TL;DR: Pick the commercial topics that matter, build prompts from real buyer questions, and baseline your visibility. Diagnose the actual gap before acting: content, SEO, retrievability, evidence, third-party sources, positioning, or brand demand. Fix that layer, then track topic visibility and business impact.
There’s too much content online about how AI search is changing, but much less real, detailed, tactical-level advice on what to actually do about it.
So I decided to sit down, do a lot of research, gather my own experience and expertise, and make a detailed tactical workflow for myself.
I mean, even experienced pilots still go through checklists every time they fly. And with how quickly AI is changing the marketing landscape, we all need a checklist now.
A week later, I had everything from the whys to the tactical steps I should actually take, so I decided to turn it into an article so people like me could have everything in one place.
We’ll go step by step through how to decide what your brand should be known for, identify the buyer questions that matter, track the right prompts, measure current visibility, diagnose why your brand is missing, choose the right fix — content, SEO, retrievability, evidence, third-party sources, or positioning — and then measure whether that work actually improves visibility and business impact.
It’s tactical, detailed, and we’re going to get into the weeds a little, so buckle up.
Strengthen your SEO foundation to support AI visibility
AI-search tactics can’t compensate for Bad SEO. They still depend heavily on information that is discoverable, accessible, understandable, and well connected across the web. If your important pages can’t be crawled, indexed, understood, or surfaced reliably, there’s little point diagnosing more advanced AI-visibility problems first.
So before running the workflow below, make sure the basic SEO foundation is sound.
1. Decide what you want AI to associate your brand with
Your brand should be strongly associated with the specific topics, problems, use cases, and buying situations where it has a real right to win, with that association reinforced across the web.
AI systems may encounter information about your brand across your website, review platforms, publications, communities, and other sources. When influential sources describe your brand differently, those inconsistencies may also appear in AI answers. Research from Ahrefs found a strong correlation between web mentions and brand visibility across ChatGPT, AI Mode, and AI Overviews, although correlation does not prove that mentions caused the visibility.
So ask yourself: for which topics, problems, use cases, and buying situations should AI mention us?
For example, imagine a CRM built for small sales teams.
Target associations:
| Primary category | Buyers | Problems | Buying situations | Product narrative |
| CRM software | Small businesses | Keeping track of leads | Best CRM for a small business | Easy to set up |
| Sales CRM | Startup sales teams | Remembering follow-ups | Easy CRM for a 10-person sales team | Built for small teams |
| Lead management software | Founder-led sales teams | Managing the sales pipeline | CRM with built-in follow-up automation | Simple pipeline management |
| — | — | Keeping customer information in one place | Affordable alternatives to Salesforce | Follow-up automation without complicated workflows |
I reduce it to one clear association map:
Brand → Topic → Buyer → Problem → Differentiator
For example:
Your brand → CRM software → small sales teams → tracking leads and follow-ups → simple setup with built-in automation
This becomes the anchor for your prompt research, content strategy, product messaging, third-party outreach, and measurement.
2. Find the questions buyers actually ask and turn them into a prompt set
Start with your brand, ICP, and product-positioning documents so you understand who the product is for, what it solves, and how it is different. Then go to customer-facing sources for the language your buyers actually use.
Gather:
- 10–20 sales calls, if available
- Customer interviews
- Demo and onboarding calls
- Support tickets
- Closed-lost notes
- Gong transcripts
- Reviews on G2 and Capterra
- Reddit and community discussions
- Search Console queries
From these sources, extract:
- Problems
- Questions
- Objections
- Buying triggers
- Products being compared
- The exact words customers use
Then turn that language into search queries and AI prompts. For example:
| Buyer language | Problem | Stage | Search query | AI prompt |
| “We keep forgetting to follow up with leads.” | Lead follow-up | Consideration | CRM with follow-up reminders | Which CRM is best for automatically reminding a small sales team to follow up? |
| “We need everyone’s customer notes in one place.” | Scattered customer information | Consideration | CRM for small sales teams | What is the easiest CRM for a small team that needs to share customer notes? |
| “Salesforce looks too complicated for us.” | Complex setup | BOFU | Salesforce alternatives for small businesses | What are the best simple alternatives to Salesforce for a small business? |
Next, group them by topic.
