OpenAI is pitching premium-priced ads in ChatGPT — with far less data than advertisers are used to getting.
What’s happening. According to a report, OpenAI is pricing ChatGPT ads at roughly $60 per 1,000 impressions — about three times higher than typical Meta ads. Despite the cost, advertisers will receive only high-level reporting, such as total impressions or clicks, with no insight into downstream actions like purchases.
Why we care. ChatGPT is emerging as a brand-new, high-attention ad environment — but one that comes with trade-offs. The high CPMs and limited reporting mean early tests will be more about brand exposure and learning than performance efficiency.
For marketers willing to experiment, this offers a first-mover chance to understand how ads perform inside AI conversations before the format scales or measurement improves.
The tradeoff. OpenAI has left the door open to expanding measurement in the future, but it has publicly committed to never selling user data to advertisers and keeping conversations private. That stance limits the kind of targeting and attribution advertisers expect from platforms like Google or Meta.
Who will see ads. The first ads will roll out in the coming weeks to users on ChatGPT’s free and lower-cost Go tiers, excluding users under 18 and conversations involving sensitive topics such as mental health or politics.
Between the lines. OpenAI is positioning ChatGPT ads as a premium, trust-first product — betting that context, attention, and brand safety can justify higher prices even without granular performance data.
Bottom line. ChatGPT ads may appeal to brands willing to pay more for visibility in a new AI-driven environment, but the lack of measurement will make performance-focused advertisers think twice.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/ChatGPT-Image-The-agentic-web-is-here-Why-NLWeb-makes-schema-your-greatest-SEO-asset-G9MQd5.webp?fit=1920%2C1080&ssl=110801920http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-26 19:17:302026-01-26 19:17:30ChatGPT ads come with premium prices — and limited data
AI Overviews, which place generated answers directly at the top of search results, are improving the search experience for users.
For businesses that rely on content to drive traffic from search engines, the impact is far less positive.
Google has been moving toward more “helpful” results for years, and zero-click searches are nothing new.
AI Overviews accelerate that shift, absorbing much of the traffic opportunity that search has historically provided.
How AI changes the work of search
For years, search followed a familiar pattern:
A user entered a short query, such as “team building companies.”
Google returned a page of paid and organic results.
The user did the work of reviewing and refining.
Most of the effort happened at the end of the process.
Google organized results based on intent and behavioral signals, but users still had to click through listings, conduct follow-up searches, and piece together an answer.
AI reverses that flow:
The user asks a more detailed question.
AI runs multiple searches and processes the results.
AI delivers a summarized response.
Traditional search allows for refinement, but each new query effectively resets the experience.
AI, by contrast, is conversational. Each interaction builds on the last, narrowing in on what the user actually wants.
The result is a faster, cleaner path to an answer – with far less effort required from the user.
The path of least resistance
This shift matters because it aligns with a basic human tendency.
People generally choose the easiest available option. If something is easier and produces a better result, adoption follows quickly.
Seeking the path of least resistance is an evolutionary trait that likely served humans well in earlier eras.
Today, however, it often shapes behavior in less intentional ways, including how people interact with ads and information.
AI is not perfect, but it is typically faster, easier, and more effective than digging through traditional search results.
That advantage makes widespread adoption inevitable, especially as AI continues to be integrated into the websites, apps, and devices people already use.
Generative answers are shifting where users enter the funnel, with engagement increasingly starting mid-funnel around content that demonstrates experience and expertise.
This is the type of content users historically would only engage with on a company’s website, or through other owned channels such as YouTube.
This does not mean top-of-the-funnel content is no longer important. Blogs, guides, and videos still matter, videos in particular. However, it may be worth reconsidering how that content is distributed rather than relying solely on traditional organic search.
With the rise of AI tools such as Gemini and ChatGPT, users can now handle much of this comparison work through AI, saving significant time.
For example, the shift looks like this:
From “Mid market ERP platforms.” Where the user must sift through results, compare options, build spreadsheets, and conduct extensive manual review.
To “Which mid-market ERP platforms work best for manufacturing firms, integrate with our existing stack of X, Y, and Z, and won’t collapse during implementation?”
This changes where the user must exert effort.
A more detailed question or input produces a far stronger response or output.
You could argue that traditional search had degraded into a form of garbage in, garbage out (GIGO), where short, generic queries produced ad-heavy, blended results that were time-consuming to mine for real answers.
The result is user fatigue. Endless clicking, avoiding ads, and sorting through widely varying content has become a chore.
AI offers a cleaner, faster, and less cluttered experience, delivering summarized pros, cons, and supporting evidence at each stage of the decision-making process.
All of this can happen inside an AI tool, without the user ever needing to visit the site where the content originated.
AI is increasingly becoming the default interface for information. These are still early days, and the experience will continue to improve, becoming faster, smoother, and more effective over time.
If you want AI to recommend your brand or include it in increasingly nuanced research, your most important content must be visible and accessible so it can be retrieved and used to generate AI answers through retrieval-augmented generation, or RAG.
Frameworks such as “They Ask, You Answer” (TAYA) by Marcus Sheridan are particularly effective here.
The premise is simple: If customers ask the question, you should answer it.
The framework focuses on five core areas, identified through extensive research, that address customer needs, drive engagement, and provide AI with the detailed information it needs to map to real user questions.
This approach works because it makes sense. It benefits users, improves visibility, drives leads, and supports sales. It is not an abstract AI strategy. It is good marketing.
These are the five key areas that TAYA focuses on:
Pricing and cost: If users search for pricing and cannot find it, they do not assume they should call for details. They often assume the product is too expensive or that information is being withheld, and they move on, or ask AI for a competitor’s pricing. Even when pricing is custom, you should explain the factors that influence cost.
Problems: Address the obvious issues. This includes problems with your product, your industry, and the drawbacks of specific solutions. Being transparent about limitations builds trust more effectively than excessive positivity.
Versus and comparisons: Buyers are choosing between alternatives. If you do not create comparison content, someone else will. Be objective. If a competitor is better for a specific use case, say so and focus on your ideal customer profile.
Reviews and ratings: People look for the best options and trust peer opinions more than brand claims. Create honest reviews of products and services in your space, including competitors. This process is informative for both users and brands.
Best in class: Users frequently search for “best” solutions. Lists such as “Top AI marketing agencies in [city]” are effective, even when they include competitors. Including alternatives demonstrates that customer fit matters more than self-promotion.
From an AI and SEO perspective in 2026, these five topics represent some of the highest-value data points for RAG systems.
Tools such as the Value Proposition Canvas and SCAMPER can support ideation and content variation, helping AI better understand your offerings.
Checklist: RAG-friendly formatting tips
Do not break content into meaningless fragments. Instead, use formatting that helps RAG systems navigate comprehensive resources:
Use question-based headers: Mirror real user questions in H2s and H3s, such as “How much does X cost?”
Lead with the answer: Apply the inverted pyramid. Start with the direct response, then add context.
Use bulleted lists for attributes: Bullets help RAG systems extract structured information.
Define key terms: Provide clear, one-sentence definitions for industry jargon.
Link to evidence: Cite sources for statistics and results to support credibility.
Treat blog posts as a knowledge base for AI. The clearer and more specific the information, the more retrievable your brand becomes.
Write for humans, not for bots
It bears repeating: Content should not be simplified solely for AI.
The environment is shifting, and new tools are changing how people find information and make decisions. Yet many fundamentals remain.
SEO tactics still apply, but AI now acts as a superconsumer and summarizer of the information that influences choice.
The task is to identify, create, and structure that information so that when users ask a question, you have already answered it and are part of the conversation.
Everybody wants smoother workflows and fewer manual tasks. And thanks to AI models, automation is at the center of conversations in marketing departments across all industries.
But most rarely get the results they’re looking for.
According to Ascend2’s State of Marketing Automation Report, only 28% of marketers say their automation “very successfully” supports their objectives.
While 69% felt it was only somewhat successful.
While this specific stat is from 2024, I imagine the broad idea is still true. Especially since there are so many more automation options and tools. It can get overwhelming to decide a go-forward plan and implement effectively.
So if you feel stuck in the camp of “not bad, but not great” marketing automation, you’re not alone.
The good news?
Once you understand the core building blocks, you can turn messy, half-automated systems into workflows that actually move the needle.
A good marketing automation usually involves four basic steps:
A trigger: A catalyst event that starts the automation
An action: One or more steps that happen in sequence after the trigger
An output: The end result
A loop or exit point: A new trigger, or an event that stops the automation
In this article, we’re going to discuss how to use these steps to automate:
The mechanics of content creation (and no, we won’t just be telling you to “write it with AI”)
Beyond the basics of email nurtures
Your PR strategy
Social media engagement
Automate the Mechanics of Content Creation
Content marketers are creative people. We don’t want to automate away the creative work that drives results.
That said, we can automate marketing workflows that come before and after creating. (So we can spend more time on high-impact work.)
Here are some simple ways to get started.
1. Basic Brief Builder
Tools required:
Make (free for 1,000 credits per month, paid plans start at $9/month)
Your favorite keyword research tool (plans vary)
Project management platform (tools like Asana offer a free plan)
Google Sheets, Google Docs (free plan available)
Every week, content marketers around the world spend hours researching keywords, pulling search data, creating new briefs, and adding tasks to their project management systems.
What if you could do most of that with one automation?
Here are the basics of how this works:
Trigger: A new row is added to a Google Sheet (your new keyword)
Action: That keyword is run through your SEO tool, which pulls keyword difficulty, search volume, related terms, and top organic results
Output: A new Google Doc with the data inside, and a new task in your project management tool
In the end, the automation will look like this:
And if this seems scary, don’t worry: I’m going to walk you through each step to create this with Make. (Or, you can go ahead and copy this Scenario into your own Make account here.)
First, you’ll need a Google Sheet for your source.
Start with columns for your new keyword, status, brief URL, and task URL. To get started faster, copy this template here.
Next, add Google Sheets as the trigger step, and select “Watch New Rows.”
After that, select the Google Sheet you want to watch.
This runs the automation every time you add a new keyword to that sheet.
Now, it’s time to gather information from your SEO tool. For this example, we’re going to use Semrush. (You could also use an API like DataForSEO.)
Our first Semrush module will be “Get Keyword Overview.” (You might see different options depending on the specific tool you use.)
You can choose whether to see the keyword data in all regional databases, or just one region.
