Finish a round of keyword research and you are usually left with a few hundred rows. The file you exported is not usable as it stands: ten different phrasings of the same question are scattered through it, and two questions with nothing in common sit next to each other because they happen to start with the same letter. Turning that list into intent groups is the first step of any content plan, and it is genuinely where AI earns its keep.
Why group at all
The reason not to write one article per keyword is straightforward. “Speed up WordPress” and “WordPress site is slow” are different strings but the same question. Write both and the two articles compete with each other. Meanwhile “WordPress speed testing tools” looks similar and asks something else entirely — the first two want a fix, this one wants a comparison.
So the grouping criterion is not string similarity but sameness of intent. Phrases that want the same answer go together, and one group becomes one page. That single rule removes most cannibalisation before it ever gets written.
Where AI is strong: sorting by intent
This is pattern recognition rather than judgement. Reading several hundred short phrases and putting like with like is dull work for a person, and the criteria drift as you tire. AI is fast at it and, more usefully, consistent from the first row to the last.
The more specific the request, the better the output. Something like: “group these keywords by search intent; name each group, write one sentence describing the answer that group wants, and say what format of page would serve it — explainer, comparison, how-to, or service page.” When it comes back, point at the two or three vague groups and ask for those to be split further.
Never take a search volume from it
One warning deserves its own heading. Ask for the groups as a table and AI will helpfully fill in a monthly search volume column too. The figures look plausible, the orders of magnitude feel right, and the relative sizes between groups make intuitive sense. They also have no source whatsoever.
It is not querying a volume database; it is producing numbers that fit the context. Copy those into a plan and you have just set six months of content priority using values nobody measured. Volumes always come from the tool, directly. When you ask for the table, it is safer to say explicitly that there must be no volume column at all — an empty column is an invitation to fill it.
It cannot tell you which group to pick
Thirty groups does not mean thirty articles. The criterion is not descending volume; it is whether you actually hold the answer.
AI has no idea what your business has done. The measurements you took, the problems you have actually fixed, the requests you have turned down — the groups where those exist are the groups you can win. A group you have no first-hand grounding in is already occupied by people who do, however large the volume looks, and a late rearrangement of公 knowledge will not take that position from them.
Once the groups are settled, the next question is what page each becomes and how they link to one another. The pillar-and-cluster structure is covered in the designing a content hub series, and other uses of AI in content work live in the AI archive. If the technical ground under the plan needs sorting first, a technical SEO review is part of our optimization program.