Some product types just naturally fit Reddit’s community culture, including:
Technical or complex tools: SaaS, software, or tools where users want support and feature breakdowns
Niche ecommerce brands: Mattresses, supplements, and other high-consideration DTC products people love to compare and review
Finance and service tools: Banks, brokers, and budgeting apps where transparency matters
Gaming and entertainment: Games or media with built-in fandoms
Consumer tech: Gadgets and devices that need troubleshooting and setup discussions
News and media brands: Outlets and publishers where audiences already debate coverage and breaking stories
Are You Committed to Building a Community?
If your only goal is to “control the narrative,” stop right here.
(I can already hear the Reddit mob sharpening their pitchforks.)
Yes, a brand subreddit can absolutely strengthen your reputation. But only as a byproduct of serving your community first.
Your reason for being should be to create a space where users can connect and feel heard.
For example, r/fidelityinvestments is a customer care channel with official Fidelity associates.
But it’s also a community.
Where members troubleshoot for each other, share feedback, and even defend the brand when criticisms pop up.
Do You Have an Assigned Moderator?
Someone has to own your brand subreddit.
And they need to be there every day:
Sparking conversations and posting prompts. Plus, modelling the tone you want until the community naturally mirrors it.
That takes a rare mix of skills:
Technical familiarity with your product
Context across marketing, support, and PR
Sharp community instinct and tone awareness
Without that person, keeping your subreddit healthy will always feel like a grind.
Are You Cool with Public Scrutiny?
Even the best teams take hits on Reddit.
The question is: Can you handle it?
Because you will get complaints, and you will get called out.
Sometimes, it’s a full-blown PR storm. Like when REI’s CEO hosted an AMA and got flooded with employee complaints about wages, hours, and sales quotas.
Other times, it’s smaller.
Like when a Sonos marketing email revealed someone’s password.
Big or small, the spotlight’s the same.
And the internet expects one thing:
That you stand there, take it, and handle it in stride.
(To their credit, both the REI CEO and u/keithfromSonos did just that.)
So, ask yourself:
“Do we have a team that can handle that pressure and keep the tone steady?”
If not, skip the brand subreddit rather than lose your cool in public for everyone to screenshot.
Alternatives to a Brand Subreddit
If you don’t meet the above conditions, it doesn’t mean you can’t be on Reddit.
You can still build visibility without launching an official community.
Start by getting active in existing unofficial brand-related subreddits.
GoPro, for example, doesn’t run r/gopro.
Yet, it’s one of the most vibrant product spaces on the platform.
Another option is to create a non-branded subreddit around your niche.
For example, if you sell hiking gear, launch r/TrailTips or r/UltralightKit.
You still get visibility without the pressure of running an official branded space.
Another alternative is using your user account as your brand’s central presence.
Many media companies do this well. Like The Washington Post at u/washingtonpost/ and Drop.com at u/drop_official/.
How to Create a Company Subreddit (5 Steps)
Think a company subreddit fits your brand?
Perfect! When done right, it can deliver real results, including:
Deeper customer insights
A self-sustaining community
More visibility in SEO and large language models (LLMs)
“Our share of voice has definitely improved. Two months ago, Reddit Answers didn’t even mention Favikon when I searched for the best influencer marketing platforms. Now, it’s up there in Reddit’s search results.”
– Olena Bomko
Ready to build yours? Let’s get into it.
Step 0: Meet the Minimum Requirements
Before creating a subreddit, become a Redditor first.
Spend time on the platform and learn the culture.
Observe how conversations flow, how moderators maintain order, and what earns trust.
(We’ll talk about cadence, staffing, and moderation in later steps.)
Plus, when everyone knows the “why,” every post naturally lines up with it.
Side note: Your community can support other goals. But your primary goal should define how you measure success. That’s what makes it easier to see whether it’s actually working.
For example, a support-first subreddit focuses on speed, accuracy, and trust.
It needs moderators who know the product and can solve problems publicly.
r/fidelityinvestments is an example of this.
Verified associates answer customer questions, while pinned announcements guide users through service updates.
And, if they were tracking key performance indicators (KPIs), they’d likely focus on response time and resolution rate.
Now, compare that to a community-first subreddit.
It usually thrives on curation, conversation, and peer support.
Moderators act more like hosts, encouraging user-generated content (UGC) and keeping discussions flowing.
r/LifeOnPurple runs this way.
The mattress brand posts lightly, shares occasional updates, and lets UGC drive momentum.
Their key metrics probably include:
Percentage of UGC
Active users
Returning posters
Common Brand Subreddit Goals
Here are the top three core goals most brand subreddits serve.
Choose one, commit to it, and let the rest orbit naturally to keep your subreddit focused.
Goal Type
Main Tasks
Typical Post Types
Brand Presence & Awareness
Customer Care
Reduce support load and create a searchable archive
FAQs, tutorials, outage updates, support megathreads
UGC ratio, non-brand posts/week, returning posters
Engagement rate, sentiment, referral traffic
Step 2: Put People (and Rules) in Place
Once you’ve set your goals, decide who’ll run the subreddit. And how.
The right person (or team) makes sure that:
Questions get answered quickly
Moderation feels fair
Brand messaging stays consistent
Start by assigning one primary moderator.
They’ll be accountable for growth, moderation quality, and reporting insights.
In most teams, that’s your community manager, social media lead, or support head.
Preferably, someone who knows the product and understands community dynamics.
But a great subreddit is rarely a one-person show.
So, make sure your moderator has access to others in the company.
Here’s how that can look depending on your subreddit type:
Support-heavy subreddits: Include a product specialist or customer service rep who can jump in fast
Community-first spaces: Bring in someone from marketing or content to spark conversations or highlight great posts
Developer or technical subs: Involve a product manager or engineer who can step in when discussions get technical
For example, r/SEMrush is run by Semrush employees who actively join conversations and clarify product questions when needed.
In contrast, r/hubspot’s moderators are a combination of members from the HubSpot support team and a power user.
Bring Key People to Your Subreddit
You should also have a few “guest stars” lined up.
These are your execs, product managers (PMs), or team leads.
They don’t need to be available all the time.
But, having them join conversations signals two things: access and accountability.
For example, as Favikon builds its company subreddit in its early stages, the team regularly runs AMAs with leaders and associates.
Define Your Ground Rules
Everyone who represents your brand on Reddit should know exactly how to show up.
So, create an internal guide — like a company subreddit playbook — outlining how your brand speaks and behaves on Reddit.
At a minimum, cover these areas:
Brand tone: How your company sounds when it speaks
Disclosure: Make it clear you’re speaking for the brand. Use verified handles or flairs like “Official Response” or “From the CEO.”
Confidentiality: Define what can be shared publicly vs. what stays internal
Escalation: Outline how moderators flag issues to support, PR, or product teams
Response guidelines: When to jump in, when to step back, and when to let the community self-resolve
Moderation scenarios: How to handle misinformation, conflict, or spam consistently and fairly
Crisis protocols: Who leads if a post goes viral, a complaint snowballs, or a product issue surfaces
Reality check: You don’t need an extensive playbook on day one. Start with the essentials that help moderators act confidently. Then, evolve it as your subreddit — and your instincts — mature.
Step 3: Set Up Your Subreddit
With your moderators and rules ready, it’s time to build the actual space.
To set it up, use a desktop. It’s much smoother than mobile.
Start by clicking “Start a ccommunity” in the left-hand sidebar.
You’ll see a pop-up window that walks you through setup.
Here’s what matters most in each step.
Pick the Right 3 Topics
First, you’ll be asked to choose three topics your community belongs to.
These help Reddit’s discovery algorithm surface your subreddit to the right users.
So, your topic choice could affect who finds you.
In other words:
Treat topic selection like SEO for community discovery.
Choose Your Community Type
Next, decide how open your subreddit will be:
Public: Best for most brand launches
Restricted: Useful for soft launches
Private: Good for internal pilots or early betas
Mature (18+): Only if your content genuinely requires age restriction.
Most brands should go “Public” for organic reach.
But there are also situations where “Private” or “Restricted” makes sense.
For example, if you want to keep everything hidden while you build, set it to “Private.”
And, if you’re not launching yet — but you want to own the URL before someone else grabs it — go “Restricted.”
Just remember, switching later requires Reddit’s approval.
Name Your Subreddit
Next comes naming your community.
This one’s permanent. So, check spelling and capitalization.
Stick with r/YourBrand or r/yourbrand when possible.
If it’s taken, use a clear variant such as r/YourBrandOfficial, r/YourProduct, or r/YourBrandSupport.
Here are a few examples:
r/0xPolygon (Polygon Labs)
r/SEMrush (Semrush)
r/LifeOnPurple (Purple Mattress)
Next, add a short description in the field below the subreddit name.
You can update this anytime.
So, keep it simple for now. (Unless you’ve already got a strong one.)
An effective subreddit description should:
Say who it’s for
Say what members can do
Set expectations
For example, Favikon’s description clearly states what the community is for and what the brand will provide.
It’s obvious that the space serves both the community (creators) and the brand’s updates.
Fidelity’s description, on the other hand, is clear that it’s a customer care channel. With Fidelity associates answering product-related questions.
It also clarifies that they don’t handle account-specific issues:
A small but crucial detail that manages expectations early.
Add Visuals to Make It Look Official
After writing your description, it’s time to add visuals:
Specifically, your icon and banner.
For your icon, upload a recognizable asset, such as your logo.
This helps users instantly see that the subreddit is official.
Next, add your banner.
A 1920 x 384 pixel image works best, though Reddit also allows slimmer options like 1920 x 256 or 1920 x 128.
Your banner should reflect your brand identity without feeling like an ad.
The r/LifeOnPurple subreddit, for example, uses the Purple Mattress logo and a clean purple banner consistent with its brand design.
But r/MobileLegendsGame uses detailed artwork that fits its gaming audience.
Once you’ve uploaded your logo and banner, click “Create Community.”
And voila! That’s your subreddit live.
Step 4: Personalize and Prepare for Launch
Once your subreddit exists, the next step is to make it feel alive.
Do these four things to make it feel welcoming:
Add clear community rules
Write and pin a welcome post
Add a few starter threads
Set up sticky highlights
Let’s walk through each.
Define Community Rules
Every subreddit needs community rules.
They define the kind of space you’re building.
You don’t need a long list, especially at the start. Four to six guidelines are enough to set expectations and boundaries.
Cover the basics first:
No spam
Be respectful
Don’t share personal information
Then, add one or two brand-specific rules.
For example, r/mintmobile, a community with heavy customer engagement, adds a rule against spreading false information.
Plus, a reminder not to post personal details.
While r/hubspot, a fairly new subreddit, has only three rules.
To add rules, click “Mod Tools” at the top right sidebar of your subreddit page.
Then, scroll down to the “MODERATION” section in the left sidebar.
Click “Rules” > “Create Rule.”
Pro tip: Spend time exploring Mod Tools. That’s where you customize your subreddit’s look, rules, and automation. The more familiar you are with that panel, the smoother your moderation as the community grows.
Write the Welcome Post
A welcome post helps new visitors understand what the subreddit’s for and how to participate.
There’s no single right format.
Just make it clear and approachable.
r/reolinkcam, for example, uses a pinned “Please Read This Before Posting” thread.
It starts with short, practical guidance, followed by a quick intro, links to product setup guides, and an FAQ section.
r/Comcast_Xfinity takes a different approach.
Its welcome post lays out the community code of conduct, explains how to use flairs, and summarizes key rules.
To create your first post, click “Create Post.”
It’s at the top right corner of your subreddit page.
Post Conversation Starters
Once your welcome post is live, add a few early posts to make your community feel active.
Some threads you can write include:
FAQ: Answer common support or sales questions your team already gets
Product updates or announcements: Share new releases to keep people in the loop
Community guidelines: Restate the rules and add context, like where to report bugs or how to tag posts
How to/tutorial: Solve a top recurring problem. It reduces tickets and becomes a reusable resource.
Pin Community Highlights
Sticky posts are the first thing visitors see when they land on your subreddit.
They’re pinned to the top of your feed.
When used well, they double as trust signals. A kind of proof that your brand is active and organized.
Start by pinning your “Welcome Post,” then layer in others as your community grows.
For example, r/SEMrush keeps its biggest updates (like the AI Visibility Toolkit launch) and company news pinned.
This way, new visitors instantly see what’s new.
Meanwhile, r/fidelityinvestments often features
Engagement prompts
Weekly Q&As
Official announcements
To make any post sticky, open the post, scroll down, and click the shield icon.
Then, select “Add to highlights.”
