AI-powered search gutted LinkedIn’s B2B awareness traffic. Across a subset of topics, non-brand organic visits fell by as much as 60% even while rankings stayed stable, the company said.

• LinkedIn is moving past the old “search, click, website” model and adopting a new framework: “Be seen, be mentioned, be considered, be chosen.”

By the numbers. In a new article, LinkedIn said its B2B organic growth team started researching Google’s Search Generative Experience (SGE) in early 2024. By early 2025, when SGE evolved into AI Overviews, the impact became significant.

• Non-brand, awareness-driven traffic declined by up to 60% across a subset of B2B topics.
• Rankings stayed stable, but click-through rates fell (by an undisclosed amount).

Yes, but. LinkedIn’s “new learnings” are more like a rehash of established SEO/AEO best practices. Here’s what LinkedIn’s content-level guidance consists of:

• Use strong headings and a clear information hierarchy.
• Improve semantic structure and content accessibility.
• Publish authoritative, fresh content written by experts.
• Move fast, because early movers get an edge.

Why we care. These tactics should all sound familiar. These are technical SEO and content-quality fundamentals. LinkedIn’s article offers little new in terms of tactics. It’s just updated packaging for modern SEO/AEO and AI visibility.

Dig deeper. How to optimize for AI search: 12 proven LLM visibility tactics

Measurement is broken. LinkedIn said its big challenge is the “dark” funnel. It can’t quantify how visibility in LLM answers impacts the bottom line, especially when discovery happens without a click.

• LinkedIn’s B2B marketing websites saw triple-digit growth in LLM-driven traffic and that it can track conversion from those visits.
  • Yes, but: Many websites are also seeing triple-digit (or more) growth in LLM-driven traffic. Because it’s an emerging channel. That said, this is still a tiny amount of overall traffic right now (1% or less for most sites).

What LinkedIn is doing. LinkedIn created an AI Search Taskforce spanning SEO, PR, editorial, product marketing, product, paid media, social, and brand. Key actions included:

• Correcting misinformation that showed up in AI responses.
• Publishing new owned content optimized for generative visibility.
• Testing LinkedIn (social) content to validate its strength in AI discovery.

Is it working? LinkedIn said early tests produced a meaningful lift in visibility and citations, especially from owned content. At least one external datapoint (Semrush, Nov. 10, 2025) suggested that LinkedIn has a structural advantage in AI search:

• Google AI Mode cited LinkedIn in roughly 15% of responses.
• LinkedIn was the #2 most-cited domain in that dataset, behind YouTube.

Incomplete story. LinkedIn’s article is an interesting read, but it’s light on specifics. Missing details include:

• The exact topic set behind the “up to 60%” decline.
• Exactly how much click-through rates “softened.”
• Sample size and timeframe.
• How “industry-wide” comparisons were calculated.
• What tests were run, what moved citation share, and by how much.

Bottom line. LinkedIn is right that visibility is the new currency. However, it hasn’t shown enough detail to prove its new playbook is meaningfully different from doing some SEO (yes, SEO) fundamentals.

LinkedIn’s article. How LinkedIn Marketing Is Adapting to AI-Led Discovery

Innovations are coming at marketers and consumers faster than before, raising the question: Are we actually ready for the agentic web?

To answer that question, it’s important to unpack a few supporting ones:

• What’s the agentic web?
• How can the agentic web be used?
• What are the pros and cons of the agentic web?

It’s important to note that this article isn’t a mandate for AI skeptics to abandon the rational questions they have about the agentic web. 

Nor is it intended to place any judgment on how you, as a consumer or professional, engage with the agentic web.

With thoughts and feelings so divided on the agentic web, this article aims to provide clear insight into how to think about it in earnest, without the branding or marketing fluff.

Disclosure: I am a Microsoft employee and believe in the path Microsoft’s taking with the agentic web. However, this article will attempt to be as platform-agnostic as possible.

What’s the agentic web? 

The agentic web refers to sophisticated tools, or agents, trained on our preferences that act with our consent to accomplish time-consuming tasks.

In simple terms, when I use one-click checkout, I allow my saved payment information to be passed to the merchant’s accounts receivable systems. 

Neither the merchant nor I must write down all the details or be involved beyond consenting to send and receive payment.

For fun, I put this question to four different AI models, and the responses are telling: 

• Copilot: “The agentic web is an emerging layer of the internet where AI agents can reason and take actions on behalf of people or organizations, turning expressed intent into outcomes while preserving user choice, transparency, and control.” 
• Gemini: “The agentic web is an evolution of the internet where autonomous AI agents – rather than just human users – interact with websites, APIs, and each other to perform complex tasks and make decisions on our behalf. “
• Perplexity: “The agentic web is an emerging phase of the internet where autonomous AI agents act as the primary interface, navigating, interpreting, and taking actions across websites and services on behalf of users with minimal human involvement.” 
• Claude: “The agentic web is an evolution of the internet where AI agents autonomously navigate, interact with, and complete tasks across websites and services on behalf of users.” 

