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.
In the good ol’ days of blogging, traffic was the main goal, and it was relatively easy to get.
Now, especially for ecommerce blogs, it’s getting harder to stay visible.
The number of Google searches that end with a click is slowly decreasing, while the number of searches that end with no clicks has increased.
While the number changes are small, they’re continuing to move in the direction of no-click searches. AI Overviews give people the answers they need at a glance, and website traffic is taking a toll as a result.
Aside from these trends in Google search, ecommerce blogs also face an uphill battle against big players like Amazon or Walmart.
With all of this in mind, you might be wondering: is it still worth the effort to build an ecommerce blog?
Here’s a real world example that shows why it still matters:
Pet care brand Petlibro has been around since 2020, but they didn’t start posting on their blog until 2022. Semrush’s Domain Overview suggests their organic growth has been pretty substantial since then.
Their website is ranking organically for over 25,000 keywords and stands in the first result for almost 1,500 of those.
And not only that: Petlibro is being mentioned and cited by AI search engines — more than 700 times.
AI search references Petlibro’s blog articles and mentions the brand directly in its response.
Their blog isn’t a separate entity to their ecommerce site. It’s a strategic tool that helps their brand get seen both in Google and in AI search — and get more conversions in the process.
Here’s the point: blogging is still valuable, especially for ecommerce brands, even in the era of AI search.
The difference between today and ten years ago is that the main goal isn’t traffic: it’s delivering clear, distinctive value for the reader.
Basically, you need to build something that AI can’t.
We’re going to dive deeper into ecommerce blog examples that are currently seeing big results and show you how to apply their strategies to your own brand.
What Makes an Ecommerce Blog Successful?
The more you study top ecommerce blogs, the more patterns start to emerge.
Before we explore each of the following examples in depth, keep an eye out for these key aspects of successful ecommerce blogs:
They know exactly who they’re talking to: All the top ecommerce blog examples we’ll discuss have a very clear target audience. And the content speaks directly to those people.
They understand intent: People search for certain terms just to gain information. Others search to learn about products, and others search because they’re ready to buy. The best ecommerce blogs know the difference between those different search intents. Then, they can create content that matches the intent of the search.
They present information in a way that’s easy to read and understand: There’s no specific format that guarantees success. But each example uses blog design essentials to make the information understandable. Their content also includes strong introductions and content that’s unique and interesting.
They integrate their store directly with their blog: The most successful ecommerce blogs are focused on conversions over traffic, and use smart integrations to showcase their products on the blog.
They prepare content to do well in the age of AI search: These blogs show up consistently in AI search by producing the kind of material AI loves to reference and mention. You’ll see how they create content that’s well structured, authoritative, and unique.
Now let’s see seven ecommerce blogs that exemplify these principles.
The goal of any ecommerce blog is to do more than just build traffic. You also want to build authority, win visibility in both Google and AI search, and nudge readers closer to buying.
The following examples cover a range of categories and company sizes. While they may not all have tens of thousands of visits per month, they’re all using their blog as a conversion tool and a way to get seen both in Google and in AI search.
And they all have something to teach you about staying visible, memorable, and findable as an ecommerce blog.
Note: We got the numbers for each of these from Semrush’s SEO Toolkit. Traffic numbers aren’t going to be 100% accurate (only the brands themselves will have the most up-to-date numbers). But it’s still useful for understanding broad trends.
1. Garmin
Industry: Consumer electronics
Organic blog traffic: 61.8K
Backlinks: 77.7K
Keywords: 46.1K
In the world of smartwatches and specialty sports gear, Garmin truly stands out. Their blog has grown consistently since mid 2022.
So, what makes this ecom blog stand out?
First off, the articles are a healthy mix of informational and commercial content.
For example, this article on finding your V02 max ranks for 4.6k keywords, and ranks #1 for 95 of those. It even shows up in the AI overview for a couple of difficult keywords.
The article is a deep-dive into a complex topic their audience is interested in. And while someone searching “good v02 max” may not be immediately interested in buying a watch, Garmin still includes plenty of ways to explore their products from this blog post.
