Google Ads launched VTC-optimized bidding for Android app campaigns, letting advertisers toggle bidding toward conversions that happen after an ad is viewed rather than clicked.
Previously, VTC worked as a hidden signal inside Google’s systems. Now, it’s a clear, explicit optimization option.
Sergey Brin, Google’s co-founder, admitted that Google “for sure messed up” by underinvesting in AI and failing to seriously pursue the opportunity after releasing the research that led to today’s generative AI era.
Google was scared. Google didn’t take it seriously enough and failed to scale fast enough after the Transformer paper, Brin said. Also:
Google was “too scared to bring it to people” because chatbots can “say dumb things.”
“OpenAI ran with it,” which was “a super smart insight.”
The full quote. Brin said:
“I guess I would say in some ways we for sure messed up in that we underinvested and sort of didn’t take it as seriously as we should have, say eight years ago when we published the transformer paper. We actually didn’t take it all that seriously and didn’t necessarily invest in scaling the compute. And also we were too scared to bring it to people because chatbots say dumb things. And you know, OpenAI ran with it, which good for them. It was a super smart insight and it was also our people like Ilya [Sutskever] who went there to do that. But I do think we still have benefited from that long history.”
Yes, but. Google still benefits from years of AI research and control over much of the technology that powers it, Brin said. That includes deep learning algorithms, years of neural network research and development, data-center capacity, and semiconductors.
Why we care. Brin’s comments help explain why Google’s AI-driven search changes have felt abrupt and inconsistent. After years of hesitation about shipping imperfect AI, Google is now moving fast (perhaps too fast?). The volatility we see in Google Search is collateral damage from that catch-up mode.
Where is AI going? Brin framed today’s AI race as hyper-competitive and fast-moving: “If you skip AI news for a month, you’re way behind.” When asked where AI is going, he said:
“I think we just don’t know. Is there a ceiling to intelligence? I guess in addition to the question that you raised, can it do anything a person can do? There’s the question, what things can it do that a person cannot do? That’s sort of a super intelligence question. And I think that’s just not known, how smart can a thing be?”
One more thing. Brin said he often uses Gemini Live in the car for back-and-forth conversations. The public version runs on an “ancient model,” Brin said, adding that a “way better version” is coming in a few weeks.
The video. Brin’s remarks came at a Stanford event marking the School of Engineering’s 100th anniversary. He discussed Google’s origins, its innovation culture, and the current AI landscape. Here’s the full video.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/0nlnx94fcue-CrSf2v.jpg?fit=1280%2C720&ssl=17201280http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-15 17:15:162025-12-15 17:15:16Sergey Brin: Google ‘messed up’ by underinvesting in AI
But AI search adds a new layer your team needs to master.
Here’s what I mean:
Traditional SEO gets your pages ranking in top search positions.
AI SEO gets your brand visible in AI-generated answers — through brand mentions, citations, or both.
You’re expanding what SEO covers. Not replacing it.
Let me break down what’s changed and what it means for your team.
What’s Changed
Search behavior itself has evolved a lot over recent years.
A growing number of people don’t just “Google” anymore. They discover, compare, and decide across multiple platforms. (And this has been the case since long before ChatGPT came along.)
Someone might start on TikTok, check Reddit reviews, search on Google, and ask ChatGPT for a summary before taking action. And they might revisit these platforms at various stages of the journey.
That journey looks less like a straight line and more like a network.
Here are five other changes reshaping how search works today:
Whole-web signals: AI pulls from your website and everywhere else your brand appears online. Your entire digital footprint influences your AI visibility.
Entity recognition: AI understands your brand as a concept it can connect to products, industries, and related topics, not just keywords to match (learn more in our guide to entity SEO)
Passage-level retrieval: AI extracts specific sections from your content to use in its answers, not entire pages. This means it needs to be clear what each section of your content is about.
Conversational search behavior: AI search queries tend to be longer and more specific. People describe problems in detail rather than typing short keywords, which means the AI often cites highly specific content rather than generic guides.
Zero-click reality: Users can now get complete answers without visiting websites. Traffic from search is no longer guaranteed, even with strong visibility.
What This Means for Your Team
These changes don’t require you to rebuild your team from scratch.
But they do require expanding what your team focuses on:
Your content team still writes. But now they also need to structure content so AI can easily understand it and extract sections for its answers.
Your technical SEO team still optimizes site architecture. But priorities shift toward AI crawlability, performance, and schema implementation.
Your strategist still tracks performance. But now they also need to measure citations and brand mentions across AI platforms.
Most of these skills build on what your team already knows. Again, they’re extensions, not replacements.
4-12 months is a typical timeline to get your team comfortable with AI SEO fundamentals.
You’ll need some combination of internal training, external guidance, and selective hiring — depending on your current gaps. I’ll talk more about this later.
First, let’s break down the specific skills your AI SEO team needs.
Essential AI SEO Skills Your Team Needs
Not everyone needs to be an AI SEO expert in all areas.
One person (typically a lead or strategist) needs strategic understanding. They understand how AI search works and can adapt when platforms change.
The rest of your team needs execution capability. They can follow guidelines and apply best practices.
It’s helpful if they show interest in understanding AI SEO, but it’s not required.
Here are the key skills that bridge traditional SEO and AI search.
Understanding AI Retrieval
AI platforms find and reference content differently from Google’s traditional ranking systems.
Some platforms, like Perplexity, search the web in real-time.
Others, like ChatGPT, can search the web or pull from their training data.
And AI Overviews use Google’s existing index and Gemini’s training data.
To optimize for and appear in these places, your team needs to understand how these systems select what to cite and mention.
When someone asks a question, these platforms look for content that directly answers the query. They prioritize sources that are clearly structured and contextually relevant.
Note: AI systems also use a process called query fan-out. This involves expanding one user prompt into multiple related sub-queries behind the scenes.
That means your content can surface even if it doesn’t match the original question exactly. If it covers a related angle or entity that the AI connects to the topic, it can be cited or mentioned.
Your SEO lead or strategist typically owns this skill.
They already understand search intent and ranking logic — the same foundations that AI retrieval builds on.
In smaller teams, a content strategist can also take this on with a shallow learning curve.
Typically, they’ll spend 2-3 hours monthly testing how your brand appears across AI platforms. Document patterns in what gets cited. And adjust content strategy based on what’s working.
Writing for AI Extraction
AI search tools don’t respond to user queries with entire articles. Instead, the AI pulls specific passages that answer those queries.
If a passage requires a lot of surrounding context to make sense, AI may be less likely to understand its relevance and therefore be less likely to use it.
This means each section of your content needs to still make sense even when taken out of the context of the rest of the article.
Each section should answer a specific question on its own, without relying on references to other parts of the article.
This is generally just good writing practice. If you find yourself making too many unique points in one section, it’s probably best to split it into subsections.
But clarity here is also key.
For example, avoid: “As we mentioned earlier, this approach works well…”
Instead, write: “Structuring content into self-contained passages helps AI extract and cite your information more effectively.”
Here’s another example of effective writing for AI extraction:
The second version makes sense whether someone reads your full article or sees just that paragraph in an AI response.
This doesn’t mean every sentence needs a complete context. It means key passages should stand alone.
Who Can Own It?
Your content or editorial team can handle this.
SEO provides the framework and guidelines. Writers implement it in their daily work.
For example, editorial reviews the article structure before publishing, ensuring each section has a clear, standalone takeaway.
Sometimes that means breaking a 500-word section into three shorter subsections with specific headers.
By the way: As a content marketer myself, I don’t think this shift is dramatic.
Most great content teams already write clearly and structure information logically. This just prioritizes ensuring key passages work independently.
Building AI-Readable Structure
AI needs clear signals to understand your site’s structure and how content relates to other pages on your site.
For example, schema markup makes your data more structured by defining what your content represents.
This can make it easier for AI systems to interpret and cite your content accurately.
While the full impact is still unclear, structured data makes your content easier to parse, which is helpful for search engines anyway. And since Gemini can lean on Google’s search infrastructure, it’s not all that unreasonable to expect that schema could at least indirectly affect your visibility in places like AI Overviews and AI Mode, now or in the future.
Similarly, internal linking shows how topics connect.
And a clear site hierarchy indicates which pages are most important.
Think of it as creating a map.
Instead of making AI infer relationships, you’re explicitly defining them.
Beyond your site: Entity databases
Once you have the basics down, consider registering your brand and products in databases like Wikipedia, Wikidata, or Crunchbase.
These knowledge bases help AI systems understand entity relationships and how your brand fits into broader industry contexts.
This bridges on-site structure (like schema markup) with off-site presence. You’re helping AI systems recognize your brand across the web, not just on your site.
You don’t need this starting out. But it’s worth exploring once your core AI SEO structure is in place.
