Adobe LLMO Partners

Why a single run tells you almost nothing

Ask a language model the same question three times and you will often get three different answers, differing in which companies are named and in what order. This is normal behavior, not a fault. The systems sample from probability distributions, personalize on context, and change as models are updated and retrieval indexes refresh. Any methodology that records one response per question and treats it as a measurement will produce a dataset dominated by noise, and teams will spend meetings explaining movements that never happened.

The defense is repetition and disclosure. Each tracked question should be run several times per collection cycle, and the recorded result should be a frequency, such as how often your brand was mentioned across those runs, rather than a yes or no. Sessions should start clean so that personalization and conversation history do not contaminate results. Ask any prospective partner directly how many samples they take per question, how they handle session state, and what they consider the margin of error on their reported figures. A partner who cannot answer that is presenting noise as insight.

Separating a genuine shift from model variance

• Set a threshold before you look — decide what size of change over what period counts as significant, so you are not deciding after seeing a chart you want to believe.
• Check whether competitors moved too — if several brands shift in the same direction at once, you are almost certainly seeing a model or index update rather than the effect of your work.
• Note model versions and dates — a provider updating its model can reset patterns overnight, and without version notes in the dataset that break is indistinguishable from a change you caused.
• Look for a mechanism — a real improvement usually has a traceable cause, such as a new page now appearing in the cited sources, and a shift with no mechanism deserves scepticism.
• Require the change to persist — an improvement that holds across two or three consecutive collection cycles is a result; one that appears and vanishes was variance.

Can the machines actually read your pages

Before any content work, confirm that automated clients can retrieve your content at all. Several common setups quietly prevent it. Content rendered entirely by client-side JavaScript may be invisible to retrievers that do not execute scripts. Bot protection and rate limiting frequently block or challenge the user agents these systems use. Aggressive geographic redirection can serve something unexpected to a request from an unfamiliar region. Consent interstitials can sit in front of the content for any client that has not accepted them.

The audit is unglamorous and pays back quickly. Fetch key pages as the relevant automated clients, with JavaScript disabled, from several regions, and compare what comes back against what a person sees. Review your robots directives and your content delivery and security rules to see which AI clients are allowed, since these are often configured by an infrastructure team with no knowledge of the marketing consequence. It is entirely possible to spend two quarters improving content that no retriever has ever been permitted to fetch.

Structured data that states your facts unambiguously

Structured data is machine-readable markup embedded in a page that states explicitly what the page is about, rather than leaving it to be inferred from prose. The practical benefit here is precision on the facts that most often get repeated wrongly: what your organization is called and what it does, what your products are and which are current, how your pricing is structured, who authored a piece and what qualifies them, and the direct answers to common questions.

Implement the types that correspond to real things on the page and keep them truthful and current, because markup that contradicts the visible content is worse than none. The highest-value items are usually organization details, product and offer information, question and answer markup on genuine support and comparison content, and author information on expert material. Treat this as a maintenance commitment rather than a project: structured data describing a product tier you retired last year is actively teaching machines something false about you.

Write so a specific passage can stand alone

Retrieval systems generally work with passages rather than whole documents. A section of your page is matched to a question, pulled out, and used to compose an answer, often without the surrounding context. That has direct consequences for how you write. A section that begins by referring to what was established above, or that answers a question only after four paragraphs of framing, is much harder to use than one that states its answer in the first sentence and then supports it.

In practice this means leading each section with the direct answer, using headings that read as the question a person would ask rather than as clever labels, and keeping each section self-contained enough that a reader arriving at it cold understands what it is about. Define terms at the point of use rather than relying on a definition several sections earlier. Long-form depth still helps, but it should be built as a sequence of individually complete units rather than a single argument that only makes sense read from the top.

Build the authority these systems actually reward

Content that gets drawn on tends to share recognisable characteristics. It states specifics rather than generalities: actual figures, named constraints, concrete conditions under which something works and does not. It is attributed to an identifiable person with demonstrable expertise rather than published anonymously. It is current and visibly maintained, with a meaningful update history rather than a refreshed date stamp. And it is corroborated elsewhere, because a claim that appears only on your own domain carries less weight than one supported by independent sources.

The uncomfortable implication is that thin content produced at volume performs poorly here, and that the fastest gains often come from areas where your organization has genuine proprietary knowledge that nobody else has published. Original data, detailed technical documentation, honest treatment of limitations and edge cases, and clear explanations of how your product behaves in specific situations all tend to earn citations. Marketing copy restating category commonplaces does not, because a model already has that material from a hundred other sources.

A tested process for when an assistant states something false

At some point an assistant will confidently tell a prospect something wrong about you: a price you do not charge, a feature you do not have, a limitation you removed two years ago, occasionally an accusation that belongs to a different company. You want a rehearsed response rather than an improvised one, because the first time this happens it usually arrives via a sales team that has already lost a deal to it.

The process has four steps. Reproduce the error and record it precisely, capturing the exact wording, the question that triggered it, the surface and the date. Trace the likely source, which is often an outdated page of your own, a stale third-party profile, or a forum thread that the citation data will point you to. Correct the source material, updating your own content first and pursuing third-party corrections where you can, while publishing a clear, well-marked authoritative statement of the correct fact. Then re-test on a schedule, because these corrections propagate over weeks rather than immediately, and only sustained absence of the error counts as resolution.

Set expectations on correction speed

• Your own pages move first — content you control can be corrected and re-crawled relatively quickly, which makes it the obvious starting point for any remediation.
• Third-party sources take negotiation — directories, review sites and publications each have their own correction process, and some will not update at all without a relationship.
• Model training is not a lever you hold — a fact baked into a model rather than retrieved is only corrected when that model is retrained, which is outside your control and your timeline.
• Provider feedback channels are worth using — several assistants accept reports of factual errors, and while the response is inconsistent it costs little relative to the damage of a persistent error.
• Track each error to closure individually — an aggregate accuracy score lets a serious error sit unresolved behind an improving average.

Measuring AI-sourced referrals and connecting them to pipeline

Traffic arriving from AI assistants is genuinely harder to measure than search traffic. Referrer data is inconsistent across surfaces, some assistants send no identifiable referrer at all, and a substantial share of the influence is invisible because the person reads the answer and later arrives through a direct or branded route. Anyone promising precise attribution here is overselling what the data supports.

Build the measurement you can defend. Identify known assistant referrers in your analytics and segment them, so the volume you can see is at least visible. Add a self-reported source question to high-intent forms, which reliably captures influence that analytics misses. Watch branded direct traffic and branded search volume for movement that coincides with your published work. And track the behavior of the identifiable segment, since AI-referred visitors frequently arrive further through their evaluation and convert differently from search traffic. That difference in quality, connected to pipeline rather than to sessions, is usually the strongest argument for continuing the investment.

Programs rarely stop at one product. Buyers hiring for Adobe LLMO often pair it with Adobe Analytics partners , Adobe Brand Visibility partners or Adobe Campaign partners , or review the whole Adobe landscape before committing.