The Technical Signals AI Search Uses That Most SEOs Still Aren’t Optimizing
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Our audit of 50 major websites reveals where the technical gaps are – and what to do about it. Reza Moaiandin 39 seconds ago ⋅ 14 min read Reza Moaiandin Co-founder at SALT.agency Bio Follow For the last couple of years, the topic of AI visibility has dominated SEO discussions. SEO teams have cleaned code and chunked content, all the easier for AI bots to access and ingest. If your brand has started earning regular citations in AI-generated answers or showing up in AI Overviews, you might be forgiven for thinking the battle for AI visibility is nearly won. None of that is wrong, and none of it is wasted effort. It’s just not the whole story. However, our audit of 50 major websites found that, while most have made it easier for AI to find them, almost none have made it possible for AI to truly understand them. And nearly two-thirds leave the question of which AI bots can access which content entirely to luck. Ask a marketing team to define “AI visibility,” and the answer will likely focus on mentions and citations. Should someone ask any of the leading AI platforms a question relevant to your category, you want your brand and/or product to have a better than average chance of appearing in the response. Defining AI visibility purely in terms of citations and mentions is old SEO thinking – get ranked, get found, get clicked – carried over to a new and very different form of search. But visibility in AI isn’t simply about getting your brand, product and relevant links in front of the right eyeballs. It’s also about how AI understands your information and interacts with your website. Once a child has mastered both reading and comprehension, they can put what they learn into action – follow a recipe to cook a meal, write their own stories or essays, carry out further research to find answers to questions arising from the initial text, and so on. Reading is only one step towards comprehension. And comprehension is only one step towards agency; what you decide to do with that information once you have it. This is why we developed an audit framework for AI readiness with three distinct layers: Can AI fetch and parse your content without stumbling? This is the reading bit, the foundational layer most SEO teams already optimize for, directly related to AI visibility in the sense of citations and brand mentions. But unless you also optimize for the second layer, you’re basically crossing your fingers and hoping AI interprets your content correctly. Can AI determine what your pages are about and who owns them? This is how AI knows, without simply guessing, which number on the page is the product price, and which is the discount price available exclusively to loyalty card holders. It’s how AI determines who the brand and/or author really is, and whether to consider them a trusted authority on the subject. It’s how AI tells whether an article is about Jaguar the car brand, or one of the many Jaguar football clubs. Giving AI greater confidence that it has correctly interpreted your content does two things: It increases the likelihood of your content being cited in relevant AI responses, while also reducing the risk of those responses misrepresenting your brand, your products or your claims. Can AI agents access your website’s capabilities to carry out tasks, such as completing transactions? This final layer is about agency, and it’s the difference between AI merely parroting information back to the user and being able to interact with your business on their behalf. Under each of these layers, we grouped the likely protocols SEO teams might implement to achieve those outcomes. For example, implementing ARIA labeling or including AI user-agent directives in the robots.txt would largely relate to Retrievability, while the presence of an entity map or JSON-LD schema would relate to Attribution and Meaning. Altogether, our framework includes 27 audit elements – 11 in Layer One, three in Layer Two, and 13 in Layer Three – rated and scored according to their current maturity as industry standards: Armed with this framework, we audited 50 major websites across retail, SaaS, travel, publishing and finance to see where the most common gaps in AI readiness might be. I’ll admit it’s a tad unfair of me to apply the same framework and scoring system across the entire cohort, regardless of industry and business model. When using our AI readiness framework with clients, we weight the scoring according to their industry. But using the framework as a benchmarking tool across a varied cohort wouldn’t work if we measured different things or applied different scoring to each website. The purpose was to highlight where the biggest gaps are overall, rather than guessing at the nuances of every individual business case or commercial decision. A low score doesn’t necessarily mean a site is underprepared for AI if other clues suggest they’ve deliberately adopted this approach. See also: Machine-First Architecture: How To Build Websites Machines Can Identify, Read, Cite & Use Using an instrumented browser, we were able to capture live HTTP responses, the rendered DOM, raw server HTML, and machine-discovery endpoints. All data was captured on the same day: June 12, 2026. We then scored 12 Established signals against our agentic readiness framework (0/1/2), with the final score rendered as a percentage of the possible maximum. While we also tracked Emerging and Frontier protocols, we excluded these from the scoring. While that might sound like a pass mark, it isn’t. When you pull the three layers apart, it becomes clear where there’s still work to be done. This is the layer that overlaps most heavily with conventional technical SEO, including elements that may have already been in place, making any new tweaks to optimize for AI retrievability easier to implement. Unsurprisingly, most sites perform reasonably well, with only three scoring below 50%. And there could be legitimate reasons for those low scores, as you’ll see. Scores drop away sharply between the first and second layers. The good news is we detected JSON-LD structured data on the homepage of 35 out of 50 websites (70%), with all but three scoring the 2-point maximum. The bad news is that still leaves nearly a third without any at all. Schema is a vital part of how AI understands what your content actually means: This is a product, this is the price, and this is the brand that sells it. Poor or non-existent schema probably won’t undermine your website’s AI retrievability, and the LLM will still make its best guess at what everything means. But that best guess can also be wrong, leading to inaccurate AI responses or even outright hallucinations. Perhaps more telling is that only five of the 50 audited sites have implemented Cloudflare’s Content Signals Policy. This is a set of directives within the robots.txt file that spell out exactly what crawlers are permitted to do with your content in relation to search indexing, live AI query responses, and model training. A Content Signals Policy means your AI strategy isn’t limited to a simplistic binary choice – “block all AI” or “allow all AI” – allowing you to take a more strategic approach. Agentic browsers and agentic commerce have only been around since late 2025. As a result, of the 13 protocols we identified as directly related to Layer 3, we could only class two as Established. Both ebay.com and wikipedia.org block same-origin fetch via CSP. But of the 48 sites where endpoint testing was possible, 46 scored zero. And while airbnb.com and vercel.com have both implemented OAuth authorization server metadata, neither has implemented OAuth protected resource metadata, meaning they only scored 50% each for Layer 3. OAuth discovery and OAuth protected metadata make it possible for AI to interact with your site. For example, the OAuth discovery metadata tells a client application what it’s allowed to access, how to identify itself, and where to get the information it needs to securely access an OAuth authorization server or protected API. However, our team is closely monitoring nine other Emerging elements in Layer 3 that could see the space evolve quickly.
Source: Search Engine Journal
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