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How to make automation work for lead gen PPC

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B2B advertising faces a distinct challenge: most automation tools weren’t built for lead generation.

Ecommerce campaigns benefit from hundreds of conversions that fuel machine learning. B2B marketers don’t have that luxury. They deal with lower conversion volume, longer sales cycles, and no clear cart value to guide optimization.

The good news? Automation can still work.

Melissa Mackey, Head of Paid Search at Compound Growth Marketing, says the right strategy and signals can turn automation into a powerful driver of B2B leads. Below is a summary of the key insights and recommendations she shared at SMX Next.

The fundamental challenge: Why automation struggles with lead gen

Automation systems are built for ecommerce success, which creates three core obstacles for B2B marketers:

  • Customer journey length: Automation performs best with short journeys. A user visits, buys, and checks out within minutes. B2B journeys can last 18 to 24 months. Offline conversions only look back 90 days, leaving a large gap between early engagement and closed revenue.
  • Conversion volume requirements: Google’s automation works best with about 30 leads per campaign per month. Google says it can function with less, but performance is often inconsistent below that level. Ecommerce campaigns easily hit hundreds of monthly conversions. B2B lead gen rarely does.
  • The cart value problem: In ecommerce, value is instant and obvious. A $10 purchase tells the system something very different than a $100 purchase. Lead generation has no cart. True value often isn’t clear until prospects move through multiple funnel stages — sometimes months later.

The solution: Sending the right signals

Despite these challenges, proven strategies can make automation work for B2B lead generation.

Offline conversions: Your number one priority

Connecting your CRM to Google Ads or Microsoft Ads is essential for making automation work in lead generation. This isn’t optional. It’s the foundation. If you haven’t done this yet, stop and fix it first.

In Google Ads’ Data Manager, you’ll find hundreds of CRM integration options. The most common B2B setups include:

  • HubSpot and Salesforce: Both offer native, seamless integrations with Google Ads. Setup is simple. Once connected, customer stages and CRM data flow directly into the platform.
  • Other CRMs: If you don’t use HubSpot or Salesforce, you can build a custom data table with only the fields you want to share. Use connectors like Snowflake to send that data to Google Ads while protecting user privacy and still supplying strong automation signals.
  • Third-party integrations: If your CRM doesn’t integrate directly, tools like Zapier can connect almost anything to Google Ads. There’s a cost, but the performance gains typically pay for it many times over.

Embrace micro conversions with strategic values

Micro conversions signal intent. They show a “hand raiser” — someone engaged on your site who isn’t an MQL yet but clearly interested.

The key is assigning relative value to these actions, even when you don’t know their exact revenue impact. Use a simple hierarchy to train automation what matters most:

  • Video views (value: 1): Shows curiosity, but qualification is unclear.
  • Ungated asset downloads (value: 10): Indicates stronger engagement and added effort.
  • Form fills (value: 100): Reflects meaningful commitment and willingness to share personal information.
  • Marketing qualified leads (value: 1,000): The highest-value signal and top optimization priority.

This value structure tells automation that one MQL matters more than 999 video views. Without these distinctions, campaigns chase impressive conversion rates driven by low-value actions — while real leads slip through the cracks.

Making Performance Max work for lead generation

You might dismiss Performance Max (PMax) for lead generation — and for good reason. Run it on a basic maximize conversions strategy, and it usually produces junk leads and wastes budget.

But PMax can deliver exceptional results when you combine conversion values and offline conversion data with a Target ROAS bid strategy.

One real client example shows what’s possible. They tracked three offline conversion actions — leads, opportunities, and customers — and valued customers at 50 times a lead. The results were dramatic:

  • Leads increased 150%
  • Opportunities increased 350%
  • Closed deals increased 200%

Closed deals became the campaign’s top-performing metric because they reflected real, paying customers. The key difference? Using conversion values with a Target ROAS strategy instead of basic maximize conversions.

Campaign-specific goals: An underutilized feature

Campaign-specific goals let you optimize campaigns for different conversion actions, giving you far more control and flexibility.

You can set conversion goals at the account level or make them campaign-specific. With campaign-specific goals, you can:

  • Run a mid-funnel campaign optimized only for lead form submissions using informational keywords.
  • Build audiences from those form fills to capture engaged prospects.
  • Launch a separate campaign optimized for qualified leads, targeting that warm audience with higher-value offers like demos or trials.

This approach avoids asking someone to “marry you on the first date.” It also keeps campaigns from competing against themselves by trying to optimize for conflicting goals.

Portfolio bidding: Reaching the data threshold faster

Portfolio bidding groups similar campaigns so you can reach the critical 30-conversions-per-month threshold faster.

For example, four separate campaigns might generate 12, 11, 0, and 15 conversions. On their own, none qualify. Grouped into a single portfolio, they total 38 conversions — giving automation far more data to optimize against.

You may still need separate campaigns for valid reasons — regional reporting, distinct budgets, or operational constraints. Portfolio bidding lets you keep that structure while still feeding the system enough volume to perform.

Bonus benefit: Portfolio bidding lets you set maximum CPCs. This prevents runaway bids when automation aggressively targets high-propensity users. This level of control is otherwise only available through tools like SA360.

First-party audiences: Powerful targeting signals

First-party audiences send strong signals about who you want to reach, which is critical for AI-powered campaigns.

