Managing local search marketing for one location is straightforward.
But managing multi-location SEO — whether it’s 10, 50, or 100 branches — gets complicated fast.
Each location needs unique content.
A single mistake in your business info can mislead customers and hurt trust.
And it’s tough to see which branches are actually driving results.
Everything changes when you’re managing SEO for multiple locations.
Our six-step system below tackles these challenges in order of priority.
You’ll learn exactly how to:
Create high-performing location pages
Optimize Google Business Profiles (GBPs) across every branch
Manage reviews, citations, and backlinks efficiently
Track performance by location to see what’s really working
Plus, you’ll get our free toolkit to help you build a scalable SEO strategy for multiple locations.
Let’s dive in.
Step 1. Create Location Landing Pages
Every branch needs its own home online.
Without a dedicated location landing page, your GBP has nowhere reliable to link. And customers looking for local hours, directions, or services may bounce straight to a competitor.
So, start by confirming the basics.
Talk with branch managers or franchise owners to verify core business details — official name, address, phone number, operating hours, and available services.
Copy our location details sheet and use it to gather and confirm accurate data for every branch.
Once it’s filled out, this sheet becomes your single “source of truth” — helping you prevent endless downstream errors when managing dozens of listings and citations later on.
Do Location-Focused Keyword Research
Once you’ve gathered accurate data, move into keyword targeting.
Each page should focus on one primary keyword set that combines your core service with its city or neighborhood modifier (e.g., “dentist in Austin”).
Doing this avoids keyword cannibalization between branches while signaling clear relevance for local searchers.
To scale efficiently, create a modular framework for every location page. This ensures consistency across branches while letting you customize local details.
Start with a simple, SEO-friendly URL structure.
Use subfolders (e.g., example.com/locations/austin).
Why?
They inherit more domain authority and are easier to maintain across large sites.
Each page should include these essential content blocks:
Name, address, and phone number (NAP)
An embedded map and clear driving directions
Local photos and customer reviews
A concise overview of services offered
A strong, localized call to action
Once your template is set, link to these pages internally so search engines and users can easily find them.
Add links from your main navigation or a dedicated HTML sitemap, and cross-link between related locations or service pages when relevant.
This type of modular setup helps every page stay on-brand while still serving unique, location-specific content.
Want a shortcut?
That’s where our Location Page Template comes in.
It’s a plug-and-play framework that keeps pages consistent while giving you room to localize copy, visuals, and CTAs.
Instead of rebuilding from scratch, just fill in the blanks and launch pages faster.
Publish Unique, Optimized Content
Even with templates, every location page should feel distinct and relevant to its community. Boilerplate content can hurt engagement and limit your local visibility.
So, add local flavor wherever you can — photos of the branch exterior or team, nearby landmarks, or community involvement.
These small touches make each page authentic and help prevent duplicate content issues.
But don’t just stop there.
Rotate seasonal offers, update photos, and feature new testimonials to show both search engines and customers that your locations are active and trusted.
Finally, dial in your SEO details.
Titles, headers, image alt text, and LocalBusiness schema should all include the branch’s city or neighborhood.
These signals help Google connect each page to the right local search intent.
Pro tip: Start with your highest-traffic or flagship markets first. Once those pages are performing, use the same structure and workflow and apply it to the rest.
Step 2. Build and Optimize Google Business Profiles for Every Location
Multi-location SEO starts with accuracy and consistency in your GBPs.
One wrong detail — or a suspended profile — can tank visibility for that branch. And when you’re handling dozens of listings, a small mistake can spread fast.
Next, check every listing against your master spreadsheet from Step 1.
Make sure the name, address, phone number, hours, and landing page URL all match. Even one typo can hurt rankings.
Then, add UTM tracking to your website links.
This lets you see which branches drive traffic, leads, and sales in Google Analytics (GA4) or your customer relationship management (CRM) system.
Optimize Your GBPs Completely
Verification is just the start.
If you’re doing SEO for multiple locations, it’s not a one-time job — it’s a system you have to run efficiently across every branch.
Start with categories.
One wrong choice can confuse Google, so build a shared list of approved options every branch can use.
Precision matters more than volume. So, pick one main category and a few secondary ones that match what that branch actually offers.
Not sure which categories competitors use?
Tools like GMBspy show the primary and secondary categories of top-ranking businesses in your market.
From there, focus on consistency and automation across every profile:
Standardize visuals: Give each manager a short photo checklist (e.g., storefront, interior, team, and one or two local highlights) to keep listings current.
Use a brand-approved description template: Maintain a consistent tone but personalize each listing with local details.
Keep data aligned: Hours, URLs, and phone numbers should always match your website and location pages. Even one mismatch can cause issues across your network.
Automate updates: Tools like Semrush Local or BrightLocal can push edits, track reviews, and monitor changes in bulk.
Pre-load FAQs: Seed each profile’s Q&A section with verified, brand-approved answers before customers fill in the gaps.
Pro tip: Want to make life easier? Use our GBP optimization checklist to stay consistent across every location.
Post and Update Regularly
Google rewards freshness.
Regular posts, photos, and updates show that your business is active. And they help each location stand out in Maps and the local pack.
Share short posts for promos, events, and new services. Rotate new photos or short videos every few months to keep your listings looking current.
Even small updates like adding seasonal offers or highlighting staff can make a difference in clicks and calls.
And don’t forget the Q&A section.
Add common customer questions yourself with accurate, brand-approved answers. Then, monitor it regularly so you can respond fast when new ones appear.
The hard part?
Doing this for dozens — or hundreds — of branches. Manually updating each profile is exhausting and easy to fall behind on.
Tools like Semrush Local can make it easier by letting you manage posts, photos, and info for all your locations from a single dashboard.
Step 3. Collect and Manage Reviews
Reviews drive both rankings and trust.
At scale, the challenge isn’t getting one review — it’s managing hundreds across locations every month without dropping the ball.
Automate Review Acquisition
Start by collecting customer contact info at checkout or after service.
That lets you send automated review requests by text or email through your point of sale (POS) system or CRM.
Each branch should have its own short review link or QR code so customers can find the right profile fast.
Add those links to receipts, follow-up emails, and even in-store signage. Small touches like that can boost response rates over time.
Most customers don’t ignore review requests on purpose, they just forget.
A simple reminder can make a big difference in review volume.
Centralize Review Monitoring
Tracking reviews one branch at a time wastes hours.
Set alerts for negative reviews so you can respond quickly and win back unhappy customers.
Over time, you’ll start spotting trends — like which cities get the most reviews or which teams need more support.
Standardize Responses
Consistency matters as much as speed.
Create a few brand-approved templates for positive, neutral, and negative reviews. Then, teach local staff how to personalize them with names or specific details from the customer’s experience.
Small touches like that make responses feel authentic while staying on brand.
You can also make a copy of our Review Response Templates to speed things up and keep messaging consistent.
The goal is to sound human without going off-script. That balance keeps your tone aligned across every branch while still making each customer feel heard.
List the official name, address, phone number, hours, Google Business Profile URL, and landing-page URL for every location.
Keep it updated — this one file keeps every branch aligned.
Next, make it easy to see what’s current and what’s not. Use the “Last Verified” column to track when each location’s details were last checked.
If different people manage different regions, assign ownership right in the sheet. That one small habit prevents duplicate edits and conflicting updates later on.
Automate Distribution
Once your data is solid, automation makes running multiple locations easier and saves hours of manual updates.
They also make it easy to update details like hours, phone numbers, and URLs whenever something changes.
Audit and Monitor Listings Regularly for Accuracy
Your listings won’t stay accurate forever. That’s where routine maintenance makes all the difference.
Run a quarterly NAP audit to catch inconsistencies before they snowball. Your listings tool can scan every profile and flag details that don’t match your master sheet.
Then, spot-check the platforms that matter most: GBP, Apple Maps, Yelp, and Facebook. If you’re in a specialized industry, check directories like ZocDoc or FindLaw, too.
Keep a running log of what you fix each quarter.
Over time, patterns will reveal which platforms or regions slip most often. That insight helps you tighten your process and prevent repeat issues.
Step 5. Build Local Backlinks That Actually Move the Needle
With one location, a few chamber of commerce links or directory listings can boost authority.
But when you’re managing dozens of branches, growing that process across your entire network takes more than luck. It takes systems.
Focus on Community and Local Partnerships
Local links help boost visibility and build trust.
They show that real people in each community engage with your business.
So, encourage branch managers to get involved. Sponsor events, join community groups, or collaborate with nearby businesses.
These efforts often lead to natural mentions and backlinks that show local relevance to search engines.
To streamline the process, collect ideas that work and turn them into a shared playbook.
Pro tip: Use your location landing pages as link destinations instead of the homepage. They’re more relevant to searchers in each market and can strengthen those pages’ ability to rank locally.
Systematize Outreach
Multi-location SEO relies on repeatable systems that make expansion easier.
Document what’s working so every branch can replicate it.
Use our Local Backlink Opportunity Tracker as your central database to log outreach, track live links, and measure results across all locations.
Add notes on what type of partnership or content earned each link so others can reuse the same playbook.
Centralize research at the brand level to save time. Identify sponsorship pages, community events, and local publishers that align with your audience before branches start outreach.
Over time, you’ll start to see what works best.
Certain link types, partner categories, or content formats will consistently deliver stronger results.
Use those insights to refine your playbook and make link acquisition faster, easier, and more predictable across your entire network.
Use Tools to Prioritize and Track
Link research tools come to the rescue in automating link opportunity discovery for every branch.