Who Can Own It?
Your technical SEO can take ownership of this skill.
They already handle the fundamentals like implementing schema markup, managing site architecture, and optimizing internal linking structures.
The approach doesn’t change much. They’re just applying the same technical skills with AI systems in mind.
Tracking AI Performance
Traditional SEO metrics (like rankings, organic traffic, and click-through rates) still matter.
But they don’t say anything about your brand’s AI search visibility.
You need different metrics now, including:
Platform breakdown: Where you’re showing up (ChatGPT, Perplexity, Google AI Overviews, etc.)
Citation frequency: How often your content gets cited as a source in AI responses
Mention rate: How often your brand appears in AI-generated answers or recommendations
Mention sentiment: Whether those mentions are positive, neutral, or negative
These numbers indicate whether your AI SEO strategy is working.
Without specialized tools, you’ll need to manually search key queries across platforms and track when your brand appears.
Who Can Own It?
Your SEO analyst or whoever handles performance reporting can own this.
They’re already tracking traditional metrics. AI performance metrics become an addition to that dashboard.
If using AI visibility tools, they’ll monitor your visibility score and citation trends monthly.
Without specialized tools, they’ll need to manually search key queries across platforms, document when and how your brand appears, and track changes over time.
AI tools go beyond just looking at your website and pull from everywhere your brand is mentioned online. Including:
G2 reviews comparing tools
Reddit threads discussing your product
Forum conversations about your industry
News articles mentioning your company
If those mentions are sparse or outdated, AI has less information to pull from when someone searches for your brand specifically or asks about your product category.
This is where AI search extends beyond your domain.
Who Can Own It?
No single person can own this entirely.
PR, community management, and customer success each control different pieces of the puzzle.
Someone from SEO can take the coordination role, ensuring these teams understand how their work affects AI visibility.
In practice, this often means your SEO lead or director works cross-functionally to align off-site efforts with AI discoverability goals.
For example, they work with customer success to encourage reviews on platforms like G2 or Trustpilot.
They also monitor where your brand gets mentioned across forums, social platforms, and community discussions.
Different AI platforms retrieve and display information in their own ways.
For example:
Perplexity searches the web in real-time and shows numbered citations
ChatGPT can search the web or pull from its training data
Google’s AI Overviews draw from Google’s search index and Gemini’s training data
What gets you cited on one platform won’t automatically work on another because each platform follows patterns in what it mentions and cites.
For instance, I searched “which is the best camera phone of 2025” across three platforms.
ChatGPT cited multiple YouTube videos, a Reddit thread, Tom’s Guide, Yahoo, and Tech Advisor.
Google’s AI Mode cited one YouTube video along with a bunch of other websites — no Tom’s Guide, Yahoo, or Tech Advisor.
Claude cited Quora and Android Authority twice. No Reddit threads, YouTube, or Tom’s Guide.
Same query, completely different sources and mentions.
Your team needs to understand these differences when optimizing for AI visibility.
You don’t need separate strategies for each platform. But knowing how different platforms prioritize sources helps you structure your entire approach, from content to technical implementation to off-site presence.
Who Can Own It?
Your SEO lead or strategist can typically own this.
They can track how your brand appears across platforms and identify what’s working where.
They’ll spot gaps in coverage on LLMs that matter to the brand. For example, strong presence in ChatGPT but weak in Perplexity.
Then they work with content, technical, and other teams to adjust the overall strategy.
Query Intent Mapping
People search differently in AI platforms than they do in Google.
Traditional Google: “best CRM software”
ChatGPT: “I need a CRM for a 50-person sales team, budget around $10K annually, must integrate with Salesforce”
The queries are longer. More conversational. More specific.
I checked my own most recent 100 prompts to ChatGPT. They averaged 13 words each.
Compare that to traditional Google searches, which typically run 3-4 words.
Understanding these prompt patterns helps you create content that answers the actual questions people ask AI.
You need to think beyond traditional keywords.
What detailed questions are the people in your audience asking? What context are they providing? What outcome do they want?
Who Can Own It?
Whoever leads keyword research or content planning can take this on, usually your SEO strategist or content planner.
This builds directly on existing keyword research skills.
You’re expanding from “what keywords do people use?” to “what problems are people trying to solve?”
(Which you should have been doing all along, but now with a stronger focus.)
This person will analyze how people search in AI platforms and document the longer, conversational queries they use.
Then they’ll build content briefs that address those specific questions and scenarios.
The Build, Buy, or Borrow Decision: Getting AI SEO Skills on Your Team
You know which skills your team needs.
Now comes the practical question: how do you actually get them?
You have three options:
Build internally
Hire new talent
Bring in outside expertise
Here’s a snapshot of the pros and cons of all three:
Most teams end up doing some combination of all three. The key is knowing which approach works best for specific skills.
Let’s look at each one in detail.
1. When to Build (Develop Internally)
Upskilling your current team is almost always the smartest first move.
They already know your brand, your workflows, and your audience. That context shortens the learning curve dramatically.
Focus on developing skills that evolve naturally from what your team already does.
For example:
Train writers to structure content for AI extraction
Help your SEO lead understand AI retrieval patterns and how citations work
Encourage your analyst to track AI visibility metrics alongside rankings
These are logical extensions of existing expertise. Not entirely new disciplines.
Now, training doesn’t have to mean building a full internal curriculum.
Start small. For example:
Run short internal workshops to explain how AI search retrieves and cites content
Review recent AI-generated answers for your top keywords and note which competitors get mentioned
Compare their cited passages to yours, and update one or two articles using those patterns
To make internal training effective, use this quick checklist:
Upskilling may not be the fastest route to output. It can take a few months before you see real traction.
But it is the most sustainable.
Once your team starts applying AI-first thinking, you’ll see compounding returns with every new SEO campaign.
Best For
Startups and mid-sized teams that already have strong SEO foundations but a limited budget for new hires.
Watch Out For
Don’t overload your team with theoretical “AI SEO” training.
Focus primarily on skills that directly connect to visibility outcomes, like structure, clarity, and retrievability.
Also watch for skill concentration. If one person (like your SEO lead) ends up owning 3+ new AI skills, that’s a bottleneck. Consider hiring or borrowing expertise to spread the load.
2. When to Buy (Hire New Talent)
When you need expertise faster than you can build it internally, it’s time to hire.
Bringing in new talent makes sense when the skill is both specialized and strategic.
Something that gives your brand a long-term edge, not just a short-term fix.
For example:
Hiring a data or visibility analyst who understands how to measure citations and brand mentions across AI platforms
Bringing in a technical SEO who can model entities and implement structured data at scale
Adding an AI content strategist who can guide how your content aligns with AI retrieval patterns
These hires extend the capabilities of your existing SEO team. They don’t replace it.
The key to finding the right people?
Clarity before you post the job. Decide what outcome you’re hiring for.
Do you need faster technical execution, deeper analytics, or dedicated AI visibility leadership?
Before you start recruiting, here’s a quick checklist to work through:
With clear hiring criteria, you’ll know which expertise to prioritize and what title makes sense for your organization.
Best For
Mid-sized and enterprise teams that have budget flexibility and want to move faster than internal training allows.
Watch Out For
Don’t over-index on shiny new “AI SEO” titles. Few people have that exact label yet.
Instead, look for specialists in areas like data, structured content, and retrieval systems. These are people who can bridge SEO and AI.
3. When to Borrow (Outsource or Consult)
Not every skill is worth building or hiring for.
Some are highly specialized. Others you only need for a short period.
That’s where borrowing expertise makes sense — through consultants, freelancers, or agencies.
Outsourcing works best when you need to move fast on projects that require niche expertise.
For example:
Hiring a consultant to set up AI visibility tracking before your analyst takes over
Partnering with a content firm to scale passage optimization across hundreds of pages
Bringing in a Reddit marketing expert to boost your brand’s presence in relevant subreddits
This approach gives you access to deep expertise without expanding headcount.
You can bring in specialists to handle complex projects, fill capability gaps, or run pilot programs that would slow your internal team down.
Sometimes that means a one-off engagement.
Other times, it’s a recurring partnership that supports your strategy long-term.
The goal isn’t to offload responsibility. It’s to fill gaps your team can’t cover yet and to get critical work done without slowing down larger projects.
When evaluating potential partners, here’s a quick checklist to follow:
Best For
Teams that need quick access to specialized expertise or extra hands for complex, time-bound projects.
Watch Out For
Don’t treat outsourcing as a default fix.
If a skill becomes core to your strategy, consider bringing it in-house. But for niche or technical projects, keeping trusted external support can be more practical.
Choose partners who understand your brand voice. AI-first SEO still needs human context.
In practice, it’s rare that a team is fully built, bought, or borrowed.
You’ll probably use all three, often at the same time.