Group related buyer prompts by commercial topic. Buyers can ask the same underlying question in different ways, so don’t track one exact prompt as if it represents the whole opportunity. For each important topic, build a small set of prompts that covers the main questions buyers ask as they move toward a decision.
Keep buyer prompts separate from retrieval queries. Buyer prompts are the questions people ask. Retrieval queries are the narrower searches an AI system may run to gather the information needed to answer them. Google confirms that AI Mode and AI Overviews may use query fan-out, issuing multiple related searches across subtopics and data sources to develop a response.
I observed the same general pattern in a small ChatGPT browsing test. I asked: “What’s the best CRM for a five-person sales team?”
ChatGPT then ran narrower searches for supporting facts, in this case the official pricing pages for Pipedrive, HubSpot, Close, and Freshsales.
Ok so this is just one illustrative test, not proof that ChatGPT always generates or executes searches this way. But it does show why the buyer prompt and the searches used to support its answer should be treated as different layers. So for a topic like CRM software for small sales teams, my tracked prompt set might include:
| Prompt type | Example prompts |
| Category | What are the best CRM tools for small sales teams?Which CRM is easiest for a small business to set up? |
| Problem | How can a small sales team keep track of leads?What is the best way to make sure sales reps follow up on time? |
| Comparison | HubSpot versus Salesforce for a small businessWhat are the best Salesforce alternatives for small teams? |
| Objection | Is a CRM worth paying for if we only have five salespeople?How long does it take to set up a CRM? |
| Product fit | Which CRM includes automatic follow-up reminders?What is the best CRM for managing leads without complicated workflows? |
This is your core AI visibility prompt set: the questions you run across relevant AI tools to see whether your brand is mentioned, cited, recommended, and described correctly.
3. Measure how your brand shows up in AI today
Now run your prompt set through the AI tools your buyers are using. You’ll learn: where your brand appears, how it is positioned, which competitors appear instead, and which sources shape the answer.
Keep this as your fixed benchmark set so you can rerun the same prompt clusters over time. Record the engine, date, model/mode, and testing conditions, and report results by engine and topic rather than blending everything into one AI visibility score.
For each prompt, record:
- Whether your brand is mentioned
- Where it appears in the answer
- Whether it is recommended
- Whether your website is cited
- Which competitors appear
- Which sources are cited
- Whether the product is described correctly
| Prompt | Brand mentioned? | Position | Recommended? | Our site cited? | Competitors | Sources cited | Description accurate? |
| Best CRM for a small sales team | Yes | 3 | Yes | No | HubSpot, Pipedrive | G2, Forbes | Mostly |
| CRM with automatic follow-up reminders | No | — | No | No | HubSpot, Zoho | Reddit, G2 | — |
| Easy Salesforce alternative for a small business | Yes | 2 | Yes | Yes | HubSpot, Pipedrive | Our product page, Capterra | Yes |
Do this across ChatGPT, Perplexity, Gemini / AI Mode where relevant, and Claude if your ICP uses it.
Warning: Don’t treat one response as a finding. AI answers and their sources can vary between runs, prompt wording, engines, and modes.
For a lightweight manual baseline, run your most important prompts two or three times in fresh chats and look for patterns across the prompt cluster. Treat that as directional evidence. For more reliable monitoring, test several prompt variations repeatedly and report results separately by engine, mode, and topic.
Tip: You can automate this too. Use a simple script or Claude Code workflow to send the same benchmark prompts through the relevant model APIs, save the raw responses and citations, extract the fields above, and write them into a CSV or Sheet. Keep API results separate from manual testing in the consumer apps, though, because the model, search mode, personalization, and retrieval environment may differ.