Start with Semrush’s Backlink Analytics to see which local websites link to your competitors. Those same sponsors, media outlets, and directories are strong prospects for your own branches.
You can also build city-specific prospect lists using searches like “our sponsors” + city name or “community partners” + city.
Try prompting AI tools like ChatGPT or Google’s AI Mode to surface local organizations, events, and publications worth contacting.
Review your data regularly to see which branches or regions are earning coverage and which need extra support.
If some locations have fewer opportunities, that’s normal.
Smaller towns and rural areas often have limited local media or sponsorship options. In those cases, expand your search to nearby cities or regional publishers.
Step 6. Track and Attribute Performance by Location
Tracking performance can get complicated, especially when you’re running a local SEO strategy for multiple locations.
Without clear attribution, you can’t prove which branches — or tactics — are driving results.
Use UTMs + Location IDs Everywhere
Building a consistent local SEO strategy for multiple locations means tracking every branch the same way — from clicks and calls to conversions and revenue.
Multi-location tracking starts with structure.
Add UTM tags to every GBP link, ad campaign, and email.
They make it possible to separate traffic, leads, and conversions by branch inside GA4 and your CRM system.
Use a clear naming convention so you can filter results without digging through rows of messy data.
Phone calls and form fills are two of the strongest conversion signals in local SEO.
Don’t lose them in a generic tracking setup.
Use tools like CallRail to assign unique phone numbers to each branch. That way, you can see which campaigns and locations are driving calls directly from search or ads.
For web forms or booking widgets, embed hidden location IDs so submissions are tagged automatically to the right branch. It takes a few minutes to set up, but it eliminates hours of manual cleanup later.
Centralize in a Multi-Location Dashboard
You can’t improve what you can’t measure.
Use a platform like Looker Studio. It can combine GBP insights, GA4 data, call-tracking results, and CRM metrics into one dashboard.
At a glance, you’ll see how all locations perform side by side. Then, drill into individual cities or stores to find what’s working and what needs attention.
Optimize Based on Insights
Once you have consistent tracking, insights start to stand out.
Spot underperforming branches early and dig into the “why.”
Maybe reviews are trending negative, citations are inaccurate, or local pages haven’t been updated in months.
At the same time, identify top-performing branches and replicate their wins across the rest of your network. Share these insights regularly with local managers so strategy and execution stay aligned.
Level Up Your Multi-Location SEO Game
Consistency is the quiet advantage in multi-location SEO.
Why?
Because brands that systemize how each branch builds trust, relevance, and citations win the long game in local search.
In short: The top performers don’t rely on guesswork. They build repeatable frameworks.
If you’re ready to scale smarter, explore our Local SEO Tools comparison.
You’ll find the platforms and features that make local SEO for multiple locations faster, easier, and far more effective — no matter how many branches you manage.
But no major AI platform has confirmed that they use it.
Not yet, anyway.
And there’s no evidence that any major large language model (LLM) actually uses it when crawling.
So, why are some SEOs and site owners already adding it to their sites?
Because LLM traffic is projected to explode over the next few years.
Which means AI models could soon become your biggest traffic source.
Remember: robots.txt was once optional, too.
Today, it’s essential for managing search crawlers.
LLMs.txt could follow a similar path — becoming the standard way to guide AI to your most important content.
In this guide, you’ll learn how llms.txt files work, the key pros and cons, and the exact steps to create one for your site.
You’ll also see different llms.txt examples from real sites.
First up: a quick explainer.
What Is LLMs.txt?
LLMs.txt is a plain-text file that tells AI models which pages to prioritize when crawling your site.
This proposed standard could make your content easier for AI systems to find, process, and cite.
Here’s how it works:
You create a text file called llms.txt
List your most important pages with brief descriptions of what each covers
Place it at your site’s root directory
In theory, LLM crawlers would then use the file to discover, prioritize, and better understand your key pages
For example, here’s what Yoast SEO’s llms.txt file looks like:
Does LLMs.txt Replace Robots.txt?
Short answer: No.
They serve different purposes.
Robots.txt tells crawlers what they’re allowed to access on a site.
It uses directives like “Allow” and “Disallow” to control crawling behavior.
LLMs.txt suggests which pages AI models should prioritize.
It doesn’t control access — it just provides a curated list. And makes it easier for crawlers to understand your content.
For example, you might use robots.txt to block crawlers from your admin dashboard and checkout pages.
Then, use llms.txt to point AI systems toward your help docs, product pages, and pricing guide.
Here’s a full breakdown of the differences:
LLMs.txt
Robots.txt
Purpose
Provides a curated list of key pages that AI models may use for information and sources
Sets rules for search engine crawlers on what to crawl and index
Target audience
LLMs like ChatGPT, Gemini, Claude, Perplexity
Traditional search engine bots (Googlebot, Bingbot, etc.)
Syntax
Markdown-based; human-readable
Plain text, specific directives
Enforcement
Proposed standard; adherence is not confirmed by major LLMs
Voluntary; considered standard practice and respected by major search engines
SEO/AI impact
May influence AI-generated summaries, citations, and content creation
Directly impacts search engine indexing and organic search rankings
Layout and Elements
So, what goes inside this file — and how should you structure it?
LLMs.txt should be created as a plain-text file and formatted with markdown.
Markdown uses simple symbols to structure content.
This includes:
# for a main heading, ## for section headings, ### for subheads
> to call out a short note or tip
– or * for bullet lists
[text](https://example.com/page) for a labeled link
Triple backticks (“`) to fence off code examples when you’re showing snippets in a doc or blog post
This makes the file easy for both humans and AI tools to read.
You can see the main elements in this llms.txt example:
# Title
> Description goes here (optional)
Additional details go here (optional)
## Section
- [Link title](https://link_url): Optional details
## Optional
- [Link title](https://link_url)
Now that you know how to format the file, let’s break down each part:
Title and optional description at the top: Add your site or company name, plus a brief description of what you do to give AI systems context
Sections with headers: Organize content by topic, like “Services,” “Case Studies,” or “Resources,” so crawlers can quickly identify what’s in the file
URLs with short descriptions: List key pages you want prioritized. Use clear, descriptive SEO-friendly URLs. And add a concise description after each link for context.
Optional sections: Consider adding lower-priority resources you want AI systems to be aware of but don’t need to emphasize — like “Our Team” or “Careers”
To put all the pieces together, let’s look at some examples.
Here’s how BX3 Interactive, a website development company, structures its llms.txt file:
It features:
The company’s name
Brief description
List of key service pages with URLs and one-sentence summaries
Top projects and case studies
Citation and linking guidelines
BX3 Interactive also includes target terms and specific CTAs for each URL.
If adopted, this approach could shape how LLMs reference the brand, guiding them toward BX3 Interactive’s preferred messaging and phrasing.
LLMs.txt files can also be more complex, depending on the site.
Like this example from the open-source platform Hugging Face:
It organizes hundreds of pages with nested headings to create a clear hierarchy.
But it goes well beyond URL lists and summaries.
It includes:
Step-by-step installation commands
Code examples for common tasks
Explanatory notes and references
This way, AI systems would get direct access to Hugging Face’s most valuable documentation without needing to crawl every page.
This could reduce the risk of key details getting missed or buried.
Keep in mind that the ideal structure depends on the scope of your site. And the depth of information you want AI to understand.
It’s possible that an llms.txt file could boost your AI SEO efforts over time.
But that would require widespread adoption.
No major AI platform has officially supported the use of llms.txt yet.
And Google has been especially clear — they don’t support it and aren’t planning to.
But big players like Hugging Face and Stripe already have llms.txt files on their sites.
Most notably, Anthropic, the company behind Claude, also has an llms.txt file on its website.
If one of the leading AI companies is using it themselves, it could mean they see potential for these files to play a bigger role in the future.
Note: While Anthropic has an llms.txt file on its site, it hasn’t publicly stated that its crawlers use or read these files.
Bottom line?
Treat llms.txt as a low-risk experiment, not a guaranteed way to boost AI visibility.
Potential Benefits
Right now, the benefits are theoretical.
But if llms.txt catches on, you could benefit in multiple ways:
Control what gets cited: Spotlight your blog posts, help docs, product pages, and policies so AI tools reference your best pages first instead of less important or outdated content
Make parsing easier: Your llms.txt file gives AI models clean markdown summaries instead of forcing them to parse through cluttered pages with navigation, ads, and JavaScript
Improve your AI performance: Guide AI models to your most valuable pages, potentially improving how often and accurately they cite your content in responses
Analyze your site faster: A flattened version of your site (a single, simplified file listing your key pages), makes it easier to run a keyword analysis and site audit without crawling every URL
Key Limitations and Challenges
The skepticism around llms.txt is valid.
Here are the biggest concerns:
No one’s officially using it yet: No major platforms have announced support for these files — not OpenAI, Google, Perplexity, or Anthropic
It’s a suggestion, not a rule: LLMs don’t have to “obey” your file, and you can’t block access to any pages. Need access control? Stick with robots.txt.
Easy to game: A separate markdown file creates an opportunity for spam. For example, site owners could overload it with keywords, content, and links that don’t align with their actual pages. Basically, keyword stuffing for the AI era.
You’re showing competitors your hand: A detailed llms.txt file hands your competitors a lot of info they might have to use dedicated tools to get otherwise. Your site structure, content gaps, messaging, keywords, and more.
Creating an llms.txt file is pretty simple — even if you don’t have much technical experience.
One caveat: You may need a developer’s help to upload it.
Step 1: Pick Your High-Priority Pages
Start by selecting the pages you want AI systems to crawl first.
Pro tip: Don’t dump your whole sitemap into your llms.txt file. Focus on your most valuable pages — not an exhaustive inventory.