That post will now appear at the top of your subreddit.
Step 5: Launch Your Subreddit
Now that everything’s in place, it’s time to spark the first lights of community.
Invite Founding Members
Founding members help set the tone and the tempo of your brand subreddit.
Ideally, they’re your superfans. People who already share your enthusiasm.
They’re usually:
Power users who love your product
Loyal customers who actively engage
Industry peers who enjoy sharing what they know
These voices bring authenticity and fill your first threads with real conversation.
They’ll also help define your culture.
So, treat them like subreddit co-founders, not just early users.
How do you get them?
Start with a simple, genuine invitation.
A one-on-one message always beats a mass announcement.
“Hey [name],We’re launching a small community on Reddit. It’s going to be a place to share ideas, ask questions, and help shape how our products evolve. You’ve been one of the most insightful voices in our space. I would love for you to be part of it from the start.”
[Your name]
Announce It Publicly (But Frame It Right)
Once you’ve got a few active members and threads, announce your subreddit in your owned channels, including:
Frame it as a shared space where your team and users exchange insights, solve problems, and showcase projects.
You can also invite followers from other platforms when there’s something happening — like an AMA or live discussion.
The way Olena does it on X, for example.
This approach builds awareness and attracts people who genuinely want to be part of your community.
Cross-Promote in Related Subreddits (Carefully)
If you or your team already participates in related subreddits, mention your new community when it genuinely adds value to a discussion.
Side note: Always check each subreddit’s rules first. Many ban self-promotion.
This tactic works best when your user account already has credibility in that subreddit.
If people recognize your username from your past helpful comments, the subreddit mention feels natural, not sneaky.
Pro tip: NEVER ask employees to pose as independent users to promote your brand. That’s called astroturfing — and it’s one of the fastest ways to destroy credibility on Reddit.
How to Keep Your Brand Subreddit Alive
Once your founding members are active, the real work begins:
Keeping your subreddit alive and thriving.
You don’t need dozens of posts a day, but you do need steady participation.
Moderate and Engage Consistently
How often you show up depends on your subreddit’s purpose, but the principle stays the same:
Be present.
Respond quickly: Aim to reply within 24 hours
Enforce rules fairly: Remove spam and toxic behavior, but don’t over-police
Check in daily (or at least on weekdays): Even 15–20 minutes a day keeps threads from going unanswered
For example, moderators in r/Comcast_Xfinity regularly pin troubleshooting threads and reply to outage questions.
From their flairs alone, you can tell they’re listening and available.
Side note: A flair is a small label that appears next to a username or post title. It adds instant context to every interaction. You can customize flairs in Mod Tools.
Start Meaningful Rituals and Events
Rituals keep communities alive and give people a reason to come back.
Some easy ones to start include:
Weekly or monthly megathreads for support or feedback
Recurring posts like “Feedback Friday” or “Tutorial Tuesday”
Regular AMAs with your CEO or product team
Community contests or creative prompts
Keep these rituals going long enough, and people start showing up out of habit.
It becomes a place where regulars connect through shared threads and interests.
And that’s how your subreddit turns from just another space into a familiar home.
Not sure where to start?
Look at non-brand subreddits for inspiration.
For example, r/bullcity — Durham, North Carolina’s official subreddit — has a biweekly anything goes thread.
This is where people can add any posts that “would otherwise be considered spam” into the thread.
It’s pinned in the community highlights and keeps local conversations active and open.
Encourage User Contributions
Invite members to share their own tips, advice, and projects.
Then, amplify their participation:
Make a special flair for “Top Contributor”
Highlight the most useful tips
Feature a “Member of the Month”
These small bits of recognition let people know their voice matters. And can turn a casual user into a loyal regular.
Pro tip: Reddit’s spam filter can be overzealous. Keep an eye on auto-removed posts so real users don’t lose motivation.
Handle Criticism Transparently (and With Grace)
Negative posts are inevitable, and deleting them is the worst move you can make.
Instead, respond honestly. Acknowledge the issue, and explain what’s being done about it.
Even if your answer isn’t perfect, that transparency helps build credibility.
To see how it’s done well, look at how other brands handle criticism or answer tough questions.
For example, Beardbrand owner, u/bandholz, once replied to the question:
“Is Beardbrand just not great anymore?” in a calm and factual way.
This turned a critical post into a constructive discussion.
Track Your Subreddit Engagement and Growth
To grow your subreddit, think less about control and more about connection.
And always watch the engagement:
Are members helping each other? Are discussions happening without you prompting them?
When activity dips, nudge it with a new prompt or AMA.
When it grows, resist the urge to overmanage.
Then, use Reddit Analytics to see whether the community is growing or slowing.
This helps you quickly gauge what’s working.
“I spend time in Reddit’s native analytics tools. They’re not super detailed, but I can track member growth and weekly contributions. I can also see daily numbers for posts, comments, and unique users. For what I do — and what I need to track right now — that’s more than enough.”
Make Your Brand Subreddit the Hub
Your brand subreddit works best as part of a complete Reddit presence, not in isolation.
Once it’s well-established, blend it with smart Reddit marketing, including ads, partnerships, and organic participation.
That’s when Reddit stops being just another forum and becomes an ecosystem that grows your visibility and your credibility at the same time.
But AI search adds a new layer your team needs to master.
Here’s what I mean:
Traditional SEO gets your pages ranking in top search positions.
AI SEO gets your brand visible in AI-generated answers — through brand mentions, citations, or both.
You’re expanding what SEO covers. Not replacing it.
Let me break down what’s changed and what it means for your team.
What’s Changed
Search behavior itself has evolved a lot over recent years.
A growing number of people don’t just “Google” anymore. They discover, compare, and decide across multiple platforms. (And this has been the case since long before ChatGPT came along.)
Someone might start on TikTok, check Reddit reviews, search on Google, and ask ChatGPT for a summary before taking action. And they might revisit these platforms at various stages of the journey.
That journey looks less like a straight line and more like a network.
Here are five other changes reshaping how search works today:
Whole-web signals: AI pulls from your website and everywhere else your brand appears online. Your entire digital footprint influences your AI visibility.
Entity recognition: AI understands your brand as a concept it can connect to products, industries, and related topics, not just keywords to match (learn more in our guide to entity SEO)
Passage-level retrieval: AI extracts specific sections from your content to use in its answers, not entire pages. This means it needs to be clear what each section of your content is about.
Conversational search behavior: AI search queries tend to be longer and more specific. People describe problems in detail rather than typing short keywords, which means the AI often cites highly specific content rather than generic guides.
Zero-click reality: Users can now get complete answers without visiting websites. Traffic from search is no longer guaranteed, even with strong visibility.
What This Means for Your Team
These changes don’t require you to rebuild your team from scratch.
But they do require expanding what your team focuses on:
Your content team still writes. But now they also need to structure content so AI can easily understand it and extract sections for its answers.
Your technical SEO team still optimizes site architecture. But priorities shift toward AI crawlability, performance, and schema implementation.
Your strategist still tracks performance. But now they also need to measure citations and brand mentions across AI platforms.
Most of these skills build on what your team already knows. Again, they’re extensions, not replacements.
4-12 months is a typical timeline to get your team comfortable with AI SEO fundamentals.
You’ll need some combination of internal training, external guidance, and selective hiring — depending on your current gaps. I’ll talk more about this later.
First, let’s break down the specific skills your AI SEO team needs.
Essential AI SEO Skills Your Team Needs
Not everyone needs to be an AI SEO expert in all areas.
One person (typically a lead or strategist) needs strategic understanding. They understand how AI search works and can adapt when platforms change.
The rest of your team needs execution capability. They can follow guidelines and apply best practices.
It’s helpful if they show interest in understanding AI SEO, but it’s not required.
Here are the key skills that bridge traditional SEO and AI search.
Understanding AI Retrieval
AI platforms find and reference content differently from Google’s traditional ranking systems.
Some platforms, like Perplexity, search the web in real-time.
Others, like ChatGPT, can search the web or pull from their training data.
And AI Overviews use Google’s existing index and Gemini’s training data.
To optimize for and appear in these places, your team needs to understand how these systems select what to cite and mention.
When someone asks a question, these platforms look for content that directly answers the query. They prioritize sources that are clearly structured and contextually relevant.
Note: AI systems also use a process called query fan-out. This involves expanding one user prompt into multiple related sub-queries behind the scenes.
That means your content can surface even if it doesn’t match the original question exactly. If it covers a related angle or entity that the AI connects to the topic, it can be cited or mentioned.
Your SEO lead or strategist typically owns this skill.
They already understand search intent and ranking logic — the same foundations that AI retrieval builds on.
In smaller teams, a content strategist can also take this on with a shallow learning curve.
Typically, they’ll spend 2-3 hours monthly testing how your brand appears across AI platforms. Document patterns in what gets cited. And adjust content strategy based on what’s working.
Writing for AI Extraction
AI search tools don’t respond to user queries with entire articles. Instead, the AI pulls specific passages that answer those queries.
If a passage requires a lot of surrounding context to make sense, AI may be less likely to understand its relevance and therefore be less likely to use it.
This means each section of your content needs to still make sense even when taken out of the context of the rest of the article.
Each section should answer a specific question on its own, without relying on references to other parts of the article.
This is generally just good writing practice. If you find yourself making too many unique points in one section, it’s probably best to split it into subsections.
But clarity here is also key.
For example, avoid: “As we mentioned earlier, this approach works well…”
Instead, write: “Structuring content into self-contained passages helps AI extract and cite your information more effectively.”
Here’s another example of effective writing for AI extraction:
The second version makes sense whether someone reads your full article or sees just that paragraph in an AI response.
This doesn’t mean every sentence needs a complete context. It means key passages should stand alone.
Who Can Own It?
Your content or editorial team can handle this.
SEO provides the framework and guidelines. Writers implement it in their daily work.
For example, editorial reviews the article structure before publishing, ensuring each section has a clear, standalone takeaway.
Sometimes that means breaking a 500-word section into three shorter subsections with specific headers.
By the way: As a content marketer myself, I don’t think this shift is dramatic.
Most great content teams already write clearly and structure information logically. This just prioritizes ensuring key passages work independently.
Building AI-Readable Structure
AI needs clear signals to understand your site’s structure and how content relates to other pages on your site.
For example, schema markup makes your data more structured by defining what your content represents.
This can make it easier for AI systems to interpret and cite your content accurately.
While the full impact is still unclear, structured data makes your content easier to parse, which is helpful for search engines anyway. And since Gemini can lean on Google’s search infrastructure, it’s not all that unreasonable to expect that schema could at least indirectly affect your visibility in places like AI Overviews and AI Mode, now or in the future.
Similarly, internal linking shows how topics connect.
And a clear site hierarchy indicates which pages are most important.
Think of it as creating a map.
Instead of making AI infer relationships, you’re explicitly defining them.
Beyond your site: Entity databases
Once you have the basics down, consider registering your brand and products in databases like Wikipedia, Wikidata, or Crunchbase.
These knowledge bases help AI systems understand entity relationships and how your brand fits into broader industry contexts.
This bridges on-site structure (like schema markup) with off-site presence. You’re helping AI systems recognize your brand across the web, not just on your site.
You don’t need this starting out. But it’s worth exploring once your core AI SEO structure is in place.
Who Can Own It?
Your technical SEO can take ownership of this skill.
They already handle the fundamentals like implementing schema markup, managing site architecture, and optimizing internal linking structures.
The approach doesn’t change much. They’re just applying the same technical skills with AI systems in mind.
Tracking AI Performance
Traditional SEO metrics (like rankings, organic traffic, and click-through rates) still matter.
But they don’t say anything about your brand’s AI search visibility.
You need different metrics now, including:
Platform breakdown: Where you’re showing up (ChatGPT, Perplexity, Google AI Overviews, etc.)
Citation frequency: How often your content gets cited as a source in AI responses
Mention rate: How often your brand appears in AI-generated answers or recommendations
Mention sentiment: Whether those mentions are positive, neutral, or negative
These numbers indicate whether your AI SEO strategy is working.
Without specialized tools, you’ll need to manually search key queries across platforms and track when your brand appears.
Who Can Own It?
Your SEO analyst or whoever handles performance reporting can own this.
They’re already tracking traditional metrics. AI performance metrics become an addition to that dashboard.
If using AI visibility tools, they’ll monitor your visibility score and citation trends monthly.
Without specialized tools, they’ll need to manually search key queries across platforms, document when and how your brand appears, and track changes over time.