If HubSpot or Salesforce is connected to Google Ads, you can import audiences and use them strategically:

  • Customer lists: Use them as exclusions to avoid paying for existing customers, or as lookalikes in Demand Gen campaigns.
  • Contact lists: Use them for observation to signal ideal audience traits, or for targeting to retarget engaged users.

Audiences make it much easier to trust broad match keywords and AI-driven campaign types like PMax or AI Max — approaches that often feel too loose for B2B without strong audience signals in place.

Leveraging AI for B2B lead generation

AI tools can significantly improve B2B advertising efficiency when you use them with intent. The key is remembering that most AI is trained on consumer behavior, not B2B buying patterns.

The essential B2B prompt addition

Always tell the AI you’re selling to other businesses. Start prompts with clear context, like: “You’re a SaaS company that sells to other businesses.” That single line shifts the AI’s lens away from consumer assumptions and toward B2B realities.

Client onboarding and profile creation

Use AI to build detailed client profiles by feeding it clear inputs, including:

  • What you sell and your core value.
  • Your unique selling propositions.
  • Target personas.
  • Ideal customer profiles.

Create a master template or a custom GPT for each client. This foundation sharpens every downstream AI task and dramatically improves accuracy and relevance.

Competitor research in minutes, not hours

Competitive analysis that once took 20–30 hours can now be done in 10–15 minutes. Ask AI to analyze your competitors and break down:

  • Current offers
  • Positioning and messaging
  • Value propositions
  • Customer sentiment
  • Social proof
  • Pricing strategies

AI delivers clean, well-structured tables you can screenshot for client decks or drop straight into Google Sheets for sorting and filtering. Use this insight to spot gaps, uncover opportunities, and identify clear strategic advantages.

Competitor keyword analysis

Use tools like Semrush or SpyFu to pull competitor keyword lists, then let AI do the heavy lifting. Create a spreadsheet with columns for each competitor’s keywords alongside your client’s keywords. Then ask the AI to:

  • Identify keywords competitors rank for that you don’t to uncover gaps to fill.
  • Identify keywords you own that competitors don’t to surface unique advantages.
  • Group keywords by theme to reveal patterns and inform campaign structure.

What once took hours of pivot tables, filtering, and manual cleanup now takes AI about five minutes.

Automating routine tasks

  • Negative keyword review: Create an AI artifact that learns your filtering rules and decision logic. Feed it search query reports, and it returns clear add-or-ignore recommendations. You spend time reviewing decisions instead of doing first-pass analysis, which makes SQR reviews faster and easier to run more often.
  • Ad copy generation: Tools like RSA generators can produce headlines and descriptions from sample keywords and destination URLs. Pair them with your custom client GPT for even stronger starting points. Always review AI-generated copy, but refining solid drafts is far faster than writing from scratch.

Experiments: testing what works

The Experiments feature is widely underused. Put it to work by testing:

  • Different bid strategies, including portfolio vs. standard
  • Match types
  • Landing pages
  • Campaign structures

Google Ads automatically reports performance, so there’s no manual math. It even includes insight summaries that tell you what to do next — apply the changes, end the experiment, or run a follow-up test.

Solutions: Pre-built scripts made easy

Solutions are prebuilt Google Ads scripts that automate common tasks, including:

  • Reporting and dashboards
  • Anomaly detection
  • Link checking
  • Flexible budgeting
  • Negative keyword list creation

Instead of hunting down scripts and pasting code, you answer a few setup questions and the solution runs automatically. Use caution with complex enterprise accounts, but for simpler structures, these tools can save a significant amount of time.

Key takeaways

Automation wasn’t built for lead generation, but with the right strategy, you can still make it work for B2B.

  • Send the right signals: Offline conversions with assigned values aren’t optional. First-party audiences add critical targeting context. Together, these signals make AI-driven campaigns work for B2B.
  • AI is your friend: Use AI to automate repetitive work — not to replace people. Take 50 search query reports off your team’s plate so they can focus on strategy instead of tedious analysis.
  • Leverage platform tools: Experiments, Solutions, campaign-specific goals, and portfolio bidding are powerful features many advertisers ignore. Use what’s already built into your ad platforms to get more out of every campaign.

Watch: It’s time to embrace automation for B2B lead gen 

Read more at Read More

Web Design and Development San Diego

Why governance maturity is a competitive advantage for SEO

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How SEO governance shifts teams from reaction to prevention

Let me guess: you just spent three months building a perfectly optimized product taxonomy, complete with schema markup, internal linking, and killer metadata. 

Then, the product team decided to launch a site redesign without telling you. Now half your URLs are broken, the new templates strip out your structured data, and your boss is asking why organic traffic dropped 40%.

Sound familiar?

Here’s the thing: this isn’t an SEO failure, but a governance failure. It’s costing you nights and weekends trying to fix problems that should never have happened in the first place.

This article covers why weak governance keeps breaking SEO, how AI has raised the stakes, and how a visibility governance maturity model helps SEO teams move from firefighting to prevention.

Governance isn’t bureaucracy – it’s your insurance policy

I know what you’re thinking. “Great, another framework that means more meetings and approval forms.” But hear me out.

The Visibility Governance Maturity Model (VGMM) isn’t about creating red tape. It’s about establishing clear ownership, documented processes, and decision rights that prevent your work from being accidentally destroyed by teams who don’t understand SEO.

Think of it this way: VGMM is the difference between being the person who gets blamed when organic traffic tanks versus being the person who can point to documentation showing exactly where the process broke down – and who approved skipping the SEO review.