Think about the evergreen content that best represents what you do — your core product pages, high-value guides, FAQ sections, key policies, and pricing details.
For example, BX3 Interactive lists this web development service page first in its llms.txt file:
Why? Because it’s a core service they offer.
And by featuring it in llms.txt, they’re signaling to AI crawlers that this page is central to their business.
Step 2: Create Your File
Next, open any plain-text editor and create a new file called llms.txt.
Options include Notepad, TextEdit (on Mac), and Visual Studio Code.
Pro tip: Don’t just list bare URLs. Add a brief description for each one that explains what the page covers and who it’s for. This context could help AI understand when and how to cite your brand.
Not comfortable with markdown formatting?
Ask your developer to handle it (if you have one).
Or let an LLM do the work — ChatGPT and Claude can generate these files instantly.
Here’s a prompt to get you started:
Create an llms.txt file in markdown format using this information:
Company Name: [Your Company Name]
Company Description: [One sentence about what you do]
Important Notes (optional):
[Key differentiator or important detail]
[What you do or don’t do]
[Another key point]
Products/Services
URL: [https://yoursite.com/product-1]
Description: [What it does and who it’s for]
URL: [https://yoursite.com/product-2]
Description: [What it does and who it’s for]
Blog/Resources
URL: [https://yoursite.com/blog-post-1]
Description: [What readers will learn]
URL: [https://yoursite.com/blog-post-2]
Description: [What readers will learn]
Company Pages
About: [https://yoursite.com/about] – [Company background and mission]
Contact: [https://yoursite.com/contact] – [How to reach you]
If setup is quick and you’re curious to experiment, it’s worth doing.
Worst case, nothing changes.
Best case, you’re ahead of the curve if AI platforms start paying attention.
In the meantime, don’t neglect proven SEO fundamentals.
Structured data, high-authority backlinks, and helpful content are what help AI — and traditional search engines — understand, trust, and surface your pages.
Want to boost your AI visibility now?
Check out our AI search guide for a framework that’s already working.
What Is AI Optimization (And Why You Should Care)?
AI optimization is the process of making your website accessible and understandable to AI-powered search tools. Like ChatGPT, Claude, Gemini, Perplexity, Google AI Overview, and Bing Copilot.
Some call it “AI search optimization.” Others “AI content optimization.”
Terminologies vary, but they’re all about the same thing:
Make your site easy for large language models (LLMs) to find, understand, and reference in their answers.
It’s not a brand-new strategy. It’s built on the core SEO principles.
Only now, you’re optimizing for tools that pull, summarize, and use your information — not just rank.
But why is AI optimization so important now?
AI tools are expected to drive more traffic than traditional search engines by 2028.
And here’s the kicker:
This traffic pool is only getting bigger.
Over 700 million people use ChatGPT every week. Millions more use Perplexity, Gemini, and other AI platforms.
Google’s AI Mode already has more than 100 million monthly active users. And that’s just in the US and India.
As it rolls out globally, adoption will only grow.
AI search optimization helps you be visible to these users.
It ensures your site appears in AI-powered search results, increasing your chances of getting referral traffic and finding new customers.
How AI Search Works
LLMs find relevant content across the web based on users’ prompts, then combines it into one comprehensive answer with source links.
There are three broad steps:
1. Understanding Your Prompt
First, AI interprets what you’re asking.
Some platforms (and specific models) may even expand or tweak your query for better results.
For instance, if I search “best sneakers,” ChatGPT’s o3 model searches for more specific phrases like “best running shoes 2025.”
2. Retrieval
Next, the AI platform searches for information in real time.
Different platforms use different sources (Google’s index, Bing, curated databases, etc.). But they all work the same way.
They gather relevant content from across the web for your expanded query.
3. Synthesis
Finally, AI decides which sources to include.
How?
The exact criteria aren’t public. But these factors seem to matter the most:
Authority: Recognized brands (entities it knows) and established experts
Structure: Clear, scannable content with direct answers
Context: Content that covers topics semantically (related concepts, not just keyword matches)
The most relevant sources get cited. The rest get ignored.
Which means ranking well isn’t enough. Your content also needs to be properly structured for AI systems.
I Analyzed 10 Queries Across Multiple AI Search Platforms: Here’s What I Found
Before we move forward to discuss how to optimize for AI search, I wanted to understand three things:
Do different AI platforms cite different types of content?
Which domains consistently appear across platforms?
Does multi-platform presence actually matter for AI visibility?
So I ran a simple experiment.
I searched 10 queries across ChatGPT 5, Claude Sonnet 4, Perplexity (Sonar model), Gemini 2.5 Flash, and Google’s AI Mode — a mix of commercial, informational, local, and trending topics.
And I found some interesting insights.
How Each Platform Chooses Sources
Platforms
Citation Behavior
ChatGPT
Acts like a community aggregator. Mixes Reddit discussions with Wikipedia and review sites.
Claude
Prefers recent, authoritative sources. Zero Reddit citations. Focuses on 2024-2025 content
Perplexity
Most diverse. Balances buying guides, YouTube reviews, and some Reddit.
Gemini
Relies mostly on training data. And since there’s no option to turn on web search, you can’t get it to cite sources for most of your queries.
Google AI Mode
Pulls from beyond top search results. 50% of citations weren’t on page one of Google.
The “Citation Core” Effect
Certain domains have achieved what we call the “citation core” status.
Citation core (n.): A small group of sites and brands that every major AI search tool trusts, cites, and uses as default sources.
Wikipedia showed up 16 times. Mayo Clinic owned health queries. RTINGS controlled electronics reviews.
These sites have become AI’s default sources.
What This Means for Brand Sites
One pattern jumped out: Official brand websites were underrepresented.
In my test, they made up around ~10% of all citations.
But that doesn’t mean your site doesn’t matter for informational or educational queries.
It means most sites aren’t yet AI-friendly. And that’s the opportunity.
When your site is structured, detailed, and optimized, it becomes one of the few brand-owned sources AI can actually cite for product specs, features, case studies, and stats. Information third-party sites can’t provide.
Think of it like this: Your website gives you the authoritative base layer. Off-site presence just amplifies it.
These findings aren’t surprising. But they reinforce what we’ve suspected all along.
In fact, a lot of what we do here at Backlinko aligns with these patterns:
Google’s guideline says good SEO is good AI optimization.
Their official guidelines mostly rehash standard SEO practices, with a few AI-specific points. Like using preview controls and ensuring structured data matches visible content.
But the foundation to make your site AI search-ready starts with three teams working in sync:
Developers: They make your site technically accessible to AI crawlers
SEOs: They structure content so AI can extract and understand it
Content teams: They create information worth extracting
Most companies treat these as separate projects.
That’s a mistake.
Leigh McKenzie, Head of SEO at Backlinko, explains why:
“Ranking in Google doesn’t guarantee you’ll show up in AI tools. SEO is still table stakes. But generative engines don’t just lift the top results. They scan at a semantic level, fan queries out into dozens of variants, and stitch together answers from multiple sources.”
You’ll need a coordinated effort to execute.
Let’s look at exactly what each team needs to do for effective AI search optimization.
Note: Most traditional SEO practices work for AI optimization too.
I’m not covering the basics here, like using sitemaps and including metadata. You should already be doing those.
Instead, I’m focusing on factors that specifically impact AI search visibility. These are insights based on my own experience, analyzing what’s working across different sites, and comparing notes with other SEOs.
Want the complete list?
I’ve created an AI Search Engine Optimization Checklist that covers everything — the well-known tactics, the experimental ones, and the “can’t hurt to try” optimizations that might give you an edge.
Developer Tasks
Understanding how to optimize for AI search starts with your developers. Because they control whether AI can actually access and understand your content.
No access means no citations.
Here’s what they need to check:
1. Make Your Site Accessible to AI Crawlers
AI crawlers need permission to access your site through your robots.txt file.
If you block them, your content won’t appear in AI search results.
Here are the main AI crawlers:
GPTBot (OpenAI/ChatGPT)
Google-Extended (Google’s AI Overview)
Claude-Web (Anthropic/Claude)
PerplexityBot (Perplexity)
To check if you’re blocking them, go to yoursite.com/robots.txt.
Look for any lines that say “Disallow” next to these crawler names.
If you find them blocked (or want to make sure they’re allowed), add these lines to your robots.txt:
This tells AI crawlers they can access your entire site.
Wondering if you need an LLMs.txt file? The short answer is that you don’t necessarily need it, but it doesn’t hurt, either. Check out our LLMs.txt guide for more details.
2. Whitelist AI Bots in Your Firewall
Cloudflare, Sucuri, and other web application firewalls (WAFs) sometimes block legitimate AI crawlers as “suspicious bots.”
For instance, Cloudflare now blocks AI bots from accessing its clients’ websites by default.
You have to turn off this security feature to ensure AI bots can crawl your site.
Check your firewalls and security tools.
See if they’re blocking requests from AI user agents or giving 403 errors. And address that issue.
Remember: no access, no citations.
3. Use Semantic HTML So AI Knows What’s Important
AI needs to understand what’s important on your page.
Your developers handled the technical requirements. AI can now access your site.
But access doesn’t guarantee visibility in AI results.
Your SEO team controls how AI discovers, understands, and prioritizes your content.
Here’s what they need to control in your AI SEO strategy:
7. Structure Pages for Fragment-Friendly Indexing
AI pulls specific fragments from your pages — sentences and paragraphs it can use in responses.
Your page structure affects how easily AI can extract these fragments.
Start with a clean heading hierarchy.