I begin with how different models answer the question because it’s important to understand that each one is trained on different information, and outcomes will inevitably vary.

It’s worth noting that with the same prompt, defining the agentic web in one sentence, three out of four models focus on diminishing the human role in navigating the web, while one makes a point to emphasize the significance of human involvement, preserving user choice, transparency, and control.

Two out of four refer to the agentic web as a layer or phase rather than an outright evolution of the web. 

This is likely where the sentiment divide on the agentic web stems from.

Some see it as a consent-driven layer designed to make life easier, while others see it as a behemoth that consumes content, critical thinking, and choice.

It’s noteworthy that one model, Gemini, calls out APIs as a means of communication in the agentic web. APIs are essentially libraries of information that can be referenced, or called, based on the task you are attempting to accomplish. 

This matters because APIs will become increasingly relevant in the agentic web, as saved preferences must be organized in ways that are easily understood and acted upon.

Defining the agentic web requires spending some time digging into two important protocols – ACP and UCP.

Dig deeper: AI agents in SEO: What you need to know

Agentic Commerce Protocol: Optimized for action inside conversational AI 

The Agentic Commerce Protocol, or ACP, is designed around a specific moment: when a user has already expressed intent and wants the AI to act.

The core idea behind ACP is simple. If a user tells an AI assistant to buy something, the assistant should be able to do so safely, transparently, and without forcing the user to leave the conversation to complete the transaction.

ACP enables this by standardizing how an AI agent can:

• Access merchant product data.
• Confirm availability and price.
• Initiate checkout using delegated, revocable payment authorization.

The experience is intentionally streamlined. The user stays in the conversation. The AI handles the mechanics. The merchant still fulfills the order.

This approach is tightly aligned with conversational AI platforms, particularly environments where users are already asking questions, refining preferences, and making decisions in real time. It prioritizes speed, clarity, and minimal friction.

Universal Commerce Protocol: Built for discovery, comparison, and lifecycle commerce 

The Universal Commerce Protocol, or UCP, takes a broader view of agentic commerce.

Rather than focusing solely on checkout, UCP is designed to support the entire shopping journey on the agentic web, from discovery through post-purchase interactions. It provides a common language that allows AI agents to interact with commerce systems across different platforms, surfaces, and payment providers. 

That includes: 

• Product discovery and comparison.
• Cart creation and updates.
• Checkout and payment handling.
• Order tracking and support workflows.

UCP is designed with scale and interoperability in mind. It assumes users will encounter agentic shopping experiences in many places, not just within a single assistant, and that merchants will want to participate without locking themselves into a single AI platform.

It’s tempting to frame ACP and UCP as competing solutions. In practice, they address different moments of the same user journey.

ACP is typically strongest when intent is explicit and the user wants something done now. UCP is generally strongest when intent is still forming and discovery, comparison, and context matter.

So what’s the agentic web? Is it an army of autonomous bots acting on past preferences to shape future needs? Is it the web as we know it, with fewer steps driven by consent-based signals? Or is it something else entirely?

The frustrating answer is that the agentic web is still being defined by human behavior, so there’s no clear answer yet. However, we have the power to determine what form the agentic web takes. To better understand how to participate, we now move to how the agentic web can be used, along with the pros and cons.

Dig deeper: The Great Decoupling of search and the birth of the agentic web

How can the agentic web be used? 

Working from the common theme across all definitions, autonomous action, we can move to applications.

Elmer Boutin has written a thoughtful technical view on how schema will impact agentic web compatibility. Benjamin Wenner has explored how PPC management might evolve in a fully agentic web. Both are worth reading.

Here, I want to focus on consumer-facing applications of the agentic web and how to think about them in relation to the tasks you already perform today.

Here are five applications of the agentic web that are live today or in active development.

1. Intent-driven commerce  

A user states a goal, such as “Find me the best running shoes under $150,” and an agent handles discovery, comparison, and checkout without requiring the user to manually browse multiple sites. 

How it works 

Rather than returning a list of links, the agent interprets user intent, including budget, category, and preferences. 

It pulls structured product information from participating merchants, applies reasoning logic to compare options, and moves toward checkout only after explicit user confirmation. 

The agent operates on approved product data and defined rules, with clear handoffs that keep the user in control. 

Implications for consumers and professionals 

Reducing decision fatigue without removing choice is a clear benefit for consumers. For brands, this turns discovery into high-intent engagement rather than anonymous clicks with unclear attribution. 

Strategically, it shifts competition away from who shouts the loudest toward who provides the clearest and most trusted product signals to agents. These agents can act as trusted guides, offering consumers third-party verification that a merchant is as reliable as it claims to be.

2. Brand-owned AI assistants 

A brand deploys its own AI agent to answer questions, recommend products, and support customers using the brand’s data, tone, and business rules.