4. Diagnose why your brand is missing
Start with what happened in the answer: are you absent, mentioned but not recommended, cited through another source, described incorrectly, or consistently displaced by competitors?
Then ask, in this order:
1. Should we actually appear for this prompt?
Check ICP relevance, commercial importance, and whether the product has a genuine right to win. If not, don’t optimize for it.
2. Do we have the information needed to answer the question?
If not, you have a genuine content gap. The missing asset might be a product page, comparison, integration page, guide, glossary entry, or original research.
3. Does the information exist but remain difficult to retrieve or understand?
If yes, the problem is on-page clarity or retrievability rather than missing content.
4. Are we mentioned or recommended, but another website supplies the evidence?
If yes, you may have a citation-ownership problem. Check whether your own site provides the relevant facts clearly and authoritatively.
5. Do recurring external sources include competitors but exclude us?
If yes, you have a source/distribution problem.
6. Are we present but described incorrectly?
If yes, you have a narrative-coherence problem. Trace the inaccurate positioning back to the influential owned and third-party sources repeating it.
7. Are the content, positioning, evidence, and source presence all reasonable, but major competitors still dominate?
Then the problem may be broader brand recognition, reputation, distribution, or category demand rather than something an on-page AEO change can solve.
A simple way to map each diagnosis to the right intervention:
| Diagnosis | Intervention |
| Genuine information gap | Create or substantially expand the right asset |
| Retrievability problem | Clarify and restructure existing information |
| Citation-ownership problem | Strengthen the canonical owned source |
| Source/distribution problem | Earn legitimate inclusion in influential external sources |
| Narrative problem | Correct inconsistent positioning |
| Brand-demand problem | Broader brand, reputation, PR and demand building |
5. Make your content easy to retrieve and understand
Your site may have the right info, but if the answer is scattered or unclear, retrieval systems may have a harder time identifying and using the relevant passage.
This is where your AEO comes in. Make sure important sections answer one clear question and still make sense if they are pulled out of the larger article.
AI systems can retrieve relevant passages rather than consume a page as one indivisible unit. Ahrefs specifically says you can’t control how different systems split a page. So create atomic content: logically grouped, self-contained sections that work for the reader and remain useful when retrieved separately.
I tested this directly too. When I asked ChatGPT how it had researched a CRM recommendation, it showed that it had surfaced relevant passages from different parts of the pricing pages, not just the opening section.
Here’s what I prefer:
Header → BLUF → supporting sentences → brief example/description
Don’t force every paragraph into an artificial AI-friendly chunk. That’ll sacrifice the page’s depth, usefulness, or organic performance. Make sure retrievability sits on top of strong SEO and useful content, but more on that later.
You can use this simple QA test: copy the section into a blank document without the rest of the page. If you can still identify the question, answer, necessary context, supporting evidence, and important limitations? You’re good! If not, the section depends too heavily on information elsewhere, making it harder for AI to retrieve/ cite it.
Here are some tactics I use:
Replace vague product language with specific facts. For example:
Instead of: Our simple and innovative CRM makes it easier to manage your sales process at every stage.
Write: Our CRM lets small sales teams keep lead details, customer notes, pipeline stages, and follow-up reminders in one place.
Then give the buyer the context they need: who the capability is for, when it matters, how it works, what it costs, and any limitations they should know about.
The same principle applies to comparisons and direct questions. If someone wants to know whether the CRM works for a five-person sales team, answer that question near the top of the section.
Useful structures depend on the information you are explaining:
| Information to make explicit | Example |
| Definition | “Lead scoring is a method for ranking leads based on how likely they are to buy.” |
| Who it’s for | “This CRM is designed for small sales teams that need to manage leads without a dedicated CRM administrator.” |
| When to use | “Use automated follow-up reminders when several salespeople manage leads across different pipeline stages.” |
| Comparison | Compare setup time, automation, integrations, pricing, and limitations across the same CRM options. |
| Evidence | “Across 5,000 customer accounts, teams using automated reminders followed up 30% faster.” |
Semrush’s 2026 study also found that clearer summaries, Q&A formatting, and stronger section structure were associated with higher citation rates in its dataset. That doesn’t prove those formats cause citations, but it does support the broader recommendation to make important answers clear, specific, and easy to identify.