Proper H2s and H3s help AI (and your readers) understand where one idea ends and another begins.
Go a step further by breaking big topics into unique subsections.
Instead of one giant guide to “healthy recipes,” create separate sections for “healthy breakfast recipes,” “healthy lunch recipes,” and “healthy dinner recipes.”
That way, you match the variations people actually search for.
Pro tip: Don’t bury your best insights in long paragraphs.
Use callouts (like this one)
Add short lists and bullets
Drop quick tables for comparisons
That’s how you turn raw text into structured fragments AI can actually use.
When your content is structured this way, every section becomes a potential answer.
8. Build Topic Clusters That Signal Full Coverage
Internal linking creates topical connections across your site.
When you link related pages together, you’re building topic clusters that show comprehensive coverage.
This is standard SEO practice that also helps AI discovery.
Create pillar pages for your main topics. These are comprehensive guides that link out to all related content.
For “project management,” your pillar would link to:
Task automation guide
Team collaboration tools
Workflow optimization
Resource planning
Each supporting page links back to the pillar and to other relevant pages in the cluster.
This helps both users and AI understand page relationships.
The cluster structure accomplishes two things:
First, it improves crawl efficiency. AI finds your hub and immediately discovers all related content through the links.
Second, it demonstrates topical depth. Organized clusters show comprehensive coverage better than scattered pages.
This structural approach helps organize your site architecture to showcase expertise through strategic internal linking.
9. Add Schema Markup to Label Your Content
When AI crawls your page, it sees text.
But it doesn’t know (without natural language processing) if that text is a recipe, a review, or a how-to guide.
Schema explicitly labels each element of the page.
It makes data more structured and easier to understand.
There are several types of schema markups.
I’ve found the FAQ schema particularly effective for AI search visibility.
Here’s how it looks:
json{
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "What is churn rate?",
"acceptedAnswer": {
"@type": "Answer",
"text": "Churn rate is the percentage of customers who cancel during a specific period."
}
}]
}
This markup tells AI exactly where to find questions and answers on your page.
The Q&A format matches how AI structures many of its responses, making your content easy to process.
Depending on the content management system (CMS) you’re using, you can add schema using plugins, add-ons, or manually.
For instance, WordPress has several good plugins.
After implementation, you can test it at validator.schema.org to ensure it’s working properly.
Note: Schema is just one type of metadata. Others include title tags, meta descriptions, and Open Graph tags.
Keeping them accurate and consistent may help AI platforms interpret your content correctly.
You can check your metadata using browser dev tools or SEO extensions, like SEO META in 1 CLICK.
10. Add Detailed Content to Category and Product Pages
Most category pages are just product grids. That’s a missed opportunity for AI search optimization.
The same goes for individual product pages with just specs and a buy button.
These pages get tons of commercial searches.
But they lack substantial content.
So, AI has limited information to work with when answering product queries.
You want to add buyer-focused information directly on these pages, like this:
They can cover:
Feature comparison tables
Common questions with clear answers
Use cases and industry applications
Technical specifications that matter
For product pages, go beyond basic descriptions.
Include materials, dimensions, compatibility, warranties, reviews — whatever matters to your buyers.
For example, GlassesUSA.com has several details on its product pages than just product specifications.
They include information that AI can use when answering specific questions.
Similarly, for category pages, add content that helps buyers choose.
What’s the difference between options? What should they consider? Which product fits which need?
Eyewear retailer Frames Direct does this well.
It has detailed content at the end of its category pages.
The key is putting this information directly on the page. Not hiding it behind tabs or “read more” buttons.
When someone asks AI about products in your category, you want substance it can quote. Not just a grid of images it can’t interpret.
11. Track Where AI Mentions Your Brand (and Where It Doesn’t)
You need to know where AI is mentioning your brand and where it isn’t.
Because if competitors appear in AI results and you don’t, they’re capturing the traffic you should be getting.
You can try checking this manually.
Run your target queries (e.g., “nutrition tracking app 2025”) across different AI platforms.
Scan the answers. And see if your brand shows up.
But that’s slow. And you’ll only catch a small slice of what’s happening.
It tracks how often your brand appears in AI-generated answers across various platforms like ChatGPT, Google AI Mode, and Google AI Overview. (In the “Visibility Overview” report.)
You can see exactly which topics and prompts your brand appears for.
And which prompts your competitors appear for, but you don’t. (In the “Competitor Research” report.)
For instance, if you find that AI cites competitors for “Cats and Feline Care” but skips your brand, that’s a clear signal to create or optimize a page targeting that exact query.
You also get strategic recommendations. So you can spot gaps, fix weak content, and double down where you’re already winning. (In the “Brand Performance” reports.)
With a tool like AI SEO Toolkit, you’re not guessing about your AI search visibility.
You’re improving based on real AI visibility data.
12. Optimize for Natural Language Prompts, Not Just Keywords
But they ask AI, “What’s the warmest jacket for Chicago winters under $300?”
Your content needs to match these natural language patterns.
Start by identifying how people actually phrase questions in your industry.
Use the AI SEO Toolkit to find high-value prompts in your industry.
Go to the “Narrative Drivers” report.
And scroll down to the “All Questions” section to see which prompts mention your brand and where competitors appear instead.
Document these prompt patterns.
Share them with your content team to create pages that answer these specific questions — not just target the base keyword.
The goal isn’t abandoning keywords.
It’s expanding from “winter jacket” pages to content that answers “warmest jacket for Chicago winters under $300.”
Content Tasks
Your site is technically ready. Your SEO is taken care of.
Now your content team needs to create valuable information and build presence across the web.
Here’s how to optimize content for AI search:
13. Publish Original Content with Data, Examples, and Insights
Generic blog posts restating common knowledge rarely perform well in AI search results.
But content with fresh angles and concrete examples does.
At Backlinko, we focus on publishing content that provides unique value through examples, original research, and exclusive insights.
Like this article:
And even if we’re talking about a common topic (e.g., organic traffic), we add fresh examples.
So how do you make your content stand out?
Run small studies or polls to produce original data. Even simple numbers can set your content apart.
Use screenshots, case studies, and workflows from real projects.
Back up your points with stats and cite credible sources.
Add expert quotes to strengthen authority.
Test tools or strategies yourself, and share the actual results.
AI systems look for concrete details they can pull into answers.
The more unique evidence, examples, and voices you add, the better.
14. Embed Your Brand Name in Context-Inclusive Copy
Context-inclusive copy means writing sentences that make sense on their own.
Each line should carry enough detail that an AI system understands it without needing the surrounding text.
But take that a step further.
Don’t just make your sentences self-contained.
Embed your brand name inside them so when AI reuses a fragment, your company is part of the answer.
Instead of: “Our tool helped increase conversions by 25%”
Write: “[Product] helped [client] increase checkout completions by 25%”
The second version keeps your brand attached to the insight when AI extracts it.
So how do you do this in practice?
With data: Tie your brand name directly to research findings or surveys you publish
With comparisons: Mention your brand alongside alternatives, so it’s always part of the conversation
With tutorials: Show steps using your product or service in real workflows
With results: Attach your brand name to case studies and examples
Here’s an example from Semrush, using their brand name vs. “we”:
The goal is simple:
Every quotable fragment should carry both context and your brand name.
That way, when AI pulls it into an answer, your company is mentioned too.
15. Create Pages for Every Use Case, Feature, and Integration
Specific pages are more likely to appear in AI responses than generic ones.
So, don’t bundle all features on one page.
Create dedicated pages for each major feature, use case, and integration.
Here’s an example of JustCall doing it right with unique pages for each of its main features and use cases:
The strategy is simple: match how people actually search.
For instance, someone looking for “Slack integration” wants a page about that specific integration. Not a features page where Slack is item #12 in a list.
Structure these pages to answer real questions, like:
What problem does this solve?
Who typically uses it?
How does it actually work?
What specific outcomes can they expect?
Get granular with your targeting. Instead of broad topics, focus on specific scenarios.
For example:
→ Ecommerce sites can create pages for each product application
→ Service businesses can detail each service variation
→ Publishers can target specific reader scenarios
The depth of coverage signals expertise while giving AI exact matches for detailed queries.
This specificity is what makes AI content optimization work. You’re creating exactly what AI systems need to cite
16. Expand Your Reach Through Non-Owned Channels
AI engines lean heavily on third-party sources. Which means your brand needs to show up in places you don’t fully control.
This goes beyond your on-site efforts.
But it’s still part of the bigger AI visibility play. And your content team can drive it by publishing externally and fueling PR.
Take this example: when I search “best duffel bags for men 2025” in Claude, it references an Outdoor Gear Lab roundup of top bags.
If you sell duffels, you’d want to be in that article.
There are two ways to expand your presence on non-owned channels.
One is publishing on other sites yourself — guest posts, bylined articles, or original research placed on authority blogs and industry outlets.
These extend your reach, position you as an expert, and increase your AI search visibility.
You’ll find guest post opportunities in several well-known sites. Like Fast Company here, which has an authority score of 67.
The other way to build visibility is getting featured by others.
Think reviews, roundups, and product comparisons that highlight your solution.
This usually involves working closely with your PR team.
But the content team fuels those opportunities with the data, case studies, and assets that make the pitch worth covering.
Either way, the goal of this AI content strategy is the same: substantive coverage.
A one-line mention usually isn’t enough. You need full features, detailed reviews, or exclusive insights that stand out.
Because the more credible coverage you earn (whether you wrote it or someone else did), the more evidence AI has to pull into its answers.
For 20 years, SEO was all about optimizing pages on your website.
Now it’s expanded far beyond that.