This maturity model:

  • Protects your work from being undone by releases you weren’t consulted on.
  • Documents your standards so you’re not explaining canonical tags for the 47th time.
  • Establishes clear ownership so you’re not expected to fix everything across six different teams.
  • Gets you a seat at the table when decisions affecting SEO are being made.
  • Makes your expertise visible to leadership in ways they understand.

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The real problem: AI just made everything harder

Remember when SEO was mostly about your website and Google? Those were simpler times.

Now you’re trying to optimize for:

  • AI Overviews that rewrite your content.
  • ChatGPT citations that may or may not link back.
  • Perplexity summaries that pull from competitors.
  • Voice assistants that only cite one source.
  • Knowledge panels that conflict with your site.

And you’re still dealing with:

  • Content teams who write AI-generated fluff.
  • Developers who don’t understand crawl budget.
  • Product managers who launch features that break structured data.
  • Marketing directors who want “just one small change” that tanks rankings.

Without governance, you’re the only person who understands how all these pieces fit together. 

When something breaks, everyone expects you to fix it – usually yesterday. When traffic is up, it’s because marketing ran a great campaign. When it’s down, it’s your fault.

You become the hero the organization depends on, which sounds great until you realize you can never take a real vacation, and you’re working 60-hour weeks.

Dig deeper: Why most SEO failures are organizational, not technical

What VGMM actually measures – in terms you care about

VGMM doesn’t care about your keyword rankings or whether you have perfect schema markup. It evaluates whether your organization is set up to sustain SEO performance without burning you out. Below are the five maturity levels that translate to your daily reality:

Level 1: Unmanaged (your current nightmare)

  • Nobody knows who’s responsible for SEO decisions.
  • Changes happen without SEO review.
  • You discover problems after they’ve tanked traffic.
  • You’re constantly firefighting.
  • Documentation doesn’t exist or is ignored.

Level 2: Aware (slightly better)

  • Leadership admits SEO matters.
  • Some standards exist but aren’t enforced.
  • You have allies but no authority.
  • Improvements happen but get reversed next quarter.
  • You’re still the only one who really gets it.

Level 3: Defined (getting somewhere)

  • SEO ownership is documented.
  • Standards exist, and some teams follow them.
  • You’re consulted before major changes.
  • QA checkpoints include SEO review.
  • You’re working normal hours most weeks.

Level 4: Integrated (the dream)

  • SEO is built into release workflows.
  • Automated checks catch problems before they ship.
  • Cross-functional teams share accountability.
  • You can actually take a vacation without a disaster.
  • Your expertise is respected and resourced.

Level 5: Sustained (unicorn territory)

  • SEO survives leadership changes.
  • Governance adapts to new AI surfaces automatically.
  • Problems are caught before they impact traffic.
  • You’re doing strategic work, not firefighting.
  • The organization values prevention over reaction.

Most organizations sit at Level 1 or 2. That’s not your fault – it’s a structural problem that VGMM helps diagnose and fix.

Dig deeper: SEO’s future isn’t content. It’s governance

How VGMM works: The less boring explanation

VGMM coordinates multiple domain-specific maturity models. Think of it as a health checkup that looks at all your vital signs, not just one metric.

It evaluates maturity across domains like:

  • SEO governance: Your core competency.
  • Content governance: Are writers following standards?
  • Performance governance: Is the site actually fast?
  • Accessibility governance: Is the site inclusive?
  • Workflow governance: Do processes exist and work?

Each domain gets scored independently, then VGMM looks at how they work together. Because excellent SEO maturity doesn’t matter if the performance team deploys code that breaks the site every Tuesday or if the content team publishes AI-generated nonsense that tanks your E-E-A-T signals.

VGMM produces a 0–100% score based on:

  • Domain scores: How mature is each area?
  • Weighting: Which domains matter most for your business?
  • Dependencies: Are weaknesses in one area breaking strengths in another?
  • Coherence: Do decision rights and accountability actually align?

The final score isn’t about effort – it’s about whether governance actually works.

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What this means for your daily life

Before VGMM-style governance:

  • Product launches a redesign → You find out when traffic drops.
  • Content team uses AI → You discover thin content in Search Console.
  • Dev changes URL structure → You spend a week fixing redirects.
  • Marketing wants “quick changes” → You explain why it’s not quick (again).
  • Site goes down → Everyone asks why you didn’t catch it.

After governance maturity improves:

  • Product can’t launch without SEO sign-off.
  • Content AI usage has review checkpoints.
  • URL changes require documented SEO approval.
  • Marketing requests go through defined workflows.
  • Site monitoring includes automated SEO health checks.

You move from reactive firefighting to proactive prevention. Your weekends become yours again.

The supporting models: What they actually check

VGMM doesn’t score you on technical SEO execution. It checks whether the organization has processes in place to prevent SEO disasters.

SEO Governance Maturity Model (SEOGMM) asks:

  • Are there documented SEO standards?
  • Who can override them, and how?
  • Do templates enforce SEO requirements?
  • Are there QA checkpoints before releases?
  • Can SEO block launches that will cause problems?

Content Governance Maturity Model (CGMM) asks:

  • Are content quality standards documented?
  • Is AI-generated content reviewed?
  • Are writers trained on SEO basics?
  • Is there a process for updating outdated content?

Website Performance Maturity Model (WPMM) asks:

  • Are Core Web Vitals monitored?
  • Can releases be rolled back if they break performance?
  • Is there a performance budget?
  • Are third-party scripts governed?