How much you lean on each one depends on factors like:
Your current team’s strengths and bandwidth
Budget flexibility for hiring or contracting
The urgency of upcoming SEO goals
How quickly AI search is evolving in your industry
Leadership’s appetite for experimentation
In my experience, many teams land somewhere near a 70-20-10 split. Which is roughly 70% built internally, 20% borrowed through outside experts, and 10% bought as new hires.
The exact ratio matters less than how deliberately you manage it.
Here’s how to keep that balance right:
Prioritize by impact: Build skills that sustain long-term visibility. Borrow when you need speed or experimentation. Buy only when a role becomes essential to your strategy.
Keep ownership internal: Even if outside partners execute the work, ensure someone on your team owns the outcome and applies the learnings.
Plan for rotation: As new AI SEO trends emerge, your mix will likely shift. What starts as a borrowed skill may become core within six months.
Audit regularly: Review your mix every quarter to see which skills rely too heavily on outside help. If a borrowed skill becomes recurring, start building it internally.
Follow this quick team review checklist to keep stock of your built, bought, and borrowed setup.
The key is flexibility and adaptability.
As priorities shift, don’t hesitate to rebalance how your team works.
That might mean promoting someone internally to take ownership of AI visibility, bringing in a freelancer to handle off-site optimization, or hiring a new analyst to deepen your data capability.
Adjust your structure based on what delivers the most impact, not what’s written on the org chart.
Your AI SEO Adoption Roadmap
You don’t need a massive reorg to evolve your SEO team for AI search.
You need a plan that helps your team build capability, test what works, and scale what proves effective.
This roadmap gives you that plan.
It breaks down:
What to focus on in each phase
How to build momentum
What progress should look like along the way
By the end, your team will know how to apply AI SEO principles consistently.
Note: This timeline is a starting point, not a rule.
Startups with smaller teams might compress this into 6 months. Enterprises coordinating across departments might need 15-18 months.
The timeline matters less than starting now and making steady progress.
Phase 1: Foundation
Start by taking stock of where your team stands.
Before diving into new tactics, align everyone around what AI SEO means for your brand and how your current approach fits into it.
This stage sets direction and gives your team the confidence to move with purpose.
Here’s what to focus on in the first three months:
Assess current capabilities: Review your team’s strengths across content, technical, and analytical areas. Identify which AI-era skills exist internally and which ones you’ll need to hire for or outsource.
Establish your visibility baseline: Search your most important topics in tools like ChatGPT, Perplexity, and Google AI Overviews. Track if (and how) your brand shows up.
Pick 2-3 priorities to act on: Choose the areas with the clearest opportunity to improve. That might mean tightening content clarity, mapping entities, or aligning off-site mentions.
Run a small pilot: Select a few representative pages and update them based on what you’ve learned. Then recheck whether those updates help your brand appear more often in AI answers.
Document key learnings: Capture what worked and what didn’t in a short internal memo. This becomes the foundation for next quarter’s priorities.
Goal: Build clarity, alignment, and a shared understanding of how AI search changes what your team prioritizes.
By the end of this phase, your team should understand what makes content discoverable in AI search, have a documented baseline to track progress, and have at least one small win that proves the approach works.
Phase 2: Acceleration
Once you’ve built your baseline, it’s time to turn insights into action.
The second phase focuses on building capability and momentum. This involves scaling what worked in your pilot, closing skill gaps, and introducing systems that help your team move faster together.
Here’s what to focus on over the next few months:
Strengthen capability: Run short training sessions to deepen AI SEO understanding across functions. If a skill gap exists, bring in a freelancer, consultant, or new hire to fill it quickly.
Encourage cross-functional collaboration: Bring content, SEO, analytics, product, and brand together under one shared visibility goal. Clarify ownership so responsibilities don’t overlap.
Expand your pilot: Apply what worked from Phase 1 to more pages or campaigns
Build repeatable workflows: Turn early learnings into working systems. Standardize how technical, analytical, and content tasks are executed for AI-driven discovery. Each function should know what “AI-ready” means in its area.
Use shared dashboards: Track AI visibility metrics in one place and review them as a team so everyone sees how their work contributes to results
Run monthly reviews: Check how well your team is adapting to new systems and responsibilities. Identify where people need support, additional training, or outsourced help.
Goal: Build capability, consistency, and accountability across your team’s AI SEO initiatives.
By the end of this phase, your team should operate with clear workflows and defined ownership across technical, analytical, and content areas.
You should also have unified dashboards that let all stakeholders track progress and collaborate without duplicated work.
Phase 3: Scale
This final phase turns AI-first thinking into how your team operates by default.
The goal now is to make the new skills, workflows, and decision habits permanent. This way, your AI SEO capability grows without needing constant resets.
Here’s what to focus on in the next six months:
Integrate what works: Expand the proven approaches from earlier pilots across your full SEO and content programs. Keep the frameworks that consistently improve visibility; drop the ones that don’t.
Solidify roles and ownership: Define who leads AI-related strategy, measurement, and experimentation. Clarify responsibilities so the team stays agile even as you scale.
Strengthen internal training: Turn what your team learned into short onboarding sessions, playbooks, or process docs. This keeps new hires aligned and prevents knowledge loss.
Plan for selective specialization: As your AI SEO programs mature, assign ownership where consistent work is required. That could mean promoting a team member to lead AI visibility reporting, assigning an SEO specialist to oversee off-site signals, or partnering long-term with a proven external expert.
Create leadership visibility: Share quarterly reports on AI-driven results and learnings with senior stakeholders. This keeps support (and budgets) growing with your progress.
Goal: Make AI-first execution routine and scalable across your team.
By the end of this phase, your team should operate with defined roles and responsibilities. You should have internal systems for training, reporting, and process consistency.
Leadership should have visibility into AI performance outcomes so the team treats AI SEO as an integrated function, not an experiment.
Measuring AI SEO Team Success
You can measure your AI SEO team’s success by tracking how often your brand appears in AI-powered answers.
Here are important AI SEO metrics to track:
Citation frequency: How often AI platforms cite your content as a source
Brand mention rate: How often your brand appears in AI responses
Platform coverage: Which AI platforms reference you (ChatGPT, Perplexity, Google AI Overviews, etc.)
Sentiment: Whether those mentions align with your brand positioning
It shows your AI Visibility Score and how many times your brand is mentioned across different AI platforms.
It also shows which prompts your brand appears for, revealing which topics your team’s content strategy is successfully targeting.
In your Brand Performance report, you can compare your brand’s visibility against multiple competitors.
The report includes insights like your Share of Voice (percentage of mentions compared to competitors) and sentiment analysis. This tells you whether AI platforms present your brand positively or negatively.
For larger organizations, Semrush offers Enterprise AIO, with team collaboration features and advanced analytics.
Specifically, your AI Visibility Score is a good overall indicator of your AI SEO team’s performance.
If it has improved over 3-12 months, it means your team is executing well. The skills are translating into real visibility.
If results aren’t showing after two quarters, revisit your priorities. You might be focusing on the wrong skills first or need to adjust your build/buy/borrow mix.
Pro tip: When you start building your team’s AI SEO skills, benchmark your brand’s AI Visibility Score alongside five competitors.
After 3-12 months, compare growth rates, not just final scores.
Your score might increase from 30 to 40 (+10 points). But if competitors jumped from 40 to 60 (+20 points), not only are they more visible — they’re also outpacing you.
Track relative growth to understand your true competitive position.
Get a Custom AI SEO Team Plan in 20-30 Minutes
AI SEO is built on traditional SEO. But there are more layers to it.
Your SEO team needs updated systems and upgraded skills so your brand gets mentioned (and your website cited) in AI search results.
We created the free AI SEO Team Building Assistant to turn everything you just read into a custom action plan for your team.
Download the file, upload it into your AI platform of choice (Claude, ChatGPT, Gemini), and follow the conversation.
This is an interactive session that adapts to your specific team, budget, and constraints. It’s not just a cookie-cutter report after a basic prompt.
It takes around 20 minutes to work through (but you should take your time with it). At the end, you’ll walk away with a complete implementation plan.
Here’s an example of the output, starting with the one-page plan:
You’ll also get a “Skills Ownership Map” showing which team member owns which skill. And which skills to build, borrow, or buy.
Plus a Phased Roadmap, KPI Tracking Framework, Leadership Brief, and 30-day checklist.
Everything is tailored to the specific inputs you provide in the interactive conversation.