AI search looks beyond your site. It sees your brand everywhere it appears — in reviews, Reddit threads, YouTube videos, press coverage, and product listings.
Your website is still your base.
But your visibility now depends on how clearly AI systems understand and connect your brand across the web.
That’s where Entity SEO comes in.
It’s how you build your Digital Brand Visibility — the thing every business should be optimizing for right now.
You’re about to learn what that means, why it matters, and how to do it.
What Are Entities in AI Search?
Entities are the building blocks of AI understanding.
An entity is any “thing” that search engines and AI models can identify, categorize, and relate to other things.
When AI systems generate answers, they don’t just pull keywords from web pages. They pull entities — structured representations of brands, products, people, and topics — and combine them to build meaning.
Let’s use the email marketing company Omnisend as an example.
Through the lens of an AI model, Omnisend isn’t just a website about email marketing. It’s a connected network of entities:
Use cases: “welcome series,” “abandoned cart recovery”
Here’s what the entities look (hypothetically) like to a large language model (LLM):
These records become the foundation for AI answers.
LLMs do more than find keywords on your page. They retrieve entities, place them in vector space, and choose the ones that best answer the question.
Vector space explained: It’s a mathematical method that AI models use to understand relationships between concepts. Imagine a 3D map where similar items group together. For example, “Apple,” the company, is close to “iPhone” and “Tim Cook.” Meanwhile, “apple,” the fruit, is near “banana” and “orchard.”
For example, ask Google: “What’s the best email marketing tool for my Shopify store?”
You’ll see brand entities like Klaviyo, Omnisend, Brevo, Mailchimp, Privy, and MailerLite mentioned. This makes sense because the entities are closely related in the AI’s understanding.
Notice: the brand mentions aren’t linked to the websites. It’s just building the answer and then linking to the brand SERP on Google.
Why Entities Matter More Than Websites
AI models are constantly mapping relationships between entities when serving up answers.
When someone types “best email marketing tool for Shopify,” LLMs spread out the query. They turn that one question into multiple related searches.
Think of AI doing lots of Google searches at the same time.
The system simultaneously explores “What integrates with Shopify?”, “Which tools handle abandoned carts?” and “What do ecommerce stores actually use?”
Your brand can appear through any of these paths, even if you didn’t optimize for the original query.
Classic SEO relied a lot on keyword density and page authority.
But AI uses dense retrieval, where it’s looking for semantic meaning across the web, not just word matches on your page.
Dense retrieval explained: AI systems focus on meaning, not just exact keywords. They find related content, even if different words are used.
A Reddit comment that clearly explains “We switched from Klaviyo to Omnisend because the Shopify integration actually works” carries more signal (assuming the model prioritizes authentic discussions) than a page stuffed with “best email marketing Shopify” keywords.
The AI understands the relationship between the entities (Klaviyo, Omnisend, Shopify) and the context (switching, integration quality).
PR folks have been fighting for this moment: mentions without links still count.
For the longest time, we’ve obsessed over backlinks as the currency of SEO.
But AI systems recognize when brands get mentioned alongside relevant topics, using these as relationship signals.
So when Patagonia appears in climate articles without a hyperlink, when Notion shows up in productivity discussions on Reddit, when your brand gets name-dropped in a podcast transcript — these all strengthen your entity in AI’s understanding.
Here’s a real example that clarified this for me:
Microsoft OneNote often shows up high in AI recommendations for “note-taking tools.”
In ChatGPT:
In Perplexity:
And in Google AI Overviews:
But EverNote dominates Google’s number one ranking spot for “note taking tools”.
Why?
OneNote’s integration with the Microsoft ecosystem means it gets mentioned constantly in productivity discussions, enterprise software comparisons, and Office tutorials. This creates dense entity relationships in AI training data.
Evernote, by contrast, has focused on SEO and earned strong backlinks that dominate traditional search rankings.
How Entities Get Recognized
So how does Google (and other AI systems) actually know that Omnisend is an email marketing platform and not, say, a meditation app?
The answer sits at the intersection of structured data, human conversation, and pattern recognition…at massive scale.
Entity Databases and Product Catalogs
Google maintains what they call Knowledge Graphs and Shopping Graphs.
Other AI systems have similar entity databases, just with different names.
The idea is the same: huge databases that map every product, company, and person along with their attributes and relationships.
When Nike releases the Pegasus 41, it doesn’t just become a new product page on Nike.com. It becomes an entity in Google’s Shopping Graph, connected to “running shoes,” “Nike,” “marathon training,” and hundreds of other nodes.
The system knows it’s a shoe before anyone optimizes a single keyword.
Human Conversation as Training Data
AI systems learn just as much from informal mentions as they do from structured markup.
When an Outdoor Gear Lab review casually mentions testing Patagonia’s Torrentshell 3L against the expensive Arc’teryx Beta SL, that relationship gets encoded.
When a podcast guest says, “I moved from Asana to Notion for task and project management,” this competitive link adds to the training data.
Reddit and Quora have become unexpectedly powerful for entity recognition. (Google explicitly stated they’re prioritizing “authentic discussion forums” in their ranking systems.)
A single comment on why someone picked Obsidian over Notion for knowledge management matters more than you might realize.
These platforms capture what websites struggle to do: real people sharing real decisions with real context.
Multimodal Recognition
AI systems extract entities from audio and video. They do this by turning speech into text through transcription.
Every mention in a transcript, every product on screen, and every comparison in a talking-head segment is processed.
A 10-minute YouTube review of project management tools turns into structured data that compares ClickUp, Notion, and Asana. It includes feature comparisons and maps out use cases.
The New SEO Power Dynamic
You can’t game entity recognition the way you could game PageRank.
You can’t manufacture authentic Reddit discussions. You can’t fake your way into natural podcast mentions. The system rewards genuine presence in genuine conversations, not optimized anchor text.
Think about what this means:
Your engineering team’s conference talk that mentions your product’s architecture? That’s entity building.
Your customer’s YouTube walkthrough of their workflow? Entity building.
That heated Hacker News thread where someone defends your approach to data privacy? Entity building.
We’ve spent the longest time optimizing for robots. Now the robots are optimized to recognize authentic human discussion. (Ironic.)
5 Ways to Optimize Your Brand for Entities (Not Just a Website)
Using Omnisend as an example, here are five approaches for evaluating and optimizing entity presence in AI-powered search results.
1. Assess Your Entity Foundation
To start, you need a baseline understanding of your current entity relationships.
For Omnisend, this means mapping how AI systems currently categorize them relative to competitors.
Begin by verifying schema markup across key pages.
Testing Omnisend’s homepage with the Schema Markup Validator shows they use Organization and VideoObject schema.
And the Organization schema is relatively basic.
Omnisends competitor, Klaviyo, uses Organization schema as a container for multiple software offerings.
Klaviyo’s approach maintains brand-level authority while declaring specific software categories and capabilities. This potentially gives them stronger entity associations for queries about email marketing, SMS marketing, and marketing automation.
Next, check your entity presence in major knowledge sources like Wikidata and Crunchbase.
On Wikidata, Omnisend’s records are OKAY.
There’s basic info, like what Omnisend does, the industry, inception date, URL, and social media profiles.
But Klaviyo, again, is all over it. They have multiple properties for industry, entity type, URLs, offerings, and even partnerships.
There’s a clear opportunity for Omnisend to update its Wikidata with more details.
2. Test Query Decomposition
AI systems break down queries into entities and relationships. Then, they may try multiple retrievals.
For example, in Google Chrome, I prompted ChatGPT:
“What’s the best email marketing tool for ecommerce in 2025? My priority is deliverability.”
In the chat URL, copy the alphanumeric sequence after the /c/ directory. For me, it was 68d4e99e-4818-8332-adbd-efab286f4007.
Note: You need to be logged into ChatGPT to get this sequence
Right-click on the page and click “Inspect”.
Choose the “Network” tab, paste the alphanumeric sequence in the filter field, and reload the page.
In the “Find” section, search for “search_model_queries“. Then, click on the search results.
Each decomposed query represents a different competitive pathway.
Omnisend might surface through deliverability discussions, but miss general tool comparisons.
Mailchimp could dominate broad searches while competitors own specialized angles.
This explains why you appear in AI answers for searches you never optimized for. The semantic understanding creates visibility through unexpected entity relationships rather than keyword matching.
You can check this yourself. Run the extracted queries in separate chats and note which brands appear where.
But maybe don’t build a strategy around exploiting this technique.
The methodology depends on undocumented functionality that OpenAI could change without notice.
Important finding: Simple queries produce simple results. When I prompted “Best email marketing tool for ecommerce,” it triggered exactly one internal search with basically the same language. No decomposition.
3. Map Competitive Entity Relationships
Traditional SEO competitive analysis asks “Who ranks for our keywords?”
Entity analysis asks “When do AI systems group us together?”
I tested this with Omnisend to understand when they appear alongside different competitors.
I ran 15 variations of email marketing queries through Google AI Mode to see which brands consistently appear together.
Note: I tested logged out, using a VPN set to San Francisco, in private browsing mode to minimize personalization bias.
I began with simple terms like “best email marketing for ecommerce” and “abandoned cart recovery tools.” Then, I tried different angles like “email automation for Shopify stores.”
Here’s what I found:
Query Context
Omnisend Present
Most Co-Mentioned
Klaviyo Present
Ecommerce email
5/5 queries
Klaviyo, Mailchimp
4/5 queries
General email
5/5 queries
Mailchimp, Brevo
2/5 queries
Deliverability focus
2/5 queries
Brevo, Mailchimp
0/5 queries
Omnisend appeared in 12 of 15 total queries — stronger entity presence than I expected.