You get the idea. Each domain has its own checklist, and VGMM shows leadership where gaps create risk.

Dig deeper: SEO execution: Understanding goals, strategy, and planning

How to pitch this to your boss

You don’t need to explain VGMM theory. You need to connect it to problems leadership already knows exist.

  • Frame it as risk reduction: “We’ve had three major traffic drops this year from changes that SEO didn’t review. VGMM helps us identify where our process breaks down so we can prevent this.”
  • Frame it as efficiency: “I’m spending 60% of my time firefighting problems that could have been prevented. VGMM establishes processes so I can focus on growth opportunities instead.”
  • Frame it as a competitive advantage: “Our competitors are getting cited in AI Overviews, and we’re not. VGMM evaluates whether we have the governance structure to compete in AI-mediated search.”
  • Frame it as scalability: “Right now, our SEO capability depends entirely on me. If I get hit by a bus tomorrow, nobody knows how to maintain what we’ve built. VGMM establishes documentation and processes that make our SEO sustainable.”
  • The ask: “I’d like to conduct a VGMM assessment to identify where our processes need strengthening.”

What success actually looks like

Organizations with higher VGMM maturity experience measurably better outcomes:

  • Fewer unexplained traffic drops because changes are reviewed.
  • More stable AI citations because content quality is governed.
  • Less rework after launches because SEO is built into workflows.
  • Clearer accountability because ownership is documented.
  • Better resource allocation because gaps are visible to leadership.

But the real win for you personally: 

  • You stop being the hero who saves the day and become the strategist who prevents disasters. 
  • Your expertise is recognized and properly resourced. 
  • You can take actual vacations. 
  • You work normal hours most of the time.

Your job becomes about building and improving, not constantly fixing.

Getting started: Practical next steps

Step 1: Self-assessment

Look at the five maturity levels. Where is your organization honestly sitting? If you’re at Level 1 or 2, you have evidence for why governance matters.

Step 2: Document current-state pain

Make a list of the last six months of SEO incidents:

  • Changes that weren’t reviewed.
  • Traffic drops from preventable problems.
  • Time spent fixing avoidable issues.
  • Requests that had to be explained multiple times.

This becomes your business case.

Step 3: Start with one domain

You don’t need to implement full VGMM immediately. Start with SEOGMM:

  • Document your standards.
  • Create a review checklist.
  • Establish who can approve exceptions.
  • Get stakeholder sign-off on the process.

Step 4: Show results 

Track prevented problems. When you catch an issue before it ships, document it. When a process prevents a regression, quantify the impact. Build your case for expanding governance.

Step 5: Expand systematically

Once SEOGMM is working, expand to related domains (content, performance, accessibility). Show how integrated governance catches problems that individual domain checks miss.

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Why governance determines whether SEO survives

Governance isn’t about making your job harder. It’s about making your organization work better so your job becomes sustainable.

VGMM gives you a framework for diagnosing why SEO keeps getting undermined by other teams and a roadmap for fixing it. It translates your expertise into language that leadership understands. It protects your work from accidental destruction.

Most importantly, it moves you from being the person who’s always fixing emergencies to being the person who builds systems that prevent them.

You didn’t become an SEO professional to spend your career firefighting. VGMM helps you get back to doing the work that actually matters – the strategic, creative, growth-focused work that attracted you to SEO in the first place.

If you’re tired of watching your best work get undone by teams who don’t understand SEO, if you’re exhausted from being the only person who knows how everything works, if you want your expertise to be recognized and protected – start the VGMM conversation with your leadership.

The framework exists. What’s missing is someone in your organization saying, “We need to govern visibility like we govern everything else that matters.”

That someone is you.

Dig deeper: Why 2026 is the year the SEO silo breaks and cross-channel execution starts

Read more at Read More

Tips and tricks to write SEO-friendly blog posts in the AI era

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It is no secret that publishing SEO-friendly blog posts is one of the easiest and most effective ways to drive organic traffic and improve SERP rankings. However, in the era of artificial intelligence, blog posts matter more than ever. They help establish brand authority by consistently delivering fresh, valuable content that can be cited in AI-generated answers.

In this guide, we will share a practical, detailed approach to writing SEO-friendly blog content that not only ranks on Google SERPs but is also surfaced by AI models.

Table of contents

Key takeaways

  • SEO friendly blog post now means writing with search intent, ensuring content is clear and quotable for AI systems
  • Key factors for SEO friendly blog posts include trustworthiness, machine-readability, answer-first structure, and topical authority
  • Conduct thorough keyword research and find readers’ questions to match search intent effectively
  • Use clear headings, improve readability, include inclusive language, and add relevant media to engage readers
  • Write compelling meta titles and descriptions, link to existing content, and focus on building authority to enhance visibility

What does an SEO-friendly blog post mean in the AI era?

The way people search for information has changed, and with it, the meaning of an SEO-friendly blog post. Before the rise of generative AI, writing an SEO-friendly blog post mostly meant this:

‘Writing content with the intention of ranking highly in search engine results pages (SERPs). The content is optimized for specific target keywords, easy to read, and provides value to the reader.’

That definition is not wrong. But it is no longer complete.

In the AI era, an SEO-friendly blog post is written with search intent first, answering a user’s question clearly and efficiently. It is not just about placing keywords in the right spots. It is about creating an information-dense piece with accurate, well-structured, and quotable sentences that AI systems can confidently extract and surface as direct answers.