In this task, you’ll map the “Phrase” to the “Keyword” column from your Google Sheet. Then, choose what you want to get as an output. (In this case, I only want to see the search volume.)
Now, let’s create another Semrush model to “Get Related Keywords” to gather relevant keywords from Semrush.
Again, you’ll map the “Phrase” to the keyword column from our Google Sheet, and choose what data you want to export. (I chose the keyword and search volume.)
You can also decide:
How the results are sorted
Whether to add filters
How many results to retrieve
Now, you’ll need to add a text aggregator into your workflow. This tool compiles the results from Semrush so we can use them in a Google Doc later on.
Here, simply map the source (our Semrush module).
Then, in the “Text” field, map the data as you want it to appear.
Next, we’ll create a Semrush module that runs “Get Keyword Difficulty.”
Again, we’ll map the “Phrase” to our keyword from the Google Sheet, and choose to export the “Keyword Difficulty Index.”
Next, run the “Get Organic Results” module from Semrush to export the sites that are ranking for your new target keyword.
Select the “Export Columns,” or the data that you want to see, and limit the number of results you get (we chose 10).
Since we’re getting multiple results, this module will also need a text aggregator to transform those results into plain text for our Google Doc.
We’ll set it up exactly the same way, but this time map the “Get Organic Results” module.
In the “Text” field, I’ve added “Bundle order position” (where that result is ranking in the SERP), and the URL of the ranking page.
Now, for the fun part.
It’s time to build your basic content brief in a Google doc.
Before you add this into Make, you’ll need to create a Google Doc as a template. This template should have variables that can be mapped to the results you get in your automation.
To show up as variables, you’ll need to wrap them in curly brackets. So, your template will look something like this:
Now, you’ll create a new module in your Make scenario to “Create a Document from a Template.”
Once you connect the Google Doc template you created, you’ll see all of the variables you added in curly brackets as fields in the configuration page.
Now, all you have to do is map those variables to the results you’ve gotten from Semrush and your text aggregators.
Now it’s time to add this new brief into your project management tool. Make lets you connect several tools, including Asana, Trello, Monday, and Notion.
In this scenario, I already have an Asana project for content production.
So I choose the “Create a Task or a Subtask” module for Asana, and map that existing project.
I can also add project custom fields (like a link to the brief in Google Docs), choose the task name (like the keyword), and automatically assign it to someone on my team.
Lastly, I want to go back and update my original Google Sheet so that I can see which keywords have already been run, and where their briefs and tasks live.
So, I add Google Sheets again as the final step in the automation and connect the same spreadsheet that we had at the beginning. Under “Values,” I can map the brief URL from Google Docs and the new task URL from Asana to columns in my spreadsheet.
I also set this so the “Status” column is updated to “Done.”
Now, let’s run this scenario and see what happens.
First, I add a new keyword to my Google Sheet.
This triggers the automation to run.
The first thing that’s produced is a brand new Google Doc with all of the SEO data from Semrush. You’ll see this new doc appear in your Drive, and you’ll find the link in Asana.
Next, I’ll see a new task appear in my Asana project (with the brief link included).
And finally, the Google sheet will be updated to show us that the task has been completed.
Plus, it adds in the links to the new brief in Google Docs and the new task in Asana.
And there you go: you now have a basic content brief builder automation.
Are these complete briefs? No. But the information provides a great start, gives the writer SERP context, and frees up more time to fill out other important content brief elements.
Resources for this automation: To get started faster, use these templates:
Tools required: Your favorite project management tool (paid or free options available)
Project management tools are great for organizing your content workflow.
But the more tasks you create over time, the harder it is to keep track of and manage those systems.
Many project management platforms give you built-in automation tools to help things run more smoothly. Let’s talk about automations that can help your content workflow specifically.
Triggers might include:
A new task is added to a project
A custom field changes
A new assignee is added
A subtask is completed
Due date is changed (or coming up soon)
A task is overdue
And actions could be:
Add to a new project
Auto-assign to a team member
Update a status
Move task to a new section
Create a subtask
Add a comment
For this example, we’re going to use the Rules system in Asana, but the same basic principles apply to almost any major project management tool.
To start, click the “Customize” button in the upper-right corner of your content management project, and create some custom fields.
Especially important here is the “Status” field. The options here should follow the steps in your content process, and will probably mirror the sections in your Project.
Once your “Sections” and “Fields” are set up, you can create some rules.
These can help dictate what happens when a new brief enters your content workflow and assign it to whoever is in charge of moving it forward in the process.
Use a Rule to auto-assign someone on your team (for example, your content manager or editor) to the task.
Now, let’s say a new article is now in progress with a writer.
Create a rule that moves the task to the corresponding section of your project when the status is set to “Writing.”
If your content tasks have subtasks (like “create outline,” “write article,” “edit,” or “design”), you can track completion and use that to move pieces forward.
In this case, you can set a rule that once all subtasks are complete, the task moves to the “Ready to Publish” section.
Once the task moves to that section, set a rule to auto-assign it to the team member who publishes posts.
Then, when the status is set to “Published,” the task could be moved into a separate project where completed tasks of published content are stored.
This allows you to clear the tasks from your main production workflow, but still keep them on hand in case the piece needs to be updated in the future.
What if a piece of content isn’t completed by its deadline?
Set up an automation that checks in with the team to see what the status is.
There are plenty of other automations you can run in Asana or other tools.
But these basic workflow automations will help your content production process have better handoffs and less friction.
We do this at Backlinko using Monday.com as our project management tool.
Email nurtures are relatively easy to put together in any basic email tool: for example, sending a welcome email to a new newsletter subscriber, or a transactional email to a new customer.
But let’s talk about some ways to take those automations even further.
A trigger: Such as someone signing up for an email list
An action: The new contact is added to a list or segment
An output: They new receive a series of pre-made emails
An exit condition: The sequence finishes once all the emails are sent, or once the contact takes a specific action, like buying a product
Exit conditions are especially important, because you don’t want people to receive another email from you after they’ve already completed an action. (Hello, promo email that arrives after I already made a purchase.)
Let’s walk through how to use marketing automation tools for email.
3. Behavior-Based Nurtures and Follow-Ups
Tools required: ActiveCampaign (paid plans start at $15/month, although other email platforms offer automation capabilities too)
When you trigger an email sequence based on real behavior, you’re catching people in the moment when they’re more likely to engage.
For example, if you want to help a new user get to know your platform, you can trigger onboarding emails based on the actions they’ve taken so far.
Or, if you want to reduce cart abandonment, you can send a special promotion for customers who have items in their cart.
This improved targeting can lead to better engagement from your email list.
All you have to do is match the right trigger to the right action. For example:
Trigger
Action
Someone downloads a resource
They receive a series of emails on that topic
A customer purchased a product a few months ago
They get a reminder to replenish their stock
A contact browses a product category, but doesn’t make a purchase
They get an email reminding them of what they looked at
A new user subscribes to your platform
They get a series of emails walking them through specific actions
Your exit condition could be when the person:
Completes their purchase
Books a call
Starts a free trial
Replies to your email
For example, let’s say you want to send a series of emails reminding someone that their subscription is reaching its end date. It could look something like this:
Trigger: End date is within 20 days from now
Action: Send series of three emails up to the last day of their subscription (we don’t want to send too many)
Exit condition: Customer responds to the email, or renews their subscription
Here’s a great example for home insurance renewal:
Or, let’s say a new lead just signed up for a free trial or freemium account.
You could create a workflow that pulls information from the onboarding survey in your tool, and builds a personalized, 1:1 email sequence.
Check out this example from HubSpot:
When I signed up for the account, I identified myself as a self-employed marketer. HubSpot pulled that information into this new trial campaign to make the email even more personalized.
So the question is: how do you get started?
Here’s a quick overview of how you could build a behavior-based email nurture automation in ActiveCampaign.
Let’s say you want to send an email sequence to a known contact who visited a certain page on your website. For example, imagine someone who subscribes to your email newsletter, but isn’t a customer, just visited your pricing page. (In other words, they may be close to signing up — they just aren’t quite convinced yet.)
Before you start this automation, you’ll need to enable Site Tracking on your account in ActiveCampaign. To do this, install the tracking code on your website so ActiveCampaign can see page views.
To start the automation, you’ll add new contacts who enter through any pipeline.
Now, when a known contact (someone who’s already in your database) visits a tracked page, ActiveCampaign associates that page view with the contact’s record, and can start an automation.
The real trigger is the next step: “Wait until conditions are met.”
In this case, the condition is that the contact has visited an exact URL on your website.
Pro tip: You can also adjust this so the email series only runs when the person visits a page multiple times, showing a higher level of interest.
Next, set a waiting period from the time the person sees the page to when the email is sent.
And finally, write your email and add it to the workflow.
After that, you could:
Wait a certain amount of time, then send another email
Set an exit condition if the contact replies or makes a purchase
All of this effort turns into an email like this one that I received from Brooks after visiting one of their product pages:
This makes me way more likely to revisit the shoes I was looking at than a generic reminder email (or no email at all).
4. Webinar Lifecycle Automation
Tools required:
Demio (plans start at $45/month)
HubSpot (limited free plan available)
Webinars are an entire customer journey, including promotion, confirmation, reminders, and post-event follow-ups.
The trigger is normally one event: Someone signed up for your webinar.
The actions include:
Confirmation email
Day before and day-of reminders
“Happening now” email
Post-event replay email
For example, here’s a great reminder email from Kiwi Wealth:
Immediately after the webinar is finished, you might send an email like this one from Beefree:
And you’ll also want to follow up later with a replay and some action items for people who attended, like this:
Note: We got these examples from Really Good Emails, which is a great resource for getting inspiration for your own campaigns.
So, how do you create this automation?
Most great webinar tools allow you to do this. Demio, for example, allows you to automate marketing emails when you create a new event:
If you want to get really fancy, you can segment your post-webinar follow-up emails by whether or not the contact attended the webinar:
Demio’s built-in email is somewhat limited beyond an actual event.
So, you can connect it to HubSpot to add a new layer of segmentation to your lists.
Once this connection is live, Demio will import webinar attendance data into HubSpot.
For example, you can import data like:
Contacts who registered for the webinar
People who registered, but missed the event
People who attended the event
How long a contact stayed in the webinar
People who watched the replay
You can even add new contacts to lists directly in Hubspot if they don’t exist there already.
This automation will help your pre- and post-webinar flows run more smoothly. And hopefully get you more valuable engagement with those webinars.