But mentions shifted dramatically by context.
In ecommerce discussions, Klaviyo dominated as the top tool.
In general email marketing, Mailchimp took over as the main reference point.
The mention order revealed something important. Klaviyo appeared first in 5 of 5 ecommerce queries, with more positive language around their positioning.
Omnisend routinely ranked second or third. This suggests they’re part of the discussion but not at the forefront.
Here’s what’s interesting:
Klaviyo completely disappeared from deliverability-focused queries while Omnisend maintained some presence.
This shows entity relationships are radically contextual.
Being the leader in ecommerce email doesn’t mean presence in deliverability conversations.
4. Optimize For Entities in Your Content
Entity recognition works best when it has context-rich passages. This helps AI systems extract and understand information more easily.
Take generic descriptions like “Our automation features help ecommerce businesses increase revenue through targeted campaigns.”
An AI system may struggle to identify which product you mean, its automation features, or how it compares to others.
Compare that to: “Omnisend’s SMS automation integrates with Shopify’s abandoned cart data to trigger personalized recovery messages within 2 hours of cart abandonment, without requiring manual workflow setup.”
This version establishes multiple entity relationships (Omnisend → SMS automation → Shopify integration → abandoned cart recovery) within a single extractable passage.
LLMs prefer to use their training data for answers. But when they pull info from the web, strong entity connections help a lot.
You’re reducing friction for both bots and human readers.
As a test, run key passages from your most important pages through Google’s Natural Language API to see what entities get recognized. This can also be video scripts.
Content with strong entity density tends to get cited more often than content requiring additional context.
5. Build Strategic Co-Citations
Entity authority builds through consistent mention alongside relevant entities in trusted sources. This moves the focus from link building to building relationships where natural comparisons happen.
For Omnisend, this means being present in authentic discussions. It’s about genuine comparisons, not forced mentions, that strengthen specific relationships.
A Reddit thread comparing “Klaviyo vs Omnisend for Shopify stores” carries a different entity weight than appearing in generic “email marketing tools” content.
The specific context (Shopify integration) strengthens both brands’ association with ecommerce email marketing.
The most valuable co-citations happen in:
Reddit discussions comparing tools for specific use cases
YouTube reviews demonstrating multiple platforms
Industry roundups grouping tools by specialization
Podcast discussions of marketing technology stacks
This Reddit thread shows strategic co-citation in action. The original post creates dense entity relationships (Klaviyo → Omnisend → pricing → Shopify store). While the comment adds even more context (pricing concerns → business scaling → “pretty good” user experience).
The discussion goes way beyond optimized content. It’s genuine decision-making that strengthens both brands’ entity associations with ecommerce email marketing.
This approach emphasizes genuine participation. Your category is discussed and evaluated by actual users who make real decisions. This is better than having artificial mentions in content made mainly for search engines.
Moving Forward with Entity SEO
If you’ve built a strong brand across various channels, you’ve laid the foundation.
Quality SEO is still crucial.
Genuine mentions in industry talks, real customer chats, and multi-channel distribution matter too.
Begin with your key product line. Organize it well, track its appearances in AI responses, and then expand to other entities.
AI answers are taking over search. More people are turning to Google AI Overviews, ChatGPT, and Perplexity for recommendations.
And if your brand isn’t showing up in those AI answers? You’re missing out on a huge (and growing) slice of your market.
That’s why Semrush built the AI SEO Toolkit. It’s a major unlock for marketers trying to understand how AI is impacting their
business.
Today, I’m going to show you how to use it — step by step — with a real example.
TL;DR: Measure Your AI Search Visibility
Here’s what you need to know about Semrush’s AI SEO Toolkit:
What it does:
Tracks how your brand appears across ChatGPT, Google AI Overviews, Google AI Mode, and Perplexity — showing which prompts include you and where you’re missing
Provides prompt tracking, content audits, and competitor comparisons
What it costs:
$99/month per domain (no trial)
Step 0: Start With a Brand
Before we analyze anything, let’s pick a brand to make this walkthrough concrete.
I went to Exploding Topics, browsed the ecommerce category, and picked Petlibro — a trending startup that sells smart pet feeders and water fountains.
I have zero affiliation with Petlibro. This isn’t sponsored. I just wanted a brand that’s growing fast and has enough search demand to make this example interesting.
Step 1: Get Your Search Baseline
Before we look at AI, we want to know how Petlibro is doing in traditional search. It’s super valuable context that will help us understand how they’re performing in LLMs.
Enter the brand’s domain name and look at the last 18 months. Looking at petlibro.com, they’ve been growing a TON.
They get most of their traffic from the U.S., rank for more than 25,000 keywords, and have a domain Authority Score of 43 with backlinks from 2.8K referring domains.
And they rank well in traditional SERPs for a bunch of highly relevant category and product keywords.
If your brand has so far neglected SEO, now is the ideal time to tackle that with a solid AI SEO strategy (which this audit will help you form).
Step 2: Check Your AI Visibility
Now for the fun part.
Back in the Semrush dashboard, look for AI SEO in the sidebar.
Enter petlibro.com, and a few minutes later, your Brand Performance dashboard will be ready for review.
On the right side, you can see the Share of Voice versus Sentiment Score.
The most interesting thing I noticed right away is that Petlibro has relatively low Share of Voice (6%) in regular ChatGPT, without Search.
That’s because ChatGPT 5 without search enabled has a training data cutoff of September 30, 2024.
And as we saw in traditional search, Petlibro has been growing a LOT in the last year.
Fortunately, they’re performing much better in SearchGPT, Google AI Mode, and Perplexity. All three of which use live search to generate their answers. For example, Petlibro’s Share of Voice in Google AI Mode is 27.8%:
Pro tip: Keep this in mind when analyzing your own brand too. These tools might not have your newest content in their training data. This can affect your apparent visibility, so be sure to check your visibility when search is enabled (as search-powered experiences are becoming more common).
This tab gives you a broad overview of your brand’s visibility. The next step will help you get more granular.
Step 3. Gauge Visibility at the Prompt Level
You can get prompt-level details by heading to the Visibility Overview tab.
Note: Things are evolving fast in the AI SEO space. This tool is brand new at the time of writing, so there isn’t much in the way of historical data right now. But tracking your visibility here over time will help you understand how well optimized your site is for an increasingly AI-based search landscape.
Scroll down and you’ll be able to quickly understand:
Your top-performing topics
Opportunities to improve your brand’s visibility
Popular sources for prompts relevant to your industry
Where your competitors are being cited that you’re not
Where you are being cited as a source
Click on any of the topics (or select Prompts) to see exact prompts and the AI response that you appear as part of.
To get more data on the prompts your rivals are appearing for that you’re not, head to the Narrative Drivers tab. First, you’ll see your brand’s Share of Voice by platform.
This gives you an overview of where your rivals are winning on each AI platform. But we want to scroll down to Share of Voice and switch to the Average Position view.
You can then toggle each competitor individually to get a better idea of how you perform against key rivals over time.
This view essentially gives you a snapshot of your brand’s visibility for key prompts.
To understand which prompts you are and are not appearing for compared to your rivals, you want to scroll down to the Breakdown by Question section.
You’ll see your position, which is where you show up in the answer snippet compared to your competitors.
You can see which ones your rivals appear for that you don’t by using the filters:
For example, Petlibro isn’t appearing for a few prompts that multiple competitors are mentioned in:
Identify the most relevant queries you want to start appearing for, and do this for each AI tool (using the toggle at the top left).
Note these down somewhere, as these will help frame your AI optimization strategy. Think of this part like the keyword research stage in a traditional SEO campaign.
Step 4. Review Your Brand’s Trust Factors
Next, you want to understand where your brand is doing a good job of appearing trustworthy to both your users and the LLMs themselves.
To do this, head back to the Brand Performance tab and scroll down to Key Business Drivers.
This essentially shows where your brand is strong compared to your competitors in various areas that help convey trust to users.
It might look overwhelming at first.
But basically:
The numbers illustrate how often key business drivers (i.e., trust factors) appear in answers where your brand is also mentioned. The bigger the number, the better.
(Look for the trophy icon to see where you’re currently ahead of your competitors.)
For example:
Searchers may value smart home integration when selecting a smart pet feeder.
When AI tools mention PetSafe, they also sometimes mention the fact it has these features.
This makes the brand more likely to appear in AI search responses when a user is looking for smart pet feeders with features like smart home integrations.
If Petlibro offers this, the brand needs to do a better job of conveying that in their content, or they’re going to struggle to appear in AI responses for relevant prompts.
Meanwhile, PetSafe is being mentioned for this kind of user prompt:
Go through this tab and identify trust factors you want to appear for.
If you spot areas competitors are strong but you’re not being picked up, make sure you:
Include trust factors and unique selling points on your website homepage
Add mentions of relevant features to product pages
Write helpful FAQ questions on product pages and blog posts that cater to these trust factors
Step 5. Audit Brand Sentiment in AI Tools
The next step involves diving deeper into how AI tools (and by proxy your users) perceive your brand.
To do this, we’ll head to the Perception report and scroll to the Key Sentiment Drivers section.
This will show you Brand Strength Factors and Areas for Improvement.
This is a great snapshot to see where you’re already doing well. And where you might need to focus new efforts on improving your brand’s perception in AI responses.
Brand strength factors are essentially areas where the AI tools talk positively about your brand.
In Petlibro’s case, these are factors like app connectivity, mechanical jams, and customer support.
Pro tip: Look for anything that’s not accurate here. You don’t want AI tools to be recommending your brand for things you don’t offer — this will just lead to disappointed customers.