The new definition clearly shows that strong SEO foundations still matter, and they matter more than ever. What has changed is how content is evaluated and discovered. Search engines and AI models now look beyond clicks and rankings to understand whether your content is trustworthy, helpful, and easy to interpret.

Here are some key factors that play a key role in determining whether a blog post is truly SEO-friendly:

  • Trustworthiness (E-E-A-T): Demonstrating real-world experience, expertise, and credibility helps your content stand out from low-value AI-generated rehashes
  • Machine-readability: Clear structure, clean HTML, and technical signals such as schema markup help search engines and AI systems understand what your content is about
  • Answer-first structure: Placing concise, direct answers at the beginning of sections makes it easier for AI models to extract and reference your content
  • Topical authority: Publishing interconnected, in-depth content around a subject is far more effective than creating isolated blog posts

9 tips to write SEO-friendly blogs for LLM and SERP visibility

Now we get to the core of this guide. Below are some foundational tips to help you plan and write SEO-friendly blog posts that are genuinely helpful, easy to understand, and focused on solving real reader problems. When done right, these practices not only improve search visibility but also shape how your brand is perceived by both users and AI systems.

1. Conduct thorough keyword research

Before you start writing a single word, start with solid keyword research. This step helps you understand how people search for a topic, which terms carry demand, and how competitive those searches are. It also ensures your content aligns with real user intent instead of assumptions.

You can use tools like Google Keyword Planner, Ahrefs, or Semrush for this. Personally, I prefer using Semrush’s Keyword Magic Tool because it quickly surfaces thousands of relevant keyword ideas around a single topic.

keyword magic tool by semrush for keyword researcg
Keyword Magic Tool by Semrush for the relevant keyword list

Here’s how I usually approach it. I enter a broad keyword related to my topic, for example, ‘SEO.’ The tool then returns an extensive list of related keywords along with important metrics. I mainly focus on three of them:

  • Search intent, to understand what the user is really looking for
  • Keyword Difficulty (KD%), to estimate how hard it is to rank
  • Search volume, to gauge demand

This combination helps me choose keywords that are realistic to rank for and meaningful for readers.

If you use Yoast SEO, this process becomes even easier. Semrush is integrated into Yoast SEO (both free and Premium), giving you keyword suggestions directly in Yoast SEO. With a single click, you can access relevant keyword data while writing, making it easier to create focused, useful content from the start.

Looking for keyphrase suggestions? When you’ve set a focus keyword in Yoast SEO, you can click on ‘Get related keyphrases’ and our Semrush integration will help you find high-performing keyphrases!

Also read: How to use the Semrush related keyphrases feature in Yoast SEO for WordPress

2. Finding readers’ questions

Keyword research tells you what people search for. Questions tell you why they search.

When you actively look for the questions your audience is asking, you move closer to matching search intent. This is especially important in the AI era, where search engines and AI models prioritize clear, answer-driven content.

For example, consider these two queries:

What are the key features of good running shoes?

This shows informational intent. The searcher wants to understand what makes a running shoe good.

What are the best running shoes?

This suggests a transactional or commercial intent. The searcher is likely comparing options before making a purchase.

Both questions are valid, but they require very different content approaches.

There are two simple ways I usually find relevant questions. The first is by checking the People also ask section in Google search results. By typing in a broad keyphrase, you can see related questions that Google itself considers relevant.

people also ask section on google serps
The People also ask section showing questions related to the broad keyphrase ‘SEO’

The second method is to use the Questions filter in Semrush’s Keyword Magic Tool. This helps uncover question-based queries directly tied to your main topic.

Apart from these methods, I also like using Google’s AI Overview and AI mode as a quick research layer. When I search for my main topic, I pay close attention to AI-cited sources, as they often surface broad questions people are actively seeking. The structured points and highlighted terms usually reflect the answers and subtopics that matter most to users. If I want to go deeper, I click “Show more,” which reveals additional angles and follow-up questions I might not have considered initially.

google ai overview citing resources
AI cited sources by Google AI Overview

Finding and answering these questions helps you do lightweight online audience research and create content that feels genuinely helpful. It also increases the chances of your blog post being referenced in AI-generated answers, since LLMs are designed to surface clear responses to specific questions.

3. Structure your content with headings and subheadings

In our 2026 SEO predictions, we highlighted that editorial quality is no longer just about good writing. It has become a machine-readability requirement. Content that is clearly structured is easier to understand, reuse, and surface across both search and AI-driven experiences.

How LLMs use headings

AI models rely on headings to identify topics, questions, and answers within a page. When your content is broken into clear sections, it becomes easier for them to extract key information and include it in AI-generated summaries.

Why headings still matter for SEO

Headings help search engines understand the hierarchy of your content and the main points you are trying to rank for. They also improve scannability and usability, especially on mobile devices, and increase the chances of earning featured snippets.

Good structure has always been a core SEO principle. In the AI era, it remains one of the simplest and most effective ways to improve visibility and discoverability.

4. Focus on readability aspects

An SEO-friendly blog post should be easy to read before it can rank or get picked up by AI systems. Readability helps readers stay engaged and helps search engines and AI models better understand your content.