Grow Your PR Strategy
For small marketing teams, PR outreach can use up a lot of valuable time.
Here are some easy automations to keep doing inbound and outbound PR requests, without spending your entire week on it.
Resource: Get your free PR Plan Template to help you pick the right goals, discover journalists, and make pitches that get press coverage.
5. PR Radar
Tools required:
BrandMentions (paid plans start at $79/month)
Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
Google Sheets (free option available)
Want to keep an eye on new articles that are related to your brand that you could potentially get featured in or a backlink from? Let’s build an automatic PR radar.
Note: Most monitoring tools send alerts, but those notifications disappear into your inbox. This workflow creates a shared, searchable log your whole team can access without extra logins—plus you’ll have a historical record for spotting PR trends over time.
This workflow looks like:
Trigger: A new article mentions your brand or related topics
Action: Pull all new mentions into one place to scan through them easily
Output: A simple, regularly-updated list of PR mentions
There are several tools that do this, but for this example, we’re going to use BrandMentions.
Once you set up your account and your project, head into settings to adjust which sources you’ll collect data from.
Remove social media, and just leave the web option. That way, you’ll get a clean list of articles and webpages that mention your brand or the keywords you added.
Once this is set up, you can connect your BrandMentions project to Zapier.
This will trigger the automation to start when any new mentions are added.
You can choose whatever output works best for you: whether that’s a Slack message, a new row in Airtable, or an addition to an ongoing Google Sheet.
For this example, I chose Google Sheets as my output. All I had to do was tie the data pulled from BrandMentions to the right columns in my spreadsheet.
Once that’s done, the automation adds new articles like this automatically into my spreadsheet:
Pro tip: Want to add a reminder? You can add another step that sends a daily Slack message summarizing all the newly added rows.
6. Media Request Matchmaker
Tools required:
RSS.app (free plan available)
Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
Airtable (free plan available)
PR would be nothing without the relationships we build with journalists and writers.
But it’s hard to know who’s writing about a topic that’s related to your brand. Or where your company’s internal subject matter experts can add their thoughts to promote your brand.
So, let’s build an automation to match new requests to your internal experts.
This involves:
Trigger: A new media request that matches relevant topics
Action: Classify new requests and match them to the internal expert with the most relevant expertise
Output: New requests are automatically routed to the right person
One of the most frequently updated places to find PR requests is on X/Twitter.
Search the hashtag #journorequest, and you’ll see hundreds of writers asking for expert contributions.
To prepare this for your automation, start by setting up an RSS feed with the hashtag #journorequest or #prrequest along with a relevant keyword.
For the simplest version of this, you can connect RSS.app directly to Slack and send a new message every time a new request is added to the feed.
But let’s be real: that could get overwhelming pretty quickly.
So, we’ll use Zapier for a more in-depth automation.
Start by adding “RSS by Zapier” as the trigger, and paste your RSS feed link into the configuration.
Pro tip: If you want to track journo requests for multiple topics, change the trigger event to “New Items in Multiple Feeds.” Then, simply paste in all of the RSS feed links. That way, they’ll all run through the same automation.
Next use “Formatter by Zapier” to extract the necessary information from the tweets.
First, in Formatter, choose the Action event “Text.”
Then, in the Configure menu, select “Extract Email Address,” and map the input to the description from your RSS feed.
Next, with another Formatter step, select “Text,” and “Extract Pattern.”
The input is still the same description (the original tweet).
In the Pattern box, in parentheses, add the keywords you want to track separated by a vertical bar, like this:
(cybersecurity|fintech|pets|saas)
Make sure that IGNORECASE is set to “Yes” so that the search isn’t case sensitive.
Now, it’s time to add that to a system you can use to keep track of new requests and route them to SMEs.
For this example, I’ve chosen to use Airtable. If you want to use this exact database, you can copy it here and we’ll use it as we move forward.
This database has tabs to keep track of your SMEs, the topics they can respond to, and the new requests that come in.
So, let’s connect that Airtable base to Zapier.
Our first step will be to find the right SME for the topic of our journo request.
To start, set the Action as “Find Record,” and link your Airtable base. We’ll pull from the SMEs table, and for “Search by Field” we’ll choose “Topics,” where we’ve previously added our SME’s favorite topics into the Airtable base.
Lastly for this step, map the “Search Value” to the previous step’s result (the topic from the PR query on X/Twitter).
Now, we’re going to create a new row in our “Requests” table in Airtable.
Add Airtable as the next step in this Zap, and select “Create Record” as the action. Link the same Airtable base, but this time select “Requests” as the Table.
Then, map the columns in that base to the information you’ve gathered. In this case, that would include:
Source = X/Twitter
Raw Text = The “Description” from RSS feed
Contact name = The “Raw Creator” from RSS feed
Contact Email = The output from our first Formatter step, which pulled the email from the original post
URL = Link from RSS feed
Topics = The output from our second Formatter step, which pulled the topic from the original post
SMEs = The “Fields Name” from our Airtable search step
Status = New
In the end, it should look like this:
And a new record is added into Airtable, like this:
If you want to get fancy with this, you can dig down into:
Which publications are requesting expertise, and rank them by their credibility
Automate messages to your SMEs to let them know there’s a new request for them
Get the Most Out of Social Media
For busy marketers, social media can be an incredible time-suck.
Keeping track of trends. Trying to post consistently.
All without getting stuck in an infinite doomscroll.
But a few simple automations can help you get back some of the time you spend on manually managing your socials.
7. Video Clip Automator
Tools required:
Zoom (free plan available)
Dropbox (free plan available)
OpusClip (plans start at $15/month)
Zapier (free for 100 tasks/month, paid plans start at $19.99/month)
Short-form video has been gradually gaining a bigger voice in marketing.
If you’re already creating long-form video (or even just doing recorded interviews with in-house experts), we have a handy automation to help you create video clips faster.
Here’s how it works:
Trigger: New Zoom cloud recording is ready
Action: Auto-create clips, burn captions, and create a new task in Asana
Output: You get social-ready video clips, and a new task to publish them
First, adjust your Zoom settings so your recordings upload automatically into a folder in Dropbox.
Next, head over to Zapier.
Your trigger step will be a new video uploaded to that folder in Dropbox.
Your next step will use OpusClip, an AI video editing tool. Select “Clip Your Video,” and map that new video file to the one uploaded in Dropbox.
OpusClip will then take your long-form video from Dropbox and use AI to clip key pieces. It also crops the video for vertical sharing and embeds captions.
You can also add your own brand template so that videos are edited with your brand’s colors and font.
Now that you have new video clips to share, it’s time to add a task to review and publish them.
So the final step in your Zap is “Create Task” in Asana (or your preferred project management tool).
You’ll tie this to a project you’ve already created in Asana, and link the project ID from OpusClip.
In the end, you’ll have a few video clips prepared and ready — all you have to do is download, review, and publish them to your social channels.
8. Comment & Community Nudge
Tools required:
Social media monitoring tool (like BrandMentions, paid plans start at $79/month)
Automation tool (like Zapier, free for 100 tasks/month, paid plans start at $19.99/month)
Are people talking about your brand online?
To keep positive sentiment high, you need to engage in those conversations. But finding the right conversations, and knowing how to reply, can take a lot of time.
Using a tool like BrandMentions, you can create a similar automation to what we built for the PR Radar earlier:
Trigger: A new mention of your brand appears on Reddit, Facebook, or LinkedIn
Action: Those new mentions are added to a Google Sheet, and you get a daily Slack message summarizing new mentions
To build this, all you’d need to do is swap out the Sources in your BrandMentions settings. Instead of Web, you’d include all of the social media channels you want to track.
If you want to get notifications for every new mention, you could connect the workflow to Slack. Then, a new message will be sent in the channel every time your brand is mentioned.
This basic automation could work for smaller brands.
But when you start getting hundreds of mentions per day, this will quickly become chaotic.
Here’s an example of how one company faced with this issue was able to automate this process in a deeper way:
Webflow was getting over 500 mentions per day. Their two-person team couldn’t keep up with monitoring and responding (alongside their regular workload).
So, they built an automation.
With Gumloop, they monitor, analyze, and flag only the posts that require a response.
They started with a Reddit scraper to pull relevant threads.
Then, they added an AI analyzer to gauge sentiment, rank priority, and assign a category.
After that, they added a step that would send all high-priority mentions to Slack for a team member to handle directly.
The result?
After testing and scaling this process, they were able to build an automation that processes 500+ mentions per day and escalates only the 10-15 that need immediate attention.
If you’ve ever thought, “How can I use AI to automate my marketing tasks?”
This is a great example of an AI automation that works for you without taking over your job.
Is Automation the Right Move? Ask Yourself These Questions First
Automation is the hottest trend.
But it’s hard to know what’s going to save you time and money, and what’s just another fad.
If you’ve ever spent more time trying to automate a task than it would’ve taken you to do the task manually, you’ll know what I mean.
To weigh up whether an automation is worth building, ask yourself these questions:
How much time does it take me to do this task manually every week?
Is the automation available with a tool I currently use, or would I have to pay for a new tool?
Is there a documented automation/integration I can follow?
Would this task still require human intervention (even with automation)?
Does this fit easily into our current workflow or process?
If the task:
Doesn’t take much time to do manually
Would still require human intervention even when automated
Isn’t easy to build an automation for
…it may not be worth your time.
On the other hand, if the task:
Is repetitive
Uses up hours of your workweek
Can be automated in tools you already have in your stack
…it’s probably time to give automation a try.
Build Your Automation Foundations, Then Keep Growing
The hype cycle of automation and AI can be overwhelming.
But don’t feel like you’re behind just because you haven’t automated away your entire marketing team yet.
Instead, focus on the automations that save you time and are sustainable.
We’ve just discussed eight different automations. Why not choose one or two that are most relevant to your business and team?
Start with the foundational automations that help smooth out your existing processes.
Then, you’ll have a better basis for building more complex automations.
To automate even more areas of your marketing workflows, check out our curated list of our favorite AI marketing tools right now.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-22 21:47:412026-01-22 21:47:41How to Automate Marketing With 8 Simple Workflows
Marketing budgets aren’t collapsing in 2026, but they are making a shift. That’s the part many teams miss.
That distinction matters. Rising media costs, weaker attribution, privacy changes, and AI-driven search shifts have created real pressure, but the data shows budgets are still moving into marketing. They’re just moving with more intent.
