How Brands Earn AI Recommendations | AEO Framework

Build an expertise supply chain that turns real buyer questions into credible, corroborated answers

When a buyer asks ChatGPT, Gemini or Perplexity to recommend a product, the answer may draw on several sources: a company website, an industry article, a LinkedIn post, a Reddit discussion, a review site or a product comparison.

For marketers, this creates a different challenge. Traditional search marketing concentrated on helping individual pages rank. Answer engine optimisation (AEO) also requires the wider web to contain enough consistent, credible evidence for an AI system to understand where a brand belongs and when it deserves consideration.

That distinction matters because AI visibility has at least three outcomes:

• A mention names the company or product.

• A citation points to a source used in the response.

• A recommendation presents the brand as an appropriate choice for a particular need.

A useful article may be cited while its product is absent from the shortlist. A famous company may be mentioned but recommended for the wrong audience. Recommendation is the harder outcome because it requires both relevance and trust.

Most companies already possess the raw material for that trust: product decisions, customer patterns, implementation lessons and hard-won judgement. Their problem is turning that knowledge into a connected body of evidence.

The rise of AEO is becoming a forcing function for building an expertise supply chain: a repeatable way to find consequential buyer questions, draw first-hand knowledge from the right people and place that knowledge where buyers and answer engines can encounter it.

Why most AEO programmes start too late

Much of the advice about AEO focuses on the finished page: headings, structured data, crawlability. These are useful practices, and established SEO foundations still matter because content must be discoverable before it can be retrieved.

But formatting cannot make a generic answer distinctive. Google's guidance for generative search emphasises unique, useful, non-commodity content grounded in experience. It also says there is no special requirement to break pages into tiny chunks or pursue artificial mentions. [1]

The original research that introduced generative engine optimisation found that citations and relevant statistics improved visibility in its test environment, while keyword stuffing did not. The exact effects vary by query and system, but the broader lesson is useful: evidence adds more value than cosmetic optimisation. [2]

The real bottleneck often appears before anyone opens the blog editor. It is the difficulty of discovering the right questions, obtaining technically rich answers from busy experts and doing this consistently enough to shape how the market understands the company.

The three conditions behind an AI recommendation

There is no universal formula for appearing in AI answers, but marketers can assess their readiness through three practical conditions: clarity, evidence and corroboration.

1. Clarity: Can the system tell where your brand belongs?

A company should be consistently associated with:

• the category it belongs to;
• the customers it serves;
• the problems it solves;
• the use cases where it is strongest;
• the situations where it may not be the right choice.

Many companies describe themselves differently across their homepage, product pages, executive profiles, review listings and press coverage. One page calls the product an analytics platform. Another presents it as a workflow tool. The CEO describes an AI assistant, while reviewers compare it with a different category altogether.

The answer is not to paste identical marketing copy everywhere. It is to maintain a stable underlying position while adapting the format and language naturally to each channel.

2. Evidence: Have you contributed something worth retrieving?

A useful question for every content brief is: what new information would retrieving this page add to the answer?

This is where evidence becomes valuable. Evidence gives the market a reason to believe it. Strong evidence can include:

• original research or product data;
• patterns observed across customer implementations;
• a detailed explanation of why a common approach fails;
• an informed disagreement with conventional advice.

Compare two articles answering the same buyer question. The first summarises ten familiar practices gathered from existing search results. The second explains what the company observed across 200 implementations, identifies the three conditions associated with better results and describes two cases where its recommendation does not apply. Only the second has added meaningful evidence to the web.

AI can organise, edit and repurpose that material. It cannot independently supply the company's first-hand experience. That knowledge usually sits with a product leader, engineer, consultant, customer success manager, salesperson or founder who has not had time to write it down.

3. Corroboration: Does anyone beyond the company support the claim?

Every company can praise itself on its own website. A recommendation becomes more credible when relevant external sources support the same category association, capability or outcome.

That evidence usually falls into three groups:

• Owned sources: website, research, product documentation.

• Editorial sources: publications, podcasts, analyst coverage, independent comparisons.

• Community sources: Reddit, LinkedIn, reviews, specialist forums.

Distribution and corroboration are related but different. Publishing the same claim through a company blog and five executive posts makes the claim easier to discover. It does not make it independent. Customer experience, credible editorial coverage, practitioner discussion and third-party testing provide stronger corroboration.

Community evidence can influence retrieval even without viral engagement. In Semrush's study of 248,000 Reddit posts surfaced by ChatGPT Search, Perplexity and Google AI Mode, Q&A, comparison and discussion threads dominated citations, while most cited posts had fewer than 20 upvotes and 20 comments. [3]

This does not justify manufacturing forum mentions. Artificial participation creates reputational risk and may be treated as spam. The objective is to contribute genuine expertise where real evaluation is already happening.

The expertise supply chain

Clarity, evidence and corroboration describe what a brand needs. An expertise supply chain describes how to produce it consistently.

Step 1: Listen for real buyer questions

Many AEO programmes begin by asking a model to generate prompts. That can help with brainstorming, but synthetic prompts should not be confused with observed demand.