AI tools go beyond just looking at your website and pull from everywhere your brand is mentioned online. Including:
G2 reviews comparing tools
Reddit threads discussing your product
Forum conversations about your industry
News articles mentioning your company
If those mentions are sparse or outdated, AI has less information to pull from when someone searches for your brand specifically or asks about your product category.
This is where AI search extends beyond your domain.
Who Can Own It?
No single person can own this entirely.
PR, community management, and customer success each control different pieces of the puzzle.
Someone from SEO can take the coordination role, ensuring these teams understand how their work affects AI visibility.
In practice, this often means your SEO lead or director works cross-functionally to align off-site efforts with AI discoverability goals.
For example, they work with customer success to encourage reviews on platforms like G2 or Trustpilot.
They also monitor where your brand gets mentioned across forums, social platforms, and community discussions.
Different AI platforms retrieve and display information in their own ways.
For example:
Perplexity searches the web in real-time and shows numbered citations
ChatGPT can search the web or pull from its training data
Google’s AI Overviews draw from Google’s search index and Gemini’s training data
What gets you cited on one platform won’t automatically work on another because each platform follows patterns in what it mentions and cites.
For instance, I searched “which is the best camera phone of 2025” across three platforms.
ChatGPT cited multiple YouTube videos, a Reddit thread, Tom’s Guide, Yahoo, and Tech Advisor.
Google’s AI Mode cited one YouTube video along with a bunch of other websites — no Tom’s Guide, Yahoo, or Tech Advisor.
Claude cited Quora and Android Authority twice. No Reddit threads, YouTube, or Tom’s Guide.
Same query, completely different sources and mentions.
Your team needs to understand these differences when optimizing for AI visibility.
You don’t need separate strategies for each platform. But knowing how different platforms prioritize sources helps you structure your entire approach, from content to technical implementation to off-site presence.
Who Can Own It?
Your SEO lead or strategist can typically own this.
They can track how your brand appears across platforms and identify what’s working where.
They’ll spot gaps in coverage on LLMs that matter to the brand. For example, strong presence in ChatGPT but weak in Perplexity.
Then they work with content, technical, and other teams to adjust the overall strategy.
Query Intent Mapping
People search differently in AI platforms than they do in Google.
Traditional Google: “best CRM software”
ChatGPT: “I need a CRM for a 50-person sales team, budget around $10K annually, must integrate with Salesforce”
The queries are longer. More conversational. More specific.
I checked my own most recent 100 prompts to ChatGPT. They averaged 13 words each.
Compare that to traditional Google searches, which typically run 3-4 words.
Understanding these prompt patterns helps you create content that answers the actual questions people ask AI.
You need to think beyond traditional keywords.
What detailed questions are the people in your audience asking? What context are they providing? What outcome do they want?
Who Can Own It?
Whoever leads keyword research or content planning can take this on, usually your SEO strategist or content planner.
This builds directly on existing keyword research skills.
You’re expanding from “what keywords do people use?” to “what problems are people trying to solve?”
(Which you should have been doing all along, but now with a stronger focus.)
This person will analyze how people search in AI platforms and document the longer, conversational queries they use.
Then they’ll build content briefs that address those specific questions and scenarios.
The Build, Buy, or Borrow Decision: Getting AI SEO Skills on Your Team
You know which skills your team needs.
Now comes the practical question: how do you actually get them?
You have three options:
Build internally
Hire new talent
Bring in outside expertise
Here’s a snapshot of the pros and cons of all three:
Most teams end up doing some combination of all three. The key is knowing which approach works best for specific skills.
Let’s look at each one in detail.
1. When to Build (Develop Internally)
Upskilling your current team is almost always the smartest first move.
They already know your brand, your workflows, and your audience. That context shortens the learning curve dramatically.
Focus on developing skills that evolve naturally from what your team already does.
For example:
Train writers to structure content for AI extraction
Help your SEO lead understand AI retrieval patterns and how citations work
Encourage your analyst to track AI visibility metrics alongside rankings
These are logical extensions of existing expertise. Not entirely new disciplines.
Now, training doesn’t have to mean building a full internal curriculum.
Start small. For example:
Run short internal workshops to explain how AI search retrieves and cites content
Review recent AI-generated answers for your top keywords and note which competitors get mentioned
Compare their cited passages to yours, and update one or two articles using those patterns
To make internal training effective, use this quick checklist:
Upskilling may not be the fastest route to output. It can take a few months before you see real traction.
But it is the most sustainable.
Once your team starts applying AI-first thinking, you’ll see compounding returns with every new SEO campaign.
Best For
Startups and mid-sized teams that already have strong SEO foundations but a limited budget for new hires.
Watch Out For
Don’t overload your team with theoretical “AI SEO” training.
Focus primarily on skills that directly connect to visibility outcomes, like structure, clarity, and retrievability.
Also watch for skill concentration. If one person (like your SEO lead) ends up owning 3+ new AI skills, that’s a bottleneck. Consider hiring or borrowing expertise to spread the load.
2. When to Buy (Hire New Talent)
When you need expertise faster than you can build it internally, it’s time to hire.
Bringing in new talent makes sense when the skill is both specialized and strategic.
Something that gives your brand a long-term edge, not just a short-term fix.
For example:
Hiring a data or visibility analyst who understands how to measure citations and brand mentions across AI platforms
Bringing in a technical SEO who can model entities and implement structured data at scale
Adding an AI content strategist who can guide how your content aligns with AI retrieval patterns
These hires extend the capabilities of your existing SEO team. They don’t replace it.
The key to finding the right people?
Clarity before you post the job. Decide what outcome you’re hiring for.
Do you need faster technical execution, deeper analytics, or dedicated AI visibility leadership?
Before you start recruiting, here’s a quick checklist to work through:
With clear hiring criteria, you’ll know which expertise to prioritize and what title makes sense for your organization.
Best For
Mid-sized and enterprise teams that have budget flexibility and want to move faster than internal training allows.
Watch Out For
Don’t over-index on shiny new “AI SEO” titles. Few people have that exact label yet.
Instead, look for specialists in areas like data, structured content, and retrieval systems. These are people who can bridge SEO and AI.
3. When to Borrow (Outsource or Consult)
Not every skill is worth building or hiring for.
Some are highly specialized. Others you only need for a short period.
That’s where borrowing expertise makes sense — through consultants, freelancers, or agencies.
Outsourcing works best when you need to move fast on projects that require niche expertise.
For example:
Hiring a consultant to set up AI visibility tracking before your analyst takes over
Partnering with a content firm to scale passage optimization across hundreds of pages
Bringing in a Reddit marketing expert to boost your brand’s presence in relevant subreddits
This approach gives you access to deep expertise without expanding headcount.
You can bring in specialists to handle complex projects, fill capability gaps, or run pilot programs that would slow your internal team down.
Sometimes that means a one-off engagement.
Other times, it’s a recurring partnership that supports your strategy long-term.
The goal isn’t to offload responsibility. It’s to fill gaps your team can’t cover yet and to get critical work done without slowing down larger projects.
When evaluating potential partners, here’s a quick checklist to follow:
Best For
Teams that need quick access to specialized expertise or extra hands for complex, time-bound projects.
Watch Out For
Don’t treat outsourcing as a default fix.
If a skill becomes core to your strategy, consider bringing it in-house. But for niche or technical projects, keeping trusted external support can be more practical.
Choose partners who understand your brand voice. AI-first SEO still needs human context.
In practice, it’s rare that a team is fully built, bought, or borrowed.
You’ll probably use all three, often at the same time.
How much you lean on each one depends on factors like:
Your current team’s strengths and bandwidth
Budget flexibility for hiring or contracting
The urgency of upcoming SEO goals
How quickly AI search is evolving in your industry
Leadership’s appetite for experimentation
In my experience, many teams land somewhere near a 70-20-10 split. Which is roughly 70% built internally, 20% borrowed through outside experts, and 10% bought as new hires.
The exact ratio matters less than how deliberately you manage it.
Here’s how to keep that balance right:
Prioritize by impact: Build skills that sustain long-term visibility. Borrow when you need speed or experimentation. Buy only when a role becomes essential to your strategy.
Keep ownership internal: Even if outside partners execute the work, ensure someone on your team owns the outcome and applies the learnings.
Plan for rotation: As new AI SEO trends emerge, your mix will likely shift. What starts as a borrowed skill may become core within six months.
Audit regularly: Review your mix every quarter to see which skills rely too heavily on outside help. If a borrowed skill becomes recurring, start building it internally.
Follow this quick team review checklist to keep stock of your built, bought, and borrowed setup.
The key is flexibility and adaptability.
As priorities shift, don’t hesitate to rebalance how your team works.
That might mean promoting someone internally to take ownership of AI visibility, bringing in a freelancer to handle off-site optimization, or hiring a new analyst to deepen your data capability.
Adjust your structure based on what delivers the most impact, not what’s written on the org chart.
Your AI SEO Adoption Roadmap
You don’t need a massive reorg to evolve your SEO team for AI search.
You need a plan that helps your team build capability, test what works, and scale what proves effective.
This roadmap gives you that plan.
It breaks down:
What to focus on in each phase
How to build momentum
What progress should look like along the way
By the end, your team will know how to apply AI SEO principles consistently.
Note: This timeline is a starting point, not a rule.
Startups with smaller teams might compress this into 6 months. Enterprises coordinating across departments might need 15-18 months.
The timeline matters less than starting now and making steady progress.
Phase 1: Foundation
Start by taking stock of where your team stands.
Before diving into new tactics, align everyone around what AI SEO means for your brand and how your current approach fits into it.
This stage sets direction and gives your team the confidence to move with purpose.
Here’s what to focus on in the first three months:
Assess current capabilities: Review your team’s strengths across content, technical, and analytical areas. Identify which AI-era skills exist internally and which ones you’ll need to hire for or outsource.
Establish your visibility baseline: Search your most important topics in tools like ChatGPT, Perplexity, and Google AI Overviews. Track if (and how) your brand shows up.
Pick 2-3 priorities to act on: Choose the areas with the clearest opportunity to improve. That might mean tightening content clarity, mapping entities, or aligning off-site mentions.
Run a small pilot: Select a few representative pages and update them based on what you’ve learned. Then recheck whether those updates help your brand appear more often in AI answers.
Document key learnings: Capture what worked and what didn’t in a short internal memo. This becomes the foundation for next quarter’s priorities.
Goal: Build clarity, alignment, and a shared understanding of how AI search changes what your team prioritizes.
By the end of this phase, your team should understand what makes content discoverable in AI search, have a documented baseline to track progress, and have at least one small win that proves the approach works.
Phase 2: Acceleration
Once you’ve built your baseline, it’s time to turn insights into action.
The second phase focuses on building capability and momentum. This involves scaling what worked in your pilot, closing skill gaps, and introducing systems that help your team move faster together.
Here’s what to focus on over the next few months:
Strengthen capability: Run short training sessions to deepen AI SEO understanding across functions. If a skill gap exists, bring in a freelancer, consultant, or new hire to fill it quickly.
Encourage cross-functional collaboration: Bring content, SEO, analytics, product, and brand together under one shared visibility goal. Clarify ownership so responsibilities don’t overlap.
Expand your pilot: Apply what worked from Phase 1 to more pages or campaigns
Build repeatable workflows: Turn early learnings into working systems. Standardize how technical, analytical, and content tasks are executed for AI-driven discovery. Each function should know what “AI-ready” means in its area.
Use shared dashboards: Track AI visibility metrics in one place and review them as a team so everyone sees how their work contributes to results
Run monthly reviews: Check how well your team is adapting to new systems and responsibilities. Identify where people need support, additional training, or outsourced help.
Goal: Build capability, consistency, and accountability across your team’s AI SEO initiatives.
By the end of this phase, your team should operate with clear workflows and defined ownership across technical, analytical, and content areas.
You should also have unified dashboards that let all stakeholders track progress and collaborate without duplicated work.
Phase 3: Scale
This final phase turns AI-first thinking into how your team operates by default.
The goal now is to make the new skills, workflows, and decision habits permanent. This way, your AI SEO capability grows without needing constant resets.
Here’s what to focus on in the next six months:
Integrate what works: Expand the proven approaches from earlier pilots across your full SEO and content programs. Keep the frameworks that consistently improve visibility; drop the ones that don’t.
Solidify roles and ownership: Define who leads AI-related strategy, measurement, and experimentation. Clarify responsibilities so the team stays agile even as you scale.
Strengthen internal training: Turn what your team learned into short onboarding sessions, playbooks, or process docs. This keeps new hires aligned and prevents knowledge loss.