Our latest NP Digital research on how marketers are spending their money in 2026 shows a clear pattern: teams are reallocating toward channels that defend ROI, compound value, and hold up under volatility. This article breaks down what’s changing, why it’s happening, and how to think about your own marketing budget for 2026 without relying on outdated assumptions.
Key Takeaways
Marketing budgets in 2026 are not shrinking. They’re being consolidated around confidence, efficiency, and defensibility.
Channels tied directly to conversion, retention, and owned data are absorbing spend, while those with declining signal quality or unclear ROI are losing ground.
SEO and content are not disappearing, but expectations have shifted toward extractability, authority, and measurable downstream impact.
Paid media still plays a critical role, but marginal efficiency now determines where dollars stay or move.
Teams that can reallocate budget quickly, based on real performance signals, are gaining a structural advantage.
The State of the Marketing Budget in 2026
Let’s start with the context that’s shaping every budget decision this year.
Media costs continue rising across search and social. CPCs aren’t coming down, and competition for attention keeps intensifying. At the same time, privacy changes have reduced signal quality, making it harder to target precisely and measure accurately.
Economic uncertainty is pushing marketers to defend ROI more aggressively than ever. Every dollar needs a clear path to revenue, and channels that can’t prove their value are getting cut.
AI adoption has accelerated faster than most teams can operationalize. Nearly everyone is experimenting, but few have figured out how to turn that experimentation into systematic advantage. The gap between “using AI” and “getting results from AI” is wider than you’d think.
Here’s the good news: budgets are not disappearing. They are being reallocated with intent. The marketers who understand where efficiency lives and where it’s eroding are the ones capturing share.
What’s Driving Budget Decisions
The shift in spending comes down to a few core factors:
Purchase journeys are more complex. 94% of purchase journeys now involve multiple touchpoints. Search and social are the most influential, appearing in 79% and 73% of journeys respectively. But they rarely operate in isolation. Budgets are being distributed to support visibility across the full path to purchase, not just the final click.
Attribution is noisier. Third-party signals keep degrading, so budgets are following channels that stay measurable. Paid search, email, and CRO all offer clearer attribution than many emerging channels. In uncertain conditions, that clarity matters.
Organic reach is declining.Zero-click searches now account for roughly 58-60% of Google searches. Organic listings are being pushed below the fold by AI Overviews, ads, and SERP features. This is reducing organic click opportunities and increasing reliance on paid coverage.
Efficiency matters more than volume. When media costs rise and margins compress, growth comes from doing more with what you have. That’s why CRO, lifecycle marketing, and retention are getting more investment even as some acquisition channels face cuts.
The marketers who are winning in 2026 understand that budget decisions aren’t about chasing trends. They’re about matching investment to where performance can be proven and defended.
Where Budgets Are Growing, Holding, and Declining
Let’s look at the actual spending patterns across channels. We’ll start with the big picture, then break down what’s happening in each major category.
Overall Marketing Budget Direction
61% of B2B marketers are increasing overall spend this year, with 20% holding flat and 19% decreasing. B2C is slightly more cautious: 57% are increasing, 32% holding flat, and 11% decreasing.
The takeaway? Growth budgets still exist, but they’re being deployed more carefully than in previous years.
The Biggest Budget Shifts Since 2025
Here’s where the reallocation is happening:
SEO spend has rebounded sharply. After a softer 2025, 61% of marketers are now increasing SEO budgets (up from 44% last year). The return of confidence in organic search reflects a few things: better AI tools for content production, clearer ROI measurement, and recognition that organic visibility still matters even in a zero-click environment.
AI SEO investment is accelerating dramatically. 98% of marketers plan to increase AI SEO spend in 2026. This isn’t just hype. Teams have figured out that AI can accelerate research, content production, and optimization cycles without sacrificing quality.
CRO and UX remain a priority. 52% are increasing spend, and only 25% are planning decreases. When traffic is harder to earn, you optimize what you have. CRO delivers measurable improvements regardless of where visitors come from.
Content creation growth has slowed. Only 32% plan increases, while 31% plan to reduce spend. This reflects a shift away from volume-based content strategies toward fewer, higher-quality assets that can be repurposed across channels.
Organic social media is facing the steepest pullback. 64% of marketers are planning budget decreases. Organic reach has declined to the point where most brands treat social as a support channel, not a growth engine.
Email and lifecycle budgets have stabilized. 60% are keeping spend flat and 23% are increasing. Email remains one of the most reliable channels for retention and conversion, especially as first-party data becomes more valuable.
The pattern across all of this? Increased focus on channels tied to conversion and retention. Reduced investment in traditional advertising channels with declining efficiency signals. And a shift away from broad content volume toward targeted execution.
Channel-by-Channel Breakdown
Now let’s get specific. Here’s what’s happening in each major channel category.
SEO and Organic Search
SEO budgets are rebounding, but the strategy is changing. Digital channels now represent 61.1% of total marketing spend, and organic search remains a major piece. But zero-click searches and AI Overviews are changing how value gets captured.
Search is becoming answer-first. Google increasingly resolves intent directly in the SERP through AI Overviews, featured snippets, and knowledge panels. This means fewer clicks but doesn’t make SEO irrelevant, just less predictable on its own. SEO needs to optimize for visibility and citation, not just click-through.
Treat rankings as one output among several that matter. Visibility in AI Overviews and featured snippets matters as much as position one. Prioritize topics tied to revenue intent and customer lifecycle stages. Build content that can win both ways: clicks and citations. Measure organic success across visibility, assisted conversion, and brand lift. More brands are pairing search with other channels, like community, that capture attention off the SERP.
AI systems increasingly resolve intent directly in the SERP, which concentrates click opportunities into fewer, higher-intent moments. Brands that show up consistently in AI-generated answers are building trust and authority even when users don’t click.
Content and Thought Leadership
Content budgets are being reallocated toward assets that influence discovery, trust, and conversion across channels. Thought leadership is increasingly used to earn inclusion in search results and AI-generated answers.
Content still fuels discovery, even when the click doesn’t happen immediately. Strong content is what AI systems summarize, cite, and pull into answers. In a noisy market, a differentiated perspective is one of the few advantages you can own.
Design content for multiple outputs: search, AI summaries, social, sales. Prioritize fewer topics with deeper authority and a clearer point of view. Shift from publishing volume to publishing leverage. Use AI for research acceleration and synthesis, but keep humans in charge of insight, brand voice, and editorial judgment.
Creators especially matter here as a result. They help brands move beyond renting attention and toward building long-term loyalty that holds up even as platforms and algorithms change. This is important because things like original insight, point of view, brand voice, and credibility are not things AI can manufacture on its own. Editorial judgment and prioritization are still very human decisions.
AI can help scale content, but the trust, experience, and perspective that influencers, creators, and SMEs offer gives content weight and relevance with an audience.
Paid Search
Paid search remains a core demand capture channel, but expectations have reset. CPC inflation and competition continue to compress efficiency. Reduced organic click availability increases reliance on paid coverage.
Shift from keyword expansion to coverage efficiency. Prioritize high-intent, defensible queries over volume. Use fewer keywords with tighter control. Coordinate more closely with SEO and CRO. Put higher emphasis on marginal ROI rather than raw spend growth.
AI and automation now control bidding, targeting, and pacing by default. Competitive advantage shifts to inputs: structure, data quality, conversion signals.
Paid Social
Paid social remains the most flexible scaled reach channel. Platform-level shifts show TikTok leading growth at 57%, YouTube at 53%, and Instagram at 46%. Facebook is under pressure, with 36% decreasing spend and only 18% increasing.
Creative velocity matters more than audience hacks. Message clarity beats novelty. Platform-native formats outperform repurposed ads. Measurement focuses on incremental lift, not just ROAS. Close alignment with lifecycle and email capture turns paid social prospects into owned relationships.
Organic Social
Some cuts are dramatic—and predictable.
Organic social: 64 percent decreasing investment.
Content creation volume: Only 32 percent increasing; 31 percent decreasing.
Traditional display: Banner ads are essentially frozen (63 percent flat).
Facebook paid: Thirty-six percent decreasing.
The pattern is clear: Teams are cutting channels with declining reach, opaque ROI, or inflated costs.
But that doesn’t mean content or social isn’t important—it simply means they’re no longer funded as volume engines. The strategy is changing, not disappearing.
Influencer Marketing
Community building is one of the strongest growth areas in 2026 budgets, with 69% of marketers increasing spend. Influencer marketing is seeing even stronger growth at 78%. These channels support retention, referrals, and brand defensibility.
Friend and direct traffic drive more conversions than any paid channel. Don’t just focus on the channels that cause direct conversions. Focus on the channels that create brand awareness and influence purchase decisions earlier in the journey.
Email + Lifecycle
Email and lifecycle budgets remain resilient because performance is driven by trust, relevance, and timing. 60% are keeping spend flat and 23% are increasing. First-party data enables consistent message delivery when paid reach and signal quality decline.
Customer acquisition isn’t the only scalable lever anymore. Retention is the controllable one. Retention programs stabilize margins as media costs, auctions, and platforms stay volatile.
AI enables real-time message sequencing based on behavior, dynamic content assembly across email and SMS, and faster iteration without rebuilding entire lifecycle programs.
CRO and UX
CRO and UX are treated as defensive investments that improve performance regardless of traffic source. 52% are increasing spend. Traffic is harder to earn and easier to lose. Fewer clicks mean every visit carries more revenue weight.
AI-assisted test generation allows faster signal detection across variants and continuous optimization tied to real behavior. Competitive advantage shifts to inputs: structure, data quality, and conversion signals.
A Simple Framework: How to Build a Smarter 2026 Marketing Budget
Here’s a practical framework for budget agility.
Anchor spend in proven demand. Protect budgets tied directly to revenue and high-intent activity. These are your foundation channels.
Build flexibility around performance signals. Shift dollars based on real outcomes. Don’t lock yourself into annual commitments for channels that aren’t delivering.
Separate experimentation from core investment. Test intentionally without destabilizing what works. Set aside 10-15% of budget for testing new channels and tactics.
Reallocate faster than your competitors. Speed of adjustment becomes a competitive advantage in volatile conditions. Review performance monthly and be willing to move budget mid-quarter.
The winners in 2026 will be faster, not just bigger. Budgets are consolidating around fewer, higher-confidence channels. Efficiency and retention now matter as much as acquisition. AI is reshaping how value is captured, not just how work gets done. Visibility, conversion, and experience must be planned together.
