A new layer is forming
For the past two decades, the question that shaped digital marketing was: how do we rank on Google? Entire industries, budgets, and career paths were built around Search Engine Optimisation. Get to page one. Stay there.
That question has not gone away. But a second question has emerged alongside it, and most marketing teams have not caught up yet: how does AI decide which brand to recommend?
When a buyer types "what is the best project management tool for a remote team" into ChatGPT, they do not get a list of ten blue links. They get a recommendation. One or two brands named with confidence. A brief explanation of why. And then the conversation moves on.
That recommendation is not random. It is shaped by everything the AI has absorbed about those brands – their content density, their citation authority, their narrative consistency across sources, their trust signals, how other sources describe them. The brands that appear are not necessarily the ones with the best product. They are the ones the AI has enough structured, authoritative information about to recommend with confidence.
That is the layer AEO and GEO are trying to address.
What AEO actually is
AEO stands for Answer Engine Optimisation. If SEO is about ranking on Google, AEO is about being recommended by AI.
The terminology is new – most marketing textbooks do not mention it yet. But the underlying principle is not complicated. AI models like ChatGPT, Claude, Gemini, and Perplexity are answer engines. You type a question. They produce an answer. The question for brands is: are you in that answer, and in what position?
AEO is the practice of structuring your brand's digital presence so that AI systems can find, understand, and cite you clearly. In practice this means:
FAQ schema and structured content – clear, concise answers to questions your customers are likely to ask AI about your category.
Authoritative sourcing – being cited by credible third parties that AI models treat as reliable inputs. Your own website is not enough.
Consistent brand narrative – ensuring that every source describing your brand tells the same coherent story. Inconsistency creates ambiguity for AI. Ambiguity kills recommendation.
Knowledge panel health – your entity in Google's Knowledge Graph, Wikipedia, and other structured knowledge sources that AI pulls from directly.
AEO is not a replacement for SEO. It is an additional layer on top of it, specifically aimed at how AI systems process and recommend your brand when someone asks a direct question.
What GEO is – and how it differs from AEO
GEO, Generative Engine Optimisation, is the broader discipline. Where AEO focuses on being the answer to a specific query, GEO is about your overall performance inside AI-generated responses – your share of voice, the authority with which you are cited, how your brand is described across different types of prompts.
The distinction matters in practice:
AEO – narrow and query-level
Focuses on a specific question and a specific answer. "Recommend a CRM for a 20-person sales team." Are you in that answer?
Tactical. Content-driven. Relatively immediate.
GEO – broad and brand-level
Focuses on how AI represents your brand across all queries in your category – including queries you did not anticipate.
Strategic. Authority-driven. Slower to build, harder to reverse.
Think of AEO as the sprint and GEO as the marathon. AEO gets you into specific answers. GEO builds the underlying authority that makes AI default to you across the category.
Most brands need both. And neither works without a baseline understanding of where you currently stand.
Why both are emerging right now
The timing is not a coincidence. Three things converged in the past two years that made this layer commercially significant.
First, AI models crossed a usage threshold. ChatGPT reached 100 million users faster than any consumer application in history. Perplexity is now processing hundreds of millions of queries a month. Gemini is integrated into Google's own search surface. Claude is embedded in enterprise workflows. These are not experimental tools anymore. They are where decisions begin.
Second, AI moved from retrieval to recommendation. Early AI tools surfaced information. Current models synthesise it and offer a verdict. That is a categorically different role in the purchase journey. A search engine shows you options. An AI model tells you which option to choose.
Third, the commercial impact is becoming measurable. Brands are starting to trace pipeline to AI recommendation patterns. Investors are asking ChatGPT about companies before taking meetings. Journalists are using Perplexity to background stories. The narrative AI holds about a brand is shaping outcomes in the real world – and most of those brands have no visibility into what that narrative is.
Why measurement has to come before optimisation
Here is where most early AEO and GEO investment goes wrong.
Brands read about the category, understand the opportunity, and move straight to execution – publishing more structured content, improving schema, building citation campaigns. And those actions can work. But without a baseline, you are optimising in the dark.
You do not know which dimension of your AI perception is weakest. You do not know whether ChatGPT and Gemini describe you the same way, or whether there is a 40-point gap between them. You do not know whether the problem is trust signals, narrative clarity, citation authority, or competitive positioning. You cannot prove to a CMO or a board that the investment is moving anything – because you have no number to point to.
This is exactly the problem that the Decision Ranking Index was built to solve. A DRI score gives you a single measurable baseline across six dimensions of AI perception. Run the audit before you invest. Run it again 60 to 90 days later. The delta is your proof of progress – or your signal to change approach.
Position & Share of AI Voice – how often and how prominently your brand appears across AI responses in your category.
Competitive Decision Engine – your win rate in direct head-to-head comparison queries, the highest-intent prompts in any buyer journey.
Trust & Risk Signals – the credibility AI attributes to your brand, and what damaging narratives it surfaces unchallenged.
Messaging Clarity – how accurately and consistently AI articulates your value proposition across models.
Emotional Resonance – the tone AI uses when describing you. Enthusiastic recommendation versus neutral acknowledgement is a commercially significant difference.
Citation & Structured Authority – the data structures and knowledge sources underpinning AI's narrative about you.
What a DRI score tells you that AEO and GEO metrics don't
AEO and GEO tools measure inputs – content structure, schema coverage, citation volume, keyword presence. Those are useful signals. But they are inputs to the question, not answers to it.
The question that matters commercially is: when someone asks AI which brand to choose in my category, does AI choose me?
That is what the DRI score measures. Not whether your content has FAQ schema. Not whether your Wikipedia page is accurate. Whether AI, across four models and six analytical dimensions, is recommending your brand or recommending your competitor.
Most brands sit in the 56 to 69 range – what we call Developing Presence. AI mentions them. It rarely recommends them. The gap between mention and recommendation is where revenue shifts, deals stall, and investors form first impressions before your team ever enters the room.
AEO and GEO are how you close that gap. The DRI score is how you know how wide the gap is, which dimension is causing it, and whether the work you are doing is closing it.
That is the measurement layer. See how it fits into the full AI stack →