We love digital 0330 353 0300
If you’re still measuring organic search the same way you were three years ago, there’s a good chance you’re missing a big chunk of what’s actually influencing your customers.
Traditional SEO gave us something fairly predictable: a ranked list of links, click data, and a measurement model centred almost entirely on Google. AI search is a different beast. ChatGPT, Perplexity, Gemini, Copilot, Google’s AI Overviews; these platforms synthesise answers, vary outputs between sessions, and can influence a purchase decision without ever generating a click.
A potential customer could ask an AI assistant which PR tool is best for their agency, read the answer, form a clear preference, and then type your competitor’s name directly into Google. The AI platform drove that decision. Your analytics doesn’t capture this.
So the old model of measuring what’s measurable and calling it done is no longer enough on its own.
The good news: there’s a smarter way to approach this.
SEO consultant Aleyda Solis has developed a practical framework for AI search measurement that is genuinely useful. It breaks performance down into three connected layers, each answering a different question.
This is the starting point. Before you can fix anything, you need to know whether your brand is appearing in the AI answers that matter, and how it’s being represented when it does.
Presence measurement isn’t just about whether your name comes up. It covers:
If an AI assistant is describing your business incorrectly to thousands of people every week, that’s a commercial problem, even if your rankings look fine.
Readiness is the diagnostic layer. It explains why your visibility looks the way it does.
If you’re appearing but not getting links, that could point to how your content is structured. If your recommendation rate is low, it might be a differentiation or trust issue. If you’re being described incorrectly, entity consistency across the web is likely the culprit.
The point of Readiness work is to stop throwing generic optimisation at a problem you haven’t diagnosed properly. Different visibility gaps have different root causes. This layer helps you find the right lever.
This is where measurement gets honest. AI referral traffic is a useful signal, but it’s the floor, not the ceiling, of AI’s contribution to your business.
A large proportion of AI-influenced conversions never show up as AI referral sessions. Users see your brand in an AI answer, don’t click, and then search for you directly or type in your URL. That conversion gets attributed to branded organic or direct traffic. The AI influence is invisible unless you’re measuring for it.
Business Impact measurement combines observed data (actual AI referral sessions), proxy signals (branded search trends, direct traffic lifts, survey data), and modelled estimates to give a more complete picture. None of these individually tells the whole story. Together, they get you much closer to the truth.
The value of this three-layer approach isn’t the metrics themselves, it’s how they connect.
Presence tells you what’s happening. Readiness tells you why. Business Impact tells you whether it matters commercially. When you run these in sequence, each one hands a hypothesis to the next. That’s what turns a reporting exercise into something you can actually act on.
We’ll be going deeper on each layer over the next few posts in this series. But if you want to get started now, the first step is simple: stop treating AI visibility as a binary. Your brand either showing up or not isn’t the question. How it shows up, where, and with what effect, that’s where the real work is.
In the next post, we’ll look at how to build a prompt library that actually represents how your customers use AI and why most businesses are getting this badly wrong.