Conclusion
Marketing in 2026 requires a different approach to budgeting. The channels that worked three years ago still work, but they work differently. The measurement that mattered in 2023 doesn’t tell the full story anymore. The strategies that justified budget in 2024 need updating for how search, social, and AI have evolved.
The marketers who thrive this year will be the ones who allocate budget where performance is provable, build systems that compound value over time, and move faster than their competitors when signals change.
If you need help translating these budget signals into a channel-specific growth plan, aligning SEO, paid media, content, and lifecycle into one system, or building measurement models that reflect zero-click and AI-driven behavior, we can help. Reach out to discuss your 2026 strategy.
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-20 20:00:002026-01-20 20:00:00How Marketers Are Spending in 2026
Google Shopping API migration deadlines are approaching, and advertisers who don’t act risk disrupted Shopping and Performance Max campaigns.
What’s happening. Google is sunsetting older API versions and pushing all merchants toward the Merchant API as the single source of truth for Shopping Ads. Advertisers can confirm which API they’re using in Merchant Center Next by checking the “Source” column under Settings > Data sources, where any listing marked “Content API” requires action.
Why we care. Google is actively reminding advertisers to migrate to the new Merchant API, with beta users required to complete the switch by Feb. 28th, and Content API users by Aug. 18th. If feeds aren’t properly reconnected, campaigns that rely on product data — especially those using feed labels — may stop serving altogether.
The risk. Feed labels don’t automatically carry over during migration. If advertisers don’t update their campaign and feed configurations in Google Ads, Shopping and Performance Max setups that depend on those labels for structure or bidding logic can quietly break.
What to do now. Google recommends completing the migration well ahead of the deadline, reviewing feed labels, and validating campaign delivery after reconnecting feeds. The transition was first outlined in mid-2024, but enforcement is now imminent as Google moves closer to fully retiring legacy APIs.
Bottom line. This isn’t a cosmetic backend change — it’s a technical cutoff that can directly impact revenue if ignored.
First seen. This update was spotted by Google Shopping Specialist Emmanuel Flossie, who shared the warnings he received on LinkedIn.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/shopping-api-reminder-2-piZqPR.jpg?fit=1044%2C1220&ssl=112201044http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-20 18:56:342026-01-20 18:56:34Google Shopping API cutoff looms, putting ad delivery at risk
The debate around llms.txt has become one of the most polarized topics in web optimization.
Some treat llms.txt as foundational infrastructure, while many SEO veterans dismiss it as speculative theater. Platform tools flag missing llms.txt files as site issues, yet server logs show that AI crawlers rarely request them.
Google even adopted it. Sort of. In December, the company added llms.txt files across many developer and documentation sites.
The signal seemed clear: if the company behind the sitemap standard is implementing llms.txt, it likely matters.
Except Google pulled it from its Search developer docs within 24 hours.
Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.
The llms.txt research
We wanted data, not debates.
So we tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care — 90 days before implementation and 90 days after.
We measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and what else these sites changed during the same window.
The results:
Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt wasn’t the cause.
Eight sites saw no measurable change.
One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file
The Neobank: 25% growth
This digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, AI traffic was up 25%.
Here’s what else happened in that window:
A PR campaign around its banking license, with coverage in major national publications.
Product pages restructured with extractable comparison tables for interest rates, fees, and minimums.
Twelve new FAQ pages optimized for extraction.
A rebuilt resource center with new banking information and concepts.
Technical SEO issues, like header structures, fixed.
When a company gets Bloomberg coverage the same month it launches optimized content and fixes crawl errors, you can’t isolate the llms.txt as the growth driver.
The B2B SaaS platform: 12.5% growth
This workflow automation company saw traffic jump 12.5% two weeks after implementing llms.txt.
Perfect timing. Case closed. Except…
Three weeks earlier, the company published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. Functional tools, not content marketing, drove the engagement behind the spike.
Google organic traffic to the templates rose 18% during the same period and continued climbing throughout the 90 days we measured.
Search engines and AI models surfaced the templates because they solved real problems and launched an entirely new site section — not because they were listed in an llms.txt file.
The 8 sites where nothing happened after uploading llms.txt
Eight sites saw no measurable change. One declined by 19.7%.
The decline came from an insurance site that implemented llms.txt in early September. The drop likely had nothing to do with the file.
The same pattern showed up across all traffic channels. Llms.txt neither prevented the decline nor created any advantage.
The other seven sites — ecommerce (pet supplies, home goods, fashion), B2B SaaS (HR tech, marketing analytics), finance, and pet care — all documented their best existing content in llms.txt. That included product pages, case studies, API docs, and buying guides.
Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file didn’t alter that.
Sites that launched new, functional content saw gains. Sites that documented existing content saw no gains.
Why the disconnect?
No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.
“None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That’s the reality. The file exists. The advocacy exists. The adoption by platforms doesn’t show it (yet!).
The token efficiency argument (and its limits)
The strongest case for llms.txt is about efficiency. Markdown saves time and tokens when AI agents parse documentation. Clean structure instead of complex HTML with navigation, ads, and JavaScript.
This matters — but almost exclusively for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency improves integration.
For ecommerce selling pet supplies, insurance explaining coverage, or B2B SaaS targeting nontechnical buyers, token efficiency doesn’t translate into traffic.
llms.txt is a sitemap, not a strategy
The most accurate comparison is a sitemap.
Sitemaps are valuable infrastructure. They help search engines discover and index content more efficiently. But no one credits traffic growth to adding a sitemap. The sitemap documents what exists; the content drives discovery.
Llms.txt works the same way. It may help AI models parse your site more efficiently if they choose to use it, but it doesn’t make your content more useful, authoritative, or likely to answer user queries.
In our analysis, the sites that grew did so because they:
Created functional assets like downloadable templates, comparison tables, and structured data.
Earned external visibility through press and backlinks.
Fixed technical barriers such as crawl and indexing issues.
Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It didn’t drive them.
What actually works
The two successful sites show what matters:
Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced these because they solved real problems, not because they were listed in a markdown file.
Structure content for extraction. The neobank rebuilt product pages with comparison tables with interest rates, fees, and account minimums. This is data AI models can pull directly into answers without interpretation.
Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models can’t access your content, no amount of documentation helps.
Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assess authority.
Optimize for user intent. Both sites answered specific queries: “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users are asking, not content that’s merely well documented.
None of this requires llms.txt. All of it drives results.
Should you implement an llms.txt file?
If you’re a developer tool where AI coding assistants are a primary distribution channel, then yes — token efficiency matters. Your audience is already using agents to interact with documentation.
For everyone else, treat llms.txt like a sitemap: useful infrastructure, not a growth lever.
It’s good practice to have. It won’t hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.
Those tactics have shown real ROI in AI discovery. Llms.txt hasn’t — at least not yet.
The lesson isn’t that llms.txt is bad. It’s that we’re reaching for control in a system where the rules aren’t written yet. Llms.txt offers that comfort: something concrete, actionable, and familiar, shaped like the web standards we already know.
But looking like infrastructure isn’t the same as functioning like infrastructure.
Focus on what actually works:
Create useful content.
Structure it for extraction.
Make it technically accessible.
Earn external validation.
Platforms and formats will change. The fundamentals won’t.
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“LLMs have trained on – read and parsed – normal web pages since the beginning,” he said in a recent discussion on Bluesky. “Why would they want to see a page that no user sees?”
His comparison was blunt: LLM-only pages are like the old keywords meta tag. Available for anyone to use, but ignored by the systems they’re meant to influence.
So is this trend actually working, or is it just the latest SEO myth?
The rise of ‘LLM-only’ web pages
The trend is real. Sites across tech, SaaS, and documentation are implementing LLM-specific content formats.
The question isn’t whether adoption is happening, it’s whether these implementations are driving the AI citations teams hoped for.
Here’s what content and SEO teams are actually building.
llms.txt files
A markdown file at your domain root listing key pages for AI systems.
The format was introduced in 2024 by AI researcher Simon Willison to help AI systems discover and prioritize important content.
Plain text lives at yourdomain.com/llms.txt with an H1 project name, brief description, and organized sections linking to important pages.
Stripe’s implementation at docs.stripe.com/llms.txt shows the approach in action:
markdown# Stripe Documentation
> Build payment integrations with Stripe APIs
## Testing
- [Test mode](https://docs.stripe.com/testing): Simulate payments
## API Reference
- [API docs](https://docs.stripe.com/api): Complete API reference
The payment processor’s bet is simple: if ChatGPT can parse their documentation cleanly, developers will get better answers when they ask, “how do I implement Stripe.”
They’re not alone. Current adopters include Cloudflare, Anthropic, Zapier, Perplexity, Coinbase, Supabase, and Vercel.
Markdown (.md) page copies
Sites are creating stripped-down markdown versions of their regular pages.
The implementation is straightforward: just add .md to any URL. Stripe’s docs.stripe.com/testing becomes docs.stripe.com/testing.md.
Everything gets stripped out except the actual content. No styling. No menus. No footers. No interactive elements. Just pure text and basic formatting.
The thinking: if AI systems don’t have to wade through CSS and JavaScript to find the information they need, they’re more likely to cite your page accurately.
/ai and similar paths
Some sites are building entirely separate versions of their content under /ai/, /llm/, or similar directories.
You might find /ai/about living alongside the regular /about page, or /llm/products as a bot-friendly alternative to the main product catalog.
Sometimes these pages have more detail than the originals. Sometimes they’re just reformatted.
The idea: give AI systems their own dedicated content that’s built for machine consumption, not human eyes.
If a person accidentally lands on one of these pages, they’ll find something that looks like a website from 2005.
Instead of creating separate pages, they built structured data feeds that live alongside their regular ecommerce site.
The files contain clean JSON – specs, pricing, and availability.
Everything an AI needs to answer “what’s the best Dell laptop under $1000” without having to parse through product descriptions written for humans.
You’ll typically find these files as /llm-metadata.json or /ai-feed.json in the site’s directory.
# Dell Technologies
> Dell Technologies is a leading technology provider, specializing in PCs, servers, and IT solutions for businesses and consumers.
## Product and Catalog Data
- [Product Feed - US Store](https://www.dell.com/data/us/catalog/products.json): Key product attributes and availability.
- [Dell Return Policy](https://www.dell.com/return-policy.md): Standard return and warranty information.
## Support and Documentation
- [Knowledge Base](https://www.dell.com/support/knowledge-base.md): Troubleshooting guides and FAQs.