Here are some tips for getting the most out of this assistant:
Block 30 uninterrupted minutes so you can really engage with the conversation
Have your current team structure in mind
Be specific in your answers (vague input = generic output)
Be honest about constraints (like budget, time, and capabilities)
http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png00http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-12 17:58:082025-12-12 17:58:08How to Build an AI-Ready SEO Team: A Complete Guide
Word count is not a ranking factor in itself, but it still plays a significant role in SEO. A minimum number of words helps search engines understand your topic, helps users understand your message, and supports content quality and relevance. The right length for your content depends on search intent, topic depth, competition, and purpose. In this guide, you will learn why word count matters, when length helps or hurts, and how to decide the right length for every page you publish.
Aim for over 300 words for posts and 200 words for product descriptions to enhance SEO and user experience.
Word count helps Google understand context and relevance, though it is not a direct ranking factor.
Longer content provides opportunities for the inclusion of keyphrases, synonyms, and internal links, thus supporting SEO.
Prioritize quality and clarity over simply hitting a word count; irrelevant filler can damage user experience.
Always align your content length with user intent and ensure it adds real value to readers.
What does word count mean for SEO?
Word count refers to the total number of words on a page, including headings, body text, and lists. In SEO, word count is often used as a rough indicator of the amount of information a page contains about a topic. It is not a quality signal by itself, but it strongly influences how much context, explanation, and clarity a page can provide.
Search engines aim to understand what a page is about and whether it satisfies the user’s search intent. A page with sufficient text provides both readers and search engines with the signals they need to interpret meaning, relevance, and usefulness. When word count reflects real depth and not just filler, it supports SEO. If it turns into padding, it works against you. That’s not all, though; in fact, longer articles contribute to SEO in several ways.
Longer content will naturally contain your keyphrase more often. This also gives you more opportunities to use synonyms and related keyphrases, too. Additionally, longer content enables you to utilize more headings, links, and images. These elements help support your keyphrase and enhance how well your page aligns with user intent.
Longer text can also help you rank long-tail variants of your keyphrase. That’s because you have more opportunities to address various topics in a lengthy text. What’s more, if you do some clever internal linking, you’ll drive more organic traffic to your site.
Why very short content often struggles
Pages with extremely low word counts often fail to perform well in search results. This is usually not because they are short, but because they lack sufficient context, depth, and usefulness. Very short pages often leave important questions unanswered. They also provide little supporting explanation and struggle to show expertise and build trust.
From a user perspective, thin content rarely feels complete. From a search engine perspective, it provides fewer clues about relevance and topic coverage. This combination makes it harder for very short pages to compete in most informational and commercial search results. Thin content also weakens your overall site quality signals, which can affect more than just one URL.
Minimum word count guidelines
Minimum word counts exist to help prevent thin content, not to guarantee rankings. As general thresholds:
Regular posts and pages typically require a minimum of 300 words
Product descriptions typically require a minimum of 200 words
Cornerstone content typically requires a minimum of 900 words
These numbers act as a quality floor. You can go above them when a topic requires more explanation, and you can sometimes go below them when the intent is extremely narrow. What matters is whether the page truly fulfills its purpose.
What does Yoast SEO check when it comes to text length?
Yoast SEO checks the length of your content as part of the SEO analysis. You can find this check in the SEO tab of the Yoast SEO meta box or in the Yoast SEO sidebar while you are editing a page. It simply calculates how many words you have added and evaluates whether that amount is likely to be sufficient to support your SEO goals. The same check is also available in the Yoast SEO for Shopify app.
Every page on your site needs to contain a certain number of words to be helpful for your site visitors and for Google. The minimum length of your text depends on the type of page. Taxonomy pages, or collections if you use Shopify, usually require less content than blog posts, while cornerstone content is often your most important content and therefore needs to contain a significant number of words.
How the Yoast SEO text length check works
This length check exists to help you avoid publishing pages that are too thin to be useful. A page with too few words often lacks context, misses important details, and struggles to demonstrate relevance or expertise. By flagging very short pages, Yoast SEO helps you improve the overall quality of your content.
The text length check in Yoast SEO
It is essential to note that this check serves as a guideline only and does not guarantee rankings. Adding more words alone will not make a page rank. The goal is to ensure that your page contains sufficient, meaningful content to explain the topic properly, align with user intent, and enhance overall content quality.
In the table below, you can see how Yoast SEO assesses the different types of pages on your site. If a page contains fewer than the advised minimum number of words, you will see a red traffic light in the Yoast SEO analysis. When you meet or exceed the minimum word count, you will receive a green traffic light.
Word count assessment by page type
Page type
Minimum advised word count
Post or page
More than 300 words
Cornerstone post or page
More than 900 words
Taxonomy description
More than 30 words
Product description
More than 200 words
Cornerstone product description
More than 400 words
Product short description
Between 20 and 50 words
Content depth vs content length
One of the most common SEO mistakes is confusing length with depth. Content length is the number of words you use. Content depth refers to the thoroughness with which you cover the subject.
Depth means that your content answers the main question clearly and addresses relevant subtopics. It also anticipates follow-up questions and provides enough context for users to understand what they are reading. A page can achieve strong depth with a few hundred words for simple topics, while complex subjects may require far more.
Search engines are increasingly evaluating whether a page demonstrates genuine understanding rather than superficial keyword usage. That understanding comes from depth, not from word count alone. This is also where concepts like E-E-A-T become important.
How user intent determines ideal length
User intent is the foundation of every word count decision. Once you understand why someone is searching, determining the appropriate length becomes much easier.
Informational searches usually need more explanation, context, and structure. Navigational searches often need only a few words to guide users to the right place. Transactional searches prioritize clarity, trust, and persuasion over lengthy educational content.
When length matches intent, users feel understood. If it does not, they struggle to find what they need. They can also feel overwhelmed by unnecessary information. Our guide on analyzing search intent explains how to align your content with what users actually want.
Cornerstone content and long-form pages
Cornerstone content represents the most important, comprehensive pages on your site. These articles define your expertise around core themes and often serve as hubs for related content through internal linking.
Because of their role, cornerstone articles are naturally longer and more detailed. They typically cover a broad topic comprehensively, address multiple subtopics, and provide a clear structure for both readers and search engines. While 900 words may be a starting point, many strong cornerstone pages grow far beyond that. This happens when the subject matter demands more detail.
When building cornerstone content, ensure that you also mark it correctly in your site structure and internal linking strategy. Our guide on how to create cornerstone content walks you through this step-by-step.
How to decide the right length for your page
Instead of starting with a word target, start with a set of questions. What is the main intent behind this page? What does the user need to know to feel satisfied? What do the top-ranking results already explain? What additional value can you realistically add?
Outlining your content before writing makes this process easier. It also helps you stay focused while you write. When each section has a clear purpose, the final word count becomes the natural result of good coverage rather than an arbitrary goal.
Word count for product pages
Product pages require a careful balance between information and usability. Insufficient content can erode trust and hinder visibility in search results. Too much content can distract users from taking action.
A strong product page clearly explains what the product is, what it does, who it is for, and why it is worth buying. For many products, a few hundred words of clear copy is enough. More complex or high-consideration products often require more detailed explanations. This helps remove uncertainty and build confidence.
Here, clarity matters far more than hitting any specific word target. Good product pages also benefit from solid internal linking and structured data, which are covered in our guide to site structure for SEO.
Word count for blog posts
Blog posts vary widely in length because they serve a range of purposes. Some posts aim to provide a concise answer to a specific question. Others aim to explore a topic in depth and become long-term reference material.
Shorter blog posts can perform well when they are tightly focused and match a simple query. Longer blog posts often perform well for broader or more competitive topics because they allow you to explore nuances, include examples, and cover related questions that users frequently ask.
A long blog post should never feel long. When structure and readability are handled well, even detailed articles remain easy to read. If you want to improve how readable your articles are, see our article on how to improve your readability score.
Word count for landing pages
Landing pages exist to convert, not to provide in-depth education. Their success depends on whether they clearly communicate value, build trust, and guide users toward a single, actionable outcome.
Some landing pages convert best with only a few hundred words. Others need significantly more space to overcome objections and establish credibility. The right length is determined by how much explanation your audience needs before committing.
Testing real user behavior through analytics and A/B testing is the only reliable way to determine the optimal length for landing pages.
How competition affects word count
Search results show what Google already considers competitive for a query. If the top-ranking pages are detailed and comprehensive, users likely expect that level of depth. If the top results are short and direct, that usually signals simpler intent.
Before deciding on your own content length, take time to study the pages that already rank. Look at their structure, coverage, and clarity. Your goal is not to match their word count, but to match or exceed their usefulness.
This process is closely connected to keyword research and SERP analysis. If you need a refresher, our guide on keyword research covers this topic in detail.
Why readability matters more than raw length
Length only helps when people can actually read and understand the content. Long pages fail when they are filled with dense paragraphs, unclear structure, or overly complex language.
Strong readability stems from using short, clear sentences and maintaining a logical flow between paragraphs. It also depends on well-placed headings and simple vocabulary. Good structure makes even long content feel approachable and encourages users to keep reading.
