Why are you visible in some places and invisible in others?
There is a pattern we see with brands that measure AI search to begin with. They found that it showed up reasonably well for broad claims at the top of the funnel, “What are the best tools for X?” But it almost disappears completely when the user asks something like “Is (brand) better than (competitor) in (specific use case)?”
This is a sign you should pay attention to, and a readiness review is how you can find out what’s causing it.
Preparedness is not the same as vision
A common mistake is to think that if you tidy up your technical setup and create more content, your AI search visibility will improve. Sometimes this is true. Often it is not.
AI search readiness is about whether the structural conditions exist for your brand to be seen, recommended, and represented accurately. There are ten characteristics that tend to separate brands that consistently appear in AI answers from those that don’t. We’ve extracted these from Aleyda Solis’ excellent work on improving AI search:
- Accessible: Can relevant pages be accessed and displayed by AI systems? Basic, but often broken in unclear ways.
- To extract: Can AI systems unambiguously extract your key answers, locations, and highlighters from your pages? Good content that is poorly organized will not be summarized well.
- useful: Does your content answer the question better than what’s already in the AI answer? Enough is not enough.
- fresh: Are your facts, prices and dates up to date? Outdated content is as much a credibility issue with AI systems as it is with readers.
- inequality: Is your site specific and ownable, or does it look like any other brand in your category?
- Recognizable: Are your brand name, category, products and key facts clear and machine-readable on your pages and across the web?
- fixed: Do these facts match across your site, LinkedIn, Wikipedia, review platforms, and press coverage? Inconsistency breeds misinformation
- Supported by: Do independent external sources support your position and claims?
- reasonable: Do third-party sources actually carry weight? Being mentioned in low-authority sites won’t move the needle the way coverage in respected publications will.
- Negotiable: Is information like pricing, plans, and feature comparisons clear enough that an AI system can help someone decide which option is right for them?
Start from your gaps in vision, not from blank scrutiny
A readiness audit becomes really useful when you’re not trying to audit everything at once. Instead, start from where your attendance data already appears.
Different vision patterns indicate different gaps in preparedness. For example:
- If you’re showing up but not getting cited links, the issue is likely extractable or accessible
- If your recommendation rate is low, look at “Confirmed, Trusted, and Outstanding” first
- If you are being misdescribed, the problem is almost certainly recognizable or consistent
- If you’re invisible at the business end of the funnel, ‘transactionable’, ‘confirmed’ and ‘useful’ are places to start
This is the direct link between measuring attendance (what we covered in the first post) and the work of preparedness. The insight gaps you find in the first layer become the hypotheses you test in the second layer. This makes the entire framework function as a diagnostic suite rather than a separate reporting suite.
Practical example
Take, for example, a regional moving company. Their presence data shows that they frequently appear for general claims such as “Best Removal Companies in the UK”. But your search for “best long distance moving company with piano storage in the Midlands” will disappear.
The instinct on their part is to write more site pages. The readiness audit says otherwise.
The relevant service pages exist and are technically accessible. The problem is extractable and differentiated: specialist services such as piano handling and long-term storage are mentioned in passing and not clearly structured, and there is no third-party support; There are no trade association listings, no specialty directory coverage, and no reviews mentioning those specific services by name.
The AI systems that respond to constrained, high-intent prompts like these rely on sources that clearly and consistently link the brand to those details. This company is not in those sources.
The fix is not a new batch of content. It restructures existing service pages so that key details are easier to extract, and gains visibility into directories and review platforms that AI systems already cite for specialized takedown queries. Targeted, specific and more useful than another round of generic blog posts.
Prioritize what needs to be fixed
Once you have mapped your readiness gaps to potential causes, prioritize using simple logic: the potential impact on the visibility gap multiplied by the business importance, divided by the effort required.
Some repairs are straightforward and need to be done quickly; An outdated pricing page causes you to disappear from “cheapest option” queries, for example. Others, such as building meaningful analyst coverage or obtaining reviews on major comparison platforms, take longer but carry more weight. Both are important. They just sit in different parts of the roadmap.
The point is not to fix everything at once. It’s to fix the right things in the right order.
In our final article, we’ll look at the part that makes leadership pay attention: connecting AI vision to actual business outcomes, without over-claiming or distorting what the data shows.