How it works 

The agent uses first-party information, such as product catalogs, policies, and FAQs. 

Guardrails define what it can say or do, preventing inferences that could lead to hallucinations. 

Responses are generated by retrieving and reasoning over approved context within the prompt.

Implications for consumers and professionals 

Customers get faster and more consistent responses. Brands retain voice, accountability, and ownership of the experience. 

Strategically, this allows companies to participate in the agentic web without ceding their identity to a platform or intermediary. It also enables participation in global commerce without relying on native speakers to verify language.

3. Autonomous task completion 

Users delegate outcomes rather than steps, such as “Prepare a weekly performance summary” or “Reorder inventory when stock is low.” 

How it works 

The agent breaks the goal into subtasks, determines which systems or tools are needed, and executes actions sequentially. It pauses when permissions or human approvals are required. 

These can be provided in bulk upfront or step by step. How this works ultimately depends on how the agent is built. 

Implications for consumers and marketers 

We’re used to treating AI like interns, relying on micromanaged task lists and detailed prompts. As agents become more sophisticated, it becomes possible to treat them more like senior employees, oriented around outcomes and process improvement. 

That makes it reasonable to ask an agent to identify action items in email or send templates in your voice when active engagement isn’t required. Human choice comes down to how much you delegate to agents versus how much you ask them to assist.

Dig deeper: The future of search visibility: What 6 SEO leaders predict for 2026

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4. Agent-to-agent coordination and negotiation 

Agents communicate with other agents on behalf of people or organizations, such as a buyer agent comparing offers with multiple seller agents. 

How it works 

Agents exchange structured information, including pricing, availability, and constraints. 

They apply predefined rules, such as budgets or policies, and surface recommended outcomes for human approval. 

Implications for consumers and marketers 

Consumers may see faster and more transparent comparisons without needing to manually negotiate or cross-check options. 

For professionals, this introduces new efficiencies in areas like procurement, media buying, or logistics, where structured negotiation can occur at scale while humans retain oversight.

5. Continuous optimization over time 

Agents don’t just act once. They improve as they observe outcomes.

How it works 

After each action, the agent evaluates what happened, such as engagement, conversion, or satisfaction. It updates its internal weighting and applies those learnings to future decisions.

Why people should care 

Consumers experience increasingly relevant interactions over time without repeatedly restating preferences. 

Professionals gain systems that improve continuously, shifting optimization from one-off efforts to long-term, adaptive performance. 

What are the pros and cons of the agentic web? 

Life is a series of choices, and leaning into or away from the agentic web comes with clear pros and cons.

Pros of leaning into the agentic web 

The strongest argument for leaning into the agentic web is behavioral. People have already been trained to prioritize convenience over process. 

Saved payment methods, password managers, autofill, and one-click checkout normalized the idea that software can complete tasks on your behalf once trust is established.

Agentic experiences follow the same trajectory. Rather than requiring users to manually navigate systems, they interpret intent and reduce the number of steps needed to reach an outcome. 

Cons of leaning into the agentic web 

Many brands will need to rethink how their content, data, and experiences are structured so they can be interpreted by automated systems and humans. What works for visual scanning or brand storytelling doesn’t always map cleanly to machine-readable signals.

There’s also a legitimate risk of overoptimization. Designing primarily for AI ingestion can unintentionally degrade human usability or accessibility if not handled carefully. 

Dig deeper: The enterprise blueprint for winning visibility in AI search

Pros of leaning away from the agentic web 

Choosing to lean away from the agentic web can offer clarity of stance. There’s a visible segment of users skeptical of AI-mediated experiences, whether due to privacy concerns, automation fatigue, or a loss of human control. 

Aligning with that perspective can strengthen trust with audiences who value deliberate, hands-on interaction.

Cons of leaning away from the agentic web 

If agentic interfaces become a primary way people discover information, compare options, or complete tasks, opting out entirely may limit visibility or participation. 

The longer an organization waits to adapt, the more expensive and disruptive that transition can become.

What’s notable across the ecosystem is that agentic systems are increasingly designed to sit on top of existing infrastructure rather than replace it outright. 

Avoiding engagement with these patterns may not be sustainable over time. If interaction norms shift and systems aren’t prepared, the combination of technical debt and lost opportunity may be harder to overcome later.

Where the agentic web stands today

The agentic web is still taking form, shaped largely by how people choose to use it. Some organizations are already applying agentic systems to reduce friction and improve outcomes. Others are waiting for stronger trust signals and clearer consent models.

Either approach is valid. What matters is understanding how agentic systems work, where they add value, and how emerging protocols are shaping participation. That understanding is the foundation for deciding when, where, and how to engage with the agentic web.

Competitive research is a gold mine of insights in the world of organic discovery. Clients always love seeing insights about how they stack up against their rivals, and the insights are very easily translated into a multi-dimensional roadmap for getting traction on essential topics.