A few key readability aspects to focus on while writing:

  • Avoid passive voice where possible
    Active sentences are clearer and more direct. They make it easier for readers to understand who is doing what, and they reduce ambiguity for AI systems processing your content.
  • Use transition words
    Transition words like “because,” “for example,” and “however” guide readers through your content. They improve flow and make it easier to follow relationships between sentences and paragraphs.
  • Keep sentences and paragraphs short
    Long, complex sentences reduce clarity. Breaking content into shorter sentences and paragraphs improves scannability and comprehension.
  • Avoid consecutive sentences starting in the same way
    Varying sentence structure keeps your writing engaging and prevents it from sounding repetitive or robotic.
The readability analysis in the Yoast SEO for WordPress metabox
The readability analysis in the Yoast SEO for WordPress metabox

If you are a WordPress or Shopify user, Yoast SEO (and Yoast SEO for Shopify for Shopify users) can help here. Its readability analysis checks for passive voice, transition words, sentence length, and other clarity signals while you write. If you prefer drafting in Google Docs, you can use the Yoast SEO Google Docs add-on to get the same readability feedback before publishing.

Use Yoast SEO in Google Docs

Optimize as you draft for SEO, inclusivity, and readability. The Yoast SEO Google Docs add-on lets you export content ready for WordPress, no reformatting required.

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Good readability is not just about pleasing algorithms. It helps readers understand your message more quickly and makes your content easier to reuse in AI-generated responses.

5. Use inclusive language

Inclusive language helps ensure your content is respectful, clear, and welcoming to a broader audience. It avoids assumptions about gender, ability, age, or background, and focuses on people-first communication.

From an SEO and AI perspective, inclusive language also improves clarity. Content that avoids vague or biased terms is easier to interpret, digest, and trust. This directly supports brand perception, especially when your content is surfaced in AI-generated responses.

Yoast SEO supports this through its inclusive language check, which flags potentially non-inclusive terms and suggests better alternatives. This feature is available in Yoast SEO, Yoast SEO Premium, and in the Yoast SEO Google Docs add-on, making it easier to build inclusive habits directly into your writing workflow.

Inclusive language ensures your content is intentional, thoughtful, and clear, aligning closely with what modern SEO and AI systems value.

6. Add relevant media and interaction points

A well-written blog post should not feel like a long block of text. Adding the right media and interaction points helps guide readers through your content, keeps them engaged, and encourages them to take action.

Why media matters

Media elements such as images, videos, embeds, and infographics make your content easier to consume and more engaging. Blog posts that include images receive 94% more views than those without, simply because visuals break up large blocks of text and make pages easier to scan.

Video content plays an even bigger role. Embedded videos help explain complex ideas faster and can significantly improve organic visibility compared to text-only posts. Together, these elements encourage readers to stay longer on your page, which is a strong signal of content quality for search engines and AI systems alike.

Media also improves accessibility. Properly optimized images with descriptive alt text make content usable for screen readers, while original visuals, screenshots, or diagrams help reinforce credibility and expertise.

Use interaction points to guide and engage readers

Interaction does not always mean complex features. Even simple elements can significantly improve engagement when used well.

Table of contents and sidebar CTA used as interaction points in a Yoast blog post

A table of contents, for example, allows readers to jump directly to the section they care about most.

Other interaction points include clear calls to action (CTAs) that guide readers to the next step, relevant recommendations that encourage users to keep exploring your site, and social sharing buttons that make it easy to amplify your content. Interactive elements like polls, quizzes, or embedded tools further encourage participation and increase time on page.

7. Plan your content length

Content length still matters, but not in the way many people think it does.

A common question is what the ideal word count is for a blog post that performs well. A 2024 study by Backlinko found that while longer content tends to attract more backlinks, the average page ranking on Google’s first page contains around 1,500 words.

That said, this should not be treated as a fixed benchmark. The ideal length is the one that fully answers the user’s question. In an AI-driven era, publishing long content that adds little value or is padded with unnecessary fluff can do more harm than good.

If a topic genuinely requires a longer format, breaking the content into clear subheadings makes a big difference. I personally prefer structuring long articles this way because it improves readability, helps readers navigate the page more easily, and makes the content easier for search engines and AI systems to understand.

Must read: How to use headings on your site

If you use Yoast SEO or Yoast SEO Premium, the paragraph and sentence length checks can help here. These checks exist to prevent pages from being too thin to provide real value. Pages with very low word counts often lack context and struggle to demonstrate relevance or expertise. Yoast SEO flags such cases as a warning, while clearly indicating that adding more words alone does not guarantee better rankings.

Think of word count as a guideline, not a goal. Your focus should always be on clarity, completeness, and usefulness.

8. Link to existing content

Internal linking is one of the most underrated SEO practices, yet it does a lot of heavy lifting behind the scenes.

By linking to relevant content within your site, you help readers discover additional resources and help search engines understand how your content is connected. Over time, this strengthens topical authority and signals that your site consistently covers a subject in depth.

Good internal linking follows a few simple principles:

  • Link only when it adds value and feels natural in context
  • Use clear, descriptive anchor text so users and search engines know what to expect
  • Avoid linking to outdated URLs or pages that redirect, as this wastes crawl signals

Internal links also keep readers engaged longer by guiding them to related articles. This improves overall site engagement while reinforcing your expertise on a topic.

From an AI and search perspective, internal linking plays an even bigger role. Modern search systems analyze content structure, metadata hierarchies, schema markup, and internal links to assess topical depth and clarity. Well-linked content clusters make it easier for search engines and AI systems to understand what your site is about and which pages are most important.

For WordPress users, Yoast SEO Premium offers internal linking suggestions directly in the editor. This makes it easier to spot relevant linking opportunities as you write, helping you build stronger content connections without interrupting your workflow.

