Tracking AI Visibility in Semrush: A Practical Setup

If Semrush visibility tracking feels a little slippery right now, that’s normal. AI results move fast, prompts are messy, and a quick one-time check rarely tells you anything useful. A solid setup fixes that by turning Semrush AI Visibility and Prompt Intelligence into something you can actually use week after week.

What you’ll set up and why it matters #

The goal is simple: build a repeatable system that shows where your brand appears in AI-generated answers, where competitors keep showing up, and which prompts deserve your attention first.

“Visibility” in this context is not the same as a traditional ranking report. You are not just checking if a page sits at position three. You are tracking whether your brand is mentioned, cited, compared, recommended, or skipped entirely inside AI answers across platforms like ChatGPT, Google AI, Gemini, and Perplexity. That difference matters, because AI discovery is often less about one page winning and more about your brand being part of the answer at all.

A good setup also keeps you from chasing noise. Instead of checking random prompts whenever somebody on your team gets curious, you create a prompt set, monitor the same entities consistently, and compare changes against a baseline. That’s where Semrush visibility tracking starts becoming useful.

What you’ll need before you start #

Getting the inputs ready first saves a surprising amount of time. Ten calm minutes here can spare you an hour of cleanup later.

Semrush access and the right toolkit area #

Start by making sure your Semrush account includes access to the AI Visibility area and the Prompt Intelligence features tied to Research & Tracking. Semrush has positioned this inside its AI SEO toolkit, where you can research prompts, monitor brand presence, and review AI visibility over time on supported platforms (Semrush AI SEO Toolkit).

Before clicking around, confirm that your account has the right permissions for creating projects, editing tracked entities, and exporting data. If your access is view-only, the setup gets annoying fast. You want full project control from the start.

A starter list of brands, competitors, and topics #

Prepare a short working list before opening any tracking screen. Include your main brand name, common variations, product names, and any shortened names people actually use. If your company gets referred to in two or three different ways, write all of them down now.

Then add direct competitors, not every company in the industry. The useful comparison set is usually small. Think of the brands that keep coming up in sales calls, shortlist pages, and comparison content. Finally, note your core topic areas, product categories, and the phrases customers use when describing the problem they want solved. Those phrases often produce better AI prompts than your internal naming does.

A simple tracking goal for the first 30 days #

Pick one goal for version one. Just one.

A strong first goal sounds like this: increase brand mentions in product-category prompts, identify competitor wins in comparison prompts, or find topic gaps where your brand never appears. A weak goal sounds like “understand AI search better.” That is too broad to guide setup.

For your first month, narrower is better. You are building a dashboard you will trust, not trying to map the entire internet.

Step 1: Define the AI visibility questions you actually want answered #

This is where most messy projects go off the rails. The temptation is to track everything because AI search still feels new. Don’t.

1. Open a blank doc or spreadsheet.
2. Write down three to five questions you want Semrush visibility tracking to answer.
3. Keep each question tied to a real decision you could make from the result.
4. Remove anything vague or impossible to act on.

A useful question might be: “For product comparison prompts, how often is your brand mentioned versus Competitor A and Competitor B?” Another might be: “Which high-intent prompts in your category produce no mention of your brand at all?”

Checkpoint: if a question would not change a content, PR, or positioning decision, it does not belong in version one.

Pick a primary use case #

Choose the one use case that matters most right now. Usually that means one of four things: brand mention tracking, competitor comparison, content opportunity discovery, or answer-change monitoring.

If your leadership team keeps asking whether your brand shows up in AI tools, lead with brand mention tracking. If sales keeps hearing the same competitor names, start with comparison prompts. If your content plan feels like guesswork, focus on prompt gap discovery.

The trick is not picking the “best” use case. The trick is picking the one that helps your team act fastest.

Translate business goals into trackable questions #

Broad goals need translation. “Get discovered more in AI search” is not trackable. “Increase mentions for project management software prompts tied to remote teams” is trackable.

Write each question so it includes three elements: the prompt type, the entities involved, and the platform scope. For example, “Across recommendation-style prompts on ChatGPT and Perplexity, does your brand appear in answers about payroll software for small businesses?” That gives your tracking setup something concrete to monitor.

Set a realistic scope for version one #

Start with a small prompt universe. Twenty to forty well-chosen prompts is usually enough for a first project.