6. Create content your competitors can’t easily copy
Turn information only your company has into evidence that answers a real buying question.
That might be product data, customer patterns, internal experiments, support insights, subject-matter expertise, and the way customers actually use your product.
One look at the SERP, and you’ll see how easy generic content is to copy. Your proprietary evidence is not.
Depending on the evidence you have and what buyers need to understand, you could turn it into benchmark reports, original surveys, expert interviews, calculators, frameworks, decision trees, templates, comparison matrices, or teardown studies.
For example, instead of publishing another article called:
“How quickly should sales teams follow up with leads?”
A CRM company could publish:
“2026 Sales Follow-Up Benchmarks: 50,000 Leads Analyzed”
You could break the results down by team size, lead source, response time, number of follow-ups, pipeline stage, and conversion rate. Now you’ve given an external writer who’s comparing CRM workflows a specific finding to reference and cite.
The same applies to internal expertise. If your support team repeatedly sees the same implementation mistake, your product team has learned something unusual about a workflow, or your sales team consistently encounters the same buying objection, turn that knowledge into something concrete and defensible.
Whatever you publish, make the evidence easy to evaluate. Explain where the data came from, sample size, period covered, methodology, exclusions, and limitations.
This also gives your brand a stronger reason to be associated with the topic.
Caution: Don’t create proprietary research just because original data is more citable. Ask which buying question the evidence answers and what decision the finding could actually change.
7. Map the external pages shaping AI answers
Next, move off-site and find which external sources AI keeps using for your tracked prompts. Take the citation data from your baseline and look for patterns across the prompt set. Which specific external pages keep appearing? Which competitors do those pages mention? Do those pages also rank for commercially important queries?
For every cited page, record:
- source type
- whether it mentions your brand
- which competitors it mentions
- which prompts it appears for
- how often it appears across your prompt set
- whether it also ranks for an important commercial search query
- where it ranks
For example, suppose you run 20 prompts about CRM software for small sales teams. The same “Best CRMs for Small Businesses” article gets cited in eight of those answers. It names HubSpot, Pipedrive, and Zoho but not your product.
That tells you this particular page repeatedly enters the information environment around a category you care about. And if the same page also ranks prominently in Google for a commercial query, it may be influencing buyers through both traditional search and AI answers.
For the commercial queries tied to your tracked prompts, pull the top 10–20 Google results and note which of your mapped pages also rank, and where.
A simplified source map might look like this:
| Source | Type | Mentions us? | Mentions competitors? | Times cited | Priority |
| G2 | Review platform | Yes | Yes | 14 | High |
| Forbes Advisor | Editorial list | No | Yes | 9 | High |
| Reddit thread | Community discussion | No | Yes | 7 | Medium |
| Small-business sales blog | Comparison | No | Yes | 6 | High |
| General software directory | Directory | No | Yes | 1 | Low |
Frequency shows how often a source appeared in your tracked prompt set. It doesn’t tell you whether the site is influential everywhere.
8. Prioritize and influence the sources that matter
You may end up with hundreds of possible external pages. Don’t treat them equally. Start with commercial relevance. If the page doesn’t influence a buying situation you care about, deprioritize it regardless of how often AI cites it.
Then ask:
- Does the page recur across the relevant prompt clusters?
- Does it also rank for an important commercial or retrieval query?
- Does it include meaningful competitors while excluding us?
- Is there a legit editorial reason and realistic route for including or updating our brand?
Place each page into one tier:
Tier 1: Commercially important + recurring AI source + strong search visibility + competitor gap + realistic inclusion route
Tier 2: Influential, but weaker commercial relevance or actionability
Tier 3: Incidental citation, broad relevance, or no legitimate route to inclusion
So a page that ranks #2 in Google, recurs across your tracked prompts, targets a core BOFU query, includes 10 direct competitors, leaves you out, and has a realistic route for inclusion is a Tier 1 opportunity.
