AI is better as the SEO audit explainer than the SEO auditor
Chris Green’s Search Engine Journal workflow gets the split right: let deterministic tools collect SEO facts, let a local LLM explain them, then keep a human responsible for judgment, priority, and client-safe recommendations.
TL;DR: The safest SEO use case for AI is not replacing the audit, it is turning verified crawl data into clearer explanations while a human keeps control of judgment and action.
What should AI actually do in an SEO audit?
Search Engine Journal’s Chris Green, in “Using AI To Assist With SEO Work, Not Replace The Worker,” describes a clean split: deterministic checks for facts, a local LLM to explain those facts, and a human to judge what matters.
That split is the whole story.
SEO audits are full of tasks that look language-heavy but are really evidence-heavy. Is the page indexable? Does the canonical point somewhere strange? Are status codes correct? Is structured data valid? Did internal links change? These are not vibes questions. They should not be answered by a model guessing from a prompt.
The model is useful after the facts are known. It can explain a crawl finding in plain English. It can group related issues. It can draft a note for a developer. It can turn “these 47 pages return a 200 but contain noindex” into a client-readable paragraph, assuming the underlying check is real.
That is a practical pattern for using AI without letting it cosplay as an auditor.
I run Lucky Domains, where search visibility work lives or dies on separating evidence from explanation.
Why deterministic checks still matter
The reason deterministic checks matter is boring, which is why they matter.
A crawler, a log file parser, a schema validator, a rank tracker, Search Console export, or a simple script can give you repeatable outputs. Maybe the tool has bugs. Maybe the configuration is wrong. But if you run the same check on the same data, you expect the same answer.
LLMs do not behave that way by default. They are great at producing fluent text. They are not inherently great at knowing whether your faceted navigation is wasting crawl budget, whether a redirect chain changed last week, or whether a canonical mismatch is intentional. If the model invents a cause, the sentence may still sound polished.
That is the trap.
Green’s workflow, as reported by Search Engine Journal, uses a local LLM as an assistant around known findings, not as the source of truth. The local part is also relevant. SEO audits often include private crawl data, unpublished site architecture, staging URLs, analytics snippets, and client context. Sending all of that into a hosted model may be fine in some organizations and a non-starter in others. The post does not turn this into a grand privacy framework, and neither would I. But local models make more sense when the work is sensitive and the task is mostly summarization, explanation, and drafting.
Where does the human fit?
The human fits where the money gets spent.
A model can say a page has thin content. A human has to decide whether that page is supposed to rank, support another page, exist for compliance, catch branded traffic, or be removed. A model can explain duplicate titles. A human has to decide whether fixing them beats fixing templates, internal links, rendering, product feed issues, or stale content.
That priority layer is not decoration. It is the job.
This is also where many AI-for-SEO pitches get slippery. They show a tool producing long audit reports, then imply the work is done. Long is not the same as useful. A 90-page audit with no priority, no business context, and no clear owner is just latency with formatting.
The better workflow is smaller and stricter: collect facts with reliable tools, pass only the relevant findings to the model, ask for explanations or drafts, then review for accuracy and priority before anything goes to a client, developer, or CMS.
Practitioner’s take: try this on one narrow audit lane first, not the whole SEO function. Pick something factual, like indexability, redirects, titles, or schema validation. Generate the findings with tools you already trust. Use a local or approved model to translate those findings into human-readable notes. Then compare the output against what you would have written. The catch most teams miss is that the prompt is less important than the boundary: the model can help explain the evidence, but it should not be allowed to invent the evidence.