect personalization from brands, and 67% are frustrated when they don’t receive it.
AI is capable of offering personalized shopping that goes beyond a witty monogrammed L.L. Bean boat and tote. “AI personalization is used across industries to create relevant product recommendations and contextually appropriate experiences at scale,” according to IBM.
IBM notes that AI personalization is typically powered by a combination of machine learning (ML), natural language processing (NLP), and generative AI (GenAI).
How AI Provides Personalized Experiences
Creating a personalized experience requires lots of data. According to IBM, AI will collect information like consumers’ shopping behaviors, preferences, and location, then segment them appropriately before making tailored recommendations.
For example, maybe you aren’t sure what to pack for your first island-hopping trip around Greece. A few quick searches for Greek summer weather may yield some helpful answers, but GenAI will take it to the next level.
“Whereas traditional ecommerce search indexes take a search term and connect it with product descriptions that use those exact terms, large language models can incorporate synonyms and search intent for more robust and personalized results,” says Rob Gonzalez, chief strategy and innovation officer and co-founder at Salsify in his guide, “Becoming an AI Leader.”
Other examples of AI personalization include chatbots that interact with consumers directly and AI-generated product descriptions, blog posts, emails, targeted ads, and more.
AI and personalization are a (literal) dynamic duo — and they’re just getting started.
How Variant-Rich PDPs Enhance Discovery and Conversion
Product detail pages (PDPs) will “become increasingly more dynamic, and every experience will involve multiple PDPs,” Gonzalez says.
He gives an example of two people shopping online for a T-shirt, both requiring different fits, styles, colors, fabrics, etc. If brands want to reach these customers, they need to give as much data as possible to their AI solution to ensure each receives a personalized experience.
Essentially, brands can train their AI tools to sell and market their products for them. Better quality data equals better shopping experiences.
Amazon is a perfect example of AI-powered PDPs, as they’re releasing a new GenAI-powered audio feature that highlights key product features in an audio clip. Amazon says this will save consumers time by doing the heavy lifting for them.
Your Brand Plays a Key Role in Training AI
Your brand’s website is the perfect training ground for AI. According to Gonzalez, GenAI models may contact your digital shelf team to verify and update data, which means you can play a critical role in supplying AI with important product information.
“AI tools, retail chatbots, and search models are learning from the PDPs, brand websites, and reviews you manage,” Peter Crosby, VP of corporate marketing at Salsify, says. “Every PDP becomes training data. Every word, image, and review becomes your brand’s representative in the next generation of shopping.”
How PXM Can Streamline the AI Experience
Winning brands choose PXM platforms that integrate AI throughout the product content ecosystem.
“A comprehensive PXM solution provides the governance and structured workflows needed to make AI-powered content scale safely and effectively,” Gonzalez says.
Implementing a PXM solution is crucial for:
- Building scalable, repeatable processes;
- Reducing manual effort;
- Managing the data needed by GenAI systems;
- Executing PDP variations, and so much more.
PXMs are also helpful for scaling without chaos: “Leading PXM platforms provide the closed-loop governance to your optimization process: structured workflows, validation checks, and collaborative reviews that keep humans in that loop,” Crosby says.
Feeding the Feedback Loop
Brand leaders must think of product content as an ongoing feedback loop in which every product experience teaches AI, shaping future product experiences.
“If digital shelf teams prepare now by collecting the right data, training AI solutions with the right information, and partnering with leading brands to run pilot programs, they can become the leaders of this new world, “ Gonzalez says.
Becoming an AI Leader
Learn how brands can bring AI into their ecommerce infrastructure to lead the industry into the next generation of ecommerce.
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