Plan for selective specialization: As your AI SEO programs mature, assign ownership where consistent work is required. That could mean promoting a team member to lead AI visibility reporting, assigning an SEO specialist to oversee off-site signals, or partnering long-term with a proven external expert.
Create leadership visibility: Share quarterly reports on AI-driven results and learnings with senior stakeholders. This keeps support (and budgets) growing with your progress.
Goal: Make AI-first execution routine and scalable across your team.
By the end of this phase, your team should operate with defined roles and responsibilities. You should have internal systems for training, reporting, and process consistency.
Leadership should have visibility into AI performance outcomes so the team treats AI SEO as an integrated function, not an experiment.
Measuring AI SEO Team Success
You can measure your AI SEO team’s success by tracking how often your brand appears in AI-powered answers.
Here are important AI SEO metrics to track:
Citation frequency: How often AI platforms cite your content as a source
Brand mention rate: How often your brand appears in AI responses
Platform coverage: Which AI platforms reference you (ChatGPT, Perplexity, Google AI Overviews, etc.)
Sentiment: Whether those mentions align with your brand positioning
It shows your AI Visibility Score and how many times your brand is mentioned across different AI platforms.
It also shows which prompts your brand appears for, revealing which topics your team’s content strategy is successfully targeting.
In your Brand Performance report, you can compare your brand’s visibility against multiple competitors.
The report includes insights like your Share of Voice (percentage of mentions compared to competitors) and sentiment analysis. This tells you whether AI platforms present your brand positively or negatively.
For larger organizations, Semrush offers Enterprise AIO, with team collaboration features and advanced analytics.
Specifically, your AI Visibility Score is a good overall indicator of your AI SEO team’s performance.
If it has improved over 3-12 months, it means your team is executing well. The skills are translating into real visibility.
If results aren’t showing after two quarters, revisit your priorities. You might be focusing on the wrong skills first or need to adjust your build/buy/borrow mix.
Pro tip: When you start building your team’s AI SEO skills, benchmark your brand’s AI Visibility Score alongside five competitors.
After 3-12 months, compare growth rates, not just final scores.
Your score might increase from 30 to 40 (+10 points). But if competitors jumped from 40 to 60 (+20 points), not only are they more visible — they’re also outpacing you.
Track relative growth to understand your true competitive position.
Get a Custom AI SEO Team Plan in 20-30 Minutes
AI SEO is built on traditional SEO. But there are more layers to it.
Your SEO team needs updated systems and upgraded skills so your brand gets mentioned (and your website cited) in AI search results.
We created the free AI SEO Team Building Assistant to turn everything you just read into a custom action plan for your team.
Download the file, upload it into your AI platform of choice (Claude, ChatGPT, Gemini), and follow the conversation.
This is an interactive session that adapts to your specific team, budget, and constraints. It’s not just a cookie-cutter report after a basic prompt.
It takes around 20 minutes to work through (but you should take your time with it). At the end, you’ll walk away with a complete implementation plan.
Here’s an example of the output, starting with the one-page plan:
You’ll also get a “Skills Ownership Map” showing which team member owns which skill. And which skills to build, borrow, or buy.
Plus a Phased Roadmap, KPI Tracking Framework, Leadership Brief, and 30-day checklist.
Everything is tailored to the specific inputs you provide in the interactive conversation.
Here are some tips for getting the most out of this assistant:
Block 30 uninterrupted minutes so you can really engage with the conversation
Have your current team structure in mind
Be specific in your answers (vague input = generic output)
Be honest about constraints (like budget, time, and capabilities)
AI chat is the number one source B2B buyers use to shortlist software.
Not review sites. Not vendor websites. Not salespeople. AI chat.
G2’s 2025 survey of 1,000+ decision makers found that 87% say AI tools like ChatGPT, Perplexity, and Gemini are changing how they research software.
Half of SaaS buyers now start in AI chat instead of Google Search.
They’re “one-shotting” their research with prompts like “Give me CRM solutions for a large gym that work on iPads.”
What used to take hours of “Google —> right-click —> open new tab” is condensed to minutes.
If your product doesn’t show up when buyers ask AI to recommend solutions in your category, you’re losing deals before they begin.
This guide shows you exactly how to change that.
I’ll walk you through:
How AI visibility works for SaaS
Why some brands dominate AI answers
What you can do to make sure AI recommends you
Side note: The data in this article comes from Semrush’s AI Visibility Index (August 2025), focusing on the Digital Tech and Software category.
The 3 Types of AI Visibility for SaaS Brands
There are three ways your brand can show up in AI search:
Brand mentions
Citations
Recommendations
Type 1: Brand Mentions
Brand mentions mean your brand appears in the AI’s answer.
It’s not always an endorsement. It’s simply the AI recognizing your brand as relevant to the topic.
For example, I asked ChatGPT:
“How can remote teams stay aligned on projects?”
ChatGPT outlined a few tactics and listed several tools, naming specific brands as examples with no extra context about any of them.
At this level, how AI talks about your brand is super important. AKA: brand sentiment.
A positive tone builds early trust while a negative one sets bad expectations.
Let me show you what I mean.
I asked ChatGPT:
“What do marketers on Reddit say about top reporting dashboards.”
ChatGPT summarized Reddit’s discussions, listed a few tools, and included criticisms about some products.
If I were evaluating dashboards, the negative details about AgencyAnalytics and Looker Studio would create a subtle bias that would follow me as I continued my research.
That’s no bueno.
So make sure sentiment around your mentions leans positive.
Just go to “AI Visibility” > “Perception” and you’ll see key sentiment drivers surrounding your brand. The tool will show you Brand Strength Factors (positive influence on sentiment) and Areas for Improvement (negative sentiment factors).
Type 2: Citations
Citations are instances of AI using your content as a source.
It’s a strong signal that the AI trusts your brand and is using your content to build its answer.
In Google AI Mode, citations show up as clickable links on the right-hand side of the response.
In ChatGPT, they appear as footnotes or small inline links.
Citations come with two complications.
First, they’re not as visible as brand mentions.
The footnote-style links are easy to miss, which means you probably won’t get meaningful traffic from these citations.
The AI can use your content, but not mention your brand.
Semrush’s AI Visibility Index report calls this the “Zapier Paradox.”
In the Google AI Mode dataset, Zapier was the most-cited domain in the entire software category. It appeared in around 21% of the analyzed prompts.
Yet it ranked only #44 for brand mentions.
That means the AI trusts Zapier’s content enough to use it constantly.
But that trust hasn’t translated into more visibility for the brand itself.
That doesn’t mean citations are useless. Far from it, since they’re still the only method of sending users directly from AI search to your website.
But if you’re cited for an answer that recommends other brands (like Zapier often is), the citation isn’t super useful for your brand.
That’s why you want the third type of AI visibility.
Type 3: Product Recommendations
Product recommendations are where the AI moves from “here are some options” to “here’s what you should choose.”
To get recommended, your brand typically needs two things working in your favor:
Positive sentiment
Enough verified facts for the AI to feel confident putting your name forward
For example, when I asked:
“Which CRM is best for small businesses?”
ChatGPT recommended six CRM platforms.
Each one came with a breakdown of strengths.
That’s the AI directly influencing my consideration set.
And when the AI wraps up the answer with the top three CRMs, these three brands stay top of mind.
As the reader, I’m thinking:
“Alrighty. These are the tools I should probably compare.”
That’s the power of SaaS product recommendations in AI search.
The AI isn’t just helping me explore the category. It’s shaping the shortlist I walk away with.
How AI Models Choose Which SaaS Brands to Surface
When AI answers a query, it cross-checks sources.
It compares what you say about your product with its training data. Along with what the rest of the internet says about you.
If the details line up, you’ve got consensus and consistency: two forces that drive visibility in AI search.
Consensus
Consensus happens when many credible places describe your product in the same way.
In practice, the AI is looking for alignment across sources like:
Review sites (G2, Capterra, TrustRadius)
Industry blogs and SaaS publishers
Expert posts on LinkedIn or in public newsletters
User communities like Reddit and Quora
Your own website and documentation
Basically: anywhere your product is being talked about in a credible context.
Take Asana, for example.
It routinely appears in AI answers about project management tools.
And you can see why when you look at its footprint online.
Across multiple places, you’ll find the same core description repeated from their website to Capterra to Reddit.
All of these brand mentions alone help boost Asana’s potential visibility.
But when they also all point to the same story, that’s consensus. This helps AI feel confident surfacing the brand repeatedly.
Consistency
Consistency means the details match everywhere they appear.
When AI scans the web, it’s looking for verifiable facts. If those specifics line up, it trusts them.
But, if those signals don’t match, the model becomes unsure.
(Just like you would if five people gave you five different versions of the same “fact.”)
For example, let’s say:
Your pricing page says your Standard plan includes unlimited reports
Your help center says Standard users get 50 reports a month
Recent reviews say they hit limits after a week
Now you’ve got three conflicting stories.
When the AI sees that, it can’t tell which one is true. It might use the right one, or it might use the wrong one. Or it might not use any of them.
That’s why data hygiene matters in AI search.
The key facts about your brand should be consistent everywhere your brand is described.
The Content That Dominates SaaS AI Search
Not all content carries the same weight in SaaS AI search.
Some formats show up repeatedly because they help models verify what’s true about a product.
Review Platforms
Review platforms are some of the most heavily cited sources in SaaS AI search.
These sites, including G2, Capterra, and TrustRadius, give AI unfiltered, third-party proof about your product.
The platforms help the model validate:
Who you are
What your product actually does
How reliable it is
How users feel about it
In other words, this is where AI goes to separate your claims from real user experience.
And the data shows how influential they are.
According to Semrush’s AI Visibility Index, G2 is the 4th most-cited source for ChatGPT and 6th for Google AI Mode across the entire tech and SaaS category.
That tells us that:
Review platforms aren’t just buyer research hubs
They’re part of an AI’s verification layer
What people say about you in these places becomes part of the material the AI uses when describing your brand.
Best-of listicles and tool roundups give LLMs structured, pre-sorted information that they can easily digest.
These articles hand the AI a ready-made map of a category, including:
Who the key players are
How the tools differ
Which products consistently show up together
The AI regularly pulls from a mix of major publishers, niche SaaS blogs, and established industry media.
For example, when I asked for the top AI SEO tools, Google AI Mode’s citations included a bunch of best lists.
Every roundup, comparison post, or “best tools for X” mention becomes one more anchor AI tools can grab when they’re trying to answer a question about your category.
Pro tip: Don’t ignore your own media. AI models also use company-owned content as reference material. So create your own well-structured roundups and comparison pages in the niches where your product plays.
For example, when I asked ChatGPT whether Omnisend or Mailchimp is better for ecommerce, one of the citations was Omnisend’s own blog post comparing the two tools.
In other words: their own content helped shape the AI’s narrative.
Documentation & Product Knowledge Bases
AI also uses your product documentation to understand how your product works: what it does, who it’s for, and what its technical capabilities are.
For example, when I asked Google AI Mode, “Is Semrush good for enterprise?” the model pulled from several Semrush-owned pages:
The Enterprise landing page
A press release on the enterprise platform
A blog on “What Is Enterprise SEO”
An enterprise client case study
Together, those pages gave the model context to understand Semrush’s enterprise offering.
One more thing:
Make sure your content is well-structured, clear, and complete.
If it’s vague or lacks key details, the AI might look elsewhere to fill the gaps.
The Semrush study shows this clearly with pricing.
When SaaS brands don’t publish transparent pricing, AI models fill the blanks using community speculation. This speculation is often tied to negative sentiment.
So, how do you structure your content for better AI visibility?
Use:
Clear, explicit content using conversational language
Clean formatting that makes details easy to extract
Tables, charts, and Q&A blocks that package information neatly
Headings that signal hierarchy
Want the full breakdown? Our article on how to rank in AI search walks you through the full process.
Video Content
Text may fuel most AI answers, but video (especially YouTube) has become a meaningful signal, too.
In fact, YouTube is the 10th most-cited source in Google AI Mode for SaaS-related prompts.
This means AI isn’t just reading the web. It’s also learning from what people show and say on camera.
For SaaS brands, that’s a real visibility lever.
If your product appears in YouTube reviews, tutorials, comparisons, or walkthroughs, the AI can pull those details straight into its explanations.
For example, when I asked Google AI Mode whether the paid version of HubSpot is worth it, one of the citations was a YouTube review.
If you don’t have a YouTube presence yet, it’s worth planning for.
Start by getting your product included in other creators’ reviews and tutorials.
Then build out your own YouTube channel to control the narrative long-term.
