Every month there's a new "this-changes-everything" AI headline. Most of it won't move a single dollar of your revenue. So instead of adding to the noise, here's the roundup version of the only question that matters: what actually worked, and how do we know?
The hype worth ignoring
The loudest recent fixation was llms.txt — a file some folks are selling as a magic switch for AI visibility. Reality check: adoption is still around one in ten sites, and no major AI provider has committed to using it as a ranking signal in their live answers. Add it, sure. It's cheap. Just don't mistake a one-line file for a strategy.
The results worth chasing
What's genuinely moving the needle is unglamorous and durable: clear, question-headed answers on your site, real proof of expertise, and clean structured data so the models can read and trust your facts. Clients who did that work this quarter are showing up in more AI answers — and, more importantly, those citations are turning into inbound conversations.
The part most people skip: proving it
Being cited by ChatGPT feels good. But a citation you can't connect to a lead is a vanity metric. The whole point of this work is to trace the line from AI citation → site visit → lead → revenue — automatically, so you're not guessing. That's exactly what a proper reporting setup does: it ties where the AI engines mention you to the pipeline those mentions produce.
If you can't tie a channel to revenue, you can't defend the budget for it. AI visibility is finally measurable — so let's measure it.
Take two minutes to look at your numbers for the month. I think the results will speak louder than any headline.
Want to see where you actually stand? Get a free AI Visibility Snapshot and I'll show you which AI engines mention you today — and which ones don't.