Ask What Happens in Month One A proposal that opens with content production has skipped the diagnosis. There is no way to know what to write before you know which questions matter, which assistants answer them badly and which sources they draw on.
This explains the most common frustration brands report, which is watching a competitor with a worse website get recommended instead. That competitor is usually not better optimised. They are more written about, and the system is weighing the difference.
How to Use This Honestly in a Business Case Do not build a return calculation on a borrowed conversion rate. Applying somebody else's percentage to an estimated mention volume produces a confident looking number resting on two guesses, and it will not survive the first person who asks where the inputs came from.
Treat markup as something with a maintenance cost rather than a one off implementation. Prices change, people leave, products are discontinued, and structured data quietly keeps asserting the old version long after the visible page has been updated. Adding a schema review to whatever process already updates your pages costs minutes and prevents the most damaging failure mode, which is confidently stating something that is no longer true.
This is the pattern search followed, and there is no obvious reason for it to play out differently here. The advantage of early movement is not that the channel is large yet, it is that the positions are cheap.
What We Genuinely Do Not Know Several things are worth admitting rather than papering over. We do not know how the systems weight their signals against each other. We do not know how much residual influence training data has once retrieval is involved. We cannot reliably distinguish a change in your visibility from a change in the model's behaviour.
The terms are used almost interchangeably. Generative engine optimization usually emphasises assistants that write an answer, while answer engine optimization is sometimes used more broadly. Ask any agency what they mean by their term.
Ask What They Will Not Do Good practitioners have a list. They will not guarantee a position in an answer, because nobody controls that. They will not fabricate reviews or seed forum threads under false identities, because it is detectable, damaging and increasingly enforced against.
Why That Breaks the Old Playbook The old playbook assumed that if you occupied a high position, you got the visit. That link between position and visibility has weakened. Ahrefs looked at 15,000 long-tail prompts across four assistants in July 2025 and found roughly 80 percent of the cited pages did not rank for the original query at all.
The first is accuracy. Somebody inside the business has to confirm that what gets published about your products, pricing and capabilities is true. The second is the third party work, which occasionally needs a decision only you can make, such as whether to engage with a critical review or approach a publication.
How You Will Know It Is Working Ask for the raw answers, not a score. A credible report shows you the exact prompts, the exact text an assistant returned, and which pages were cited. You should be able to read it and form your own judgement without trusting anyone's index.
The Mistake That Undoes Everything Markup is a claim, not evidence. Structured data asserting that you own a profile only helps when that profile exists and points back at you. Markup naming an author only helps when the author can be found elsewhere.
In this case there is something real underneath. The plumbing of how people find suppliers has changed, and the work required has changed with it. Here is the whole idea explained without the acronyms, aimed at someone who wants to understand the decision rather than do the job. get recommended by ai
Now a growing share of those questions produce an answer instead of a list. The assistant reads the sources, forms the opinion and hands you a recommendation. The comparison step that used to happen in the buyer's head now happens inside a model, using sources the buyer never sees.
The Types That Rarely Earn Their Keep Elaborate breadcrumb hierarchies, speakable markup, deeply nested item lists and most of the specialised types outside their intended vertical produce little observable difference in how a brand is understood or recommended.
This claim circulates constantly and it is usually presented with more confidence than the evidence supports. It is also probably directionally true, for reasons that are structural rather than mysterious.
Entity Coherence Before a model can recommend you it has to be confident that the scattered mentions of your name refer to one company. That confidence comes from consistency across the details that identify you.
Nobody outside the labs has the full picture, and anyone claiming otherwise is guessing with confidence. What we do have is a large volume of observable behaviour, published research and the citations that several assistants display openly, and those three together support some reasonably firm conclusions.