This approach makes the most sense for ecommerce and SaaS companies that already keep their product data in databases.
They’re just exposing what they already have in a format AI systems can easily digest.
Real-world citation data: What actually gets referenced
The theory sounds good. The adoption numbers look impressive.
But do these LLM-optimized pages actually get cited?
The individual analysis
Landwehr, CPO and CMO at Peec AI, ran targeted tests on five websites using these tactics. He crafted prompts specifically designed to surface their LLM-friendly content.
Some queries even contained explicit 20+ word quotes designed to trigger specific sources.
Across nearly 18,000 citations, here’s what he found.
llms.txt: 0.03% of citations
Out of 18,000 citations, only six pointed to llms.txt files.
The six that did work had something in common: they contained genuinely useful information about how to use an API and where to find additional documentation.
The kind of content that actually helps AI systems answer technical questions. The “search-optimized” llms.txt files, the ones stuffed with content and keywords, received zero citations.
Markdown (.md) pages: 0% of citations
Sites using .md copies of their content got cited 3,500+ times. None of those citations pointed to the markdown versions.
The one exception: GitHub, where .md files are the standard URLs.
They’re linked internally, and there’s no HTML alternative. But these are just regular pages that happen to be in markdown format.
/ai pages: 0.5% to 16% of citations
Results varied wildly depending on implementation.
One site saw 0.5% of its citations point to its/ai pages. Another hit 16%.
The difference?
The higher-performing site put significantly more information in their /ai pages than existed anywhere else on their site.
Keep in mind, these prompts were specifically asking for information contained in these files.
Even with prompts designed to surface this content, most queries ignored the /ai versions.
JSON metadata: 5% of citations
One brand saw 85 out of 1,800 citations (5%) come from their metadata JSON file.
The critical detail here is that the file contained information that didn’t exist anywhere else on the website.
Once again, the query specifically asked for those pieces of information.
Instead of testing individual sites, they analyzed 300,000 domains to see if llms.txt adoption correlated with citation frequency at scale.
Only 10.13% of domains, or 1 in 10, had implemented llms.txt.
For context, that’s nowhere near the universal adoption of standards like robots.txt or XML sitemaps.
During the study, an interesting relationship between adoption rates and traffic levels emerged.
Sites with 0-100 monthly visits adopted llms.txt at 9.88%.
Sites with 100,001+ visits? Just 8.27%.
The biggest, most established sites were actually slightly less likely to use the file than mid-tier ones.
But the real test was whether llms.txt impacted citations.
SE Ranking built a machine learning model using XGBoost to predict citation frequency based on various factors, including the presence of llms.txt.
The result: removing llms.txt from the model actually improved its accuracy.
The file wasn’t helping predict citation behavior, it was adding noise.
The pattern
Both analyses point to the same conclusion: LLM-optimized pages get cited when they contain unique, useful information that doesn’t exist elsewhere on your site.
The format doesn’t matter.
Landwehr’s conclusion was blunt: “You could create a 12345.txt file and it would be cited if it contains useful and unique information.”
A well-structured about page achieves the same result as an /ai/about page. API documentation gets cited whether it’s in llms.txt or buried in your regular docs.
The files themselves get no special treatment from AI systems.
The content inside them might, but only if it’s actually better than what already exists on your regular pages.
SE Ranking’s data backs this up at scale. There’s no correlation between having llms.txt and getting more citations.
The presence of the file made no measurable difference in how AI systems referenced domains.
No major AI company has confirmed using llms.txt files in their crawling or citation processes.
Google’s Mueller made the sharpest critique in April 2025, comparing llms.txt to the obsolete keywords meta tag:
“[As far as I know], none of the AI services have said they’re using LLMs.TXT (and you can tell when you look at your server logs that they don’t even check for it).”
Google’s Gary Illyes reinforced this at the July 2025 Search Central Deep Dive in Bangkok, explicitly stating Google “doesn’t support LLMs.txt and isn’t planning to.”
Google Search Central’s documentation is equally clear:
“The best practices for SEO remain relevant for AI features in Google Search. There are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary.”
OpenAI, Anthropic, and Perplexity all maintain their own llms.txt files for their API documentation to make it easy for developers to load into AI assistants.
But none have announced their crawlers actually read these files from other websites.
The consistent message from every major platform: standard web publishing practices drive visibility in AI search.
No special files, no new markup, and no separate versions needed.
What this means for SEO teams
The evidence points to a single conclusion: stop building content that only machines will see.
“Why would they want to see a page that no user sees?”
If AI companies needed special formats to generate better responses, they would tell you. As he noted:
“AI companies aren’t really known for being shy.”
The data proves him right.
Across Landwehr’s nearly 18,000 citations, LLM-optimized formats showed no advantage unless they contained unique information that didn’t exist anywhere else on the site.
SE Ranking’s analysis of 300,000 domains found that llms.txt actually added confusion to their citation prediction model rather than improving it.
Instead of creating shadow versions of your content, focus on what actually works.
Build clean HTML that both humans and AI can parse easily.
Reduce JavaScript dependencies for critical content, which Mueller identified as the real technical barrier:
“Excluding JS, which still seems hard for many of these systems.”
Heavy client-side rendering creates actual problems for AI parsing.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2026/01/JohnMu-Lily-Ray-on-BlueSky-vgN5IJ.webp?fit=634%2C511&ssl=1511634http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2026-01-20 14:00:002026-01-20 14:00:00Why LLM-only pages aren’t the answer to AI search
Before you apply for a new role, it’s important to prepare for marketing salary negotiations and learn how to pursue fair pay with practical, realistic guidance.
Whether you work in SEO, PPC, or somewhere in between, salaries remain a contentious topic.
They are often hard to discuss, difficult to quantify, and challenging to change.
While many resources cover salary negotiations in general, this article focuses specifically on negotiating pay for marketing roles.
Difficulties with marketing salaries
Several factors make marketing roles harder to benchmark than many other professions, complicating salary expectations and negotiations.
No industry standard
Unlike fields with national governing bodies and defined career grades, marketing lacks standardization.
This makes it difficult to align salary bands across companies or compare roles on an equal footing.
Inconsistent job titles
Job titles in marketing vary widely.
A VP of marketing at one company may perform duties similar to a junior account manager elsewhere, while in another organization, the title represents the most senior marketing leader.
Because titles are used inconsistently, it can be challenging to assess role seniority and determine which salary ranges are appropriate.
Major market shifts in recent years
Marketers who last negotiated pay during the COVID-driven digital boom of 2020-2021 may find today’s job market markedly different.
Just five years ago, businesses rapidly shifted to online-first marketing, driving strong demand for digital talent.
Performance and organic marketers benefited from a candidate-favorable market, with new roles being created, frequent poaching, and rising salaries.
Today, conditions have changed. The rise of AI, global economic uncertainty, and company downsizing have reduced salary pressure for many marketing roles.
There is also more uncertainty around job stability, leading fewer marketers to change roles unless necessary.
As a result, the salary levels seen in 2020-2021 are largely a thing of the past.
Well-paid marketing roles still exist, but they are harder to find. That reality should inform your salary negotiations, not discourage them.
Some marketing channels can be misunderstood
Less marketing-savvy companies often advertise a single role intended to cover three or more distinct specializations, typically at bottom-of-the-market pay.
Even organizations that better understand marketing skill sets may struggle to grasp the full complexity and breadth of knowledge required to perform a role effectively.
This can lead to significant undervaluation of marketers.
Given that marketing salaries can be difficult for employers to navigate, how can you ensure you are fairly compensated for your experience and expertise?
The following nine tips can be broadly grouped into four areas:
Know what you bring to the table.
Know what is realistic.
Identify and demonstrate what is valuable to the company.
Stick to your boundaries.
Know what you bring to the table
We’ll start with the side of salary negotiations that, for some, can be very difficult: accurately valuing your own skill set.
If you are in a position to negotiate a salary, you have either already been offered the job or you work for the company and are hoping to secure a raise.
In either case, the company must already believe you are suitable for the role.
That does not stop it from trying to secure your services at the most economical price.
Knowing what you bring to the table is key to having the bargaining power and confidence to negotiate a fair salary.
This does not just mean how much direct experience you have in the role you have landed.
Tip 1: Demonstrate your experience in the industry
Don’t underestimate how much employers value candidates who have knowledge and experience within their sector.
You may also find that some industries struggle to hire marketing professionals, and your willingness to join that industry can command a higher salary.
If you’ve worked in notoriously difficult industries, such as gambling, adult entertainment, or pharmaceuticals, you may be able to negotiate higher pay because of it.
This can be due to the perception of difficulty in marketing within these industries.
Tip 2: Promote your prior experience in and out of similar roles
Your years of experience in a role may feel like an obvious bargaining chip when negotiating salary.
However, don’t forget that an employer may also benefit from the knowledge and experience you gained outside the role you are applying for.
Just because your previous job titles may not sound similar to the role you are negotiating now doesn’t mean the skills you developed there aren’t directly relevant.
Review your CV and compare it with the role you are applying for. Identify the parts of your work history that align with the job description.
Look beyond the obvious and consider transferable skills such as communication, problem-solving, and stakeholder management.
Tip 3: Highlight extra skills outside your job specification
Think about the skills you’ve developed over the years that may not be listed in the job description but are likely to be important for success in the role.
This can be particularly helpful if you are earlier in your career and lack directly relevant experience in similar positions.
Consider what you learned through volunteer roles, a first summer job, or even hobbies.
They may seem far removed from marketing, but you have likely gained lessons through those experiences that can support your current career.
Tip 4: Show your financial impact in previous roles
As with any ROI calculation, employers want to know whether the salary they may pay a candidate will deliver a return on that investment.
If you are negotiating a higher salary than originally offered, you need to demonstrate why it is financially worthwhile for the employer.
Be strategic in the examples you share. Rather than focusing only on increases in traffic or rankings, emphasize the revenue or cost savings you delivered.
You may be limited by NDAs and unable to share specific figures, but you can still reference outcomes, such as increasing organic search revenue by 5x or reducing a PPC budget by 20% while maintaining performance.
It’s one thing to understand your value based on the skills and experience you bring to a company. It’s another to assess that value accurately in the job market.
Ultimately, salaries are limited by what employers are willing to pay.
Tip 5: Be familiar with industry benchmarks
Do some research when considering your salary.
You may have been paid above or below the market average in your current or previous role, which can skew your expectations.
