AI Search
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Written by Asif Ali
Modern SEO teams aren’t just optimizing for rankings in traditional search anymore.
They’re also optimizing for visibility in AI-powered search and answer engines.
And that shift is showing up in job listings.
I recently came across this position:
This isn’t an outlier.
Dozens of companies are now posting similar roles, and the shift runs deeper than new job titles.
I reviewed 100+ general SEO job postings.
96% mentioned AI somewhere in the description.
AI is creating entirely new positions, but it’s also changing what existing roles require.
Why?
Because AI search works differently from traditional Google ranking.
It extracts passages, synthesizes information, and presents instant answers from multiple sources.
This shift opens up new visibility opportunities beyond ranking in traditional search engines.
SEO teams that expand their skills now can ensure their brands are visible in AI search.
In this guide, you’ll learn:
Want a faster way to apply what you’re about to learn?
Download the AI SEO Team Building Assistant.
Upload it to your preferred AI platform (like ChatGPT or Gemini). Type “START” and follow the conversation.
Once complete, you’ll get a custom one-page plan, a checklist, and more, showing exactly how to evolve your SEO team for AI-first search.
The current SEO skill set still matters.
Keyword analysis. Technical optimization. Link building. None of that goes away.
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.
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:
These changes don’t require you to rebuild your team from scratch.
But they do require expanding what your team focuses on:
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.
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.
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.
Learn more about this in Semrush’s guide to query fan-out optimization.
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.
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.
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.
AI needs clear signals to understand your site’s structure and how content relates to other pages on your site.
Things like schema markup, internal linking, and clear site hierarchy provide those signals.
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.
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.
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.
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:
These numbers indicate whether your AI SEO strategy is working.
Semrush’s AI Visibility Toolkit can help you track these key AI search metrics.
Without specialized tools, you’ll need to manually search key queries across platforms and track when your brand appears.
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.
Further reading: 5 LLM Visibility Tools to Track Your Brand in AI Search
AI tools go beyond just looking at your website and pull from everywhere your brand is mentioned online. Including:
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.
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.
Further reading: Search Everywhere Optimization Guide (+ Free Checklist)
Different AI platforms retrieve and display information in their own ways.
For example:
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.
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.
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?
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.
You know which skills your team needs.
Now comes the practical question: how do you actually get them?
You have three options:
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.
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:
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:
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.
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.
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:
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.
Instead, look for specialists in areas like data, structured content, and retrieval systems. These are people who can bridge SEO and AI.
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:
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:
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.
Further reading: SEO Consultants Guide: When to Hire and What to Expect
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:
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:
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.
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:
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.
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:
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.
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:
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.
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:
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.
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:
Semrush’s AI Visibility Toolkit makes tracking these metrics simple.
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.
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:
Download the AI SEO Team Building Assistant and start building your AI-ready team.
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