Why AI Search Rewards Problem Descriptions, Not Product Descriptions
The State of PPC 2026 asked 1,306 professionals about their biggest challenge in managing product feeds.
Over half (54%) percent said data errors and missing product information.
That number has not shifted meaningfully in years and after twelve years of building feed management infrastructure for over 17,000 brands, in my experience, there is one recurring reason – the channels keep moving.
There is now a second reason that is becoming harder to ignore. The optimization discipline itself is shifting and where performance marketing has traditionally been a creative problem solved by better copy, stronger imagery and sharper bidding, it is increasingly becoming a data infrastructure problem. B2C no longer works – it’s now about B2R (business-to-robot.)
The signals that determine whether a product surfaces in Google Shopping, Performance Max, and increasingly in AI-generated results like Gemini and Perplexity, are not creative signals but technical ones – attribute completeness, feed consistency, and data accuracy. By fixing their feed quality now, brands are not just solving a maintenance problem but building the foundation that the next generation of agentic commerce discovery runs on.
Which brings us back to the plight of the 54%.
Amazon requires ten attributes this month and adds five more the next. European regulatory requirements introduce mandatory fields including product safety documentation and compliance links that can make previously complete feeds non-compliant overnight. Google continuously updates its taxonomy and popularity of channels differs across markets.
Keeping up with changes across multiple channels simultaneously is where most brands lose ground, not because of access to technology but because of the operational discipline required.
Start with the foundation
When a brand has poor feed quality and needs to improve fast, the approach is always the same, start with the must-dos, not the nice-to-haves. Most brands trying to fix everything at once end up fixing nothing well.
Every channel has a hierarchy of requirements. There are fields that will cause your products to be rejected or suppressed if they are missing or wrong. There are fields that are recommended and will meaningfully improve performance. Then is a long tail of optional optimizations that matter once the foundation is solid. Get 100% of your products listed and eligible. That single step gets you to roughly 80% of the performance gain available from feed optimization.
The fields that do the most work across almost every channel are consistent – titles, descriptions, and core attributes. A title needs to be long enough to communicate what the product is and who it is for, but shaped to fit how that channel presents listings. A title that works on Google Shopping may be too long for Amazon and too short for a comparison site.
Every channel is unique, and treating them as interchangeable is one of the most common and most costly mistakes in feed management.
What completeness looks like is also evolving. Google recently introduced Conversational Attributes for Merchant Center – six new fields including Question & Answer, Document Link, and Popularity Rank – designed to help AI better understand and surface products. The brands adopting them early are building an advantage as Google continues to expand its AI-driven shopping experiences. Attribute completeness is not a fixed target but one that moves, and keeping up with it is part of the discipline.
The hero and underperformer framework
Once your foundation is solid, the next step is performance segmentation. Not all products deserve equal attention and not all optimization work returns equal value.
Look at your catalogue in two dimensions: clicks and revenue.
Your heroes are the products with high clicks and strong revenue – these are working. Your underperformers are the products with high clicks but low conversion. These are the ones worth prioritising for content optimization, because the demand signal is already there. Someone is finding these products and clicking on them but the data is letting them down at the point of decision.
For underperformers, the diagnosis is almost always one of three things – the title is not specific enough to set the right expectation, key attributes are missing so the product appears in too broad a match, or there is an inconsistency in the data. This means there is a price or availability mismatch that is eroding trust at the moment of comparison.
The impact of fixing this can be significant. German retailer Deiters applied this framework during carnival season, using performance segmentation to identify products receiving budget but generating little return alongside products with potential that had very limited visibility. Rather than treating the catalogue equally, they restructured campaigns around those performance segments. The result was over €500K in additional revenue while maintaining their ROAS target, with the number of products receiving zero impressions dropping from over 4,000 to around 500.
The reason the impact is so outsized is straightforward – you are not trying to create demand from scratch. You are converting demand that already exists.
Once you have worked through your underperformers, look at your heroes and ask whether they can be improved further. High performers often have headroom that brands do not explore because the results look good enough. In a competitive catalogue, good enough is not always sufficient.
How many channels are too many?
Brands can be visible across multiple channels if they don’t spread the same data thinly across all of them. Instead they must focus on how to optimize properly for each one with the right attributes, format and signals.
The reason is operational. Every channel has its own attribute requirements, its own content standards and its own pace of change. Keeping up with all of them at low investment means keeping up with none of them well.
Rather than thinking about how many channels to invest in, a better strategy is to ask “where is the demand for my category actually concentrated?” then identify the channels where your category has genuine scale, check whether you are eligible to sell there, and set up properly before expanding. One well-optimized channel will consistently outperform three under-resourced ones.
Feed quality is more than a maintenance task
In the age of B2R, brands must figure out how to get a machine to trust their product enough to surface it.
The signals are different from before. A human consumer responds to emotion, narrative, and brand recognition. An AI engine responds to attribute completeness, feed consistency, and data accuracy. A well-known brand with a thin, inconsistent feed can be outranked by a smaller competitor whose product data is precise and complete.
Think about the difference between a product titled “blue running shoe, size 10” and one described as “lightweight trail running shoe, recommended for marathon training, high-arch support, waterproof, 280g.” Both describe the same product but one answers a question and the other occupies a listing. AI systems surface products that answer questions.
Final thoughts
Of course, it is still the early days of agentic commerce.The volume of transactions being influenced by AI agents is small and the tooling to measure LLM visibility is nowhere near as mature as what exists for traditional search. But the brands that fix their data and feed now before it becomes urgent are better positioned than they realise and those that treat product data as a technical backlog item will continue to underperform in a future AI scenario.
The 54% who are still wrestling with basic data errors are not just leaving Performance Max efficiency on the table but ceding ground in a race they do not yet realise has started.
Fix the feed, because the machine is already watching.