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9. Write compelling meta titles and descriptions

Meta titles and meta descriptions help users decide whether to click on your content. While meta descriptions are not a direct ranking factor, they strongly influence click-through rates, making them an essential part of writing SEO-friendly blog posts.

A good meta title clearly communicates what the page is about. Place your main keyword near the beginning, keep it concise, and aim for roughly 55-60 characters so it doesn’t get truncated in search results.

Meta descriptions act like a short invitation. They should explain what the reader will gain from clicking and why it matters. Instead of stuffing keywords, focus on clarity and usefulness. Mention what aspects of the topic your content covers and how it helps the reader. Simple language works best.

Pro tip: Using action-oriented verbs such as “learn,” “discover,” or “read” can also encourage clicks and make your description more engaging.

If you use Yoast SEO Premium, this process becomes much easier. The AI-powered meta title and description generation feature helps you create relevant, well-structured metadata in just one click. It follows SEO best practices while producing descriptions and titles that are clear, engaging, and aligned with search intent.

Bonus tips

Once you have the fundamentals in place, a few extra refinements can go a long way. The following bonus tips help improve usability, clarity, and long-term discoverability. They are not mandatory, but when applied thoughtfully, they can make your blog posts more helpful for readers and easier to surface across search engines and AI-driven experiences.

1. Add a table of contents

A table of contents (TOC) helps readers quickly understand what your blog post covers and jump straight to the section they care about. This is especially useful for long-form content, where users often scan rather than scroll from top to bottom.

From an SEO perspective, a TOC improves structure and readability and can create jump links in search results, which may increase click-through rates. It reduces bounce rates by helping users find answers faster and improves accessibility by offering clear navigation.

By the way, did you know Yoast can help you here too? Yes, the Yoast SEO Internal linking blocks feature lets you add a TOC block to your blog post that automatically includes all the headings with just one click!

2. Add key takeaways

Key takeaways help readers quickly grasp the main points of your blog post without having to read the whole post. This is especially helpful for time-constrained users who want quick, actionable insights.

Summaries also support SEO by reinforcing topic relevance and improving content comprehension for search engines and AI systems. Well-written takeaways might increase visibility in featured snippets and “People also ask” results.

If you use Yoast SEO Premium, the Yoast AI Summarize feature can generate key takeaways for your content in just one click, making it easier to add concise summaries without extra effort.

3. Add an FAQ section

An FAQ section gives you space to answer specific questions your readers may still have after reading your post. This improves user experience by addressing concerns directly and building trust.

FAQs also help search engines better understand your content by clearly outlining common questions and answers related to your topic. While they can support rankings, their real value lies in reducing friction, improving clarity, and even supporting conversions by clearing doubts.

4. Short permalinks

A permalink is the permanent URL of your blog post. Short, descriptive permalinks are easier to read, easier to share, and more likely to be clicked.

Good permalinks clearly describe what the page is about, avoid unnecessary words, and include the main topic where relevant. They improve usability and help search engines understand page context at a glance.

5. Focus on building authority (EEAT aspect)

Building authority is critical, especially for sites that cover sensitive or high-impact topics. Demonstrating Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) helps both users and search engines trust your content.

This includes citing reliable sources, showing real-world experience, maintaining consistent quality, and clearly communicating who is behind the content. Strong E-E-A-T signals are especially important for YMYL topics, where accuracy and credibility matter most.

6. Plan content distribution

Writing a great blog post is only half the work. Distribution helps your content reach the right audience.

Sharing posts on social media, repurposing key insights into newsletters, and earning backlinks from relevant sites can drive more traffic and visibility. Distribution also increases engagement signals and helps your content gain traction faster, which supports long-term SEO performance.

Target your readers always!

In AI-driven search, retrieval beats ranking. Clarity, structure, and language alignment now decide if your content gets seen. – Carolyn Shelby

This perfectly sums up what writing SEO-friendly blog posts looks like today. Success is no longer just about rankings. It is about being clear, helpful, and easy to understand for both readers and AI systems.

Throughout this guide, we focused on the fundamentals that still matter: understanding search intent, structuring content well, improving readability, using inclusive language, and supporting your writing with media, internal links, and thoughtful metadata. These are not new tricks. They are strong SEO foundations, adapted for how search and discovery work in the AI era.

If there is one takeaway, it is this: always write for your readers first. When your content genuinely helps people, answers their questions, and respects how they search and read, it naturally becomes easier to surface across SERPs and AI-driven experiences.

Good SEO has not changed. It has simply become more human.

The post Tips and tricks to write SEO-friendly blog posts in the AI era appeared first on Yoast.

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How SEO leaders can explain agentic AI to ecommerce executives

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How to communicate agentic AI to ecommerce leadership without the hype

Agentic AI is increasingly appearing in leadership conversations, often accompanied by big claims and unclear expectations. For SEO leaders working with ecommerce brands, this creates a familiar challenge.

Executives hear about autonomous agents, automated purchasing, and AI-led decisions, and they want to know what this really means for growth, risk, and competitiveness.

What they don’t need is more hype. They need clear explanations, grounded thinking, and practical guidance. 

This is where SEO leaders can add real value, not by predicting the future, but by helping leadership understand what is changing, what isn’t, and how to respond without overreacting. Here’s how.

Start by explaining what ‘agentic’ actually means

A useful first step is to remove the mystery from the term itself. Agentic systems don’t replace customers, they act on behalf of customers. The intent, preferences, and constraints still come from a person.