Review job ads in your geographic area that require similar skills and experience, and note the lower and upper ends of the stated salary ranges.
Be careful not to compare roles based on job titles alone.
As noted earlier, marketing titles are often inconsistent, and you may be comparing your role with one that is more senior or more junior.
Also consider the industry. Salaries in charities, for example, are unlikely to match those in tech or finance.
Salary benchmarking reports can also be useful, such as:
These resources can provide a more objective view of the market.
Keep in mind that salaries vary significantly by country, so avoid comparing U.S. and UK salaries directly.
Tip 6: Find out the internal salary ranges
When applying for a role, it is always helpful to understand the salary range being offered, although this is not always possible.
Some companies wait for candidates to make the first move on salary and may avoid sharing ranges to prevent offering more than necessary.
This is why it’s important to follow Tip 5 first, so you understand what your skills and experience are worth in the broader market.
Some organizations use salary banding. For example, a senior SEO specialist may be classified as a level 4 role, while a junior SEO role may sit at level 2.
If a recruiter is unwilling to share the exact salary range, you can ask about role levels or banding instead.
This can provide insight into where the role sits within the company hierarchy and what the potential salary ceiling might be.
If you are able to identify the salary range, try to determine what qualifies a candidate for the top of the band.
Is the company looking for additional “nice to haves” to justify the highest salary? In some cases, it may be reserved for candidates with experience in a specific industry.
Once you understand which skills command higher pay, you can emphasize them in your CV and during interviews.
Identify and demonstrate the values of the company
As many candidates discover during interviews, what a company truly wants is not always clearly stated in the job description or early conversations with recruiters.
Hiring managers may not fully define what they are looking for in a successful candidate until they have interviewed several people for comparison.
As a result, you may be unclear about what matters most for the role, making it harder to demonstrate your suitability and justify the salary you are requesting.
Interviews can provide an opportunity to explore these values in more detail.
Ask interviewers what “success” looks like in the role or how they would describe the traits of their top-performing colleagues.
This can help you understand the characteristics and behaviors the company values.
Tip 7: Demonstrate how you live up to those values
Once you understand what the company values, identify how you can deliver that through the role.
For example, if “initiative” is highly valued, you can use the interview process to highlight how you demonstrate initiative in your work.
Use examples from past experience to show how you embody the company’s values, citing specific projects or situations where you demonstrated them.
If “transparency” is important to the organization, you might reference a time when you acknowledged a mistake.
Demonstrating alignment with the company, in addition to job proficiency, can make you a more attractive candidate and support a stronger salary case.
When negotiating your salary, you need to know your absolute minimum. This is not just the lowest salary you can afford to accept.
It also means identifying what you need from a role to feel respected and valued, and what the overall compensation package must include to support that.
Going into negotiations with clear boundaries makes it easier to say no when an offer does not meet them.
Tip 8: Consider other benefits that may offset a lower salary
In some situations, accepting a lower salary may make sense.
You may be moving into a different role where you have less experience and are starting at a more junior level.
The opportunity to develop new skills can justify a lower salary.
Other tangible benefits, such as strong health coverage, additional paid time off, shorter working hours, or a gym membership, may also make a lower salary acceptable.
Tip 9: Identify other positives that may justify a lower salary
You may be moving into an industry you care deeply about.
For example, joining a charity may provide enough personal satisfaction to offset lower pay.
Be sure to factor these considerations into your salary expectations when defining your boundaries.
Tip 10: Decide how little is enough for you to walk away
After working through the previous tips, you should have a clear understanding of the minimum compensation you are willing to accept, or remain in, a role for.
Keep this in mind during negotiations. You may feel pressure not to lose the role by asking for more money, or worry about appearing overly focused on pay.
Joining a company and immediately feeling underpaid is not sustainable.
At the same time, asking for a raise as soon as you start is unlikely to help you establish yourself.
You may be better off declining a role if the company cannot close the gap between its offer and your minimum salary expectations.
Use these tips to define your value, account for any mitigating factors, and arrive at a salary you are willing to accept.
Once you have that number, negotiating becomes a matter of clearly demonstrating the value you bring to the company compared with other candidates.
If the gap between what a company is willing to pay and what you believe your skills and experience are worth is too large, walking away may be the better option.
You’ve likely invested in AI tools for your marketing team, or at least encouraged people to experiment.
Some use the tools daily. Others avoid them. A few test them quietly on the side.
This inconsistency creates a problem.
An MIT study found that 95% of AI pilots fail to show measurable ROI.
Scattered marketing AI adoption doesn’t translate to proven time savings, higher output, or revenue growth.
AI usage ≠ AI adoption ≠ effective AI adoption.
To get real results, your whole team needs to use AI systematically with clear guidelines and documented outcomes.
But getting there requires removing common roadblocks.
In this guide, I’ll explain seven marketing AI adoption challenges and how to overcome them. By the end, you’ll know how to successfully roll out AI across your team.
Free roadmap: I created a companion AI adoption roadmap with step-by-step tasks and timeframes to help you execute your pilot. Download it now.
First up: One of the biggest barriers to AI adoption — lack of clarity on when and how to use it.
1. No Clear AI Use Cases to Guide Your Team
Companies often mandate AI usage but provide limited guidance on which tasks it should handle.
In my experience, this is one of the most common AI adoption challenges teams face. Regardless of industry or company size.
Vague directives like “use AI more” leave people guessing.
The solution is to connect tasks to tools so everyone knows exactly how AI fits into their workflow.
The Fix: Map Team Member Tasks to Your Tech Stack
Start by gathering your marketing team for a working session.
Ask everyone to write down the tasks they perform daily or weekly. (Not job descriptions, but actual tasks they repeat regularly.)
Then look for patterns.
Which tasks are repetitive and time-consuming?
Maybe your content team realizes they spend four hours each week manually tracking competitor content to identify gaps and opportunities. That’s a clear AI use case.
Or your analytics lead notices they are wasting half a day consolidating campaign performance data from multiple regions into a single report.
AI tools can automatically pull and format that data.
Once your team has identified use cases, match each task to the appropriate tool.
After your workshop, create assignments for each person based on what they identified in the session.
For example: “Automate competitor tracking with [specific tool].”
When your team knows exactly what to do, adoption becomes easier.
2. No Structured Plan to Roll Out AI Across the Organization
If you give AI tools to everyone at once, don’t be surprised if you get low adoption in return.
The issue isn’t your team or the technology. It’s launching without testing first.
The Fix: Start with a Pilot Program
A pilot program is a small-scale test where one team uses AI tools. You learn what works, fix problems, and prove value — before rolling it out to everyone else.
A company-wide launch doesn’t give you this learning period.
Everyone struggles with the same issues at once. And nobody knows if the problem is the tool, their approach, or both.
Which means you end up wasting months (and money) before realizing what went wrong.
Plan to run your pilot for 8-12 weeks.
Note: Your pilot timeline will vary by team.
Small teams can move fast and test in 4-8 weeks. Larger teams might need 3-4 months to gather enough feedback.
Start with three months as your baseline. Then adjust based on how quickly your team adapts.
Content, email, or social teams work best because they produce repetitive outputs that show AI’s immediate value.
Select 3-30 participants from this department, depending on your team size.
(Smaller teams might pilot with 3-5 people. Larger organizations can test with 20-30.)
Then, set measurable goals with clear targets you can track. Like:
Schedule weekly meetings to gather feedback throughout the pilot.
The pilot will produce department-specific workflows. But you’ll also discover what transfers: which training methods work, where people struggle, and what governance rules you need.
When you expand to other departments, they’ll adapt these frameworks to their own AI tasks.
After three months, you’ll have proven results and trained users who can teach the next group.
At that point, expand the pilot to your second department (or next batch of the same team).
They’ll learn from the first group’s mistakes and scale faster because you’ve already solved common problems.
Pro tip: Keep refining throughout the pilot.
Update prompts when they produce poor results
Add new tools when you find workflow gaps
Remove friction points the moment they appear
Your third batch will move even quicker.
Within a year, you’ll have organization-wide marketing AI adoption with measurable results.
Employees may resist AI marketing adoption because they fear losing their jobs to automation.
Headlines about AI replacing workers don’t help.
Your goal is to address these fears directly rather than dismissing them.
The Fix: Have Honest Conversations About Job Security
Meet with each team member and walk through how AI affects their workflow.
Point out which repetitive tasks AI will automate. Then explain what they’ll work on with that freed-up time.
Be careful about the language you use. Be empathetic and reassuring.
For example, don’t say “AI makes you more strategic.”
Say: “AI will pull performance reports automatically. You’ll analyze the insights, identify opportunities, and make strategic decisions on budget allocation.”
One is vague. The other shows them exactly how their role evolves.
Don’t just spring changes on your team. Give them a clear timeline.
Explain when AI tools will roll out, when training starts, and when you expect them to start using the new workflows.
For example: “We’re implementing AI for competitor tracking in Q2. Training happens in March. By April, this becomes part of your weekly process.”
When people know what’s coming and when, they have time to prepare instead of panicking.
Pro tip: Let people choose which AI features align with their interests and work style.
Some team members might gravitate toward AI for content creation. Others prefer using it for data analysis or reporting.
When people have autonomy over which features they adopt first, resistance decreases. They’re exploring tools that genuinely interest them rather than following mandates.
5. Your Team Resists AI-Driven Workflow Changes
People resist AI when it disrupts their established workflows.
Your team has spent years perfecting their processes. AI represents change, even when the benefits are obvious.
Resistance gets stronger when organizations mandate AI usage without considering how people actually work.
New platforms can be especially intimidating.
It means new logins, new interfaces, and completely new workflows to learn.
Rather than forcing everyone to change their workflows at once, let a few team members test the new approach first using familiar tools.
The Fix: Start with AI Features in Existing Tools
Your team likely already uses HubSpot, Google Ads, Adobe, or similar platforms daily.
When you use AI within existing tools, your team learns new capabilities without learning an entirely new system.
If you’re running a pilot program, designate 2-3 participants as AI champions.
Their role goes beyond testing — they actively share what they’re learning with the broader team.
The AI champions should be naturally curious about new tools and respected by their colleagues (not just the most senior people).
Have them share what they discover in a team Slack channel or during standups:
Specific tasks that are now faster or easier
What surprised them (good or bad)
Tips or advice on how others can use the tool effectively
When others see real examples, such as “I used Social Content AI to create 10 LinkedIn posts in 20 minutes instead of 2 hours,” it carries more weight than reassurance from leadership.