The areas for improvement are areas where you might want to:
Create optimized content to make it clear to customers what you offer
Optimize your existing product pages to better reflect their strengths
Improve your products or services to better meet your customers’ needs
That final point is worth emphasizing. Semrush’s AI SEO tools don’t just give you content ideas.
You can use the insights you gain here and the prompts real users are inputting into AI tools to understand where you can improve and expand your products/services.
The future of marketing is truly collaborative across departments. And these kinds of insights can help align both your SEO/content teams and your product and marketing divisions.
This can lead to a better user experience on your site, a better product for your customers, and increased business growth.
Pro tip: At the bottom of most of these tabs, you’ll also find “AI Strategic Insights.” These are AI-powered suggestions you can use immediately to boost your AI visibility.
Step 6. Identify More Content Ideas
Step 6 is to find more ideas for creating new content and optimizing your existing pages.
First, head to the Questions tab and scroll down to the Query Topics section.
Answer these questions with new content or in your existing content.
For example, Petlibro could create a blog post titled “How to Stop Your Cat Shaking Food Out of Its Feeder.”
They could also update their product pages to highlight that their feeders support different portion sizes for morning and evening meals, and add an FAQ section answering common branded questions.
To understand what content you might want to create (and which prompts are actually worth optimizing for), enter the relevant ones into tools like ChatGPT. (Make sure you enable web search.)
The example below returns a lot of scientific papers, so it would likely be a tough one for Petlibro to appear for.
But there is a Reddit thread in there too. Which means a Reddit marketing strategy could be worth exploring to boost visibility for these kinds of prompts.
This next one is a more likely candidate, and we can see PetSafe (a competitor) gets cited as a source. (And Reddit appears again too.)
There is also a product carousel with links further down — none of which are from Petlibro.
So this would definitely be worth digging into to see why PetSafe (and the other products) are being recommended:
Do the product pages do a better job of conveying trust signals?
Are they more descriptive?
Do they have FAQ sections that answer the prompt’s question?
Bottom line:
You need to look closer than simply the prompts themselves to understand why other brands are being recommended ahead of yours.
But once again, if you scroll to the bottom, you’ll find AI-powered insights that can give you a head start.
Turn Your AI SEO Audit Insights Into Action
An AI SEO audit is a vital first step to make your brand AI ready. And Semrush’s AI SEO Toolkit gives you everything you need to get started.
But the audit is just the first step. Use these resources to turn what you learn from the tool into action for your brand:
AI has infinitely sped up the hype cycle in marketing.
So when the term “vibe marketing” came onto the scene, you may have rolled your eyes for a moment before you said, “I have to try this.”
In basic terms, vibe marketing means using AI to run entire marketing workflows. Usually, this involves a combination of:
Vibe coding: No-code AI tools where you type what you want (e.g., “Build me a landing page”), and the tool spins it up
AI agents: Always-on assistants that handle background tasks, like checking your inbox for leads or updating your CRM
And whether or not they consider themselves “vibe marketers,” many teams are already doing this.
In a survey of marketing teams doing $100m+ in revenue, GrowthLoop found that more than a third of those teams use AI to optimize campaigns or predict customer behavior.
And those embedding AI into their processes report more effective strategies.
So, is vibe marketing the next wave of marketing methodology? Or just more AI hype?
In this guide, we’re diving into real-world case studies that show how marketers are using AI in their daily workflows.
Plus, we’ll test the hype against reality based on my own experiments and the perspective of industry experts.
Vibe Marketing vs. Traditional Marketing
With vibe marketing, things like campaigns, segmentation, and competitor analysis can happen in the background. So you can focus more on creative work and strategy.
Here’s how it stacks up against traditional marketing:
Task
Traditional Marketing
Vibe Marketing
Campaign creation
Weeks of strategy, briefs, handoffs, and approvals
Concepts, landing pages, and emails drafted in hours
Audience segmentation
Manual data exports and persona-building
AI builds real-time dynamic segments
Competitive analysis
Manual research on competitor websites, social feeds, reports
Automated data scraping and AI summaries
Performance reporting
Hours compiling data into slides
Real-time dashboards + plain-English insights
This all sounds incredible, and it’s all technically possible for marketing teams today.
But here’s the catch: AI workflows are still clunky and experimental.
Hootsuite reports that while 83% of marketers say their AI budgets have increased, 4 in 10 companies waste at least
10% of their AI budget on tools that didn’t deliver.
Bottom line: Don’t expect AI workflows to run your marketing overnight. Sometimes building them takes longer than doing the task manually (I learned that firsthand — more on that later).
So, what does vibe marketing look like when it does work?
6 Examples of Vibe Marketing in the Wild
Vibe marketing can seem like a vague concept.
But when we talk about using AI to automate social listening workflows, follow up with inbound leads, or run competitive analysis, all of a sudden this ambiguous concept takes on real-world meaning.
We’ll see six examples of brands using vibe marketing in their daily workflows.
Plus, how you can copy these ideas into your own strategy.
1. Build Enterprise-Level Campaigns Without Reliance on Technical Teams
The biggest slowdown in most campaigns isn’t the marketing work itself. It’s the wait for other teams to deliver what you need.
At the job site, Indeed, those delays stretched to an average of 3.5 months per campaign.
Even simple requests — like defining an audience segment — meant analysts had to pull data from their warehouse. Then, engineers had to reformat it before marketing could use it.
With vibe marketing, the team broke that bottleneck.
They used the AI platform GrowthLoop to turn raw customer data into ready-to-use segments.
Now, their team can type a plain-English prompt (e.g. “nurses in the U.S. who searched jobs in the last 30 days but haven’t applied”) and instantly generate that segment.
Instead of waiting a whole quarter to get in front of job seekers, the team can now react to hiring needs in almost real time.
Try It Yourself:
If you’re on an enterprise team already using a data warehouse tool, GrowthLoop’s makes it easy to type a goal, generate audiences, and send them directly into campaigns.
On the other hand, let’s say you keep customer data in a CRM or spreadsheet — names, emails, recent purchases.
With a tool like Clay, you can import those leads and use the built-in AI to enrich them with more data.
Then, you can create campaigns that automatically go out based on that enrichment.
For example, when a company has received funding in the last three months, they can be automatically added to a campaign.
In seconds, you’ve got a list ready to target.
What makes this powerful isn’t just faster data access.
It’s the AI layer that turns raw information into something marketing can actually act on, without waiting on anyone else.
2. Automate Social Listening Workflows
Getting a lot of mentions on social media is great — until it isn’t. Some social media managers can spend hours every day sifting through comments and posts that tag the brand.
More than just being a tedious task, this is completely unsustainable.
Which is exactly what Webflow’s two-person social team realized.
Between Reddit, X, YouTube, and forums, they faced 500+ daily mentions. But only a handful actually needed a human reply.
Finding those few was like looking for needles in a haystack.
So, they built an AI workflow to do the sorting for them.
The system scans every mention, tags it by sentiment and urgency, and pushes the important ones straight into Slack.
Out of 500+ daily posts, the team now sees just 10–15 that matter most — and responds within the hour.
Pick one high-volume channel — maybe Reddit, X, or even a busy community forum.
Use a tool like Gumloop or Apify to pull in mentions of your brand. Then, run them through an AI categorizer to flag sentiment and urgency.
Start small, check the tags for accuracy, and only then scale to other platforms.
Note: To take this workflow a step further, add a tool like ManyChat or Yuma.ai to generate automated responses to posts and DMs. Entrepreneur Candace Junée did this and saw a 118% increase in leads while saving 15 hours per month answering Instagram DMs.
3. Create On-Brand Content Assets
Ever tried to turn a 40-page technical document into a blog post or campaign copy?
The content is there, but shaping it into something clear — and in your brand’s voice and style — takes time.
At Pilot Company, with multiple sub-brands and channels to manage, that challenge multiplied.
Writers spent hours summarizing technical docs into usable briefs. Designers waited for copy that matched the right tone before prototypes could move forward.
And inconsistencies crept in across brands.
So, the team used Jasper to help build consistency in style and tone.
They used the tool’s summarizer to condense long technical documents into actionable outlines, and the brand-voice model to keep messaging aligned across sub-brands.
Designers could even pull realistic placeholder text without waiting on writers.
The result: Each team member saved 3–5 hours a week, freeing them up for strategy and storytelling instead of slogging through documents.
Try It Yourself:
With a tool like Jasper, you can add specific instructions about your brand voice, audience, and even include source material to show what great content looks like for your brand.
Then, you can use it to create copy and content for entire campaigns.
You can also use tools like Notion AI, Claude, or ChatGPT to turn long documentation into campaign content.
Start by inputting your brand voice, style, target audience, and any other details that might be useful. Then, upload documentation and ask the AI to turn it into specific pieces of content.
Test the tools to find your favorite. Make sure to give specific instructions on what kind of output you’re looking for.
Use AI to generate briefs, draft first passes, or speed up design prototypes — and reserve human time for the creative polish.
On paper, 500+ inbound marketing leads a day looks like a dream for a small agency.
But for Tiddle, a six-person influencer agency, it was a nightmare.
They were buried in the flood of messages, with only a few that were worth pursuing. Sorting through the noise ate up 6–8 hours a day — time that should’ve gone into client campaigns and outreach.
Instead of hiring more staff, they brought in AI.
Using Lindy, every inbound email was screened automatically.
Low-quality offers were politely declined, while promising ones were flagged and routed to the right person.
If terms weren’t a fit, the AI could even suggest counteroffers.
The team went from slogging through hundreds of emails to focusing only on the 10–15 real opportunities that mattered.