For instance, readers can see CTAs to some of their most relevant watches in the sidebar, and they also see links to product categories in the text.
But Garmin also knows how to focus their blog on buying intent, which is why they also rank for terms like “Garmin aviation watch.”
From this single keyword, Garmin’s article on aviation watches gets 3.7k monthly organic traffic by ranking for 63 keywords. (I guess pilots really like their watches.)
But more than just creating content for search, Garmin has cracked the code on creating content that gets mentioned by AI.
Just look at Garmin’s incredible AI visibility score, with over 52k mentions:
AI search loves to highlight product information directly from the brand. Which is why Garmin’s clear, detailed support documentation appears so often in AI search results.
But their blog posts are also cited by AI to respond to product-related questions, like which smartwatch has the best battery life.
Something else that Garmin has done well is combine their content efforts on their owned channels with mentions across the web. Whether it’s tech review sites, YouTube videos, fitness blogs, or Google reviews, Garmin’s products are mentioned positively in a lot of places.
The result?
Semrush’s AI Visibility Index found that Garmin ranked #4 in AI Share of Voice for consumer electronics brands. They sit right at the top with heavy hitters like Apple and Google.
Key Lessons from Garmin’s Blog
Garmin is a multi-billion dollar company, well-known in its space. But importantly, they dominate their category. When you own a category (like smartwatches), it’s much easier for AI to surface your content and products to users.
Another company doing this is Patagonia. They dominate the category of ethical fashion, and have gained 21.96% of the AI Share of Voice (for Fashion & Apparel).
Another lesson from Garmin’s blog is the importance of providing clear information about your products.
AI search results tend to cite brands as authorities on their own products. But if you don’t answer the questions searchers have about your products? AI will usually attempt to base its answers on someone else’s article (whether that information is correct or not).
Finally, remember that your blog isn’t a solo marketing effort. When you partner with content creators outside your owned channels, you can expand your visibility in AI.
The more positive mentions your brand gets, the more likely you are to see yourself in AI answers and overviews.
We’ve already introduced you to Petlibro above: showing the power of blogging for ecommerce brands. Not only do they show up in search results, Petlibro’s blog posts are also being cited and mentioned by AI.
Take this post for example:
This informational post answers the question of how often to change the filters in a cat fountain. It’s not too long, but it answers the question clearly and gives just the right amount of detail.
So, along with ranking for 44 different keywords, it’s also showing up inside the answers given by ChatGPT and other AI search tools.
Another post, explaining why cats bring you toys, ranks in the top 10 for 14 keywords, and appears in the AI overview in Google.
But Petlibro doesn’t just post informational articles. They do a great job of striking the balance of intent, focusing on content that matches what the searcher is looking for.
For example, this blog article about choosing the perfect cat tree gets more than 500 visits per month and ranks for 127 keywords. Best of all, most of these keywords have commercial or transactional intent.
Key Lessons from Petlibro’s Blog
First off, Petlibro shows it’s important to develop a healthy mix of informational and transactional content.
Going after keywords at the top of the funnel works to build your authority. But content that helps point people to the right products when they’re already in the mood to buy brings more immediate results.
Next, for your brand to be visible in both Google and AI, you need to answer the questions people are asking. You can start by doing research on forums, but also try tools like Semrush’s AI SEO toolkit for prompt research.
This can give you an idea of the prompts people are using in AI platforms, and which websites AI is currently referencing or mentioning directly.
For example, let’s try searching for “home security camera systems.”
In the Prompt Research report, you can see AI volume for that topic, how difficult it is to gain visibility, the intent of the questions in this topic, and more details about the prompts used and the brands mentioned.
This gives you a great starting point to see what people are asking about within your topic. Then, you can create content that answers those questions.
3. Great Jones Goods
Industry: Cookware
Organic blog traffic: 11.6K
Backlinks: 1.7K
Keywords: 4.9K
Great Jones Goods’ blog stands out with fantastic visuals and content that is tailored to their audience.
