In product-led categories, AI recommendations are rarely won by brand storytelling alone.

They are won by having absolute attribute clarity.

When a user asks an AI assistant for a recommendation, the model is usually trying to match a need state to a product profile. That matching process depends heavily on attributes. Not just the attributes in your feed or PDP but the attributes that are clearly expressed and consistently reinforced across your site, retail pages and supporting sources.

This is why attribute engineering is becoming one of the most practical growth levers in AI search.

Most brands already have product information. The problem is that it is often incomplete, inconsistent, buried in marketing copy, or written in language that is clear to humans but ambiguous to models. As a result, the brand may be relevant in reality but under-selected in AI recommendations. That presents a challenge and an opportunity - 

Attribute engineering is the process of fixing that.

It starts by identifying the attributes that actually drive recommendation decisions in your category. These vary by category but typically include functional performance, format, compatibility, audience, constraints and use case qualifiers.

For example, in consumer categories this might include things like sugar-free, vegan, caffeine level, refillable, waterproof, fragrance-free, travel-friendly, sensitive skin, or machine washable. In tools or electronics categories it could be battery type, runtime, input/output compatibility, material durability, or installation type.

The key is not listing more attributes for the sake of it.

The key is making the right attributes explicit in the places AI systems are most likely to infer meaning from.

In practice, the biggest issues we see are predictable.Important attributes are implied rather than stated. Product pages use lifestyle language but do not clearly express the decision criteria users actually ask for. Attributes are inconsistent across channels. The brand site says one thing, Amazon says another and third-party references use different wording again. Attributes are not mapped to real prompts. Teams optimise for internal terminology but not for the phrasing consumers use when asking AI assistants for recommendations.

To improve AI recommendation performance, brands should focus on a few high-impact fixes. 

• Define a category-specific attribute framework. Agree the decision-driving attributes that matter most for recommendation prompts in your category.

• Tighten language. Use clear category phrasing and avoid unnecessary ambiguity. AI systems can handle nuance but they still benefit from direct language.

• Align across surfaces. Product detail pages, collection pages, FAQs, retailer listings and supporting brand content should reinforce the same attribute truths.

Then of course, test and measure. Use tools like Share of Model to track whether attribute fixes improve visibility, mention rate and especially average position for relevant prompts.

As AI search grows, brands that make their products easier for models to understand will be easier for users to discover. In product-led categories, that can become a structural advantage.

The winners will not necessarily be the brands with the loudest message. They will often be the brands with the clearest product truth - which is something that helps consumers just as much as AI. 

Author - Stephen Honight, founder of The Lmo7 Agency.