That may sound too small, but honestly, a tight list is easier to read, easier to explain, and much easier to improve. A bloated list turns into dashboard wallpaper. You stop noticing what matters because everything is technically being tracked.

Step 2: Build a clean seed list of prompts in Prompt Research #

Now you are ready to build the prompt list that will feed the project.

1. Open Prompt Research in Semrush.
2. Enter your core topics, product categories, and customer language.
3. Save promising prompt ideas into a working list.
4. Ignore anything broad just because it looks impressive.
5. Aim for intent-rich prompts that resemble how people actually ask AI tools questions.

Start with core topics and product language #

Begin with the plainest version of what you sell or publish about. If you work in CRM, enter terms like “CRM for startups,” “customer relationship management software,” and “sales pipeline tool.” If you run an ecommerce brand, start with product category phrases, not just brand names.

Use the language customers use in demos, reviews, and support chats. AI prompts tend to sound more conversational than old-school keyword lists, so this is one place where “how do I” and “what’s the best” phrasing matters.

Expand into real question-style prompts #

Once you have core terms, move into natural-language prompt forms. Research prompts that sound like real requests made inside ChatGPT, Google AI, Gemini, or Perplexity.

Look for patterns like recommendation questions, comparisons, problem-solving questions, buying help, and category explanation prompts. Semrush has written about the importance of targeting the questions people ask AI systems directly, rather than relying only on classic SEO phrasing (AI visibility: What it is and how to grow yours in 2026).

A good seed list often includes prompts such as “best invoicing software for freelancers,” “what CRM works well for a small sales team,” or “alternatives to HubSpot for startups.” Those are much more useful than a lone head term.

Group prompts by intent #

Before you track anything, sort prompts into intent buckets. That makes later reporting far easier to read.

Create groups such as informational, comparison, recommendation, problem-solving, and branded discovery. Informational prompts tell you how AI explains the space. Comparison and recommendation prompts often tell you who gets considered. Problem-solving prompts can reveal hidden opportunities because they expose real pain points. Branded discovery prompts show whether your company enters the conversation once somebody knows enough to ask.

Flag high-value prompts first #

Not every prompt deserves equal attention. Mark the prompts closest to consideration and conversion first.

High-value prompts usually include recommendation language, alternatives language, use-case specificity, or a clear buying scenario. If somebody asks “best email marketing software for Shopify,” that answer can shape a shortlist. If somebody asks “what is email marketing,” the value is lower for early tracking, unless education is your main model.

Step 3: Filter and prioritize the prompts you’ll track #

Now trim the list. This is the part that makes your system useful instead of noisy.

1. Review every prompt in your seed list.
2. Remove duplicates and near-duplicates.
3. Mark each remaining prompt by type and priority.
4. Keep the first tracked set focused.
5. Save extra prompts for later expansion, not for version one.

Separate broad discovery prompts from brand-specific prompts #

You need both, but they do different jobs. Broad discovery prompts show whether your brand enters category-level conversations. Brand-specific prompts show what happens once somebody already knows your name or is comparing you directly.

If you blend both into one undifferentiated list, your results get muddy. A brand should usually do better on branded prompts than on broad category prompts. That’s normal. Keeping them separate helps you see the real gap.

Identify competitor-comparison prompts #

Comparison prompts are often the most revealing in the whole set. These are the prompts where somebody is close to making a choice.

Include phrases like “best,” “alternatives,” “vs,” “compare,” and use-case comparisons tied to your market. Prompts like “best CRM for startups” or “Semrush alternatives” can show which brands AI systems surface when buyers are actively weighing options. If your brand disappears here, that is worth knowing quickly.

Remove weak or redundant prompts #

Cut prompts that are too vague, too repetitive, or too far from business value. If two prompts are nearly identical, keep the clearer one. If a prompt has unclear intent, drop it. If it sounds like something no actual buyer would ask, let it go.

This part feels picky, but that’s the point. Clean input creates readable output.

Create a tiered prompt list #

Split your final list into tiers. Tier 1 should hold the prompts you care about most right now. Tier 2 can include adjacent prompts worth watching later. Tier 3 is your parking lot.

That structure keeps expansion easy. You are not rebuilding the project every time a new idea comes up. You are simply promoting or demoting prompts based on what proves useful.

Step 4: Set up your tracking project in Semrush #

With your prompts cleaned up, move into project creation.

