I’ve been testing a few different internal linking workflows over the past couple of months on a couple of niche sites, and I’m starting to think the results are a lot more situational than the tool vendors make it sound.

The basic setup:
– WordPress site with ~400 indexed URLs
– Mix of evergreen guides and some commercial pages
– Used AI to suggest contextual internal links based on topic clusters
– Manually reviewed the suggestions before publishing
– Compared against a more traditional “human + spreadsheet” internal linking process

What I’m seeing so far:
– On a few content clusters, crawl depth improved and some pages picked up a small lift in impressions
– But in other sections, the AI kept recommending links that were technically relevant but not commercially useful
– It also tended to over-link broader informational pages while ignoring pages that actually needed authority flow
– In a couple of cases, the anchor text got repetitive unless I manually rewrote it

My main takeaway is that AI can speed up the process, but it doesn’t seem to understand site strategy very well. It’s decent at identifying topical relevance, but weak at prioritizing pages that matter for rankings or revenue.

I’m curious how others are handling this:
1. Are you using AI for internal linking, or still doing it manually?
2. If you’re automating, are you feeding the model page intent / commercial priority, or just letting it work from content similarity?
3. Has anyone seen measurable ranking improvements, not just “more links added”?
4. Do you think internal linking automation is actually useful for smaller sites, or mainly for larger content libraries?

My current view is that AI internal linking works best as a suggestion engine, not a replacement for strategy. If you already have a solid site architecture, it can save time. If your topical map is messy, it may just scale the mess faster.

Would be interested to hear real examples, especially from anyone running affiliate sites or sites with a lot of informational content.