If you haven’t already done this, 2026 needs to be the year when you add competitive research from answer engine optimization (AEO) (I’ll use this acronym interchangeably with AI search) into your organic strategy – and not just because your executives or clients are clamoring for it (although I’m guessing they are).

This article breaks down the distinct roles of SEO and AEO competitive research, the tools used for each, and how to turn those insights into clear, actionable next steps.

SEO competitive research benefits vs. AEO competitive research benefits

Traditional SEO research is great for content planning and development that helps you address specific keywords, but that’s far from the whole organic picture in 2026.

Combined, SEO and AI competitive research can give you a clear strategy for positioning and messaging, content development, content reformatting, and even product marketing roadmapping. 

Let’s start with the tried-and-true tools of traditional SEO research. They excel at: 

• Demand capture.
• Keyword-driven intent mapping.
• Late-funnel and transactional discovery.

A few years ago, pre-ChatGPT and the competitors that followed, SEO research was the foundation of your organic strategy. Today, those tools are a vital piece of organic strategy, but the emergence of AI search has shifted much of the focus away from traditional SEO. 

Now, SEO research should be used to:

• Support AI visibility strategies.
• Validate demand, not define strategy.
• Identify content gaps that feed AI systems, not just SERPs.

AEO tools cover very different parts of the customer journey. These include:

• Demand shaping.
• Brand framing and recommendation bias.
• Early- and mid-funnel decision influence.

AEO tools operate before the click, often replacing multiple SERP visits with a single synthesized answer. They offer a new type of research that’s a blend of voice-of-customer, competitive positioning, and market perception. That helps them deliver tremendous competitive insights into: 

• Category leadership. 
• Challenger brand visibility. 
• Competitive positioning at the moment opinions are formed.

Let’s break this down a little further. Organic search experts can use insights from AI search tools to:

• Identify feature expectations users assume are table stakes.
• Spot emerging alternatives before they show up in keyword tools.
• Understand where top products are or are not visible for relevant queries in key large language models (LLMs).
• Understand why users are advised not to choose certain products.
• Validate whether your product roadmap aligns with how the market is being explained to users.

Dig deeper: How to use competitive audits for AI SERP optimization

SEO vs. AEO research tools

Aside from adding AEO functionality (leaders here are Semrush and Ahrefs), SEO research tools essentially function in much the way they did a few years ago. Those tools, and their uses, include:

Ahrefs

Ahrefs is a great source of info for, among other things: 

• Search traffic.
• Paid traffic.
• Trends over time.
• Search engine ranking for keywords.
• Topics and categories your competitors are writing content for.
• Top pages.

I also like to use Ahrefs for a couple of more advanced initiatives: 

• High-level batch analysis provides a fast overview of backlinks for any list of URLs you enter. This can give you ideas about outreach – or content written strategically to appeal to these outlets – for your backlinks strategy. 
• Reverse-engineering a competitor’s FAQs allows you to see potentially important topics to address with your brand’s differentiators in mind.
  • To do this, go to Ahrefs’ Site Explorer, drop in a competitor domain, and then click on the Organic Keywords report. 
  • From here, you’ll want to filter out non-question keywords. The result is a good list of questions from actual users in your industry. You can then use these to tailor your content to meet potential customer needs.

Dig deeper: Link intent: How to combine great content with strategic outreach

BuzzSumo

BuzzSumo sends you alerts about where your competitors receive links from their public relations and outreach efforts. 

This is the same idea as the batch analysis, but it’s more real-time and gives you good insights into your competitors’ current priorities.

Semrush

Semrush is a super-useful tool for competitive research. 

You can use the domain versus domain tool to see what keywords competitors rank for with associated metrics. You can get insights on competitor keywords, ad copy, organic and paid listings, etc. 

Armed with all of this research, a fun content maneuver I like to suggest to clients is “[Client] vs. [Competitor]” pieces of content, particularly once they have some differentiators fleshed out to play up in their content. 

With this angle, I’ve gotten some great first-page rankings and reached users with buying intent.

Using their brand name might not always get you to rank above your competitor. Still, if you’re a challenger taking on bigger brands, it’s a good way to borrow their brand equity.

On the AEO side, I love tools with a heavy measurement component, but I also make a point of digging into the actual LLMs themselves, like ChatGPT and Google AI Mode, to combine reporting tools with source data.

This is similar to how my team has always approached traditional SEO research, which balances qualitative tools with extensive manual analysis of the actual SERPs.

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The tools I recommend for heavy use are:

Profound

Profound is the most purpose-built AEO platform I’m using today. It focuses on how brands and competitors appear inside AI-generated answers, not just whether they rank in classic SERPs. Its insights help users: 

• See which brands are cited or referenced in LLM answers for category-level and comparison queries.
• Identify patterns in how competitors’ content is framed (e.g., default recommendation, alternative, warning, etc.). 
• Understand which sources LLMs trust (e.g., documentation, reviews, forums, owned content).
• Track share of voice within AI answers, not just blue links.

