Petra Kis-Herczegh encourages you to be more critical in your thinking – and not to initially accept everything at face value.

Petra says: “Embrace healthy scepticism.

In a time where the SEO industry is drowning in new AI metrics, from AIO attribution to vector index presence, we need to understand that we don't have robust, standardised methods to validate these just yet.

When tools show you things like AI visibility, you should be asking: What's the sample size behind this? What's that number or percentage based on? How are these metrics defined? Is the tool actually using LLM training data, or are they reverse-engineering the attribution models?

Embracing healthy scepticism and using critical thinking isn't a new thing. It's not a new process; we’ve had to use it before, but this situation makes it crucial. Previously, when featured snippets appeared, we had to completely rethink how we evaluated our existing Google Search Console data, because it now meant something different because of the change, and it was in a new context. We’ve had to ask these questions, and not just chase new metrics, but evaluate the data that we are basing decisions on.

Part of the problem now is that people are trying to look at these shiny new metrics as something that they can base decisions on, when they actually might change in a month or two. Everyone's still collecting data, looking at that attribution model, and trying to learn how these LLMs understand your websites, do vector embeddings, understand context, and serve answers – and the models change all the time as well, so that plays a part.

Before you bet your credibility on a shiny new dashboard, you need to ask yourself: What would you actually do differently with this data? Do you have any other way to validate this from something that you already trust and know to be proven?

If you can't answer those questions, you are just chasing the hype instead of thinking critically.”