Then choose the tactic based on the source type.
For listicles and comparison pages: Give the publisher a good enough reason to revisit the page, such as product access, verified feature details, pricing corrections, customer evidence, original data, or a clearer use-case fit.
For publications: Pitch something they can actually use: proprietary research, first-party data, expert commentary, or a defensible industry insight.
For integration ecosystems: Work toward inclusion in partner pages, marketplaces, integration directories, documentation, and workflow guides.
For review platforms: Improve profile accuracy, category placement, product descriptions, review quality, review recency, and customer specificity.
For industry experts: Give them something worth discussing, such as original data, useful findings, a strong product POV, or a benchmark.
Work LinkedIn and Reddit deliberately
These can be valuable sources if handled strategically. But use LinkedIn, Reddit, or any other platform only when your source map shows that it materially contributes to the territory you’re targeting.
For B2B SaaS, I’d usually check both because they often surface around comparisons, objections, firsthand experience, and expert commentary. But check that for your own category rather than assuming they matter everywhere.
If Reddit repeatedly appears for comparison, objection, or firsthand-experience prompts, monitor those discussions, answer factual questions transparently where appropriate, and learn from recurring complaints.
If LinkedIn posts from practitioners appear for expert or emerging-topic prompts, encourage founders, operators, SMEs, customers, and other credible voices to publish specific firsthand expertise and evidence there.
If neither platform appears in the source environment for that territory, don’t force it because it appears on an AEO checklist.
Warning: don’t manufacture the signals you’re trying to earn. Fake reviews, fake community participation, undisclosed paid recommendations, meaningless mass listings, hidden prompts, fake experts, and similar shortcuts create fragile visibility and reputational risk.
9. Fix brand narrative inconsistencies
AI may already mention your brand, but describe it in the wrong way because the web still contains outdated, inconsistent, or weak positioning.
Suppose your company currently positions itself as: “AI revenue orchestration.” But half the web still describes you as: “simple email automation.” Even if AI mentions you, it may reproduce the old positioning.
Return to the influential pages in your source map and compare how they describe your brand with the association map you defined at the start: Brand → Topic → Buyer → Problem → Differentiator.
| External page | Type | Mentions us? | Competitors mentioned | Times cited | Commercial query | SERP position |
| Best CRMs for Small Businesses | Editorial list | No | HubSpot, Pipedrive, Zoho | 8 | best CRM for small business | #3 |
| CRM recommendations thread | Community | No | HubSpot, Zoho | 5 | simple CRM small business | #8 |
| Small Business CRM Reviews | Review/comparison | Yes | HubSpot, Pipedrive | 4 | CRM reviews small business | #5 |
Then update owned profiles, request publisher corrections, refresh partner and directory copy, fix outdated pricing or feature descriptions, and keep company bios and positioning consistent.
Measure AI visibility separately from business impact
A brand can be cited without being mentioned, mentioned without being recommended, or recommended while the AI cites a third-party source instead of the brand’s website.
Track a few separate dimensions:
- Topic visibility: How consistently are you present across the commercial prompt cluster?
- Mention share: How often is your brand named?
- Recommendation share: How often are you included among suggested solutions?
- Citation share: How often are your owned pages cited?
- Narrative accuracy: Is the brand described correctly?
Measure these across prompt clusters rather than treating one exact wording as the unit of success. Individual responses fluctuate; the useful signal is whether your presence across the commercial territory strengthens over time.
Also measure against the intervention you actually made: if you improved an owned page, look for citation movement; if you earned inclusion in recurring third-party sources, look for mention or recommendation movement; if you corrected positioning, look for better narrative accuracy.
Then look downstream. Someone might discover you in ChatGPT, search your brand later, and eventually convert through organic or direct traffic. So also watch branded search, direct and AI referral traffic, demos or signups, “How did you hear about us?”, sales conversations mentioning AI, and pipeline or revenue influenced.
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