When you run an AI-readiness audit on your store, you get a number out of 100 and a letter grade. It is satisfying to see, but a score is only useful if you know what moved it and what to do next. So here is exactly what the readiness score measures, why it is built the way it is, and how to push it up.
Why a single score exists at all
AI shopping assistants do not read your store the way a person does. They pull structured data, parse it, and decide whether they understand your catalog well enough to recommend you. "AI-ready" is not one thing, it is several, and they matter in different amounts. The score rolls those into one number so you have a single thing to watch, and it breaks into four parts so you know where the points went.
The four parts are weighted by how much they actually affect whether an agent can use your store: coverage, structure quality, variant resolvability, and trust and safety.
What each part measures
Coverage (30 points). The share of your products that have valid, AI-ready structured data. This one is blunt on purpose. If only half your catalog has clean structured data, an agent can only confidently work with half your store, so coverage is worth a full 30 points. Run a sync, get every product covered, and this climbs fast. It is usually the biggest early win.
Structure quality (35 points). Whether each product's data has the required attributes and uses normalized values. This is the heaviest single component because it is what an agent actually reads: title, price, availability, identifiers, and the rest, in a shape it can parse without guessing. You lose points for missing required attributes and for values that are not normalized (a size written five different ways, a price as free text). For the underlying fields, our product schema guide walks through what "complete" looks like.
Variant resolvability (20 points). Whether an agent can tell your variants apart. A product with "Blue / Large" and "Blue / L" and "Blue-LG" as separate-looking variants is ambiguous, and an agent that cannot resolve which is which may skip the product rather than recommend the wrong thing. Clean, consistent variant structure keeps these 20 points.
Trust and safety (15 points). Whether the data carries the right confidence signals and avoids unsafe assertions. This is the smallest slice but it is what keeps an agent comfortable putting your store forward. Missing confidence flags and conflicting claims cost points here.
Add them up and you get your score out of 100.
What the letter grade means
The grade is a quick read on the number:
- A is 90 and above. Agents can read your catalog cleanly across the board.
- B is 75 to 89. Strong, with a specific axis or two left to tighten.
- C is 55 to 74. This is the line that matters most: C or better is the threshold where a product counts as "AI-ready." Below it, an agent is likely to hedge or skip.
- D is under 55. The data is incomplete or ambiguous enough that agents cannot rely on it yet.
So the first goal is to clear C across your catalog, then push toward A.
How to actually read your score
Do not stare at the total. Look at which of the four bars is lowest relative to its maximum, because that is where the cheapest points are.
- Low coverage almost always means you have products that never got synced. A sync is the fix, and it moves the most points for the least effort.
- Low structure quality means missing or messy attributes. The prioritized fix list will name the specific fields, and for images specifically, alt text is a common gap.
- Low variant resolvability means inconsistent variant naming. Standardize how options are written across the catalog.
- Low trust and safety usually means missing confidence signals on a subset of products.
Fix the lowest bar first, re-run the audit, and watch it move. It is a faster loop than trying to improve everything at once.
The honest state of this
A readiness score is a proxy. It approximates how well a machine can read your store today, and "how machines read stores" is still changing month to month. A perfect 100 is not a guarantee of sales from AI, and a B is not a crisis. The score is most useful as a direction and a diff: it tells you where the gaps are, and it tells you whether the work you just did actually helped.
It also only covers the reading layer, the structured data agents consume. The newer question of whether an agent can operate your store is scored separately, which we get into in Lighthouse's agentic browsing score. Both matter, and they are converging.
That is the whole point of putting a number on it. Being readable to AI used to be invisible work with no feedback. A score, broken into the parts that actually move it, turns it into something you can see, act on, and watch improve. That is why AgentReady leads with it, and why the first thing worth doing after install is finding your lowest bar and clearing it.