All of these insights help to move competitive research from the simple question of “who ranks” to the more important answer of “who is recommended and why.”

Ahrefs

Ahrefs remains a foundational tool for traditional SEO research, but its insights primarily reflect what ranks, not what gets synthesized or cited by AI systems.

They have, however, built in some new AI brand tracking tools worth exploring.

ChatGPT

ChatGPT is invaluable as a qualitative competitive research layer. I use it to: 

• Simulate how users phrase early-stage and exploratory questions.
• Compare how different competitors are summarized when asked things like: “What’s the best alternative to X?” or “Who should use X vs. Y?” 
• Identify language, positioning, and feature emphases that consistently show up across responses. 
• Test messaging.
• Compare narratives with competitors.
• Identify where your brand’s positioning is unclear or has gaps.

Google AI Mode

This tool is the clearest signal we have today of how AI Overviews will impact demand capture. It provides insight into: 

• Which competitors are surfaced before any traditional ranking is visible. 
• What sources Google synthesizes to build its answers.
• How informational, commercial, and navigational queries blend. (This is especially important for mid-funnel queries where users previously clicked multiple results but now receive a single synthesized answer.)

Reddit Pro

This resource combines traditional community research with AI-era discovery. 

Because Reddit content is disproportionately represented in AI answers, this has become a first-class competitive intelligence source, not just a qualitative one. It helps to surface: 

• High-signal conversations frequently referenced by LLMs. 
• Common objections, alternatives, and feature gaps discussed by real users.
• Language that actually resonates with people – and insight which often differs from keyword-driven copy.

Dig deeper: How to use advanced SEO competitor analysis to accelerate rankings & boost visibility

How to take action on your organic competitive research insights

Presenting competitive insights to clients or management teams in a digestible package is a good start (and may make its way up to the executive team for strategic planning). 

But where the rubber really meets the road is when you can make strong recommendations for how to use the insights you’ve gathered. 

Aim for takeaways like:

• “[Competitor] is great at [X], so I suggest we target [Yy.”
• “[Competitor] is less popular with [audience], which would likely engage with content on [topic].”
• “[Competitor] is dominating AI search on topics I should own, so I recommend developing or refining our positioning and building a specific content strategy.”
• “I’ve built a matrix showing the competitor product pages that draw more visibility in LLMs than our top-selling products. I recommend we focus on making those product pages more digestible for AI search and tracking progress. If we get traction, I recommend we identify the next tranche of product pages to optimize and proceed.” 

Ultimately, your clients or teammates should be able to use your insights to understand the market and align with you on priorities for initiatives to expand their footprint in both traditional and AI search. 

Custom GPTs can help SEO teams move faster by turning repeatable tasks into structured workflows.

If you don’t have access to paid ChatGPT, you can still use these prompts as standalone references by copying them into your notes for future reuse. You will need to tweak them for your team’s specific use cases, because they are intended as a starting point.

Working with AI is largely trial and error. To get better at writing prompts, practice with small tasks first, iterate on prompts, and take notes on what gets you good outputs. 

AI also tends to ramble, so it helps to give strict guidelines for formatting and to specify what not to do. You can upload resources and articles to follow and provide clear context, such as defining the role and audience upfront.

The seven prompts below are designed to help you start building custom GPTs for planning, analysis, and ongoing SEO work.

1. Project plan GPT

Using past examples of project plans, create a GPT that will help you make a draft for this year’s focus areas.

How to set it up

• Input project plans from previous years.
• Give it a specific format to follow.
• Consider how many items or sections to include.
• Add specific details based on you or your team.
• (Optional) Copy notes and feedback from your team or retrospective.

Example prompt

Based on last year’s project plan, make my project plan for this year. Here are the focus areas and problem areas to include.

Give me a bulleted list with the three most important items for me (or my team) to focus on for each quarter of this year. At least one item should cover link building.

Include a one-sentence summary of why you recommend each item and at least two KPIs to measure success.

[Insert last year’s plan.]

Now poke holes in your plan. Give me three reasons I should not focus on these items based on the risks. Include sources for your notes.

Dig deeper: How to use ChatGPT Tasks for SEO

2. Site performance GPT

Hook up your performance dashboards or custom GA reports to ChatGPT and let it do the initial legwork in identifying issues. Then make a list of items to investigate yourself.

How to set it up

• Connect your reporting tools or upload reports directly.
• Give specific direction for what to look for.
• Include the cadence you want to look at, like a daily or weekly report.
• Give examples of types of pages or categories to compare.

Example prompt

Here is the weekly site report. Give me your analysis of how the site performed compared to last week. Include a three-sentence summary of the sessions, conversions, and engagement.

List three wins and three misses in bullet format. Color-code each item based on how good or bad each item is.

[Insert report doc.]

3. Competitor analysis GPT

Check out what’s working and what’s not on competitor sites and get insights for yours. It’s most helpful to connect to a tool like Semrush or Ahrefs. 