For example, if your team already uses a tool like Semrush, your champions can demonstrate how its AI features improve their workflows.
Keyword Magic Tool’s AI-powered Personal Keyword Difficulty (PKD%) score shows which keywords your site can realistically rank for — without requiring any manual research or analysis.
Your content writers can input a topic, set their brand voice, and get a structured first draft in minutes. This reduces the time spent staring at a blank page.
Social Content AI handles the repetitive parts of social media planning. It generates post ideas, copy variations, and images.
Your social team can quickly build out a week’s content calendar instead of creating each post from scratch.
Don’t have a Semrush subscription? Sign up now and get a 14-day free trial + get a special 17% discount on annual plan.
6. No Governance or Guardrails to Keep AI Usage Safe
Without clear guidelines, your team may either avoid AI entirely or use it in ways that create risk.
They paste customer data into ChatGPT without realizing it violates data policies.
Or publish AI-generated content without approval because the review process was never explained.
Your team needs clear guidelines on what’s allowed, what’s not, and who approves what.
Free AI policy template: Need help creating your company’s AI policy? Download our free AI Marketing Usage Policy template. Customize it with your team’s tools and workflows, and you’re ready to go.
The Fix: Create a One-Page AI Usage Policy
When creating your policy, keep it simple and accessible. Don’t create a 20-page document nobody will read.
Aim for 1-2 pages that are straightforward and easy to follow.
Include four key areas to keep AI usage both safe and productive.
Policy Area
What to Include
Example
Approved Tools
List which AI tools your team can use — both standalone tools and AI features in platforms you already use
“Approved: ChatGPT, Claude, Semrush’s AI Article Generator, Adobe Firefly”
Data Sharing Rules
Define specifically what data can and can’t be shared with AI tools
“Safe to share: Product descriptions, blog topics, competitor URLs
Concerns about whether AI-generated content is accurate or appropriate
Questions about data sharing
The goal is to give them a clear path to get help, rather than guessing or avoiding AI altogether.
Then, post the policy where your team will see it.
This might be your Slack workspace, project management tool, or a pinned document in your shared drive.
And treat it as a living document.
When the same question comes up multiple times, add the answer to your policy.
For example, if three people ask, “Can I use AI to write email subject lines?” update your policy to explicitly say yes (and clarify who reviews them before sending).
7. No Reliable Way to Measure AI’s Impact or ROI
Without clear proof that AI improves their results, team members may assume it’s just extra work and return to old methods.
And if leadership can’t see a measurable impact, they might question the investment.
This puts your entire AI program at risk.
Avoid this by establishing the right metrics before implementing AI.
The Fix: Track Business Metrics (Not Just Efficiency)
Here’s how to measure AI’s business impact properly.
Pick 2-3 metrics your leadership already reviews in reports or meetings.
These are typically:
Leads generated
Conversion rate
Revenue growth
Customer acquisition
Customer retention
These numbers demonstrate to your team and leadership that AI is helping your business.
Then, establish your baseline by recording your current numbers. (Do this before implementing AI tools.)
For example, if you’re tracking leads and conversion rate, write down:
Current monthly leads: 200
Current conversion rate: 3%
This baseline lets you show your team (and leadership) exactly what changed after implementing AI.
Pro tip: Avoid making multiple changes simultaneously during your pilot or initial rollout.
If you implement AI while also switching platforms or restructuring your team, you won’t know which change drove results.
Keep other variables stable so you can clearly attribute improvements to AI.
Once AI is in use, check your metrics monthly to see if they’re improving. Use the same tools you used to record your baseline.
Write down your current numbers next to your baseline numbers.
For example:
Baseline leads (before AI): 200 per month
Current leads (3 months into AI): 280 per month
But don’t just check if numbers went up or down.
Look for patterns:
Did one specific campaign or content type perform better after using AI?
Are certain team members getting better results than others?
Track individual output alongside team metrics.
For example, compare how many blog posts each writer completes per week, or email open rates by the person who drafted them.
If someone’s consistently performing better, ask them to share their AI workflow with the team.
This shows you what’s working, and helps the rest of your team improve.
Share results with both your team and leadership regularly.
When reporting, connect AI’s impact to the metrics you’ve been tracking.
For example:
Say: “AI cut email creation time from 4 hours to 2.5 hours. We used that time to run 30% more campaigns, which increased quarterly revenue from email by $5,000.”
Not: “We saved 90 hours with AI email tools.”
The first shows business impact — what you accomplished with the time saved. The second only shows time saved.
Other examples of how to frame your reporting include:
Build Your Marketing AI Adoption Strategy
When AI usage is optional, undefined, or unsupported, it stays fragmented.
Effective marketing AI adoption looks different.
It’s built on:
Role-specific training people actually use
Guardrails that reduce uncertainty and risk
Metrics that drive business outcomes
When those pieces are in place, AI becomes part of how work gets done.
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Remember when a handful of links from sites in your niche could drive steady organic traffic? That era is over.
Today, Google’s AI Overviews and the rise of answer engines like ChatGPT raise the bar. You have to do more to stay visible. Hiring an experienced link building agency is one efficient way to meet that challenge.
It’s also one of the most important investments you’ll make. The right partner doesn’t just build links. They position your brand as a trusted, cited source in the AI era.
So how do you choose the right agency for your company?
While the interface has changed, the core ranking signals remain largely the same. What’s changed is their priority.
LLMs need credible sources to ground their answers. That makes authoritative link building more important than ever.
This article shows you how to vet and choose a link building agency that understands these new priorities and can help your brand win trust in the AI-driven SEO landscape.
How link building and SEO are changing
Gartner predicted search engine volume to drop by 25% as AI takes over more answers. That makes working with an agency that understands AI SEO essential.
But how do you know which agencies actually do?
The real indicators are holistic authority and AI visibility. Only one in five links cited in Google’s AI Overviews matched a top-10 organic result, according to an Authoritas study. Even more telling, 62.1% of cited links or domains didn’t rank in the top 10 at all.
The takeaway is simple. AI systems and search engines don’t evaluate websites the same way. We’re no longer building links just for Google’s crawler.
Link equity alone isn’t enough. Sites need topical authority, brand mentions, and real market presence. The goal is to build a footprint that AI models recognize and can’t ignore.
The new criteria: Evaluating a link building agency for AI SEO
Choosing the right link building agency comes down to how well they prioritize the factors that matter now.
This section shows you what to look for.
Prioritizing quality, relevance, and traffic
I see this mistake all the time. A marketing director evaluates link quality based only on Domain Rating (DR).
High DR matters, but at uSERP, we know it’s not the finish line. You should also look for:
Relevance: A link from a DR 60, niche-specific site in your industry often beats a DR 80 general news site that covers everything from crypto to keto.
Minimum traffic standards: If a site doesn’t rank for keywords or attract real traffic, its links won’t help you rank. That’s why strict traffic minimums matter.
When vetting an agency, ask for contractual site-traffic guarantees.
A confident agency won’t hesitate to sign a Statement of Work that guarantees every link comes from a site with a minimum traffic threshold, such as 5,000+ monthly organic visitors.
If they won’t put traffic minimums in writing, they’re likely planning to place links on “ghost town” sites. These domains appear strong, but they lack a real audience, which protects their margins rather than supporting your growth.
Look for a content-driven approach and digital PR
Links don’t exist in a vacuum. The strongest ones come from being part of a real conversation.
The best agencies no longer operate like traditional link builders. They act more like content marketing and digital PR teams.
Instead of asking for links, the best agencies create linkable assets — data studies, expert commentary, and in-depth guides that journalists and publishers want to cite – because they understand:
Google’s algorithms and AI models are continually getting better at identifying paid placements. A content-led approach keeps links natural, editorial, and valuable to readers.
Guest posting in the AI SEO era isn’t about a disposable 500-word article. It’s about thought leadership that positions your CEO as a credible expert.
Red flags: Recognizing outdated or dangerous tactics
Choosing the wrong partner doesn’t just waste your budget. It puts your brand reputation — and potentially your company’s future — at risk.
Here are the biggest red flags to avoid when hiring an agency:
Guaranteed rankings
No one can guarantee a number-one ranking on Google. Any agency that promises specific keyword positions on a fixed timeline is likely doing one of two things:
Using risky, short-term tactics to force a temporary spike.
Selling you snake oil.
These agencies often rely on private blog networks (PBNs) or aggressive anchor text manipulation to manufacture fast results.
You might see an early jump, but the crash that follows—and the risk of a penalty when Google’s spam systems catch up—is never worth it.
Lack of transparency
If an agency won’t explain how they earn links or where placements will come from before you pay, walk away.
Reputable agencies are transparent. They’ll show real examples of past placements and share relevant case studies from your industry.
Agencies that hide their inventory usually do it for a reason. Those sites are often part of a low-quality network or link farm.
Self-serve link portfolios
If you’re a marketer or SEO on LinkedIn, chances are you’ve received a message like this:
This is a common tactic among low-quality link builders: reselling backlinks from a shared inventory. I understand the appeal.
Strategic link acquisition is hard. Buying and flipping links is easy.
The problem — for you — is the footprint. If an agency can secure a link by filling out a form, anyone can. That includes casino affiliates, gambling sites, adult content, and outright scammers.
That’s not a natural link profile. Google has almost certainly already identified and burned those domains.
In the best case, you pay for a link that passes zero authority. In the worst case, Google flags your site as part of a link scheme.
Dirt-cheap packages
SEO and link building deliver incredible ROI, but they aren’t cheap.
You can’t buy a high-quality article with a real, earned link from an authoritative site for $50. Speaking as someone who runs an AI SEO agency, the true cost of quality content, editing, outreach, and relationship building is at least an order of magnitude higher.
That’s why cheap packages that promise multiple high-authority links are a major red flag. They almost always rely on:
Partnering with a link building agency for a sustainable market presence
Link building in the AI era is a long-term investment. It’s about building a durable market presence, not chasing quick wins.
The right partner sees themselves as an extension of your team. They care about:
Your backlink gap compared to competitors.
Your brand mentions across LLMs.
Your overall search and AI visibility.
They help you navigate content syndication, backlink audits, content marketing, and modern link building strategies with a unified approach.
If you’re ready to move past vanity metrics and start building authority that drives revenue and AI citations, it’s time to be selective about who you trust with your domain.
The right link building agency is out there. You just need to know how to spot them.
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