Ecommerce is changing. People are no longer relying only on search engines, marketplaces, or product filters to decide what to buy. Increasingly, they are asking AI tools to compare products, find the right option, and recommend what they should purchase.
That is where Agentic Commerce Optimization (ACO) comes in. An ACO Score is a way of measuring how well a product’s content is prepared for AI-powered shopping experiences.
In simple terms, it helps answer a practical question: Can AI understand your product well enough to recommend it to the right shopper?
Why Do You Need an ACO Score?
A product page can look perfectly fine to a human shopper and still have gaps that make it difficult for an AI shopping agent to understand.
For example, imagine someone asks an AI assistant to find a moisturizer for sensitive skin that is fragrance-free, lightweight, and suitable for daily use. If your product page does not clearly provide those details, the AI may have difficulty determining whether your product matches the request.
The problem is not necessarily that the product is bad. The problem is that the product information may not be complete, clear, or structured enough for an AI system to interpret confidently.
This is becoming more important as AI moves further into product discovery and shopping. Google, for example, has been expanding AI-powered shopping experiences, while AI agents are increasingly being used to research and compare products.
An ACO Score gives brands a way to identify these content gaps before they start affecting product visibility.
What Does an ACO Score Measure?
There is currently no single universal ACO scoring standard used by every company. Different platforms can use different methodologies.
One approach is to evaluate a product across two important areas: Information Sufficiency and Agent Indexability.
1. Information Sufficiency (Shoppers query coverage)
Information Sufficiency looks at whether the product content provides enough useful information to answer the questions a shopper or AI shopping agent may have.
This can include product features, benefits, ingredients, dimensions, compatibility, use cases, specifications, certifications, variants, pricing information, and other relevant attributes.
The idea is simple: if important information is missing, an AI system has less evidence to work with when deciding whether a product matches a shopper's requirements.
2. Agent Indexability
Agent Indexability looks at whether AI systems can understand and process the product information effectively.
This goes beyond simply having a product description. Product information may need to be organized clearly and supported by structured data, consistent attributes, and machine-readable information.
Google also recommends structured product data because it helps systems understand important product and offer information such as price, availability, and other product attributes.
Together, these areas provide a clearer picture of how prepared a product is for AI-driven commerce.
How Is an ACO Score Calculated?
There isn't one calculation that applies to every ACO Score because the methodology depends on the provider.
For example, a scoring system can examine hundreds or even thousands of product data points. It may look at the completeness of product attributes, the quality of descriptions, important shopper questions, structured information, and how easily the content can be interpreted by AI systems.
Enaiblex's ACO scoring system evaluates product listings across Information Sufficiency and Agent Indexability and uses more than 1,000 data points to assess product content.
The final score is then used as a benchmark. A low score can highlight missing or unclear information, while a higher score indicates that the product content is better prepared for AI-driven product discovery.
It is important to remember that a high ACO Score does not guarantee that an AI will recommend a product. AI recommendations also depend on factors such as the shopper's query, competition, product relevance, availability, price, reviews, and the platform's own systems.
The score is better understood as a readiness and content-quality benchmark, rather than a guarantee of visibility.
Who Provides an ACO Score, How to Get It?
Because ACO is still an emerging area, there is no single company that owns one universal "official" ACO Score.
Different companies and tools can create their own scoring frameworks for measuring AI readiness. For that reason, when comparing scores, it is important to understand what the scoring methodology actually measures.
Enaiblex provides a free ACO Score for eligible product listings. Its free tool allows brands to submit an Amazon ASIN and receive an assessment of how discoverable and AI-ready the product content is.
The free score can be useful as a starting point for identifying where a product page may need improvement before investing in a larger optimization program.
What Does a Good ACO Score Mean?
For an ACO Score, 85 or above is considered a good score, indicating that the product information is sufficiently complete and well-structured for AI systems to understand and interpret.
A score below 85 can highlight areas where product content may still have gaps, such as missing attributes, incomplete information, or poor structure. The score helps ecommerce teams identify what needs to be improved to make their products more AI-ready.
How Can Brands Improve Their ACO Score?
The first step is usually to look at the product information from the perspective of an AI shopping agent.
Ask whether the product page clearly answers the questions a potential buyer might ask. Check whether important attributes are missing, whether product claims are supported by clear information, and whether different versions of the product have consistent data.
Brands should also pay attention to structured product information. Product feeds, schema markup, attributes, pricing, availability, variants, and other machine-readable signals can all contribute to how ecommerce products are understood by digital systems.
Most importantly, ACO should not mean writing content only for machines. The best product content needs to work for both human shoppers and AI shopping agents.
ACO Score and the Future of Ecommerce
The traditional ecommerce journey was relatively straightforward: a shopper searched for something, visited product pages, compared options, and made a decision.
With agentic commerce, some of that decision-making can happen through AI. A shopper may simply describe what they need and expect an AI assistant to find and compare suitable products.
That makes product data increasingly important.
If an AI agent cannot clearly understand what a product is, who it is for, what it does, and how it compares with alternatives, the product can become difficult to recommend.
An ACO Score provides a practical way for brands to see where they stand and where their product content needs work.
Ultimately, the goal is not simply to achieve a higher number. It is to make product information complete, accurate, understandable, and useful enough for both shoppers and the AI systems helping them make purchasing decisions.
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