How to set it up

• Connect tools like Ahrefs or Semrush, or upload a report.
• Identify competitors to analyze and top pages and folders.
• List key metrics to compare.
• Set up unique prompts for page, keyword/topic, folder, and domain-level comparison.
• (Optional) Create documentation on identifying which metrics to dig deeper.

Example prompt

You are an SEO analyst performing competitor analysis to identify areas to improve your website. Check out these URLs and compare them. Give me a table with each URL in the rows and these columns: backlinks, average rank, top keyword, sessions, and estimated value.

Below that, give a two-sentence summary of who wins in each category and why. Use the criteria in this link to make your judgments, citing sources for each.

URL 1: 
URL 2: 
URL 3: 
Article reference:

Dig deeper: How to use advanced SEO competitor analysis to accelerate rankings & boost visibility

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4. SERP analyzer GPT

AI has gotten much better over the last few months at analyzing images. Plug in SERP screenshots from your own searches and compare it to a web search from the GPT. Build this into a competitive SERP landscape analysis to see things like who appears in both searches vs. only one.

How to set it up

• Identify search results and keywords to compare.
• Take screenshots in incognito mode for comparison.

Example prompt

Do a web search for [your keyword here]. Show me what you are seeing in the search results.

Compare it with this screenshot and list the differences. Then include a bulleted list of what the results seen most often have in common.

Dig deeper: How to build a custom GPT to redefine keyword research

5. UX GPT

Turn your design or UX team’s resources into an easy-to-use helper. This is especially helpful for editorial teams that do not want to search through endless documentation for quick advice.

How to set it up

• Upload your team’s documentation or your favorite UX articles.
• Find pages with poor bounce or engagement stats.
• Integrate the tool into standard page updates.

Example prompt

You are an SEO writer working on improving user engagement. Open this page. Check to make sure it follows all of our design rules.

List each violation, along with a source, explaining what is wrong and what to do instead. Then check to see whether there are any relevant page template patterns from the brand book that could apply to this type of page.

6. Tech SEO check GPT

Set up a daily or weekly tech SEO check to do the bulk of the analysis for you. 

How to set it up

• Connect any tools like Google Search Console, or upload reports.
• List the top metrics to check, like Core Web Vitals, page speed, and console errors.
• Identify top pages to run a more comprehensive check.
• Set up reminders to run it daily or weekly, or connect it to Slack to export results directly.

Example prompt

Based on the latest CWV report, identify problem pages that need a speed improvement audit. Create the list in a table, with the URLs in rows and columns for speed, issues identified, and suggested fixes. Make a separate list of pages that have improved, along with the actual scores.

Dig deeper: A technical SEO blueprint for GEO: Optimize for AI-powered search

7. Presentation GPT

While ChatGPT cannot directly create slides yet without an add-on or third-party connector, it can create the content for you to paste into your slides. Combine it with your performance, testing, tech SEO, and competitor GPTs for a well-rounded summary of overall site status with relevant context.

How to set it up

• Gather data from your other GPTs.
• Choose the ones to present.
• (Optional) Upload past presentations for reference.

Example prompt

Pretend you are setting up a slide deck. The audience is other members of the SEO team. Format this summary from my Performance GPT into a slide.

Give me a header, subheader, and key bullets and takeaways. The tone should be straightforward but professional. Limit bullets to one line. Round all numbers to zero decimals. Suggest three examples of imagery and graphics to use.

[Insert summary.]

Dig deeper: How to balance speed and credibility in AI-assisted content creation

Where custom GPTs fit into day-to-day SEO work

Custom GPTs are most useful when they sit alongside the tools and processes SEO teams already use. Rather than replacing dashboards, audits, or documentation, they can handle first passes, surface patterns, and standardize how work gets reviewed before a human steps in.

Used this way, the prompts in this article are less about automation for its own sake and more about reducing friction in common SEO tasks, from planning and reporting to SERP analysis and technical checks.

For a long time, SEO had the simplest math in marketing:

• Rank higher → Get more traffic → Fill the sales pipeline

To the dissatisfaction of marketing executives, that linear world is breaking fast.

Between AI Overviews, zero-click SERPs, and users getting answers directly from LLMs, the old “rank to get traffic and leads” equation is failing. 

Today, holding a top keyword position often yields significantly fewer clicks than it did just two years ago.

This has forced many uncomfortable conversations in boardrooms. CMOs and CEOs are looking at traffic dashboards and asking tough questions, especially:

• “If traffic is down… how do we know SEO is actually working?”

The answer forces us to confront a hard truth: The traffic model has collapsed, but executives still want measurable ROI. 

We have to stop treating SEO like a traffic faucet and start treating it like what it actually is: a brand-dependent performance channel.

Why traffic and pipeline are no longer in lockstep

Linear attribution has never fully captured the reality of organic search. 

ChatGPT is not replacing Google; rather, it is expanding its use. 