Look for questions in sales and support calls, customer interviews, product demos, webinar Q&As, reviews, Reddit threads, LinkedIn discussions and specialist communities. Frequency matters, but it is not the only signal. A less common question may deserve priority if it indicates strong commercial intent, reveals a costly misconception or repeatedly delays a purchase.

Let’s take an example of a cybersecurity platform whose prospects repeatedly ask whether its AI assistant retains or trains on proprietary source code.

In reviewing sales calls you find variations of the source-code question in a few enterprise sales calls, security reviews and developer discussions. Its commercial importance is high even if the overall volume is modest.

Step 2: Route each question to the right expert

The person who owns the content calendar is rarely the person with every answer. Match each question to the colleague with direct experience. A technical implementation question may belong with solutions engineering. A question about switching costs may be better answered by customer success. A category-level disagreement may require the founder or product leader.

Running example: Marketing sends the source-code question to the security lead and a solutions engineer, rather than drafting a generic answer from public material.

Step 3: Extract evidence, not merely an opinion

Asking an expert to "share some thoughts" usually produces a general answer. Better editorial questions reach the second or third layer of the expert's thinking:

• What have you observed directly?
• Can you give a specific example?
• What do most people misunderstand?
• When does the usual advice fail?
• What factors change your recommendation?
• What data or documentation supports this?
• What would you advise a customer not to do?

Running example: The interview surfaces the actual retention window, deployment options, customer controls, audit documentation and one limitation that buyers should understand. Those details turn reassurance into verifiable evidence.

Step 4: Create a canonical answer

Turn the expert input into the clearest and most complete version of the answer on your website. State who the advice is for, answer directly, explain the reasoning, provide evidence and acknowledge limitations. Specificity builds more trust than certainty.

Use straightforward structure because it helps people navigate the argument. Avoid forcing every heading into a question or breaking every paragraph into a tiny fragment in the hope of pleasing an algorithm. Write for comprehension first.

Running example: The company publishes a security page that answers the retention question directly, links to the relevant controls and documentation, and explains which deployment option fits each risk profile.

Step 5: Distribute the insight and earn corroboration

Adapt the underlying expertise for the places buyers learn. The website may contain the canonical explanation. A subject-matter expert can discuss the reasoning on LinkedIn. A practitioner can answer a genuinely relevant Reddit question. The evidence can support a journalist, analyst or partner creating an independent assessment.

Each channel has different expectations. LinkedIn may reward a clear point of view. Reddit usually expects practical detail and an honest response to the precise issue. An industry journalist wants evidence, relevance and a story. Keep the expertise consistent while adapting its format and tone.

Running example: The security lead explains the decision logic on LinkedIn, while customer reviews and an independent implementation guide confirm how the controls work in practice. The first distributes the company's expertise; the latter sources corroborate it.

Step 6: Observe what answer engines retrieve

Measure AEO at two levels - one for marketing at a granular level and the other for leadership team or board. The marketing team needs diagnostic information that improves the work. Business leaders need indicators that show whether the work is contributing to demand.

1. AI visibility tracker

Group a manageable set of queries by buying stage, then track mentions, citations and recommendation context over time.

Maintain two query sets. Keep a small benchmark set fixed around the topics and use cases you want to own, giving you a comparable long-term view. Use an exploratory set for new questions, emerging language and changing buyer concerns.

Run benchmark queries on a consistent cadence and under comparable conditions. Results can vary by model, wording, location, recency and personalisation, so look for sustained patterns rather than reacting to a single answer.

2. AI business impact tracker

At board level, use converging indicators rather than pretending AI attribution is precise:

• Branded search and direct traffic: track their longer-term direction alongside campaign activity. They are indicators of demand, not proof that AI caused a visit.

• Self-reported attribution: ask "How did you hear about us?", include an AI-assistant option and free text, then connect meaningful response volume to qualified pipeline and revenue.

Conclusion

Three conditions determine whether an answer engine recommends a brand, and one workflow builds all three:\

• Clarity: a consistent position across every surface a buyer or a model might encounter.

• Evidence: first-hand knowledge that a generic model could not produce on its own.

• Corroboration: independent sources that back the same claim.

The expertise supply chain builds all three on a repeatable cadence: listen for real buyer questions, route them to the expert who has actually seen the issue, extract evidence rather than opinion, write the canonical answer, distribute it to earn corroboration, and observe what answer engines retrieve.

The web is filling with generic summaries. Publishing another one is unlikely to create a durable advantage.

At Rocksalt, this is the AEO problem we are focused on: connecting the questions buyers are actually discussing with the experts who can answer them, then helping marketing teams turn those answers into authoritative content for the channels that shape discovery.

AI has made content inexpensive. Credible expertise, clearly expressed and independently corroborated, remains difficult to replicate. That is what gives a brand a reason to be recommended.

Sources

1. Google Search Central, "Optimizing your website for generative AI features on Google Search"
2. Aggarwal et al., "GEO: Generative Engine Optimization"
3. Semrush, "We Analyzed 248K Reddit Posts: What Drives Visibility in AI Search"