1. Create a new tracking project in the Semrush AI Visibility area.
2. Give it a clear name.
3. Add your brand and competitor entities.
4. Import or select your prompt set.
5. Review the settings before launching.

Checkpoint: before saving, make sure every part of the project matches the original 30-day goal.

Name the project so future reporting stays clear #

Use a naming convention that will still make sense three months from now. Include the brand, market, and purpose.

A name like “Acme_US_AIVisibility_Category+Comparison_Q3” is not glamorous, but it is easy to scan later. If you manage multiple brands, regions, or business units, this matters more than you think. Future you will thank you on a random Tuesday afternoon.

Add your brand and competitor entities #

Enter your brand exactly as it commonly appears, then add important variations if Semrush supports them in your setup. Do the same for competitors. Keep the entity set tight. You want direct comparison, not a crowded room.

This step matters because AI responses do not always use the exact legal company name. If your brand is often shortened or referred to by a product line, account for that where possible.

Choose the prompt set to monitor #

Import the final prompt list from your research stage or select saved prompts inside Semrush. Keep the scope aligned with the goal you picked earlier.

If your first goal is competitor comparison, your prompt set should lean toward recommendation and alternatives prompts. If your first goal is visibility in a product category, keep more discovery and problem-solving prompts. The setup should reflect the question you want answered.

Confirm baseline settings before launch #

Before you hit launch, review the market focus, prompt coverage, tracked entities, and platform selection. Small mismatches here can distort the whole project.

Success looks like this: the project name is clear, the prompts are relevant, the entities are accurate, and the scope matches one specific use case. If anything feels fuzzy, fix it now rather than after two weeks of data.

Step 5: Choose the AI platforms and coverage that fit your goals #

Not every platform deserves equal weight. Track where your audience actually looks.

1. List the AI platforms most relevant to your buyers.
2. Match platform choice to business model and audience behavior.
3. Start with a focused mix.
4. Expand later if the first set proves useful.

Match platforms to audience behavior #

SaaS buyers often use ChatGPT and Perplexity for research-heavy comparisons. Ecommerce shoppers may lean more into Google AI touchpoints and product-discovery queries. Publishers may care about broad informational prompts and citation patterns. B2B teams often need a mix, because buying journeys are messy and stretched out.

Use platform choice as a reflection of real search behavior, not as a badge of completeness.

Understand platform differences before comparing results #

AI systems generate answers differently. Some synthesize aggressively, some emphasize citations more clearly, and some vary more from prompt to prompt. Semrush notes that AI visibility can differ across supported platforms because answer generation itself differs (What is AI visibility?).

So if your brand appears more often on one platform than another, do not assume performance alone explains it. Sometimes the platform is simply better at surfacing your category, your citations, or your brand type.

Start with a focused platform mix #

For version one, two or three platforms are enough. That gives you comparison without scattering attention.

If you start everywhere at once, your reporting gets harder and your action items get fuzzier. Better to track the platforms that matter most, get comfortable reading the data, and expand later.

Step 6: Establish your baseline visibility snapshot #

Before making changes, capture the starting point. This is your before photo.

1. Run the project and wait for initial results.
2. Document visibility by prompt group.
3. Note competitor presence and answer patterns.
4. Save exports or screenshots in one place.

Record current visibility by prompt group #

Review your visibility across the prompt clusters you created earlier. Look at where your brand appears, where it appears consistently, and where it is absent.

Do this at the group level first, not just prompt by prompt. Patterns show up faster that way. You want to know whether you are weak in recommendation prompts overall, for example, not just whether one random prompt missed your brand.

Note competitor presence and mention patterns #

Check which competitors show up most often and in what contexts. One competitor may dominate broad discovery prompts, while another wins comparison prompts. That difference matters because it hints at different strengths.

Record this plainly. A simple note like “Competitor A appears often in tool-comparison prompts, Competitor B appears more in educational prompts” is already useful.

Review citation, mention, and answer context #

Presence alone is not enough. Look at how your brand is mentioned.

Are you directly cited? Are you listed among top options? Are you named with weak, generic context while competitors get stronger explanation? Semrush has emphasized that AI visibility is about more than raw presence, since answer framing and citation quality shape perception too (AI Search Visibility Checker).

Save the baseline for reporting #

Export the data if available, and save screenshots or summary notes in a shared location. Date everything.

