Ask 100 consumers what the best sunscreen is, and many would probably say Coppertone, the 82-year-old brand that’s been a category leader for decades.
However, just because a product is well-known or considered the best by word-of-mouth, it doesn’t mean AI engines will consider it the best to recommend.
That’s exactly what happened to Coppertone. During a live demo at the National Retail Federation’s (NRF) conference earlier this year, the keynote speaker entered a prompt asking for sunscreen recommendations. ChatGPT suggested several brands: None of them were Coppertone.
“Immediately everyone was like, ‘Whoa, why wasn't Coppertone recommended? What's going on here?’” says Ideen Rahvar, manager of ecommerce data and analytics at Beiersdorf, Coppertone’s parent company, who ironically witnessed the demo in real time. “It gave me the platform to explain many of the things that Jason [the speaker] was explaining there and go even further into, ‘Why is that the case? Why wasn't Coppertone mentioned? And what can you do to get ChatGPT to understand your brand correctly?’”
Coppertone’s experience isn’t an outlier. It’s happening to well-known and upstart brands, and every company in between. The NRF demo is one example of why optimizing product data for ChatGPT, Claude, Gemini, and other systems has become non-negotiable in the age of AI search.
Nearly two in 10 consumers now use AI search tools for product discovery, just behind brand and retailer websites as a top discovery channel, according to Salsify’s “2026 Consumer Research” report.
If you aren’t on AI’s shortlist, consumers are probably overlooking your brand. But three workflows within Salsify’s intelligent product experience management platform (PXM) can help your company identify and correct data gaps that hurt your AI visibility.
Here’s a playbook for how to make your product data AI-ready, so AI search tools accurately represent and recommend your brand.
The process: Using internal data and AI listening tools to inform your strategy, start with the specific shopper questions or use cases you want to explore. Salsify PXM has built-in content readiness and validation workflows that help you make product data more complete.
Let’s take the Coppertone example.
Through its research, Coppertone’s ecommerce team may discover that customers ask specific questions around a sunscreen’s SPF, ingredients, whether it’s good for sensitive skin, or whether it leaves a white cast after application.
Or, they may uncover interesting use cases, like women in their 40s layering Coppertone sunscreen over their moisturizer or active seniors using it religiously before they exercise outdoors.
All of these things are signals Coppertone can use to improve its product feeds, help AI better understand the usefulness of its products, and ensure different audiences understand how Coppertone sunscreen can meet their needs.
Next, Coppertone’s ecommerce team can use Salsify PXM to evaluate whether the current product attributes that feed its product detail pages (PDPs) answer these questions or highlight these use cases.
The team can then perform data quality and readiness checks to identify incomplete records and trigger automated, intelligent workflows that add these attributes to those records and route them to the appropriate owner for approval before publishing.
Salsify Intelligence Suite, an AI-powered workbench integrated into Salsify’s PXM, drives these automations, sparing teams from time-consuming manual updates. Teams can also use Angie, the platform’s conversational AI assistant, to understand how to perform various tasks within Salsify’s PXM, such as workflow creation or mapping content to different channels.
Together, these tools power a process that ensures the record isn’t only complete; it’s accurate — and gives consumers quality information to compare Coppertone to competitors and, hopefully, decide to purchase from the brand.
Workflow checklist:
Identify relevant shopper questions or use cases
Choose a specific product or assortment for analysis
Use Salsify PXM to map shopper questions to product attributes
Identify the gaps, including missing or inaccurate information
Trigger automated workflows to add or correct information
Route the changes to a team member for validation and approval
Publish the updated PDP
Problem it solves: Surface and extract valuable product data in packaging labels, spec sheets, and other assets to build a more comprehensive PDP that’s readable by AI engines.
The process: Brands are sitting on a treasure trove of valuable product data they haven’t fully activated to create a better PDP. That’s because this data is scattered across various systems and digital assets, from product images and nutrition labels to allergen disclosures and warranty information.
To get their product AI-ready, brands need to unearth this information and make it machine-readable. Salsify PXM acts as a central source of truth that makes this possible:
Within the platform, teams can build intelligent workflows to extract content from digital assets across their ecosystem and store it as structured text.
A validation layer incorporates human review and approval, ensuring these scattered details become trusted information that enriches PDPs.
Salsify customers like the Kerry Group, a global leader in sustainable taste and nutrition, have experienced an 85% reduction in manual work using a similar process to scrape information from images and ensure content completeness.
Workflow checklist:
Select the source asset (e.g., package label, safety information, comparison chart on website, etc.)
Define the fields you want to extract
Use automated workflows to retrieve the data
A team member reviews and approves the information
Store it in Salsify PXM as structured text, building a repository of trusted, first-party data to improve your PDPs
Problem it solves: Creating relevant, answer-ready content that addresses consumer questions and boosts AI visibility.
The process: Your product catalog should be a living system, but the previous workflows that have been outlined will ensure it maintains a baseline level of completeness. From there, you can use your catalog as the foundation for creating content that speaks to consumers’ intent.
Salsify’s AEO Accelerator, which is embedded in Intelligence Suite, streamlines this process.
The AEO Accelerator allows your team to create AEO content faster, from Q&As and use case landing pages to comparison charts.
Built-in governance capabilities within Intelligence Suite also let you set content quality rules and audit this content to ensure features and product capabilities are accurate before publishing.
Workflow checklist:
Go back to your library of vetted AI search questions from consumers
Create AEO content informed by these questions and trusted data from your product catalog
Validate claims and QA content before publishing
Repurpose the content as needed for different channels
Repeat this process as new shopper questions and use cases emerge
Your brand is sitting on rich data it can use to deliver better product experiences. Now, you can put this data to work to drive AI visibility.
Though you can’t control the ever-changing algorithms AI models use to determine who to recommend, you can feed AI engines higher-quality information to better understand your company, your value, and how you’re different.
These three AI-ready, Salsify PXM workflows help you identify data gaps, turn unstructured information from various assets into structured text, and then use the trusted foundation you’ve built to develop AEO content.
With its automated, intelligent capabilities, Salsify’s PXM gives brands the fundamental capabilities they need to win on the digital — and AI — shelf.