And that’s because users are skeptical of search and LLM results, so they need to validate the information they find on both platforms. 

In the past, the research loop happened inside Google’s ecosystem (clicking back and forth between results).

Today, organic search behaves like a pinball machine. Buyers bounce across channels and interfaces in ways that traditional attribution software cannot track. 

A user might find an answer in an AI Overview, verify it on Reddit, check a competitor comparison on G2, and finally convert days later via a direct visit.

This complexity has broken the correlation marketing executives are hungry for. 

In the past, if you overlaid traffic and pipeline charts, the lines moved together. Now, they often diverge.

Across B2B SaaS portfolios, I am seeing a consistent pattern:

• Organic sessions are flat or declining year over year.
• Rankings for high-intent terms remain stable.
• Pipeline and inbound demos from organic search are going up.

Dig deeper: How to explain flat traffic when SEO is actually working

This divergence doesn’t mean SEO is failing. It means that traffic is no longer a reliable proxy for business impact.

The traffic being lost to zero-click searches is often informational and low-intent. The remaining traffic is higher-intent and closer to conversion. 

We are witnessing the “atomization” of search demand. 

As Kevin Indig notes in his analysis of The Great Decoupling, demand for short-head, broad keywords is in permanent decline. 

Users are either bypassing search entirely for AI interfaces, or they are refining their queries into specific, long-tail questions that have lower volume but significantly higher intent.

The “fat head” of search – the generic terms that used to drive massive vanity traffic – is being eaten by AI. The long tail is where the pipeline lives.

The mistake many leaders make is seeing the sessions drop and instinctively pushing to “get the numbers back up.” 

But chasing lost clicks usually leads to publishing broad, top-of-funnel content that inflates session counts (and other vanity metrics) without actually driving qualified leads.

Dig deeper: How to align your SEO strategy with the stages of buyer intent

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SEO ROI is now the downstream outcome of brand traction

This is where the debate between “brand” and “performance” breaks down.

For a decade, SEO masqueraded as a pure performance channel. 

We convinced ourselves that if we just optimized the H1s and built enough backlinks, we could rank for anything. 

We treated brand awareness as a nice bonus, but not a prerequisite.

In reality, SEO has always been downstream of brand. AI interfaces are simply exposing that truth.

The rise of LLM-based search has flipped the script. These engines don’t just match keywords to pages; they synthesize reputation.

When an LLM constructs an answer, it is looking for verification across the entire web:

• What do actual customers say on G2 and Reddit?
• Is the brand cited in expert, non-affiliate content?
• Is the product mentioned alongside category leaders?

You cannot brute-force these outcomes via SEO techniques.

If your brand lacks digital authority, no amount of technical optimization will save you. That is why I call this brand-conditioned performance.

It means that your brand strength sets the ceiling for your organic performance. You can no longer out-optimize a weak reputation. 

The search engines are looking for consensus across the web, and if the market doesn’t already associate your brand with the solution, the algorithm won’t recommend you.

So, what does brand strength actually mean to an LLM? In this new environment, brand strength is composed of four specific signals:

• Topical authority: Do you own the complete conceptual map of your industry, or just a few disconnected keywords?
• Ideal customer profile (ICP) alignment: Are you answering the specific, messy questions your actual buyers ask, or just publishing generic definitions?
• Validation: Are you cited by the category-defining sources that LLMs use as training data?
• Positioning clarity: Can an AI clearly summarize exactly what you do? As Indig points out, “Vague positioning gets skipped; sharp positioning gets cited.”

Bottom line: SEO doesn’t create demand out of thin air. It captures the demand your brand has already validated. 

Dig deeper: The new SEO imperative: Building your brand

The new defensibility metrics for SEO

When traffic stops being the headline KPI, leadership still needs proof that SEO is working. 

The strongest teams are pivoting to defensible signals that track revenue and reputation rather than just volume.

We need to anchor on metrics that prove business impact, even if top-of-funnel sessions are leaking:

• Top-10 rankings for commercial and BOFU keywords remain stable. (You hold the ground where money changes hands).
• Ahrefs traffic value increases, even if sessions decline. (You are trading high-volume informational traffic for high-value commercial traffic).
• Product, solution, and comparison page traffic stabilizes. (Buyers are still finding your money pages).
• Homepage traffic grows YoY. (The strongest proxy for brand demand).
• LLM referral traffic emerges and accelerates. (The newest frontier. Tracking referral sources from ChatGPT, Gemini, or Perplexity indicates that you are part of the new conversation, even if the volume is currently low.)
• Inbound demos and pipeline from organic growth relative to traffic.

That last point is the one that changes executive thinking.

When you show that pipeline per organic visitor is rising – even as sessions fall – the conversation shifts from “SEO is broken” to “SEO is evolving.”

Dig deeper: Why AI availability is the new battleground for brands

Modern SEO is moving from acquisition to influence

The most successful SEO teams are no longer asking, “How do we get the traffic back?”