Honestly, just looking at this blog is making me want to get into the kitchen and bake something.
Their blog has two main sections: recipes and personal profiles.
You gotta love these recipe posts. Just take this one for arroz con gandules:
Each recipe has a different author. So each post has a very personal feel.
It’s just like your favorite recipe blog, but without so many layers of fluff.
The posts also mention the cookware the author used (subtly highlighting their own products).
And each recipe is also accompanied by beautiful step-by-step visuals.
This all looks great: but what about the results?
Great Jones Goods isn’t getting millions in traffic. But their content does show up in all the right places.
For example, their profiles of chefs and well-known people rank in search results:
And their recipe posts also show up in AI overviews:
Their blog is consistent and targeted at their specific audience. Instead of being “sales-y,” they focus on being part of the community that they want to sell to.
Key Lessons from Great Jones Goods’ Blog
Beautiful, descriptive visuals are a key component of high-quality blog content. Plus, it’s a great way to make your blog stand out as different. When you’re creating content for your blog, ask yourself: how can I create something that AI can’t?
Great Jones does this by including step-by-step imagery and real-world examples of their products in use. That’s something shoppers love to see, and AI can’t replicate.
Another key takeaway from this ecommerce blog example is to include your community in your content. Great Jones does this with in-depth personal profiles that talk about the joy of cooking — something their target audience shares.
People crave connection with other humans, now more than ever. You can use your blog to become part of that community.
Try including people that the community already knows and loves. This will help your blog be more personal, as well as give you new ways to promote your blog.
When your brand is dedicated to a mission, you can use your blog to promote and grow that mission. And that’s exactly what the period underwear brand Thinx has done with their “Periodical” section.
First, they chose an incredibly appropriate name for their blog. Next, they filled it with articles all about menstrual health for women and teens.
The articles are generally on the short side, but answer key questions their audience is asking. And with that, they’re able to rank for difficult keywords like “when do you ovulate,” “period blood clots,” or “period nausea.”
Just this one article on ovulation ranks for 1.3k keywords, most of which are either hard or very hard to rank for per Semrush data.
They also build educational resources around the message: Get BodyWise.
Thinx takes body literacy seriously. In fact, they have a dedicated resource page aside from their blog that is built to provide candid, accessible information for people who bleed.
This even includes a series of educational videos from Dr. Saru Bala on women’s health.
Everything they do on the blog supports their mission to make period products and education more accessible to everyone who needs it.
And while their content doesn’t heavily promote their products (possibly on purpose), they do list a handful of relevant products at the end of each blog post. Just the right mix of promotional and educational.
Key Lessons from Thinx Periodical Blog
Your company mission statement isn’t just something that lives quietly on your About page.
It should be a living, breathing part of your business ethos.
It should come through in your marketing.
When your blog has a core mission behind it, the content you create has a clear direction. You’re not just chasing keywords: you’re building educational resources that truly benefit your audience.
The result?
Thinx builds brand affinity naturally over time, increasing the chances that folks will choose Thinx over a competitor when they’re ready to buy.
5. King Arthur Baking
Industry: Cooking ingredients
Organic blog traffic: 730K
Backlinks: 133K
Keywords: 338K
King Arthur Baking’s blog ranks in the top 10 for some of the most difficult keywords in baking. That includes terms like “baguette,” “pizza,” or “types of cinnamon.”
So, how did they get here?
King Arthur Baking didn’t limit themselves to written content. They created a content ecosystem that also included multimedia content.
Currently, the King Arthur YouTube channel has over 330K subscribers. They post recipes, along with video versions of their podcast episodes.
These videos work seamlessly inside their blog posts.
For example, check out their blog post on chocolate chip cookies.
The video from their YouTube video is part of the image gallery at the top.
But it’s also spliced together with the step-by-step recipe instructions below.
Doing this increases their chances of ranking for difficult keywords. And in some cases, they even rank more than once in the search results.
Key Lessons from King Arthur’s Bakery Blog
Google and AI won’t rank what they can’t understand, so giving clear structure and formatting to your blog is an essential first step to rank better.