Readability also supports accessibility and user experience. Both of these indirectly influence SEO performance. That is why readability is a core part of how Yoast SEO evaluates content quality.
Internal linking and topical coverage
Word count influences how much topical ground you can cover and how naturally you can include internal links. Internal links help search engines understand your site’s structure and enable users to discover related content.
Longer, in-depth pages naturally create more opportunities for internal links that are meaningful. This is because they touch on more aspects of a topic. Short pages often limit those opportunities. Strong internal linking enhances topical authority and improves the performance of cornerstone content.
If you want to improve your internal linking strategy, you can start with our guide to internal linking for SEO.
Common mistakes with word count
One common mistake is writing only to hit a number. This often leads to repetition and filler that reduce clarity and trust. Another mistake is publishing large amounts of thin content at scale. This can weaken the overall quality signal of a site.
Ignoring user intent is equally damaging. A very long article for a simple query can frustrate users just as much as a very short article for a complex topic. Finally, many sites overlook updating older thin pages as topics evolve and user expectations shift.
Regular content audits help prevent this problem and keep your site aligned with what users and search engines expect.
Conclusion on word count and SEO
Word count can influence how your posts and pages perform, but it should never come at the expense of quality. Writing more words only helps when those words improve clarity, structure, and usefulness. If you stretch your text just to reach a number, you risk making your content harder to read and less helpful for your visitors.
Focus on writing readable, well-structured content that genuinely answers the user’s question. Use headings to guide readers, keep paragraphs clear and concise, and make sure every section serves a clear purpose. That is what helps users engage with your content and what search engines aim to reward.
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Headings structure your content for both readers and search engines. They help users scan a page, understand its content, and quickly locate the information they need. Search engines and AI systems use headings to interpret the topic and structure of your content. By using one clear H1, supported by well-written H2 and H3 headings, you can improve readability, accessibility, and SEO simultaneously.
Headings are the titles and sub-titles used to structure your content. In HTML, headings range from H1 to H6. These tags inform browsers, search engines, and assistive technologies about the organization of your content.
On a typical page, the H1 is used for the main topic. H2 headings divide the text into main sections. H3 headings divide those sections further. This hierarchy creates a logical outline of your page, similar to the table of contents of a book.
Without headings, your content becomes difficult to scan. With clear headings, readers can immediately see what your page is about and which sections are relevant to them.
Why are headings important for SEO?
Headings help search engines understand what your content is about and how different topics on the page relate to each other. They provide structure, context, and signals about the importance of different sections.
Your H1 usually tells search engines what the main topic of your page is. Your H2 and H3 headings support that topic by introducing related subtopics. When this structure is clear and logical, it becomes easier for search engines to interpret your content correctly.
Headings also support semantic SEO. Rather than focusing on one keyword, search engines now assess topical relevance and context. Well-written headings naturally contain related terms and concepts that reinforce the overall topic of the page. This approach works best when combined with thorough keyword research and in-depth content. You can read more about this in our guides to keyword research and high-quality content.
Headings also play a role in how content is interpreted by AI driven search systems. Clean structure makes it easier for these systems to extract accurate answers from your pages.
Why are headings important for readers?
Most visitors do not read every word of a page. They scan first. They look at the title, skim the subheadings, and only then decide which parts to read in detail. Headings support this natural reading behavior.
Clear headings improve readability by breaking long texts into manageable sections. They help readers understand what each part of the article is about before they start reading it. This lowers the effort required to engage with your content and keeps people on the page longer.
Readability is a key quality signal. If you want to go deeper into this topic, our readability guide explains how structure, sentence length, and headings work together to create content that is easy to read.
How to use headings correctly
Using headings correctly means following a logical hierarchy and writing them with the reader in mind. Each page should have one clear H1 that describes the main topic. This is usually your page title. Below that, use H2 headings for your main sections. If a section becomes lengthy or complex, use H3 headings to further divide it.
Do not skip heading levels. An H3 should always follow an H2, not jump directly from an H1. This keeps the structure logical for both users and machines.
Your headings should describe what the section is about. Avoid vague labels such as “Introduction” or “More information.” Instead, write headings that clearly explain what the reader will learn in that section.
How many H1 headings should you use?
In most cases, you should use one H1 per page. The H1 defines the main topic of the page and helps both users and search engines understand what the page is about at a glance.
Although modern HTML allows more than one H1, using multiple H1s often creates confusion about the primary focus of the page. For consistency and clarity, one H1 is still the best practice for most websites.
Your H1 should be written naturally and should not be stuffed with keywords. It should read like a real headline written for humans. If you need help with this, Yoast SEO can balance clarity and optimization in headlines and titles.
How to use H2 and H3 headings
H2 headings divide your article into its main sections. Each H2 should cover one important aspect of your topic. When someone scans only your H2 headings, they should still be able to understand the overall structure and purpose of your article.
H3 headings are used within an H2 section to break it down into smaller parts. They are useful when you explain steps, compare options, or cover several closely related points within one larger section.
You should not use H3 headings unless they add clarity. Headings are meant to support the reader, not to decorate the page.
Common mistakes when using headings
A common mistake is using headings only for visual styling. Headings are not just larger or bolder text. They define the structure of your content in the HTML. Choosing a heading level solely based on its appearance can compromise the semantic structure of your page.
Another frequent issue is skipping heading levels, such as jumping directly from H2 to H4. This disrupts the logical structure of the page and creates issues for screen readers and search engines.
Repeating the same heading text in multiple places is also a problem. Each heading should be unique so that users and search engines can clearly distinguish between sections.
Keyword stuffing is another mistake. Headings should sound natural. If they read like a list of search terms, they reduce trust and harm readability. Clear, descriptive language always works better.
Headings and accessibility
Headings are essential for accessibility. Screen readers utilize headings to assist users in navigating a page efficiently. With a proper heading structure, visually impaired users can easily navigate from section to section and understand how the content is organized without needing to listen to the entire page.
A clear and logical heading hierarchy improves usability for everyone, not just for users of assistive technology. It is also strongly aligned with how search engines assess page quality.
If accessibility is part of your broader optimization work, it should be considered alongside internal linking and overall site structure. Don’t forget that, in many cases, what’s good for accessibility is also good for SEO!
Yoast SEO uses headings as part of both its SEO analysis and its readability analysis. One of the checks it performs is on your subheading distribution, which looks at how evenly your text is divided into sections with headings. If large blocks of text appear without any subheadings, Yoast will flag this and suggest you add subheadings to improve the readability of that part.
Effective subheading distribution means readers regularly encounter clear signposts that help them navigate the page without feeling overwhelmed by long, uninterrupted paragraphs. See the video below to find out more about the subheading distribution check and the keyphrase in subheadings check in Yoast SEO:
How to get a green traffic light for your subheading distribution
What do you do if you get an orange or red traffic light in the Yoast SEO plugin for your subheading distribution? First of all, and this is quite obvious, don’t forget to use subheadings. You should try to create a subheading for every separate topic in your text. This could be for every paragraph or a couple discussing the same topic.
We suggest that you include a heading above every long paragraph or group of paragraphs that form a thematic unit. The text following a subheading should be 250-350 words.
An example heading structure
Let’s say that we have a blog post about ballet shoes. We’ve chosen “ballet shoes” as our focus keyword and written an article about why we like ballet shoes. Without headings, there’s a risk that we might end up writing a long, rambling piece that is hard to understand. But if we structure things logically using headings, we make it easier to read and help focus our writing.
Here’s what the structure of that post might look like:
H1: Ballet shoes are awesome
H2: Why we think ballet shoes are awesome
H3: They don’t just come in pink!
H3: You can use them for more than just dancing
H3: They might be less expensive than you think
H2: Where should you buy your ballet shoes?
H3: The ten best ballet equipment websites
H3: Our favorite local dancing shops
See how we’ve created a logical structure, using H2 tags to plan sections and H3 tags to cover specific topics? We’ve done the same thing in the post you’re reading right now!
This is an excellent example of how your headings should be structured in a medium-length article. You should use fewer (or more general, high-level) headings for a shorter article. If you want to go into more detail, nothing stops you from using H4 tags to create even ‘lower-level’ sections.
Adding headings
Knowing how to structure is all well and good, but how do you add headings? The best way to explain this is in two of the most popular CMSs: WordPress and Shopify!
Note: The instructions below will walk you through how to add in-text subheadings. Don’t forget to add a post title at the top of the page, too! In Yoast SEO Premium, you’ll get a reminder to do so if the ‘Title’ field is empty. In addition, if you use Yoast SEO Premium, you get various other AI features, like Yoast AI Optimize, that help you do the hard work.