What This Shift Means for Your SaaS Brand
If you’ve already put in the work on your SaaS SEO basics, you’re already in a good position.
But AI search adds a new layer, and it requires a few more steps to stay visible.
Make AI Visibility a Company-Wide Effort
AI search visibility isn’t something marketing can brute-force on its own since consensus and consistency play such a major part.
Multiple teams should keep their corners of the internet aligned in your brand story.
This means:
Marketing keeps claims factual and up to date
Product Marketing ensures documentation, changelogs, and feature pages match what’s actually live
Customer Success helps maintain accurate review-site profiles
PR/Comms monitors media mentions so nothing drifts off-message
To make that doable, create a simple internal “source of truth” every team can follow.
This doesn’t need to be a 100-page brand bible.
Start with:
Exact product names, tier names, and feature labels
The approved value props and phrasing you want repeated everywhere
Performance claims or metrics that should stay consistent across your site, docs, and press
Integration names and technical terms written the same way across all surfaces
Example of a Brand That’s Winning in AI Search (Slack)
Slack ranks ninth overall in the Digital Technology/Software category for AI visibility.
That visibility isn’t tied to one use case or category, as Slack shows up everywhere for various queries.
From prompts about remote work to team communication and the best tools for small businesses.
Here’s what they’re doing that you can steal:
Slack Owns Their Category (Not Just Brand-Specific Prompts)
Slack doesn’t only show up when someone searches for “Slack.”
They show up for everything inside their category, in prompts about:
Use cases: “team chat for remote work”
Features: “tools with shared channels”
Problems: “how to align remote teams”
Price: “team communication tools”
Showing up in these various category prompts builds early recognition.
This then affects what happens next as the user goes deeper into their buying journey.
For example, a user might start an AI conversation with:
“Which is better, Slack or Teams?”
Slack shows up in the citations because they’ve published content that answers that question.
Now, let’s say the user sees a drawback in the AI’s answer.
The user might follow up with:
“What are Slack’s security concerns?”
And Slack again shows up in the citations, this time through their own blog content.
Slack is actively shaping the conversation.
As the user moves from comparison to evaluation to decision, Slack’s content keeps appearing in the AI’s reasoning.
In short: Slack gets to influence the story at every step of the buyer journey.
Slack’s Messaging Is Clear
One thing Slack absolutely nails is message consistency.
Everywhere you look — their website, their docs, their review profiles, their blog — you get the same story about what Slack does and who it’s for.
Go to their site and you’ll see pages laying out features, use cases, and integrations. All in plain, straightforward language.
Even their blog posts break down new features in that same accessible tone.
That clarity matters because it makes it incredibly easy for AI to learn what’s what.
When your content follows a simple structure of “Here’s the feature, here’s what it does, here’s how it works,” the model can easily classify information.
But Slack doesn’t just do this on their site.
Jump over to their review profiles and you’ll find the exact same messaging — the same features, same categories, same positioning.
That consistency is a big plus.
When your messaging stays the same across every channel, you give the AI reliable information to work with.
Slack Is Present Everywhere LLMs Go for Answers
Slack has a footprint across every layer that large language models pull from.
The community layer: Reddit threads, Quora discussions, and YouTube reviews:
The expert layer: SaaS tutorials, niche SaaS blogs, and trusted industry publishers:
The verification layer: G2, Capterra, and TrustRadius:
This breadth matters because it helps LLMs find patterns.
When Slack’s value prop, features, and positioning appear the same way across all three layers, the AI treats that agreement as “high-confidence” information.
This gives the AI zero doubts about what Slack does and what it offers — and therefore what kinds of queries the AI should recommend Slack for.
Help AI Find and Feature Your SaaS Brand
For SaaS AI search, the game is simple:
Show up everywhere the AI looks.
For software companies, that means being intentional about what you publish, how you structure it, and where you show up across the web.
You don’t just need to “write more content.”
You need to create the right content, in the right places, in the right formats that AI models rely on.
AI search is reshaping how ecommerce brands get discovered.
One week, your products show up in ChatGPT. The next week, they’re replaced by competitors.
For many brands, this uncertainty can feel overwhelming.
Organic visibility now depends less on rankings and keywords, and more on how LLMs gather information, which platforms they rely on, and what signals help them highlight your brand.
In this guide, I’ll explain this crucial shift in detail.
I’ll unpack:
What actually shapes visibility inside AI answers
The business impact of compressed buyer journeys and broken attribution
How you can build lasting relevance in this new search ecosystem
The 3 Types of AI Visibility for Ecommerce Brands
If you’re familiar with SEO, getting AI visibility is similar. It starts with how search systems decide what to display.
But for years, ecommerce SEO was a linear equation: rank = visibility = traffic (and then conversions).
AI search is changing that.
LLMs summarize, compare, and recommend products, all in one place.
In short: Shoppers can discover your products, check alternatives, and make buying decisions within AI chats.
In this new setup, brands compete across three different discovery models.
Type 1: Brand Mentions
Mentions drive product discovery and build top-of-funnel LLM visibility for your brand.
This is where your brand gets featured in AI-generated answers, often without a link to your site.
Mentions often come from reputation signals like:
Reddit posts
Media coverage
User reviews
Social discussions
Put simply, you become part of the conversation.
For new or emerging brands, this is often the first touchpoint to reach shoppers through AI.
Type 2: Citations
Citations are linked references within AI-generated results, like a footnote in an essay.
With citations, LLMs attribute specific information, claims, or data points to your pages.
Your brand becomes a source of truth in AI responses and gains credibility.
How?
When an AI tool cites your brand, it signals to shoppers that you’re an authoritative voice.
Plus, citations can support your positioning. The AI tools can pull your framing and product narrative into their response. Not someone else’s.
Type 3: Product Recommendations
AI platforms actively recommend products for a shopper’s specific needs and concerns.
This is the most impactful layer for ecommerce brands.
Your products can show up with pricing, ratings, and other details.
This type of visibility effectively merges discovery and purchase in one place.
This happens when the LLM reviews the query, compares options, and picks your product as the best fit.
Showing up in the list of recommended products makes your brand a part of the decision interface.
Shoppers can compare specs, prices, and reviews — or even purchase — right in the AI chatbot or search tool itself.
How AI Models Choose Which Ecommerce Brands to Surface
AI visibility as a discipline is still evolving rapidly. But there are clear patterns to which ecommerce brands get seen and which get sidelined.
Two driving forces at play are: consensus and consistency.
Consensus
With traditional search, ecommerce brands could build domain authority through activities like link building and digital PR. Strong pages from an authority perspective tended to perform well in search results.
In AI search, LLMs don’t evaluate your website and product pages in isolation. Authority is built from a consensus across sources.
LLMs ask: “What do credible sources agree on about this product?”
To decide which brands and products deserve visibility, LLMs cross-reference multiple sources, like:
Reddit threads
YouTube videos
Industry reports
Customer reviews
Trusted publishers
Community discussions
So, a glowing review on your PDP might mean little if customers on Amazon consistently leave 1-star ratings.
And a publisher’s feature loses impact if Reddit users repeatedly recommend your competitors instead.
In other words: No single source determines your likelihood of being mentioned or cited. It’s the pattern of consensus across multiple platforms that does this.
For example:
Keychron frequently shows up when you use AI search tools to find mechanical keyboards.
This happens because the brand has earned trust through various sources:
Review sites like PCMag and Tom’s Guide rank Keychron in their top recommendations
Keychron’s Amazon pages are detailed with positive reviews and an average rating of 4.4 stars
Multiple Reddit threads in subreddits like r/MechanicalKeyboards and r/macbook recommend the brand
Several YouTube videos feature Keychron in their roundup of mechanical keyboards
Each trust signal on its own is valuable.
But when taken together, LLMs see a pattern of independent sources validating the same brand/product for a specific use case.
Consistency
LLMs don’t crawl and rank pages the way traditional search engines do.
Instead, when answering a product-related query, an AI model might pull:
Your product name from your Shopify store
Pricing from Google Merchant Center
Key specs from Amazon
Opinions from users on Reddit
If your product title is “stainless steel” on Amazon but “brushed metal” on Walmart, the LLM can’t decide which is correct. This inconsistency could make the AI tool less likely to include any information about your product. Or it could include the wrong information.
This is why data hygiene is crucial for building AI visibility.
You need to maintain a clean, synchronized identity for every product across every channel.
Your product attributes should follow the same pattern across your site, marketplaces, and feeds:
Model numbers
Dimensions
Materials
Weights
Prices
LLMs use these data points to match your products to queries and validate claims across sources.
Your Amazon listing, your Shopify store, your Google Merchant feed — all sources need to tell the same story with the same data.
So, the same SKU name, image, and product description should appear everywhere your product appears.
Finally, outdated data signals decay, and models may deprioritize products with outdated info.
When you change a price or update a key spec, that change should be visible everywhere. Stock availability, pricing, and features should always be up to date.
Types of Content That Dominate Ecommerce AI Search
We’re seeing clear patterns in what gets cited, mentioned, or ignored in AI search for ecommerce.
Understanding these patterns can be the difference between hoping you show up and knowing how to position your brand so that you do show up.
Here’s what’s currently doing well in AI search for ecommerce:
Top Cited Sources
I wanted to see which brands are cited most frequently in LLM responses for ecommerce queries — so I tested it.
I picked nine popular ecommerce niches and searched category-specific queries across ChatGPT, Claude, Perplexity, and AI Mode.
Based on the responses, I made a list of five popular brands showing up frequently for each vertical.
Then, I jumped to the “Competitor Research” tab in Semrush’s AI Visibility Toolkit to run a gap analysis for these five brands in each category.
The “Sources” tab showed which domains LLMs cite most frequently, like this for the “outdoor travel & gear” niche:
This data reveals where LLMs pull product information, and which platforms matter most in your vertical.
Here’s what this data tells you:
Reddit: Reddit is a top-cited source for nearly every industry. If people aren’t discussing your brand in relevant subreddits, invest in Reddit marketing.
YouTube: It’s another universal citation source. Video content from creators and users feeds into AI answers. That means having a YouTube presence can be a huge visibility lever for most ecommerce verticals.
Category-specific platforms: Generic sources like Amazon appear everywhere. But niche platforms (like Petco, Barbend, Sephora) carry weight in their verticals.
Wikipedia: It’s a top source for categories like outdoor gear, healthy drinks, and gadgets. This is where product context and category education matter a lot alongside the likes of specs and pricing.
Going beyond these top-cited platforms, here are the kinds of content LLMs link to most frequently for ecommerce queries:
Publisher Listicles
These are product roundups, buying guides, and comparison posts from established media outlets.
For example, I asked ChatGPT for the best Bluetooth speaker recommendations.
It cites publishers like TechRadar, Rtings.com, and Stereo Guide for this response.
Getting featured in these listicles means you’re part of the source material LLMs use to compile information.
AI models use publisher listicles as sources because they:
Compare multiple products in one place
Refresh their recommendations periodically, providing recency signals
Include specific, comparable details like price ranges, key specs, and pros/cons lists
Fulfill high editorial standards and so may appear more trustworthy than user-generated content
Retailer Product Pages
Retailers like Amazon, Walmart, and Target are among the most frequently cited sources for product queries.
When I asked Perplexity about the NutriBullet Turbo, it cited the product pages from the likes of Walmart and Macy’s.
These PDPs provide structured data points like ratings, pricing, and key specs.
AI models often rely on these product pages because they:
Include structured, machine-readable product data like specs, dimensions, materials, and pricing
Aggregate hundreds or thousands of customer reviews as social proof
Show real-time availability and pricing
Lab Tests and Expert Reviews
In-depth product testing content from experts is another important source for citations.
These websites test products systematically and publish detailed findings.
LLMs can then use this empirical data as the basis for their responses.
For example, I asked Claude to find the best mattress for side sleepers.
The tool references sites like NapLab, Consumer Reports, and Sleep Foundation for data-backed recommendations.
AI models consider lab test or expert review content for citations because they:
Compare products against consistent criteria and benchmarks
Show credibility with independent, systematic evaluation processes
Include measurable data to explain their top-ranked recommendations
Periodically update their recommendations to offer fresh, authoritative data
Reddit Threads and Community Discussions
Conversations on Reddit, Facebook groups, and YouTube comments frequently appear in AI responses.
This is especially true for subjective queries like “Is X worth it?” or “What do people actually think about Y?”
I tested this myself by asking Perplexity whether the Instant Pot Duo is worth buying.
It pulled insights from multiple Reddit threads, a Facebook group, and a YouTube video to respond based on real user input.
Brands that get mentioned positively across multiple Reddit threads build “cultural proof.”
