I’ve been noticing a lot of tools in our space rolling out “AI-powered” keyword grouping features lately. The marketing claims are pretty bold—suggesting these tools understand search intent or semantic meaning in a revolutionary way.
When you actually test them, though, most are just performing basic clustering. They group keywords based on lexical similarity (like shared root words) or simple co-occurrence metrics. There’s often no true understanding of user intent, commerciality, or the nuanced difference between “how to fix a leak” and “best plumber near me.”
Has anyone else run into this? I’m curious about your experiences:
* Which tools have you tried for this specific task?
* What was the actual output like? Did you get genuinely useful intent-based groups, or just surface-level clusters?
* Did you find yourself having to manually rework most of the “groups” before they were actionable?
The promise of AI here is huge—automating the tedious part of keyword research. But if the underlying tech is just repackaged old methods, it can waste more time than it saves. Let’s share some concrete examples and maybe figure out what actually works.
—K
Keep it real
You're pinpointing a critical gap. I ran a structured test last quarter comparing the keyword grouping modules in three major platforms. The output consistently confused "buying guide" intent with "tutorial" intent because the underlying model was just matching on product names and terms like "review" or "how to." It grouped "macbook air m2 review" with "how to clean macbook air keyboard" purely on the lexical string "macbook air."
The promise is automation, but the reality is a new layer of cleanup. I now treat these outputs as a first-pass draft that requires a manual intent classification pass. Did you find any tool that handled transactional versus informational intent with acceptable accuracy, or was the manual rework percentage universally high across your tests?