How to add a heading in WordPress
If you’re using WordPress, there are a couple of ways to do this:
Via the editor The easiest way to add headings is through the editor. If you use the block editor, click the + button and select ‘Heading’. Then, you can select which heading (H2, H3, etc.) you want to add.
Selecting a heading type in the block editor of WordPress
If you’re still using the classic editor in WordPress, it’s easy, too. Ensure you’re on the visual tab of the editor and select ‘Heading 2’ or another heading from the dropdown menu.
Change the heading type from the dropdown menu in the classic editor
Using HTML It’s also possible to add headings using HTML. In the classic editor, you will need to make sure you’re on the text tab (or directly in the code) and use heading tags <h1>, <h2>, <h3>, etc., to specify each type of heading. End each heading with a closing tag like </h1>. Like this:
Be sure to select the Text tab in the classic editor in WordPress
You can switch between the visual editor or edit as HTML in the block editor. Click on the three vertical dots in the block toolbar to do that. Then, select the Edit as HTML option. Like this:
You can also edit a post as HTML in the block editor
How to add a heading in Shopify
Adding headings in Shopify is similar to that in WordPress’s classic editor. If you’re in the content editor, you can select a piece of text and select the appropriate heading from the dropdown in the formatting menu item:
Select the text and choose a heading in Shopify
If you prefer to work in HTML, you can select the code sign in the upper right corner of the editor and create headings in HTML as described in the instructions for WordPress above.
Click the code sign to switch to HTML in the Shopify editor
Using your keyphrase in the subheadings
Headings allow you to prominently use your focus keyword (or its synonyms) to clarify what the page is about. By adding your focus keyphrase to your subheadings, you stress its importance. Moreover, if you’re trying to rank for keywords, you must write about them. You’ll probably have difficulty ranking if none of your paragraphs address the main topic.
Still, just like keyphrases, it’s important not to overdo it. Add your keyphrase where it makes sense and leave it out where it doesn’t.
Yoast SEO can help you with the keyphrase in headings assessment
After you insert your keyphrase in Yoast SEO, the keyphrase in subheadings assessment checks whether you’ve used it sufficiently. In Yoast SEO, you’ll get a green traffic light if you use the keywords in 30 to 75% of your subheadings. Please note that we’ll only review your H2 and H3 subheadings. If you have Yoast SEO Premium or if you’re using the Yoast SEO for Shopify app, you can even check your use of synonyms.
A green traffic light for the keyphrase in subheadings assessment in Yoast SEO
How to add your keyphrase in your subheadings
Whether you add your keyphrase to a subheading depends on the paragraph(s) it’s connected to. Every paragraph in your text should tell the reader something about the topic. In addition, your subheadings are nothing more than a very short outline of what you will say in one or more paragraphs. Therefore, adding your keyphrase to one or more subheadings should always be possible. If you’re still struggling to achieve this, ask yourself a couple of questions about the structure of your article.
Does my text discuss the topic described in the keyphrase? If not, should I pick other keywords?
Do my current subheadings accurately describe what I discuss below?
What paragraphs are most closely connected to the topic and the keyphrase?
What questions do these paragraphs answer concerning the topic and the keyphrase?
Most of the time, you’ll find that answering these questions helps you add the keywords to one or more of your subheadings. If you can’t, you should probably reconsider question number one. If that doesn’t solve your problems, consider educating yourself on copywriting and text structure, to get a clearer view of how a good piece is structured. Your keyphrase should be central to the topic. Therefore, you should be able to add the keywords to several subheadings.
Headings in themes
Most themes will use headings as part of their HTML code, but some don’t follow best practices. Almost all themes will automatically use the name of your article in an H1 tag. This is helpful because you don’t need to repeat the post name inside your content.
Unfortunately, some themes use tags incorrectly, in an illogical order (e.g., an H4, then an H2) or use tags messily in sidebars, headers, and footers. This can cause accessibility problems, as the order of your headings may not make sense. Users, search engines, and assistive technologies typically examine the entire page, not just your content area.
If you have a custom theme, you may be able to resolve this issue by adjusting your HTML code. You may need to contact the developers if you’re using an off-the-shelf theme. Either way, you should verify that your headings are consistent across each template type on your website.
Check your blog’s headings
Using headings well is helpful for your users. It increases the chances of people reading your article, improves accessibility, and might even contribute to SEO. So add them to your copy, but make sure you use them correctly!
The document overview is a handy button located in the upper left corner of the WordPress block editor’s content editing screen. This shows an outline of the page you’re editing. If you’ve structured your content well, it should look like this!
If you’re using Shopify or the Classic Editor in WordPress, you can test your published article via the W3 Validator.
Check the heading hierarchy in the WordPress outline feature
Final thoughts
Headings are one of the simplest and most powerful tools you have for improving both readability and SEO. They guide your readers through your content and help search engines understand what each part of your page is about.
Use one clear H1 to define your topic. Use H2s to structure your main ideas. Use H3s where they genuinely improve clarity. Write your headings for people first and let optimization support that goal.
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Today, we are excited to introduce a new feature in the Search Console Performance report: weekly and monthly views.
This new functionality allows you to adjust the time aggregation of any of the performance charts,
helping you smooth out daily changes and focus on the overall trend of traffic to your website.
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Google is rolling out a new Data Manager API that lets you plug first-party data into Google’s AI-powered ad tools with less friction. The goal: stronger measurement, smarter targeting, and better performance without the hassle of managing multiple systems.
Why we care. The Data Manager API helps you get more value from the data you already have by sending reliable first-party data into Google’s AI. This improves your targeting, measurement, and bidding. It also replaces several separate APIs with one easy connection, cutting down on engineering work and getting insights back into your campaigns faster.
About the Data Manager API. It will replace several separate Google platform APIs with one centralized integration point for advertisers, agencies, and developers. It builds on Google’s existing codeless Data Manager tool, which tens of thousands of advertisers already use to activate their first-party data.
You can use it to:
Upload and refresh audience lists.
Send offline conversions to improve measurement.
Improve bidding performance by giving Google AI richer signals.
Partnership push. To speed adoption, Google is launching with integrations from AdSwerve, Customerlabs, Data Hash, Fifty Five, Hightouch, Jellyfish, Lytics, Tealium, Treasure Data, Zapier, and others.
Available today. The API is available starting today across Google Ads, Google Analytics and Display & Video 360, with more product integrations on the way.
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Google cites retailers only 4% of the time, while ChatGPT does it 36% of the time. That 9x gap means shoppers on each platform get steered in very different ways, according to new BrightEdge data.
Why we care. Millions of shoppers now turn to AI for deals and gift ideas, but product discovery works differently on the two leading AI search platforms. Google leans on what people say, while ChatGPT focuses more on where you can buy it.
What each AI prioritizes. Google AI Overviews cite YouTube reviews, Reddit threads, and editorial sites, while ChatGPT cite retail giants like Amazon, Walmart, Target, and Best Buy.
Google AI Overviews prioritize:
YouTube reviewers and unboxings.
Reddit threads and community consensus.
Editorial reviews and category experts.
ChatGPT prioritizes:
Major retailer listings.
Brand and manufacturer product pages.
Editorial sources (secondary).
The citation divide. On Google, retailers appear only about 4% of the time. Its citations lean toward user-generated content and expert reviews. Google AI Overviews serve more as a research tool than a purchase assistant. Top sources included:
YouTube
Reddit
Quora
Editorial sites like CNET, The Spruce Eats, and Wirecutter
On ChatGPT, retailers appear about 36% of the time. ChatGPT acts as both the explainer and the shopping assistant, so retailer links show up far more often. Its top sources included:
Amazon
Target
Walmart
Home Depot
Best Buy
About the data. BrightEdge analyzed tens of thousands of ecommerce prompts across Google AI Overviews and ChatGPT during the 2025 holiday shopping season, then extracted and categorized citation sources. Domains were classified by type (retailer, UGC/social, editorial, brand) and compared across identical prompts.
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Generative systems like ChatGPT, Gemini, Claude, and Perplexity are quietly taking over the early parts of discovery – the “what should I know?” stage that once sent millions of people to your website.
Visibility now isn’t just about who ranks. It’s about who gets referenced inside the models that guide those decisions.
The metrics we’ve lived by – impressions, sessions, CTR – still matter, but they no longer tell the full story.
Mentions, citations, and structured visibility signals are becoming the new levers of trust and the path to revenue.
This article pulls together data from Siege Media’s two-year content performance study, Grow and Convert’s conversion findings, Seer Interactive’s AI Overview research, and what we’re seeing firsthand inside generative platforms.
Together, they offer a clearer view of where visibility, engagement, and buying intent are actually moving as AI takes over more of the user journey – and has its eye on even more.