And those organic discussions about your brand feed directly into AI training data and real-time search results.
AI models pull from these communities because they:
Present an aggregated sentiment from community discussions
Contain contrasting opinions and insights to objectively review products
Show different use cases and pain points that a product can tackle
Highlight a product’s pros and cons based on firsthand experience
Comparison Posts
Content that compares two or more products can also help LLMs find the right brands to mention in their response.
When I ask AI Mode for alternatives to the supplement brand Athletic Greens, it mentions five options.
The sources include several comparison articles (alongside some roundups).
Being included in this type of content (even if you’re not the winner) can help build your visibility.
This could be Brand A vs. Brand B blog posts, YouTube videos, review sites, and social media discussions.
AI models refer to these resources because they:
Answer buyers’ questions by comparing two or more products
Focus on decision-making criteria and help people make informed decisions
Let’s now consider the business impact of this AI search setup for your ecommerce brand.
The Compressed Buyer Journey
The traditional ecommerce funnel was built on multiple touchpoints.
A shopper might:
Google a product category
Read reviews on multiple different sites
Check Reddit and YouTube
Visit brand websites to compare prices
Return days later to buy
Each step was an opportunity for your brand to show up, make an impression, and win their trust.
For a lot of purchase decisions, AI search collapses this entire journey into a single interaction.
The same shoppers can now go to AI tools and ask, “What’s the best air fryer for a small kitchen?”
They get a single response with buying criteria, product recommendations, pricing, ratings, and more.
Now, clearly this isn’t going to happen for every purchase decision. These tools are still new for one thing, and it takes a lot to majorly shift buyer behavior. (And of course, SEO is not dead.)
But discovery, evaluation, and consideration CAN all happen in one response now. The AI agent performs the research labor.
That means you have fewer chances to influence buyers.
In the past, if a shopper didn’t discover you in organic search, they might find you through a review site, a Reddit thread, or a retargeting ad.
In other words: You could lose the first touchpoint and still win the sale three touchpoints later.
With AI search, you might only get one shot: the initial response.
For many ecommerce queries, AI tools give you a curated list of options. If you’re not in that initial answer, you don’t exist in the decision process.
Take action: Build an AI search strategy using our Seen & Trusted Brand Framework to increase the probability of your brand getting featured in AI responses.
The Visibility Paradox
Your brand might frequently show up in AI search. But your analytics show flat traffic and zero conversions traced back to AI tools.
Here’s why:
Not all AI visibility is created equal.
Your brand can appear in 10 different AI responses and drive 10 completely different business outcomes.
It all depends on how you’re presented.
Here’s what the visibility spectrum actually looks like for ecommerce brands:
Visibility Type
Example
Business Outcome
Mentioned without context
“Popular air fryer brands include Ninja, Cosori, Instant Pot, and Philips.”
Value: Brand awareness Purchase Likelihood: Low
Mentioned with attributes
“Cosori is known for its large capacity and intuitive controls.”
“The Cosori 5.8-quart model includes 11 presets, uses 85% less oil than deep frying, fits a 3-pound chicken, and costs around $120.”
Value: Active consideration and purchase Purchase Likelihood: High
That means getting mentioned is table stakes, not the end goal.
Building brand awareness without differentiation just makes you a part of the crowd.
To drive real sales, you need to earn citations and product recommendations.
The brands winning in AI search are:
Cited as trustworthy sources
Recommended for specific use cases
Attribution Gets Murky
When shoppers find products through AI but buy elsewhere, analytics tools can’t track the whole journey.
This creates two problems:
You can’t prove the ROI of AI search: Even if AI mentions are driving consideration, you’ll get zero or limited data on that. You won’t see the prompt the user asked or the response from the tool.
You can’t optimize what you can’t measure: When you don’t know how people are discovering you in AI answers, you can’t A/B test your way to better visibility. The feedback loop is broken.
Tools like Semrush’s AI SEO Toolkit are closing this gap by showing how your brand and competitors appear in AI search.
I used the tool to check the AI visibility and search performance for Vuori, an athleisure brand.
The brand has a score of 76 against the industry average of 82, and is frequently mentioned AND cited in AI responses.
The toolkit also identifies specific prompts where your brand is mentioned or missing.
This makes it easy to spot exactly which type of queries are driving visibility and which represent missed opportunities.
For example, here’s a list of prompts where LLMs don’t feature Vuori, but do mention its competitors.
Go to the “Cited Sources” tab to find out the websites that LLMs most commonly refer to for your industry-related queries.
For Vuori, it’s sites like Reddit, Men’s Health, Forbes, and more.
The “Source Opportunities” tab will give you a list of key sites that mention your competitors, but not you. These are sites you should aim to get your brand included on.
Besides tracking your own AI visibility, the AI SEO Toolkit also lets you monitor your competitors’ performance on AI platforms.
The “Competitor Research” report compares you to your biggest competitors in terms of overall AI visibility.
It also highlights topics and prompts where other brands are featured, but you aren’t.
Example of a Brand That’s Winning in AI Search: Caraway
If you want to see what winning in AI search actually looks like, look at the cookware brand, Caraway.
When you ask AI about the “best bakeware set” or the “best ceramic pans,” Caraway almost always makes the shortlist.
Data from Semrush’s AI SEO Toolkit shows that Caraway also outweighs its biggest competitors in AI visibility.
Let’s break down how Caraway built this advantage.
Showing Up Where LLMs Look
Caraway is frequently featured on publishers like Taste of Home, Good Housekeeping, and Food and Wine.
These are the actual sources LLMs cite when constructing answers about cookware-related queries.
For example, here’s a paragraph from the Food and Wine article ChatGPT cited as a source, which mentions the attributes ChatGPT used in its recommendation:
Caraway also earns mentions through organic discussions on Reddit, Quora, and kitchen forums.
Retailer Evidence That AI Can Cite
Caraway’s clean Amazon Brand Store and on-site product pages also make it easily citable.
These product listings and pages give LLMs concrete signals like:
Multiple in-stock SKUs with visible sales velocity (“500+ bought in the past month”)
Product rating and volume
Rich media files
These retailer PDPs become credible sources for verifying pricing, availability, or product specs.
Strong Affiliate Presence
Caraway also runs an affiliate program, and the brand makes it frictionless for publishers to feature its products through:
Affiliate networks: Links are available through major networks like Skimlinks and Sovrn/Commerce
Amazon compatibility: Editors can also use Amazon Associates links for Caraway’s stocked SKUs
Reviewer support: The brand provides an affiliate kit, including link types, banner ads, text links, and email copy
This all makes it easy for Caraway to work with influencers and other publishers to promote its products. And these publishers can then appear as citations when AI tools make their recommendations.
For example, all the highlighted sources in the ChatGPT conversation below contain Caraway affiliate links:
Part of the Category Narrative
Many style media and mainstream outlets reference Caraway in their content.
Here’s a recent example from an Architectural Digest interview featuring the cookware set as an essential kitchen item.
This creates more authority for the brand in the cookware and kitchen category.
Make AI Work for Your Ecommerce Brand
You now know how the game works and who’s winning. It’s your turn to play it.
But there’s a lot to do.
Making your site readable by LLMs, opmtimizing your structured data, and setting up automated product feeds are just stratching the surface.
Our comprehensive Ecommerce AIO Guide gives you alll of the actionable tactics to consistently show up in AI results.
You found a digital marketing agency that feels like the one.
The pitch was perfect. They “get” your goals. Their case studies are impressive.
But a few weeks later, reality starts to set in: slow responses, recycled strategies, and reports that don’t show any tangible results.
This scenario is painfully common, but it’s not inevitable.
Choosing an agency that performs as well as they sell is possible — if you know what to look for.
In this guide, I’ll cover:
Red flags that signal an agency might overpromise and underdeliver
Green flags that separate the great partners from the mediocre ones
Must-ask questions to help you spot these flags before you sign the contract
You’ll also get real-world advice from experienced marketing leaders who’ve seen both dream partnerships and nightmare contracts.
By the end, you’ll know exactly how to choose a digital marketing agency in 2026. One that drives results instead of draining your budget.
First up: Vital questions to ask before jumping into a partnership.
Before You Hire a Digital Marketing Agency, Ask These Questions
Finding the right agency starts with understanding what you need and why.
Do You Have Product-Market Fit and a Clear Target Audience?
Even the best agency can’t sell a product that doesn’t solve a real problem for a defined audience.
If product-market fit isn’t there, your results will stall.
Ask yourself:
What pain points do we solve?
Who’s willing to pay for this?
Who else is competing for this audience?
Use a market analysis tool like Semrush’s Market Overview to confirm there’s real, sustainable demand.
For example, a quick search for Purina pet food shows strong growth and evenly distributed traffic — a clear sign of opportunity.
That’s the kind of demand signal you want before investing in outside help.
Do You Have a Clear Goal for Your Marketing Strategy?
A marketing agency can help you refine your goals.
But you’ll get better results when you already know what success looks like.
Vague goals like “increase website traffic” sound good, but they’re too broad to measure. Instead, set SMART goals — specific, measurable, achievable, relevant, and time-bound.
Here’s what a SMART goal looks like in action:
“Generate 120 qualified demo requests per month within four months by improving landing page copy and optimizing Google Ads.”
Clear goals like this help you find the right agency. And give them a focus to rally around and drive results.
Do You Have the Bandwidth to Manage an Agency?
Working with an agency isn’t a set-it-and-forget-it kind of task.
Regular, consistent communication with your agency is part of this process.
Sure, the level of autonomy will depend on the agency and the work.
But generally, the best agencies keep the door to conversation open.
Here’s what you can expect:
Provide materials and align on strategy and deliverables up front
Join weekly or biweekly check-ins (typically about an hour)
Review work and share feedback monthly
Pro tip: Assign one internal “agency owner.” Their job will be to keep decisions moving, share context fast, and unblock workflows.
Do You Know What Marketing Services You Need?
“Full-service marketing” sounds great. Until you realize you’re paying for tactics that get you nowhere.
There are many types of digital marketing agencies:
SEO and content: Drive organic growth through optimized content
Branding and design: Shape your visual identity and messaging
Video: Create video content that converts
Consultant: Help define priorities before execution
But before you pick one, identify what’s already working (and what’s not).
The more specific you are about your needs, the easier it is to find a partner whose strengths align with your goals.
Start by looking at your top-performing channels, campaigns, and content in analytics tools.
If content and partnerships drive results for you, that’s a hint about where to invest.
Next, check what’s working for your competitors.
For example, Semrush’s Organic Social tool reveals how your competitors generate traffic from social media.
And tells you exactly which platforms send the most traffic to their websites.
If others in your space are thriving on social while you’re not, that’s a clue to where you could expand.
Pro Tip: Before looking for an agency, ask yourself: Do I need strategy, execution, or both?
Is Your Internal Team Aligned on What You Need?
Clear goals mean nothing if your team isn’t aligned.
Without internal buy-in, even the best agency partnership can derail fast.
Marketing leader Eric Doty learned this the hard way.
After hiring an agency for a logo redesign (and spending weeks on revisions), leadership revealed they wanted to keep the full company name.
“In the end, we wasted around $15,000 on these iterations when all the company really wanted was to change the font.”
Avoid this by:
Defining who owns the agency relationship
Deciding who signs off on deliverables
Getting stakeholder input before work gets started
Once you’re aligned internally, you’re ready to align externally with your agency.
6 Red Flags That a Marketing Agency Will Waste Your Time (and Budget)
The sales call sounds great.
But how do you know whether the relationship will work long-term?
Don’t go in blind. Here are six warning signs and how to spot them.
1. They’re Not Willing to Invest Time in You
This isn’t something an agency will just come out and say directly. But there may be indications that they’ve currently got too much on their plate.
(And you’re about to be thrown onto the back burner.)
For one, look for a high amount of employee turnover. Employees leave when stress is high.
Check LinkedIn to learn about their employees and watch for downward growth trends.
You’ll also want to pay close attention to the discovery call.
If it’s all about them and nothing about you, that’s a sign they’re not taking the time to understand your business.
An agency that “yeses” you to death without adding ideas or offering pushback is another red flag.
They’re likely more focused on producing work as fast as possible than on providing a sustainable strategy.
Pro tip: Ask for a sample strategic recommendation on the call. Something lightweight like: “How would you improve our blog content?” The right agency will share high-level insights — not just a sales script.
And it’s never a good sign if they get defensive when you ask questions.
This can be an indicator that they’re not willing to invest time in the relationship.