That sounds obvious, but baseline data gets messy fast if it lives in scattered files. Keep one folder, one summary sheet, and one naming pattern. Clean reporting habits make trend analysis much easier later.

Step 7: Build a simple reporting view for ongoing monitoring #

A reporting view should help you notice change in a couple of minutes. If it takes half an afternoon to interpret, it is too complicated.

1. Choose a short set of metrics.
2. Segment the data into useful views.
3. Decide how often to review it.
4. Share the findings in the format each audience needs.

Choose the core metrics to watch #

Focus on a handful of measures: share of mentions, prompt-level presence, competitor overlap, and trend direction. Those usually tell the story clearly enough for ongoing monitoring.

You do not need fifteen metrics in the first month. More numbers rarely create more clarity.

Create segments by topic, funnel stage, or product line #

Segment the report so it answers real business questions. A SaaS company might split prompts by product line or by use case. A publisher might segment by topic cluster. An agency might segment by client or industry.

This is how your dashboard stops being one giant list and starts behaving like a decision tool.

Set a review cadence #

For most teams, a weekly check is enough during setup, with a deeper monthly review. If you publish heavily or run active campaigns, a weekly rhythm helps you catch changes without obsessing over every fluctuation.

Consistency beats frequency here. A steady review habit is more useful than checking ten times one week and forgetting it the next.

Share findings with the right people #

SEO teams usually want prompt-level detail and competitor movement. Content marketers want topic gaps and content ideas. Managers want a concise trend summary. Clients want clear movement and recommended actions.

Same data, different packaging. If you send everyone the same export, the dashboard ends up ignored.

Step 8: Analyze prompt-level results to find quick wins #

This is where the setup starts paying back.

1. Review prompts where your brand is absent.
2. Review prompts where your brand appears weakly.
3. Look for repeated themes in answer language.
4. Turn those patterns into a short action list.

Spot prompts where competitors appear and you don’t #

These are your cleanest gap opportunities. If a prompt matters, competitors show up, and your brand does not, that is a strong signal.

Do not react to every single gap. Look for repeated gaps in high-value prompt groups. One missed prompt is interesting. Five missed recommendation prompts in the same category is a priority.

Find prompts where your brand appears weakly #

Sometimes your brand is present but not really competitive. Maybe you are listed without explanation. Maybe a competitor gets stronger descriptive language. Maybe the answer cites sources that support everyone else more clearly.

Those weak appearances are often easier to improve than total absences, because you are already in the conversation.

Look for repeated themes in AI-generated responses #

Pay attention to how AI answers describe your category and the brands inside it. Do certain competitors keep getting framed as “best for startups” or “easy to use”? Does your brand keep getting omitted from a use case you actually serve well?

Repeated wording can reveal market perception problems just as much as visibility problems.

Turn observations into a short action list #

Keep the action list short and specific. Refresh an existing comparison page. Publish a use-case explainer. Add stronger summaries and clearer product details. Support a claim with cited evidence. Improve a landing page that already sits close to the right topic.

If the list gets too long, nothing moves. Three to five actions is enough.

Step 9: Connect visibility data to content updates #

Tracking alone does not improve anything. Content changes do.

1. Map missing prompts to existing pages.
2. Identify content gaps that need new assets.
3. Improve content structure for extraction.
4. Compare results against your baseline after updates.

Map prompt gaps to pages you already have #

Start with content you already own. If a high-value prompt maps loosely to an existing page, refresh that page before creating something new.

This is usually the fastest win. A comparison page, category page, FAQ, or use-case article often needs clearer language, stronger headings, better summaries, or more explicit problem framing.

Identify net-new content opportunities #

Some gaps need new content. If prompt research reveals strong demand around a use case you do not cover, create the page.

This could mean a comparison page, a category explainer, a buyer’s guide, a “best for” resource, or a specific FAQ hub. The point is to match the language and intent you saw in prompt tracking, not to publish generic filler.

Improve extractability for AI answers #

Make content easier for AI systems to interpret and quote. Semrush has recommended structuring content for extraction by using clear summaries, strong topical organization, and direct answers near the top of relevant sections (How to improve AI search visibility).

In practice, that means tighter headings, concise definitions, explicit comparisons, straightforward tables where helpful, and less wandering prose. Think of it like setting the table neatly instead of piling everything in one drawer.