Content type popularity and engagement trends
In a robust study, the folks at Siege Media analyzed two years of performance across various industry blogs, covering more than 7.2 million sessions. It’s an impressive dataset, and kudos to them for sharing it publicly.
A disclaimer worth noting: the data focuses on blog content, so these trends may not map directly to other formats such as videos, documentation, or landing pages.
With that in mind, here’s a run-through of what they surfaced.
TL;DR of the Siege Media study
Pricing and cost content saw the strongest growth over the past two years, while top-of-funnel guides and “how-to” posts declined sharply.
They suggest that pricing pages gained ground at the expense of TOFU content. I interpret this differently.
Pricing content didn’t simply replace TOFU because the relationship isn’t zero-sum.
As user patterns evolve, buyers increasingly start with generative research, then move to high-intent queries like pricing or comparisons as they get closer to a decision.
That distinction – correlation vs. causation – matters a lot in understanding what’s really changing.
The data shows major growth in pricing pages, calculators, and comparison content.
Meanwhile, guides and tutorials – the backbone of legacy SEO – took a sharp hit.
Keep that drop in mind. We’ll circle back to it later.
Interestingly, every major content category saw an increase in engagement. That makes sense.
As users complete more of their research inside generative engines, they reach your site later in the journey or for additional details, when they’re already motivated and ready to act.
If you’re a data-driven SEO, this might sound like a green light to focus exclusively on bottom-of-funnel content.
Why bother with top-of-funnel “traffic” that doesn’t convert?
Leave that for the suckers chasing GEO visibility metrics for vanity, right?
But of course, this is SEO, so I have to say it …
Did you expect me to say, “It depends?”
Here’s a question instead: when that high-intent user typed the query that surfaced a case study, pricing page, or comparison page, where did they first learn the brand existed?
I can’t believe I’m saying this, but you’ll have to keep making TOFU content.
You might need to make even more of it.
Let’s think about legacy SEO.
If we look back – waaaaay back – to 2023 and a study from Grow and Convert, we see that while there is far more TOFU traffic…
…it converts far worse.
Note: They only looked at one client, so take it with a grain of salt. However, the direction still aligns with other studies and our instincts.
This pattern also shows up across channels like PPC, which is why TOFU keywords are generally cheaper than BOFU.
The conversion rate is higher at the bottom of the funnel.
Now we’re seeing this shift carry over to generative engines, except that generative engines cover the TOFU journey almost entirely.
Rather than clicking through a series of low-conversion content pieces as they move through the funnel, users stay inside the generative experience through TOFU and often MOFU, then click through or shift to another channel (search or direct) only when it’s time to convert.
For example, when I asked ChatGPT to help me plan a trip to the Outer Banks:
After a dozen back-and-forths planning a trip and deciding what to eat, I wanted to find out where to stay.
That journey took me through many steps and gave me multiple chances to encounter different brands and filtering or refinement options.
I eventually landed on my BOFU prompt, “Some specific companies would be great.”
From there, I might click the links or search for the company names on Google.
What matters about this journey – apart from the fact that my final query would be practically useless as insight in something like Search Console – is that throughout the TOFU and MOFU stages, I was seeing citations and encountering brands I would rely on later.
Once I switched into conversion mode, I wanted help making decisions. That’s where I’m likely to click through to a few companies to find a rental.
So, when we read statistics like Pew’s finding that AI Overviews reduce CTR by upwards of 50%, and then consider what happens when AI Mode hits the browser, it’s easy to worry about where your traffic goes. Add to that ChatGPT’s 700 million weekly active users (and growing):
And according to their research on how users engage with it:
We can see a clear TOFU hit and very little BOFU usage.
So, on top of the ~50% hit you may be taking from AI Overviews, 700+ million people are going to ChatGPT and other generative platforms for their top-of-funnel needs.
I did exactly that above with my trip planning to the OBX.
The good news is that while that vacation rental company or blue widget manufacturer might not see me on their site when I’m figuring out what to do – or what a blue widget even is – I’m still going to take the same number of holidays and buy the same number of products I would have without AI Overviews or ChatGPT, Claude, Perplexity, etc.
Unless you’re a publisher or make money off impressions, you’ll still have the same amount of money to be made.
It just might take fewer website visits to do it.
More about TOFU
Traffic at the bottom of the funnel is holding steady for now (more on that below), but the top of the funnel is being replaced quickly by generative conversations rather than visits.
The question is whether being included in those conversations affects your CTR further down the funnel.
The folks at Seer Interactive found that organic clicks rose from 0.6% to 1.08% when a site was cited in AI Overviews.
And while the traffic was far lower, ChatGPT had a conversion rate of 16% compared with Google organic’s 1.8%.
If we look at the conversion rate for organic traffic at the bottom of the funnel – which we saw above – it was 4.78%.
Users who engage with generative engines clearly get further into their decision-making than users who reach BOFU queries through organic search.
But why?
While I can’t be certain, I agree with Seer’s conclusion that AI-driven users are pre-sold during the TOFU stage.
They’ve already encountered your brand and trust the system to interpret their needs. When it’s time to convert, they’re almost ready with their credit card.
Why bottom-funnel stability won’t last much longer
Above, I noted that “traffic at the bottom of the funnel is holding steady for now.”
It’s only fair to warn you that through 2026 and 2027, we’ll likely see this erode.
The same number of people will still travel and still buy blue widgets.
They just won’t book or buy them themselves. And at best, attribution will be even worse than it is today.
I spoke at SMX Advanced last spring about the rise of AI agents.
I won’t get into all the gory details here, but the Cliff Notes are this:
Agents are AI systems with some autonomy that complete tasks humans otherwise would.
They’re rising quickly – it’s the dominant topic for those of us working in AI – and that growth isn’t slowing anytime soon. You need to be ready.
A few concepts to familiarize yourself with, if you want to understand what’s coming, are:
AP2 (Agent Payments Protocol): A standard that allows agents to securely execute payments on your behalf. Think of it as a digital letter of credit that ensures the agent can only buy the specific “blue widget” you approved within the price limit you set. Before you say, “But I’d never send a machine to do a human’s job,” let me tell you, you will. And if you somehow prove me wrong individually out of spite, your customers will.
Gemini Computer Use Model API: A model with reasoning and image understanding that can navigate and engage with user interfaces like websites. While many agentic systems access data via APIs, this model (OpenAI has one too, as do others) lets the agent interact with visual interfaces to access information it normally couldn’t – navigating filters, logins, and more if given the power.
MCP (Model Context Protocol): An emerging standard acting as a universal USB port for AI apps. It lets agents safely connect to your internal data (like checking your calendar or reading your emails) to make purchasing decisions with full context and to work interactively with other agents. Hat tip to Ahrefs for building an awesome MCP server.
Why do these protocols matter to a content strategist?
Because once AP2 and Computer Use hit critical mass, the click – that sacred metric we’ve optimized for two decades – changes function.
It stops being a navigation step for a human exploring a website and becomes a transactional step for a machine executing a task.
If an agent uses Computer Use to navigate your pricing page and AP2 to pay for the subscription, the human user never sees your bottom-of-the-funnel content.
So in that world, who – or rather, what – are you optimizing for?
This brings us back to the Siege Media data.
Right now, pricing pages and calculators are winning because humans are using AI to research (TOFU and MOFU) and then manually visiting sites to convert (BOFU).
But as agents take over execution, that manual visit disappears. The “traffic” to your pricing page may be bots verifying costs, not humans persuaded by your copy.
The 2026 strategy
This reality pushes value back up the funnel.
If the agent handles the purchase, the human decision – the “moment of truth” – happens entirely inside the chat interface or agentic system during the research phase.
In this world, you don’t win by having the flashiest pricing page.
You win by being the brand the LLM recommends when the user asks, “Who should I trust?”
Your strategy for 2026 requires a two-pronged approach:
For the agent (the execution): Ensure your BOFU content is technically flawless. Use clean schema, accessible APIs, and clear data structures so that when an agent arrives via MCP or Computer Use to execute a transaction, it encounters no friction.
For the human (the selection): Double down on TOFU. Focus on mentions and citations. You need to be the entity referenced in the generative answer so that users – and agents – trust you.
As we move toward 2026 and then 2027 (it’ll be here sooner than you think), the “click” will become a commodity more often handled by machines.
The mention, however, remains the domain of human trust. And in my opinion, that’s where your next battle for visibility will be fought.
Time to start – or hopefully keep – making the TOFU.
https://i0.wp.com/dubadosolutions.com/wp-content/uploads/2025/12/image-27-fh9XCo.webp?fit=1600%2C1364&ssl=113641600http://dubadosolutions.com/wp-content/uploads/2017/05/dubado-logo-1.png2025-12-09 15:00:002025-12-09 15:00:00Mentions, citations, and clicks: Your 2026 content strategy
Evaluating SEO tools has never been more complicated.