For example, King Arthur uses schema markup for their recipes. This helps them rank in rich results on Google.
Another lesson from King Arthur is using multimedia when it makes sense. Try creating videos that show your products in action, or clearly answer a question that your audience is asking. These can help you increase time on page and appear in more search results.
Finally, know when to push your products. King Arthur does a great job of subtly adding their products to content.
For example, their blog posts include “featured products,” a CTA to “Shop this recipe,” and “Recommended for you” products at the end of each post.
6. Keychron
Industry: Electronics
Organic blog traffic: 62.1K
Backlinks: 7.1K
Keywords: 25.8K
For a seriously niche blog and product, Keychron has a pretty hefty presence online. Their blog has had steady traffic growth since around 2020. And they rank for all kinds of keywords about keyboards.
For example, this article about hall effect switches gets over 1,700 visits per month.
The post ranks #1 for that main keyword. But it also appears in search results, AI overviews, and image packs for 137 other keywords.
Their blog posts do a great job of using visuals to explain topics about the tech. And they get to gently promote their own products when appropriate.
Of course, this kind of top-of-the-funnel content is likely to drive less traffic as more people rely on AI Overviews and other AI tools for quick answers to their questions.
But it can still drive some traffic. And careful linking and CTA placement can turn that traffic into conversions.
Key Lessons from Keychron’s Blog
One key takeaway from Keychron’s blog?
Don’t be afraid to go niche.
Your audience may have very deep knowledge of a topic (like keyboards), or they may be generalists looking for an overall view of the topic. It’s up to you to know who your audience is, and develop content for them.
Topics like “Best Keyboards for World of Warcraft” may seem niche, but it fits Keychron’s highly specific audience (and does a great job of showcasing their products).
The root domain didn’t take as much of a hit. But the blog experienced a spike and a sudden drop around early 2021.
Thankfully, Huckberry didn’t let that stop them.
They still had another card up their sleeve: their YouTube channel.
While the channel was created back in 2016, there was no consistency, and hardly any views.
But sometime after traffic dipped on the blog, we see a change in the posting pattern on YouTube. Suddenly, they’re posting consistently.
They share video series, interviews, and more (some of which get hundreds of thousands of views).
And over time, Huckberry became the go-to place for adventure content for men.
They started sharing videos about culinary travel and adventure stories with members of the community. Plus, they posted gear reviews that linked back to their products.
That multimedia strategy helped Huckberry’s blog gain consistent growth again. Plus, their YouTube channel took off — today, it boasts over 375K subscribers.
That video strategy made them adapt the way they present content on their blog as well.
Many posts include videos with gear reviews and style help. The videos are funny, personable, and mention the brand’s products without sounding like a sales pitch — it really sounds like two friends shooting the breeze.
The posts themselves also do a beautiful job of incorporating products:
Almost all their posts follow classic blog post templates, but maintain the vibe of a cool online magazine.
Key Lessons from Huckberry’s Blog
Huckberry’s key lesson is this: don’t give up after a traffic dip.
Blog traffic can dip for many different reasons, but it doesn’t mean your blog is a lost cause. When you see a dip, dig into the data.
Have you lost ranking on major keywords? Are clicks down? Run through a basic SEO checklist to make sure you’ve got your bases covered.
Then, go back to the question we’ve talked about before: What can you create that AI can’t replicate? Define how your blog is differentiated from what AI answers can deliver, and what value you can bring to your audience.
Your Ecommerce Blog Can Succeed — If You Trust the Process
You can’t build a successful ecommerce blog overnight. But the brands above prove it’s worth the effort.
When you do it right, your blog becomes more than a traffic source. It’s a growth engine that boosts visibility, builds trust, and strengthens your brand in both Google and AI search.
Keep answering your customers’ questions, stay focused on your niche, and build consistency over time.
But remember: your blog is just one piece of your overall strategy.
To go deeper into building a comprehensive marketing strategy for your ecommerce brand, check out our full ecommerce marketing guide.