Track updates against the baseline #

After updating content, compare the affected prompt groups against your original snapshot. Look for gradual improvement, not overnight magic.

If visibility improves in the exact prompt cluster you targeted, that is a useful signal. If nothing changes after a reasonable period, revisit the page match, the prompt choice, or the strength of your source support.

Step 10: Monitor changes over time without overreacting #

AI visibility shifts quickly. Calm interpretation matters.

1. Review trends over multiple checks, not one isolated result.
2. Compare movement at the cluster level.
3. Note internal changes alongside external visibility shifts.

Separate normal fluctuation from meaningful change #

Single-prompt swings happen. One day your brand appears, another day it does not. That alone is not a reason to panic.

Meaningful change usually looks broader: multiple prompts shifting in the same direction, repeated gains or losses across checks, or a competitor appearing more often in the same prompt category.

Watch for prompt clusters that rise or fall together #

Cluster movement is more trustworthy than isolated prompt movement. If recommendation prompts improve together after a content refresh, that is a stronger signal than one prompt spiking randomly.

This is why your earlier grouping work matters so much. It gives you a lens for real pattern detection.

Log content, PR, and product changes alongside visibility #

Keep a simple change log. Note new content, big page updates, PR coverage, product launches, and messaging changes. Then line those events up against visibility movement.

Without that context, reporting becomes guesswork. With it, you can say something useful, like “visibility for onboarding-software prompts improved two weeks after the comparison page refresh and product update.”

Step 11: Expand the setup once the first system is working #

Once your first dashboard feels clean and readable, then expand.

1. Review which prompts and reports proved useful.
2. Add one new layer at a time.
3. Keep the original core view intact.

Add more prompt categories #

Expand into adjacent topics, new funnel stages, or related use cases. If your first project focused on recommendation prompts, add educational prompts next. If you started with one product line, branch into the next closest category.

The key is controlled expansion. Add enough to learn more, not enough to blur the signal.

Track additional competitors or sub-brands #

Larger markets may need more entities once the base system works. Agencies may add client competitors. Multi-product companies may add sub-brands or product families.

Do this carefully. Every added entity makes interpretation more complex, so only expand where comparison creates a real decision.

Create separate views for teams, clients, or regions #

Once the process is stable, create reporting views tailored to the people using them. Regional teams may need market-specific prompts. Clients may need separate dashboards. Product teams may need topic-specific slices.

The underlying setup stays consistent. The view changes to fit the audience.

Troubleshooting common setup issues #

Even a good setup gets messy in the first month. Most problems are fixable without starting over.

Too many prompts and not enough signal #

If the dashboard feels crowded, cut back. Keep the prompts that drive clear decisions and pause the rest. Usually the best fix is reducing the set by a third, then checking whether the remaining prompts tell a cleaner story.

More prompts are not automatically better. Better prompts are better.

Visibility looks inconsistent across platforms #

That is normal. Different AI systems pull, summarize, and cite information differently. Compare trends within a platform first, then compare broader patterns across platforms.

Trying to force one-to-one comparisons too early usually creates confusion.

Brand mentions are missing or misread #

Check your entity naming. Add common brand variants, product names, and abbreviated references where relevant. Review whether AI answers are referring to your company indirectly, especially if your product name is better known than your corporate name.

Sometimes the issue is not absence. It is detection.

Teams don’t know what to do with the data #

If reporting stalls, tighten the output. Every update should answer three things: what changed, where it changed, and what action makes sense now.

Once the report starts pointing to specific page updates, content ideas, or competitor moves, teams pay attention.

What success looks like after setup #

A good setup does not just produce data. It gives you a repeatable way to spot prompt opportunities, monitor AI mentions, and catch competitor gains before they become a bigger problem.

The signals you should be able to see clearly #

By now, you should be able to answer a few questions without fumbling through spreadsheets. Which prompt groups mention your brand most often? Which competitors dominate comparison prompts? Where are you absent? Which topics show weak brand framing? Which changes are improving visibility over time?

That clarity is the real win. Not perfection, clarity.

Your next move after the first month #

After 30 days, keep the prompts that produced useful decisions, cut the ones that stayed noisy, and add one new prompt group or platform that fits your goal. Try that one thing before making the setup bigger. That’s usually enough to turn Semrush visibility tracking from an interesting dashboard into a working system.