Costs keep rising, and promises for new AI features are everywhere.
This combination is hardly convincing when you need leadership to approve a new tool or expand the budget for an existing one.
Your boss still expects SEO to show business impact – not how many keywords or prompts you can track, how fast you can optimize content, or what your visibility score is.
That is exactly where most tools still fail miserably.
The landscape adds even more friction.
Features are bundled into confusing packages and add-on models, and the number of solutions has grown sharply in the last 12 months.
Teams can spend weeks or even months comparing platforms only to discover they still cannot demonstrate clear ROI or the tools are simply out of budget.
If this sounds familiar, keep reading.
This article outlines a practical framework for evaluating your SEO tool stack in 2026, focusing on:
Must-have features.
A faster way to compare multiple tools.
How to approach vendor conversations.
The new realities of SEO tooling in 2026
Before evaluating vendors, it helps to understand the forces reshaping the SEO tooling landscape – and why many platforms are struggling to keep pace.
Leadership wants MQLs, not rankings
Both traditional and modern SEO tools still center on keyword and prompt tracking and visibility metrics. These are useful, but they are not enough to justify the rising prices.
In 2026, teams need a way to connect searches to traffic and then to MQLs and revenue.
Almost no tool provides that link, which makes securing larger budgets nearly impossible.
(I say “almost” because I have not tested every platform, so the unicorn may exist somewhere.)
AI agents raise expectations
With AI platforms like ChatGPT, Claude, and Perplexity – along with the ability to build custom GPTs, Gems, and Agents – teams can automate a wide range of tasks.
That includes everything from simple content rewriting and keyword clustering to more complex competitor analysis and multi-step workflows.
Because of this, SEO tools now need to explain why they are better than a well-trained AI agent.
Many can’t. This means that during evaluation, you inevitably end up asking a simple question: do you spend the time training your own agent, or do you buy a ready-made one?
Small teams need automation that truly saves time
If you want real impact, your automation shouldn’t be cosmetic.
You can’t rely on generic checklists or basic AI recommendations, yet many tools still provide exactly that – fast checklists with no context.
Without context, automation becomes noise. It generates generic insights that are not tailored to your company, product, or market, and those insights will not save time or drive results.
Teams need automation that removes repetitive work and delivers better insights while genuinely giving time back.
Technical SEO tools remain the most stable part of the SEO stack.
The vendor landscape has not shifted dramatically, and most major platforms are innovating at a similar pace.
Because of this, they do not require the same level of reevaluation as newer AI-driven categories.
That said, budgeting for them may still become challenging.
Leadership often assumes AI can solve every problem, but we know that without strong technical performance, SEO, content, and AI efforts can easily fail.
I will also make one bold prediction – we should be prepared to expect the unexpected in this category.
These platforms can crawl almost any site at scale and extract structured information, which could make them some of the most important and powerful tools in the stack.
Many already pull data from GA and GSC, and integrating with CRM or other data platforms may be only a matter of time.
I see that as a likely 2026 development.
What must-have features actually look like in 2026
To evaluate tools effectively, it helps to focus on the capabilities that drive real impact. These are the ones worth prioritizing in 2026.
Advanced data analysis and blended data capabilities
Data analysis will play a much bigger role.
Tools that let you blend data from GA, GSC, Salesforce, and similar sources will move you closer to the Holy Grail of SEO – understanding whether a prompt or search eventually leads to an MQL or a closed-won deal.
This will never be a perfect science, but even a solid guesstimation is more useful than another visibility chart.
Integration maturity is becoming a competitive differentiator.
Disconnected data remains the biggest barrier between SEO work and business attribution.
SERP intelligence for keywords and prompts
Traditional SERP intelligence remains essential. You still need:
Topic research and insights for top-ranking pages.
Competitor analysis.
Content gap insights.
Technical issues and ways to fix them.
You also need AI SERP intelligence, which analyzes:
How AI tools answer specific prompts.
What sources do they cite.
If your brand appears, and if your competitors are also mentioned.
In an ideal world, these two groups should appear side by side and provide you with a 360-degree view of your performance.
Automation with real-time savings
Prioritize tools that:
Cluster automatically.
Detect anomalies.
Provide prioritized recommendations for improvements.
Turn data into easy-to-understand insights.
These are just some of the examples of practical AI that can really guide you and save you time.
Strong multilingual support
This applies to SEO experts who work with websites in languages other than English.
Many tools are still heavily English-centric. Before choosing a tool, make sure the databases, SERP tracking, and AI insights work across languages, not just English.
Transparent pricing and clear feature lists
Hidden pricing, confusing bundles, and multiple add-ons make evaluation frustrating.
Tools should communicate clearly:
Which features they have.
All related limitations.
Whether a feature is part of the standard plan or an add-on.
When something from the standard plan moves to an add-on.
Many vendors change these things quietly, which makes calculating the investment you need difficult and hard to justify.
Step 4: Involve only the people who will actually use the tool
Always ask yourself who truly needs to be involved in the evaluation.
For example, we are currently assessing a platform used not only by the SEO team but also by two other teams.
We asked those teams for a brief summary of their requirements, but until we have a shortlist, there is no reason to involve them further or slow the process.
And if your company has a heavy procurement or security review, involving too many people too early will slow everything down even more.
At the same time, involve the whole SEO team, because each person will see different strengths and weaknesses and everyone will rely on the tool.
Step 5: Evaluate results, not features
Many features sound like magic wands.
In reality, the magic often works only sometimes, or it works but is very expensive. To understand what you truly need, always ask yourself:
Did the tool save time?
Did it surface insights that my current stack does not?
Could a custom GPT do this instead?
Does the price make sense for my team, and can I prove its ROI?
These questions turn the decision into a business conversation rather than a feature debate and help you prepare your “sales” pitch for your boss.
Step 6: Evaluate support quality, not just product features
Support has become one of the most overlooked parts of tool evaluation.
Many platforms rely heavily on AI chat and automated replies, which can be extremely frustrating when you are dealing with a time-sensitive issue or have to explain your problem multiple times.
Support quality can significantly affect your team’s efficiency, especially in small teams with limited resources.
When evaluating tools, check:
How easy it is to reach a human.
What response times look like.
Whether the vendor offers onboarding or ongoing guidance.
A great product with weak support can quickly become a bottleneck.
Once you have a shortlist, the quality of your vendor conversations will determine how quickly you can move forward.
And this may be the hardest part – especially for the introverted SEO leads, myself included.
How to navigate vendor conversations
I’m practical, and I don’t like wasting anyone’s time. I have plenty of tasks waiting, so fluff conversations aren’t helpful.
That’s why I start every vendor call by setting clear goals, limitations, a timeline, and next steps.
Over time, I’ve learned that conversations run much more smoothly when I follow a few simple principles.
Be prepared for meetings
If you are evaluating a tool, come prepared to the demo.
Ideally, you should have access to a free trial, tested the platform, and created a list of practical questions.
Showing up unprepared is not a good sign, and that applies to both sides.
For example, I am always impressed when a vendor joins the conversation having already researched who we are, what we do, and who our competitors are.
If you have spoken with the vendor before, directly ask what has changed since your last discussion.
Ask for competitor comparisons
When comparing a few tools, I always ask each vendor for a direct comparison.
These comparisons will be biased, but collecting them from all sides can reveal insights I had not considered and give me ideas for specific things to test.
Often, there is no reason to reinvent the wheel.
Ask how annual contracts influence pricing
Annual contracts reduce administrative work and give vendors room to negotiate, which can lead to better pricing.
Many tools include this information on their pricing pages, and we have all seen it.
Ask about any other nuances that might affect the final price – such as additional user seats or add-ons.
Don’t start from scratch with vendors you know
Often, the most effective approach is simply to say:
“This is our budget. This is what we need. Can you support this?”
This works especially well with vendors you have used before because both sides already know each other.
What to consider from a business perspective
Even if you select a tool, that does not mean you will receive the budget for it.
Proving ROI is especially difficult with SEO tools. But there are a few things you can do to increase your chances of getting a yes.
Present at least three alternatives in every request
This shows you have done your homework, not just picked the first thing you found. Present your leadership with:
The criteria you used in your evaluation.
Pros and cons of each tool.
The business case and why the capability is needed.
What happens if you do not buy the tool.
Providing this view builds trust in your ability to make decisions.
Avoid overselling
Tools improve efficiency, but they cannot guarantee outcomes – especially in SEO, GEO, or whatever you call it.
Spend time explaining how quickly things are changing and how many factors are outside your control. Managing expectations will strengthen your team’s credibility.
But even with thorough evaluation and negotiation, we still face the same issue: the SEO tooling market has not caught up with what companies now expect.
Let’s hope the future brings something closer to the clarity we see in Google Ads.
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