I once hired an agency to help run paid social ads, and they did the absolute bare minimum. I had to point this out to get any attention, and by then, our three-month trial engagement was practically over, and we saw no results. While I don’t know for a fact it’s because we were on the lower end of their engagement value, it seems likely.
Looking at recent testimonials or mentions of the agency can help.
But sometimes, asking pointed questions is the best way to get an answer.
For example:
What’s your typical engagement type?
How long are your typical engagements?
How many clients does your team normally work with at once?
By asking these questions, you’ll get a better sense of the agency’s bandwidth.
2. Their Offerings Haven’t Evolved (or Have Evolved Too Much)
It’s no secret that marketing has evolved over the past few years.
And AI has only accelerated those changes.
So, if an agency hasn’t evolved its strategy to match the industry, it’s a sign they’re coasting on an outdated approach.
Want to find this out before the discovery call?
First, check the age of their case studies. Older case studies indicate a strategy that hasn’t changed.
Next, look at the wording on their services page.
If it sounds generic or dated, that’s a red flag.
In the example below, wording like “Taking over Google” is no longer fully relevant.
Plus, there’s no mention of local search or AI results.
(Which is odd, since they target local businesses.)
Pro tip: Trend chasing is another huge red flag. If you see a digital marketing agency that’s majorly pivoted without the data or case studies to back up those decisions, then you may want to steer clear.
Make sure they’re thinking ahead — not clinging to old playbooks — by asking:
How have your offerings changed in the past year?
How has your process changed since AI came on the scene?
How much does your team use AI when creating deliverables?
What’s your perspective on marketing in the AI era?
But you don’t want to get stuck in a relationship that’s not working.
Shorter contracts may not have an out clause. But if you’re getting ready to sign a contract for a year or more, and there’s no way out of that relationship, that could be a red flag.
For longer contracts, a 30-day out clause is typical. That means you both can leave the contract if things aren’t working out.
If you ask for this clause and the agency is pushing back hard, that’s a warning sign.
Amanda agrees:
No failsafe means the agency knows retention is a problem. And they may be more focused on cash flow than results.
Again, communicating clearly is important here.
When in doubt, ask the digital marketing agency these questions:
How have you handled failed campaigns in the past? Did you course-correct mid-campaign, or offer free revisions?
What barriers to success do you see with our engagement?
What’s your policy for a 30-day out in the contract?
4. Communication Isn’t Clear or Easy
The way your agency communicates during the discovery phase is a key indicator of how they’ll communicate once that contract is signed.
Here are some key warning signs you could see early in the process:
You have to chase them for updates or next steps: If getting in contact with the agency is hard before you sign the contract, don’t expect it to improve later on.
You can’t get clear answers to your questions: Asking about timeline, resources, and processes is normal. If they can’t give you straight answers to basic questions, beware.
You have no idea who you’ll be working with: It’s typical to talk to a salesperson or account manager in the early stages. But if you get pushback when asking to speak to the people you’ll be working with, that’s a red flag.
Chelsea Castle, head of brand and content at Close, experienced this firsthand.
Here’s her agency horror story:
One of my biggest career mistakes was not speaking up sooner and louder about yellow flags with an agency. From the initial meeting, something felt off in our communication. There were bumps and issues throughout the entire nine-month engagement. We didn’t love the output, and they weren’t doing things we suspected they should be doing.
Collaboration and communication were messy. We ended up firing this agency and losing the five figures spent on them, which left us with no completed work. Talk about a challenging conversation with your CEO!
To know more about communication before signing the contract, ask questions like:
Who’s my main point of contact with your agency?
Who’s going to be working on the project with me?
Who will be included in the check-in meetings?
At what points in the process do you track metrics to assess if we’re on the right track?
5. They Promise More Than They Can Reasonably Deliver
Overselling can lead to disaster down the road. But, how do you know if an agency is selling something they can’t deliver?
First, look at the language they use to describe their services or results.
If they make exaggerated claims or promises, it’s worth pausing.
For example, this agency’s website has red flags written all over it:
(I wish this were a made-up website, but it’s not.)
Claims like this sound great, but it’s important to take a step back and look at the facts.
Can they actually back up their claims with real examples?
Can they reasonably guarantee results without knowing anything about the potential client?
Danni Roseman, a brand manager at a SaaS company, hired an agency that promised the world but didn’t live up to expectations.
I assumed a team would handle our project. We later found out that only one person had the expertise we needed. It wasn’t enough. Deadlines slipped, quality dropped, and “edits” turned into full rewrites on our end. Hand-holding your agency isn’t part of the deal.
An agency that’s focused on revenue may sell more than the team is capable of doing, and you’re left with the aftermath.
Another side to this is whether the team has experience using or integrating with your tech stack.
Eric once worked with an email marketing agency that promised big things.
But ended up having no experience integrating with Microsoft Teams (a must-have for his company).
They decided to lead a procurement process for us to find a tool that integrated with Teams. This turned into a massively bloated project, when, really, they should’ve just told me from the get-go that they had no experience with this tool.
So, how do you make sure that what the sales team is offering can actually be delivered down the road?
First, ask pointed questions like:
Who on your team has experience working with the tools in our tech stack?
How much experience does your team have with these tools?
How many years of experience does the team have in this type of project?
What’s the project (within the type of service you’re looking for) that you enjoyed working on the most?
Can you give me some names of people I can talk to about your work?
Lastly, get references.
The sales team is going to say everything right. You need something solid to back up those claims.
Most agency websites say some version of “We do X for Y.” But can they explain how?
This is something you can check for on their website.
For example, what do their case studies look like? Are they just screenshots, or do they explain the process behind the work?
Here’s an example:
What looks impressive at first glance melts away when you realize these are just screenshots.
No discussion of the work, no explanation.
Here are some other warning signs to look out for:
Their process isn’t up for discussion: If an agency tells you anything along the lines of, “Trust us, we’ll handle it,” beware
They’re using the same templated strategies for every client: On the discovery call, are they bringing ideas to the table? Do they take your unique situation into account?
Their reporting is focused on big-number vanity metrics: Case studies with numbers are great. But do those numbers tell you a story of real impact?
They can’t explain why something worked: This could mean the team has little understanding of the mechanics behind the results
If you’re not sure about their process, ask questions like:
How do you approach new engagements?
How much time do you spend determining strategy?
How is the strategy adjusted as time goes on?
How often will we meet for check-ins?
Can you tell me about a project you worked on (in this vertical/type) that didn’t go well? How did your team handle that situation?
When you’re evaluating an agency, Chelsea’s advice rings true:
Ultimately, I think the biggest flag cannot be said; it can only be felt. Intuition and how you connect with someone are crucial in selecting and building long-lasting external relationships.
6 Green Flags You’ve Found a High-Performing Marketing Agency
Despite the horror stories we’ve discussed, great agencies do exist.
Here are the most common green flags — and tips for choosing a digital marketing agency that will actually deliver on its promises.
1. They Start with Questions, Not Tactics
The right agency feels like a partner.
They’re curious about your business and invested in your success.
On the discovery call, look for all of these green flags:
They start by asking deep questions about your business model, ICP, positioning, and goals
They’re comfortable pushing back respectfully if a strategy doesn’t align with best practices
They focus on how their work ties to your business outcomes, not vanity metrics
For example, KlientBoost, a PPC agency, doesn’t just offer standard strategy packages.
They ask questions about what the client needs, their goals, and their situation.
This information lets them tailor quotes to each client’s needs.
2. You Get Good Feedback From Third Parties
Good feedback, testimonials, and reviews are always a green flag.
First, check vetted, third-party review sites like Clutch.
Look for reviews that mention:
Quality of the digital marketing agency’s work
Communication style
Costs
Timing
Some reviews even include specific numbers and results.
Another way to get feedback is to ask your network.
Ask around in your favorite Slack communities and check on Reddit or LinkedIn.
You’ll learn who’s worked with this agency and what their impressions are.
Chelsea swears by using your network to find good agencies.
The best hires for me have almost always come through network referrals. When a trusted friend or colleague makes a recommendation, they’re risking their reputation to vouch for them. So you can be confident they’re worth your time.
What should you do if you don’t have any network recommendations?
Check out industry award winners, says Chelsea:
When I needed to hire a web design agency, I looked at Webflow’s Webby winners. While many great agencies don’t get awards like this, it was a sure bet to start my search by looking at those recognized in this credible, trustworthy way. I ended up finding a fantastic partner who was great to work with.
Within awards like Webby, you’ll find some incredible projects (and the agencies that made them happen).
3. The Full Team Will Be Involved in Communication
Knowing who’s involved in your project can help you have more confidence in the work being done.
Plus, if it’s easy to talk to the team before the project gets started, it’s a good sign that communication will be top-notch after the contract is signed as well.
Ask early on who will be on calls with your team.
If you find out it’s more than just one account manager, that means multiple people are invested in your engagement.
For example, check out this about page from content agency Beam:
You see the founders of this team.
But you also see the content producers and their social profiles. This level of transparency is a green flag.
4. They’re Transparent About Scope, Pricing, Timing, and How Work Gets Done
Your agency should be very clear about vital details upfront.
This includes:
The scope of the projects they do
Timing they can commit to
Any processes they use
For example, KlientBoost creates marketing plans for clients.
But even before you give them any information or sign up for a call, they show you a sneak peek of what a marketing plan looks like for their clients.
Another aspect of transparency is pricing.
Knowing what you’ll pay (and exactly what that cost includes) is essential to the project’s success.
That’s why some agencies, like A2Media, show their pricing right on their homepage:
Of course, not every agency lists its pricing publicly.
And there are plenty of different pricing structures, each with its pros and cons.
When talking about rates, ask the agency why they take the approach they do.
Get estimates for what each type of project entails.
If you’re comfortable with those ranges and estimates, include those in the contract.
When you can get clear answers to these questions, it’s a good sign they’ll live up to their promises.
When you find an agency you like, check out their marketing.
Most of the time, it’s a good indicator of the quality of their work.
In the past year, I’ve had two fantastic experiences with marketing agencies.
And both of them had one key aspect that was a huge green flag for me: their brand marketing was on point.
Take A2Media, for example.
The founder, Ademola, regularly produces video content on LinkedIn that generates strong engagement with his niche audience.
Another example is Beam.
They offer great content services to clients.
But they also produce fantastic content on their own website that’s both interesting and fun to read.
This pattern repeats itself over and over again.
KlientBoost’s LinkedIn video ads aren’t only hilarious but also deeply relatable.
Juice, a brand and web agency, has an incredibly stylish and fun website.
If they do great work for themselves, it’s a positive sign they’ll do great work for you.
6. Your Personalities Match
Yes, personality is subjective. And judging a marketing agency on “vibes” might sound a bit woo-woo.
But remember, this is a relationship. Hopefully, a long-term one.
So, the right agency should also match your style and get your vision.
Here are some green flags when it comes to personality match:
Their team seems genuinely excited about your product and mission
They treat your team members with respect, regardless of title
Their company culture aligns with yours
You enjoy working with them
They make collaboration energizing, not draining
Chelsea saw a personality match early on with a video agency, which gave her the confidence to move forward.
From the very first call, it just felt right. The agency owner and I instantly clicked and saw eye to eye on many things. He asked thoughtful, intentional questions that signaled respect, expertise, and a desire to find the best way to work together that prioritized me and my team. We’ve been working with this partner for more than a year, and have every intention of holding onto them for as long as we can.
Bonus: They Have Proven Expertise in Your Vertical
We’ve covered the most vital factors to evaluate when choosing a marketing agency partner.
But niche experience is worth considering, too.
While it’s not a necessity, it can be a really great bonus when combined with what we’ve discussed above.
For example, this agency focuses on dental practices:
While this agency focuses on marketing for law firms:
From just those two websites, it’s clear that their approach, strategy, and personality are very different.
And they’re each uniquely qualified to help clients in their chosen industry.
Other agencies may not have experience in your specific vertical. But they can demonstrate proven experience in the services you need.
For example, let’s say you want an agency that can help you show up in AI responses.
Then, you come across a case study like this:
Obviously, this agency has adapted its services to include AI search.
And has proven expertise in exactly what you need.
Ready to Choose a Digital Marketing Agency? Trust the Patterns (and Your Gut)
Choosing the right marketing agency comes down to spotting patterns.
Red flags: Overpromising, poor communication, and teams that won’t invest time in your success
Green flags: Thoughtful questions, killer third-party reviews, and teams that practice what they preach
But don’t forget the value of your gut reaction.
If something feels off during discovery, it won’t magically disappear once the contract is signed.
The best agency relationships start with a genuine connection.