As Tiddle’s CEO, Mike Hahn, says, “Every deal we’ve closed in the last few months came from Lindy surfacing the right conversations.”
Try It Yourself:
Pick one channel where inbound volume is overwhelming (email, DMs, LinkedIn).
Define the “must-haves” for a qualified lead (budget, offer type, brand fit), then use a tool like Lindy or Clay to screen and tag incoming requests.
You can even set up conditional logic so the tool can change how it responds based on specific conditions.
Note: Small companies aren’t the only ones making use of AI for inbound leads. Ariel Kelmen, president and CMO of Salesforce, recently said that they use AI agents to handle interactive follow-ups with leads. And those agents manage the first 80% of the conversation.
5. Build Hyper-Personalization for Your Ideal Customer Profiles
“Hi [first name]…” personalization doesn’t cut it anymore. But manually tailoring every message to your ideal customer profiles (ICPs) is impossible to scale.
Oren Greenberg, a solo marketing consultant, faced this problem.
And since there was no system that fit his ideals of hyperpersonalization, Oren built his own.
He coded a workflow in Replit that filtered a 50,000-company dataset, excluded existing contacts, and generated outreach tailored to each company’s stage and challenges.
The result: outreach so specific it only makes sense for the intended recipient.
Pro tip: Hyper-personalization works only if you deeply understand your ICP — AI can’t do that thinking for you. But once you know who you’re selling to, it can scale bespoke messaging in ways you couldn’t manually.
Try It Yourself
If you’re a highly technical person with the skills and know-how to recreate something like this in a vibe-coding tool, then by all means have at it.
For the rest of us, using a tool like Clay is a fast path to get 80% of the way there.
Start by defining your ICP.
Then use Clay to pull in business data, filter it against your ICP criteria, and enrich it with extra context.
With that data in place, you can add an AI-powered column that drafts personalized outreach for each prospect.
Run a pilot batch of 50–100 and iterate until the system feels like true one-to-one messaging.
6. Run Competitive Analysis
New marketing roles often start with 30-60 days of slow discovery.
Who are the real competitors? What do customers actually care about? What language do they use?
Semrush’s former VP of Brand Marketing Olga Andrienko found a way to shortcut that process.
Before Day 1 at a new job, she suggests running an AI-powered competitive analysis.
Pull your site and the top competitors’ pages, transcribe the most-viewed YouTube reviews, and mine Reddit and forums for repeated complaints.
Then, feed that into an AI summarizer to surface frequent feature praise or criticism and real customer phrasing. Tools like Google Opal or Gemini help cross-link those insights into a positioning map.
Whether you’re stepping into a new role, launching a campaign, or scoping out a new market, the same workflow applies.
First, pick your brand and three competitors. With a scraper tool like Apify, get your website copy and grab a handful of top YouTube reviews and forum threads.
Then, feed those into a tool like Claude, Gemini, or ChatGPT to summarize and analyze the data.
Extract the top five pains and language customers use, and sketch a one-page positioning map you can bring to meetings.
That way, you start your campaign with clarity — not uncertainty.
My Disastrous Vibe Marketing Experiment (What I Learned the Hard Way So You Don’t Have To)
Giving you examples is great, but I wanted to put all this to the test and see if I could build a usable AI workflow for myself. (Spoiler: It did not go well.)
Goal: Save time replying to LinkedIn comments without losing my voice.
Constraints: Something I could test immediately, for free, and that would actually be useful.
Method: Build a workflow that scrapes comments, learns my style, and drafts replies I could approve before posting.
Time spent: 4+ hours
1st Attempt
First, I created an account in PhantomBuster, a tool that automates actions on social platforms like LinkedIn.
Then, I connected my LinkedIn account and set up the “LinkedIn Post Commenter and Liker Scraper” tool.
I asked it to retrieve only comments from my LinkedIn posts from recent days, which it did successfully.
Next, I created a new “Scenario” in Make, a no-code automation and AI agent tool, and added PhantomBuster as the start of that workflow.
Then, I built a Make AI Agent that would draw from my previous posts to learn my voice..
I added that Make AI Agent into the workflow, giving it instructions to analyze the comments scraped by PhantomBuster and produce a reply.
And finally, I added Google Docs as the final output. The idea was to create a document where I could see both the original comment and the AI-generated reply.
The whole workflow ran successfully, which I took as a win and closed up shop for the night.
But when I opened my laptop the next day to check all the wonderful replies my new AI buddy had written for me, all I found was this lovely Google Doc:
Still undeterred, I decided to try something different.
2nd Attempt
Along the same lines, I wanted to build an automated AI workflow that would scrape content from LinkedIn that I’m interested in. Then, write comments in my voice and style using my existing content as a foundation.
I used a similar workflow: PhantomBuster to scrape the content, Make AI Agents to analyze and write comments, and getting the final output in a Google Sheet.
Unfortunately, that gave me the exact same result (only this time in spreadsheet format, woohoo!):
What especially irked me was that the automations themselves were running successfully. But I still had no output.
So after more than four hours of work (and a lot of back-and-forth with ChatGPT), I finally gave up.
Could I have figured out this AI workflow eventually? Yes, I have no doubt.
But at that point, how much time would I be saving?
Does a little time saved on writing comments justify spending hours building an AI workflow (and what should’ve been a relatively simple one, at that)?
Here’s what I learned from this experiment:
If you’ve been secretly feeling a little skeptical about vibe marketing, you were right
The folks building vibe-coded apps and AI workflows in five minutes have years of practice. The rest of us can’t expect the same speed.
The tools that are currently available for vibe coding and AI automations aren’t ready yet for the average user to just jump in and build
If someone with a background in tech (me) struggled so much with a simple workflow, imagine the challenge of something more complex
And while it’s true that others are seeing success with vibe marketing (like the examples that we saw above), there are also clear downsides.
It’s Not All a Bed of Roses: The Caveats of Vibe Marketing
Vibe marketing is like any new marketing buzzword: We all love to join in the hype, even if we don’t quite get it.
The problem is, the hype can obscure reality.
After running my own experiments, I also talked with other experts in the field. What emerged was a clear pattern — vibe marketing is powerful, but the gaps between promise and practice are real.
It’s Harder Than It Looks
The idea that you can tinker around with AI for five minutes and produce a usable workflow just isn’t feasible for the majority of us.
And yet, that’s the promise we’re seeing over and over again:
This all sounds great, but we’re marketers: We know better.
Simple automations? Sure.
But robust, real-world systems usually need engineering support or serious AI chops.
Without that, you risk fragile prototypes that break the first time they’re stress-tested.
Oren Greenberg, the AI marketing consultant we talked about earlier, told me:
“The level of hype is out of this world. Vibe coding is cool, and there are a few people who’ve built a nice small business out of it. But it’s mostly the vendors who are minting cash.”
Here’s the point: Don’t get swept up in the hype. Check the source.
The Infrastructure Is Messy
AI workflows look slick in a demo. But in practice, you have to plug into your marketing stack.
And that’s where things get complicated.
For example, you might build the perfect AI agent to score inbound leads, only to realize that your CRM can’t accept the data the way you need.
As Austin Hay, Co-Founder of Clarity and MarTech teacher at Reforge, noted in a recent interview:
“Everyone’s excited about unstructured data, but unstructured data is useless when it needs to play nice with structured systems.”
For traditional marketing teams, this means your AI workflows may not play well with your company’s established martech systems.
And if your tech’s API documentation is outdated (or worse, nonexistent), it will be nearly impossible to vibe code your way to integrations between existing tools.
AI Can’t Invent Outside its Datasets
Another misconception around vibe marketing is that you can throw any messy, undefined problem at an AI agent and it will figure it out.
The reality is less glamorous.
AI thrives on patterns it’s seen before. Point it at a well-scoped, repeatable task, and it shines.
But ask it to invent outside of its training data — or solve a fuzzy, novel problem — and you’ll end up with loops, errors, and wasted hours.
Speed Only Works When You Know Where You’re Going
AI can help you move fast. But if you don’t know what metrics matter and where you want your workflows to lead, faster will just mean getting lost sooner.
Marketers who succeed with vibe coding are the ones who define the finish line first. AI then becomes a vehicle to reach those goals faster, not a substitute for setting them.
Kevin White, Head of Marketing at Scrunch AI, put it this way in a recent interview:
“AI multiplies the abilities of people who already know their craft. Treat it as a force multiplier for your expertise rather than a substitute for it.”
Vibe Marketing Tools Free Up Time…But for What?
As more marketers build AI workflows and vibe code their way to productivity, a philosophical question arises: why?
AI workflows and automations free up time (when they work). But, what are we freeing up time for?
By eliminating the busywork, we’ve saved only the most demanding tasks for ourselves. And while creating and strategizing may be what we enjoy most, it’s impossible for most people to do that kind of mentally-taxing work for eight hours straight.
“In conversations with CMOs, it’s clear that GenAI has become a core part of how modern marketing teams operate. What separates the winners is a commitment not just to scaling the technology, but to empowering the people who use it. Those CMOs investing in tools and talent are the ones rewriting the playbook.”
Ready to Try Your Own Vibe Marketing Experiment?
Vibe marketing isn’t snake oil. But it’s not a silver bullet, either.
The hype can make it feel like anyone can vibe code and automate their way to a marketing edge. But the reality is far more nuanced.
The marketers getting real value from vibe marketing are the ones with strong fundamentals, clear goals, and often a layer of engineering support behind them.
For the rest of us, the takeaway is simple:
Vibe marketing is worth experimenting with, but it won’t replace strategy, judgment, or hard-won expertise.
