The trap is easy: paste a pile of crawl data into a model and ask it to tell you what to think. But the most useful AI SEO workflows are moving in the opposite direction. As the article puts it, the aim is not “to delegate an SEO review to AI outputs” but to “make the review easier to perform by an SEO using AI.”
Deterministic where possible
Many technical SEO checks are just true/false facts. A URL returns a 404 or it does not. A canonical exists or it does not. A link destination changes between server HTML and rendered DOM or it does not. Robots.txt allows a crawler or it does not.
None of that requires a large language model. For repeated processes, a firm deterministic framework beats a probabilistic word predictor. It also gives the reviewer a much stronger starting point than pasting a page into a model and asking what might be wrong.
AI where interpretation helps
Once those facts are gathered, the output can be accurate but unpleasant to work with. That is where language models genuinely earn their place. A model can turn a dense technical bundle into:
- A concise explanation
- A summary of what changed
- A Jira ticket-ready description
- Clearer wording for a consultant
- An interpretation of an ambiguous semantic change
The crucial shift is that the model does not become the final decision-maker. The SEO is still accountable. Testing Chrome’s on-device Gemini Nano against rendered DOM differences showed why: the model described wording changes well—such as an anchor changing from descriptive text to “Learn more”—but struggled when asked to combine structured facts into a final SEO judgement. It confused inputs with outputs and invented rationale.
So the model’s job was changed: it now helps convey findings and evidence, not judge them.
Reduce friction, not the practitioner
Imagine a rendered-DOM difference containing URLs, HTTP status, canonical evidence, robots evidence and anchor changes. A human can inspect all of it, but doing it repeatedly creates tedious friction. A language model can compress that evidence into plain-language context without taking the decision away from the reviewer. If stronger reasoning is needed—or there are many areas to inspect at once—a more capable model can tag in.
A useful tool does not have to be autonomous. Saving 30 seconds twenty times a day, making evidence easier to interpret and keeping repetitive work consistent can be extremely valuable. If a model does everything and you are only there to deliver recommendations, you will not understand the work well enough to answer follow-up questions.
The framework is simple: use software to establish facts, use AI to reduce the effort of working with those facts, and use human judgement where judgement is actually required. The result may be less spectacular than “AI does your SEO audit for you,” but it is more useful—and it makes you harder to replace.
Source: Search Engine Journal
