Getting started: The fundamentals of the new AI search era

Let’s be real: search isn’t what it used to be. A few years ago, we were all optimizing for the classic blue links on a Google results page. Rank high enough, and people clicked through to your site. Game on.

But today? More and more people are skipping the click entirely. Tools like ChatGPT, Perplexity, Claude and Google’s SGE are generating answers on the spot. You ask a question, and instead of a list of links, you get a neatly summarized response, pulled together from content across the web. Yours included.

And that’s the kicker: your blog posts, product docs, customer stories and how-to guides might still be influencing decisions, educating prospects and building trust, but you’re not seeing the traffic. Because the LLMs are the ones doing the “reading”.

This is what’s known as no-click search, and it’s already reshaping how we think about content performance. It’s not just about what you’re publishing anymore, it’s about how AI tools are finding it, interpreting it and using it in their answers.

Different models behave differently too. For example:

  • ChatGPT pulls from a mix of older training data and live browsing (if enabled), often surfacing Reddit posts, Wikipedia entries and structured blog content.
  • Perplexity pulls real-time info, cites sources and heavily favors top-ranked pages and user-generated content.
  • Claude leans on more conversational data, prioritizing clarity and human tone.
  • Google’s SGE integrates AI directly into search results, sometimes showing sources, other times, just paraphrasing.

So if you’re still measuring success by clicks alone, you’re only seeing half the picture. The other half? It’s happening inside AI engines, where your content may be working hard just without the usual metrics to prove it.

If you’ve been staring at your GA4 dashboard wondering why traffic is flat even though your content output is stronger than ever, you’re not imagining things.

The reality is this: traditional web analytics tools weren’t built for an AI-first internet.

Google Analytics, Search Console and Adobe Analytics are great at tracking human behavior on your website. They can tell you who clicked, how long they stayed, what page they bounced from. But what they can’t tell you is whether ChatGPT used your blog post to answer someone’s question, or if your product page was summarized by Google’s SGE without ever generating a visit.

It’s official: the old playbook isn’t enough.

Clicks, impressions, bounce rates, those metrics still matter, but they only tell the story of human interactions with your content. What we need now is a second set of eyes, metrics built for how machines are seeing and using your content.

Because here’s what’s really happening behind the scenes:

  • AI models are retrieving your content to build answers.
  • They’re embedding your copy into vector databases.
  • They’re citing (or misattributing) your brand in generated responses.
  • And they’re doing it all invisibly, unless you know where and how to look.

So what should modern RevOps, ContentOps and MarketingOps teams start tracking to truly understand content performance in the age of generative AI?

Introducing the 10 KPIs built for the AI search era — a new lens for measuring how LLMs are discovering, evaluating and repurposing your content.

  1. AI retrieval frequency – How often is your content being pulled by LLMs?
  2. Embedding coverage – Is your content making it into the model’s memory?
  3. Semantic indexation score – How well do LLMs understand the meaning of your content?
  4. Citation frequency – How often is your site credited in AI-generated answers?
  5. Answer surface area – How much of your content is showing up in responses?
  6. Answer visibility – Is your content present in ChatGPT, Claude, Perplexity and SGE?
  7. Answer usefulness score – Are LLMs favoring your content because it’s actually helpful?
  8. Prompt match relevance – Is your content structured in a way that matches user prompts?
  9. Hallucination rate – Are AI tools misquoting or inventing things about your content?
  10. Feedback loop participation – Is your content being used to retrain or fine-tune LLMs?

These aren’t vanity metrics. These are the signals that will determine whether your content becomes part of the AI-driven knowledge layer, or gets left behind.

Although these KPIs are essential, automating their measurement remains non-trivial. Evaluating each LLM often requires individualized prompt engineering and manual KPI extraction. However, with a lightweight automation layer built using Google Apps Script, Google Sheets, and the OpenAI API, ContentOps teams can programmatically issue scheduled prompts to multiple LLM endpoints, capture structured responses along with relevant metadata, and persist this data for longitudinal analysis. This approach creates a normalized dataset that enables downstream analytics pipelines, whether in Sheets, Looker Studio, or dedicated data warehouses, to generate actionable insights with minimal ongoing manual intervention.

While this article will not cover the tactical steps to automate the measurement of these KPIs, it will expand on the types of questions to ask the LLM (or yourself) when evaluating each KPI.