They understand that the game has changed from acquisition to influence. 

They are asking:

• How does our brand show up for buying questions?
• How do we dominate consideration-stage queries?
• How do we turn organic visibility into real buying influence?

They recognize that in an AI-first world, zero-click does not mean zero-value.

If a user sees your brand ranked first in an AI Overview, reads a snippet that positions you as the expert, and remembers you when they are ready to buy – SEO did its job.

SEO is no longer a hack for cheap traffic; it is the primary way brands condition the market to buy.

One of the biggest SEO challenges right now isn’t AI. It’s the irresponsible misinformation surrounding it.

SEO isn’t dying — it’s evolving. That means it’s on us to understand how the industry is changing, and to be careful about who we listen to.

I’m not easily shocked, but some of the AEO (or GEO) talks I’ve seen over the past year have been genuinely eyebrow-raising — even for someone with Botox.

I still remember one speaker telling a room full of marketers they were “sorry for anyone still working in SEO,” then immediately recommending outdated tactics as the “secret sauce” for LLM visibility. It’s been… painful.

Thankfully, the adults have entered the room. This week, four of the industry’s most trusted voices — Lily Ray, Kevin Indig, Steve Toth, and Ross Hudgens — came together for a roundtable on the future of search. It was easily the most useful AEO session I’ve attended. Each shared specific tactics they’ve personally used to achieve LLM visibility.

Here’s what they had to say.

1. Advertorials work

LLMs don’t currently distinguish between paid and organic editorial. That means well-placed advertorials on reputable publishers can help brands show up in AI search, much like earned coverage. As with traditional PR, the publication’s credibility still matters most.

2. Syndication can scale visibility

Paid syndication can increase reach, but quality matters more than quantity. Focus on reputable, relevant publications and use this tactic carefully.

3. Map pages to every audience and use case you serve

Brands that create clearly defined pages for each audience, industry, and use case are better positioned as AI search becomes more personalized. This structure helps LLMs understand relevance and remains a strong SEO practice, with or without AI.

4. Homepage clarity

Your homepage should clearly communicate who you serve and what you do. LLMs parse homepage content far more easily than navigation menus, so relying on your nav to explain your offering is a missed opportunity.

5. Optimize your footer

Don’t overlook your footer. Brand and service signals placed here are being picked up by LLMs. Wil Reynolds shared a great case study showing how footer content can directly influence AI visibility.

6. Don’t prioritize llm.txt

Despite the speculation, no major LLM has confirmed using llm.txt files, and Google has explicitly said it does not. Your time and effort are better spent elsewhere.

7. Go multimodal

Repurpose your core content across text, video, audio, and imagery. The goal is to build brand recognition across the full range of sources an LLM may pull from.

8. Actively shape your brand narrative

Actively shape your brand narrative. It’s estimated that 250 documents are needed to meaningfully influence how an LLM perceives a brand. Brands that don’t publish and promote content consistently risk letting others define that narrative for them.

9. Freshness carries disproportionate weight

Recent content tends to perform especially well in AI search, reflecting LLMs’ preference for up-to-date information. That said, artificial “refreshing” without meaningful updates is a bad idea.

10. Social works fast

Posts on platforms like LinkedIn—including Pulse articles—can appear in AI search within hours, sometimes minutes, especially for accounts with strong followings. Reddit, YouTube, and other high-trust platforms show similar behavior.

11. Authority accelerates inclusion

Publishing on respected, niche industry sites can lead to rapid inclusion in LLM responses — sometimes within hours.

12. Don’t hide FAQs

FAQs should be visible and substantial, not hidden behind accordions. Don’t hold back on content either— eight to 10 well-answered questions can clearly signal expertise, intent, and relevance to both users and LLMs.

Is AEO the same as SEO? 

This much-debated question was addressed directly by John Mueller at Google Search Live in December. Putting the AEO cowboys in their place, he made it clear that good AEO still relies on good SEO:

• “AI systems rely on search. and there is no such thing as GEO or AEO without doing SEO fundamentals. Tricks will come out and they will work for a short time, companies that want to be around for the long term should focus on something that is proven with long term stability and not tricks.” 

The overlap makes sense when you look at how modern LLMs like GPT-5 actually work. They use Retrieval-Augmented Generation (RAG). Rather than relying only on frozen training data, RAG lets an LLM query search engines and trusted sources in real time before answering.

Put simply: if you want LLM visibility, you need to show up in search first.

• Lily Ray has an excellent video explaining this process in more detail, which is well worth watching.

So yes, good AEO is good SEO — but there’s nuance. The tactics above work right now, but they will inevitably evolve as LLMs continue to advance.

The best AI search strategy for 2026

Forget the magic button. Keep testing. Stay skeptical of the hype. And be selective about who you let into your ear — or your LinkedIn feed.

Thanks to Bernard Huang and Clearscope for hosting this excellent panel.