As Chelsea says, “In any kind of creative work, sometimes you really do just have to go off vibes.”
When you find a team that gets your vision, respects your goals, and makes collaboration energizing, that’s your signal to move forward.
Understanding what’s happening in SEO will help you ask better questions. And spot whether agencies are using outdated tactics or staying ahead of the curve.
Marketers are making bold statements about AI SEO every day.
The problem?
Most of them are half-right at best.
“SEO is dead.”
“Long-form content is pointless.”
“AI SEO is just good SEO.”
Here’s the truth:
When it comes to AI, the answer is rarely that simple.
Are you trying to show up in ChatGPT or Google’s AI Overviews?
Do you want the AI to recommend your brand or cite your content?
Is the model pulling from training data or live web results?
Each of those questions has a different approach.
Trying to generalize only causes confusion.
So, let’s skip the hype and get specific.
This guide tests today’s biggest AI myths in SEO to uncover what’s true, what’s false, what’s complicated, and what all of it really means for your marketing strategy.
Semantic HTML (clean heading hierarchy, proper use of <p> and <section>)
Schema markup
Side note:Google has confirmed that schema markup can help with AI visibility in its own products. It’s not a guarantee, but it’s smart technical hygiene. And it’s likely to become even more important as AI evolves.
That means your ranking foundation still matters, but it’s no longer enough.
Off-site credibility: Brand associations built through mentions, citations, and expert recognition
Takeaway: SEO fundamentals get you indexed. Off-site authority gets you cited. AI SEO is about expanding what “optimization” means beyond your own site.
3. True or False: All AI SEO Works the Same
False.
Marketers talk about “showing up in AI answers” like it’s one game.
It’s not.
Google dominates the search landscape so much that traditional SEO is pretty unified — one platform, one algorithm, one analytics dashboard.
But there’s no single kind of AI visibility and no single playbook for earning it.
What’s Actually Happening
Every AI platform behaves slightly differently.
They draw from unique data pipelines, weigh off-site signals differently, and credit sources in their own ways.
For example, Google’s AI tools still echo its ranking system.
Originality.AI found that many Google AI Overviews come from the top 10 ranking pages.
But for brand mentions (answers that refer to your company), ranking seems to have more of an impact on ChatGPT.
Brands that rank on page one of Google show up more often in ChatGPT answers. Seer Interactive found a 0.65 correlation between high rankings and brand mentions.
In other words, if HubSpot ranks on page one for “CRM software,” ChatGPT is more likely to name it when users ask for the best CRMs.
Takeaway: Each platform plays by slightly different rules. Treat AI SEO like an ecosystem, not a checklist.
4. True or False: If You’re Cited by AI, You’ll Also Get Mentioned
Mostly false.
Mentions and citations aren’t the same thing — and one doesn’t guarantee the other.
Mentions = when your brand appears in the answer
Citations = when your content is trusted as a source
You need both to stay visible long term.
What’s Actually Happening
If you had to choose, being mentioned matters more in the short term.
When someone asks ChatGPT for “the best CRM for small businesses,” you want your brand to show up, even without a link.
But long-term visibility compounds when you’re both seen and trusted.
Brands that are both mentioned and cited appear 40% more often in repeat AI searches, AirOps found.
And that’s harder than you might think.
According to Semrush’s AI Visibility Index, fewer than 1 in 10 brands appear in AI answers as both mentioned and cited.
Most only get one: they’re either mentioned without a link or cited without being named.
For instance, if I look up “What’s the best HR software for small businesses?” I get the following response from ChatGPT:
Of all the responses, only Rippling was mentioned as a good choice of software and cited as a source.
Getting mentioned and cited consistently means playing a longer, smarter game.
To win both, you need to shape the way AI systems talk about your brand.
Earn mentions through off-site authority — PR, reviews, credible partnerships — and citations through trustworthy, reference-worthy content.
Takeaway: Mentions get you visibility. Citations earn you trust. You need both to last.
5. True or False: AI Engines Don’t Care About E-E-A-T
It’s complicated.
AI engines tend to cite pages that look trustworthy: clear sourcing, visible citations, and credible domains.
When AI engines use query fan-out, they break one question into many.
If a short page or definition answers a single sub-question directly, it might get pulled into that specific part of an AI answer.
Still, those are situational wins, not a replacement for authority.
And there’s more nuance here:
The Muck Rack study found that when questions got subjective — like asking for advice or step-by-step guidance — AI models pulled more from corporate blogs than authoritative news sources.
But, whether the LLMs are looking at official news sites, corporate blogs, or community sources, they consistently preferred credible content.
Credibility takes different forms. But AI systems pull from sources people trust most, whether institutional or experiential.
Clarity and organization make you easier to cite, but credibility will keep you there.
Plus, E-E-A-T keeps your content people-friendly as well as AI-friendly.
Takeaway: E-E-A-T still matters. It just needs to be paired with structured, clearly scoped content that AI systems can read and reuse.
6. True or False: Content Recency Matters Even More for AI Visibility
Mostly true.
Keeping content up to date has always been best-practice SEO.
And it’s also important for AI visibility on most of the public platforms.
But the relationship between freshness and visibility isn’t one-size-fits-all.
What’s Actually Happening
Seer Interactive found that nearly 65% of AI bot visits go to content published in the last 12 months.
I checked this out for myself using ChatGPT. I asked the query:
How do I create an AI-optimized content strategy?
Then, I asked:
Can you show me the sources you used for that answer?
And it returned:
The earliest resource was from 2023.
(It didn’t find a date for the Airtable and RevvGrowth articles because they weren’t “visible in the header.”)
Finally, I asked why it chose those sources to answer the question.
It returned:
Note: It listed recency as its top criteria.
But there’s some variation in how important recency is.
Seer Interactive found that freshness matters most in fields like finance, HR, and tax, where outdated data loses credibility fast.
In travel, the window is broader.
Evergreen guides (“best destinations for weekend city breaks”) still perform, but regular updates help maintain visibility.
And in energy, for example, relevance often beats recency. Educational, evergreen pages (“green vs. renewable energy”) continue attracting AI hits years after publication.
Even instructional content in slow-moving niches can perform long after it’s published.
Seer found AI bots still visiting decking tutorials written 10–15 years ago — proof that quality evergreen content can still hold its ground.
Takeaway: Fresh content gets more bot activity. But credible, well-maintained evergreen pages still win trust. Especially when they’re the best answer for the human behind the query.
7. True Or False: Long-Form Content Is Pointless to Create Now
False.
Many marketers are making a simple mistake:
They hear “AI prefers short answers” and conclude “AI prefers short content.”
AI is more likely to use or cite content that is structured so it’s easy to understand.
But that’s not about length. That’s about structure.
What’s Actually Happening
AI systems don’t skip long pieces.
They skip messy pieces.
Content passages with clear headings helps models scan, interpret, and extract the right snippets.
There’s nothing to say your content needs to be short.
Example: Ask ChatGPT for “the best resources to learn SEO,” and you’ll often see Backlinko mentioned.
Those guides are deep, not brief.
They’re cited because they give a complete answer in a format both humans and models can follow.
Long-form content also compounds your odds of being mentioned.
AI visibility is a probability game.
The more your content earns human discussion, the more likely it is to appear when AI answers a question.
And humans don’t rave about shallow content.
People share and reference the pieces that teach them something new: frameworks, research, comparisons, stories.
Cutting them down for AI only strips out the context that makes your brand trustworthy.
Takeaway: Long-form isn’t outdated. It’s still a way to build authority, trust, and the kind of signal both readers and AI models rely on.
8. True or False: You Should Skip the ToFu Content Now
False.
This is one of the most persistent AI myths in content marketing.
“If AI answers everything, why bother with top-of-funnel (ToFu)?”
But ToFu content still matters. It just has a new job.
In the past, you could publish a big guide like “What Is SEO?” and watch it climb the rankings.
Those broad, educational posts drove traffic because people had to click through to learn.
Now, AI Overviews and large language models answer those same questions right on the results page.
But that doesn’t mean top-of-funnel content is dead.
It just means it’s working differently.
What’s Actually Happening
ToFu content isn’t the traffic engine it once was.
But it still powers two things your marketing ecosystem depends on: awareness and authority.
ToFu Builds Awareness
ToFu content helps new audiences discover your brand, even if they don’t click.
When someone searches “What is the best time to send marketing emails?” and sees your brand name in a featured snippet or short summary, that’s still visibility.
It’s like a digital billboard.
People might not visit your site right away, but they’ll start to recognize your name the next time they see it.
The more consistently your brand shows up around key industry topics, the more familiar it feels to your future buyers.
That awareness pays off later when they’re comparing vendors or deciding who to trust.
ToFu Earns Credibility
Google and AI systems both reward depth of coverage.
They look for brands that explain an entire topic — not just their own product.
A Search Engine Land analysis of 8,000 AI citations found that AI systems repeatedly pull from in-depth, trusted sources, not surface-level articles.
If your site only has bottom-of-funnel pages like “Why Choose [Your Product],” algorithms see a narrow view.
But when you also publish foundational explainers and educational content, it shows that your brand understands the full landscape.
That matters for AI visibility too.
Takeaway: ToFU content strengthens your overall site signals. Even if ToFu posts don’t drive conversions, they reinforce your brand’s expertise across the funnel.
9. True Or False: You Should Publish 10x More Content with AI
False.
In theory, more content should mean more visibility.
In practice, that’s not what’s happening.
Teams feel pressure to publish faster because AI makes production easier.
But volume isn’t the same as reach.
Most scaled AI content dies in search before it ever earns authority.
AI platforms seem to be taking the same approach. They reward original insight and authority, not sheer output.
Takeaway: If you want visibility in both Google and AI search, slow down and build credibility.
10. True or False: High-Quality Content Is All You Need to Appear in LLMs
It’s more complicated than that.
Many marketers assume that if they simply create great content, AI tools like ChatGPT, Perplexity, or Gemini will automatically surface it.
But “great” isn’t enough.
High-quality content is a requirement. It’s what gets your pages seen, crawled, and trusted in the first place.
But visibility in AI search depends on something bigger: how consistently your brand is referenced and recognized across the web.
What’s Actually Happening
LLMs generate responses using two data sources:
Training data: The static dataset the model was trained on months (or years) ago
The live web: Real-time crawling and retrieval from indexed pages, like Google AI Overviews or Perplexity
Each system rewards a different kind of visibility, and each treats “quality” in its own way.
Training-data systems reward brand association.
When a model relies on its training data, it draws on patterns it has already learned.
That includes which brands are consistently associated with which topics.
If your brand’s name and theme appear together across thousands of credible pages, that association becomes part of the model’s long-term memory.
For example, Canva is strongly associated with “simple design.” So, if you ask ChatGPT “What is the simplest design program?” it’s probably going to answer Canva.
That’s how brands build “semantic ownership” of an idea.
Over time, those associations become the model’s defaults, a durable moat that competitors can’t easily displace.
Quality still matters here.
It determines whether people read, share, and cite your work — the human behaviors that create the signals AI later learns from.
Meanwhile, web-indexed systems reward structure and authority.
When an AI system relies on live web data, the process looks more like search.
Models retrieve pages in real time, parse structure, and extract concise, factual snippets.
In this environment, “quality” means clarity, structure, and credibility.
For example, if someone asks an AI tool “best CRM software for small business,” the model pulls from pages that look like strong search results.
In this case, that would probably be list posts with clear headings, comparison tables, and trustworthy sources.
A messy blog without structure or citations wouldn’t make the cut.
Takeaway: High-quality content is your ticket in, not your winning hand. Authority, structure, relevancy, and consistent brand signals are what actually get you cited in LLM answers.
How to Level Up Your SEO Strategy for AI Visibility
You’ve seen the myths. You understand the reality.
Now, here’s what to actually do about it.
The good news? You don’t need to blow up your entire SEO strategy.
Most of what you’re already doing still works.
You just need to expand where you’re looking and what you’re measuring.
Start Measuring What You Can’t See
Your analytics are lying to you by omission.
When someone discovers your brand through ChatGPT and visits you three days later, it shows up as direct traffic or a branded search. Zero attribution to the AI mention that started the journey.
So you’ll need to:
Track the indirect signals.
Rising branded searches while organic clicks decline? That could be LLM discovery.
Direct traffic holding steady despite fewer Google clicks? Same thing.
Sales calls where prospects say “found you through AI”? You’re getting cited.
Use dedicated AI tracking tools.
Options include Peek.ai and ZipTie.Dev. For more comprehensive features, Semrush Enterprise AIO is a good option, especially if you need full-funnel visibility and advanced reporting.