What changes is who does the work.

Discovery, comparison, filtering, and sometimes execution are handled by software that can move faster and process more information than a human can.

When speaking to executive teams, a simple framing works best:

  • “We’re not losing customers, we’re adding a new decision-maker into the journey. That decision-maker is software acting as a proxy for the customer.” 

Once this is clear, the conversation becomes calmer and more practical, and the focus moves away from fear and toward preparation.

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Keep expectations realistic and avoid the hype

Another important role for SEO leaders is to slow the conversation down. Agentic behavior will not arrive everywhere at the same time. Its impact will be uneven and gradual.

Some categories will see change earlier because their products are standardized and data is already well structured. Others will move more slowly because trust, complexity, or regulation makes automation harder.

This matters because leadership teams often fall into one of two traps:

  1. Panic, where plans are rewritten too quickly, budgets move too fast, and teams chase futures that may still be some distance away. 
  2. Dismissal, where nothing changes until performance clearly drops, and by then the response is rushed.

SEO leaders can offer a steadier view. Agentic AI accelerates trends that already exist. Personalized discovery, fewer visible clicks, and more pressure on data quality are not new problems. 

Agents simply make them more obvious. Seen this way, agentic AI becomes a reason to improve foundations, not a reason to chase novelty.

Dig deeper: Are we ready for the agentic web?

Change the conversation from rankings to eligibility

One of the most helpful shifts in executive conversations is moving away from rankings as the main outcome of SEO. In an agent-led journey, the key question isn’t “do we rank well?” but “are we eligible to be chosen at all?”

Eligibility depends on clarity, consistency, and trust. An agent needs to understand what you sell, who it is for, how much it costs, whether it is available, and how risky it is to choose you on behalf of a user. This is a strong way to connect SEO to commercial reality.

Questions worth raising include whether product information is consistent across systems, whether pricing and availability are reliable, and whether policies reduce uncertainty or create it. Framed this way, SEO becomes less about chasing traffic and more about making the business easy to select.

Explain why SEO no longer sits only in marketing

Many executives still see SEO as a marketing channel, but agentic behavior challenges that view.

Selection by an agent depends on factors that sit well beyond marketing. Data quality, technical reliability, stock accuracy, delivery performance, and payment confidence all play a role.

SEO leaders should be clear about this. This isn’t about writing more content. It’s about making sure the business is understandable, reliable, and usable by machines.

Positioned correctly, SEO becomes a connecting function that helps leadership see where gaps in systems or data could prevent the brand from being selected. This often resonates because it links SEO to risk and operational health, not just growth.

Dig deeper: How to integrate SEO into your broader marketing strategy

Be clear that discovery will change first

For most ecommerce brands, the earliest impact of agentic systems will be at the top of the funnel. Discovery becomes more conversational and more personal.

Users describe situations, needs, and constraints instead of typing short search phrases, and the agent then turns that context into actions.

This reduces the value of simply owning category head terms. If an agent knows a user’s budget, preferences, delivery expectations, and past behavior, it doesn’t behave like a first-time visitor. It behaves like a well-informed repeat customer.

This creates a reporting challenge. Some SEO work will no longer look like direct demand creation, even though it still influences outcomes. Leadership teams need to be prepared for this shift.

Get the newsletter search marketers rely on.

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Reframe consideration as filtering, not persuasion

The middle of the funnel also changes shape. Today, consideration often involves reading reviews, comparing options, and seeking reassurance.

In an agent-led journey, consideration becomes a filtering process, where the agent removes options it believes the user would reject and keeps those that fit.

This has clear implications. Generic content becomes less effective as a traffic driver because agents can generate summaries and comparisons instantly. Trust signals become structural, meaning claims need to be backed by consistent and verifiable information.

In many cases, a brand may be chosen without the user being consciously aware of it. That can be positive for conversion, but risky for long-term brand strength if recognition isn’t built elsewhere.

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

Set honest expectations about measurement

Executives care about measurement, and agentic AI makes this harder. As more discovery and consideration happen inside AI systems, fewer interactions leave clean attribution trails. Some impact will show up as direct traffic, and some will not be visible at all.

SEO leaders should address this early. This isn’t a failure of optimization. It reflects the limits of today’s analytics in a more mediated world.

The conversation should move toward directional signals and blended performance views, rather than precise channel attribution that no longer reflects how decisions are made.

Promote a proactive, low-risk response

The most important part of the leadership discussion is what to do next. The good news is that most sensible responses to agentic AI are low risk.

Improving product data quality, reducing inconsistencies across platforms, strengthening reliability signals, and fixing technical weaknesses all help today, regardless of how quickly agents mature.

Investing in brand demand outside search also matters. If agents handle more of the comparison work, brands that users already trust by name are more likely to be selected.

This reassures leaders that action doesn’t require dramatic change, only disciplined improvement.

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Agentic AI changes the focus, not the fundamentals

For SEO leaders, agentic AI changes the focus of the role. The work shifts from optimizing pages to protecting eligibility, from chasing visibility to reducing ambiguity, and from reporting clicks to explaining influence.

This requires confidence, clear communication, and a willingness to challenge hype. Agentic AI makes SEO more strategic, not any less important.

Agentic AI should not be treated as an immediate threat or a guaranteed advantage. It’s a shift in how decisions are made.

For ecommerce brands, the winners will be those that stay calm, communicate clearly, and adapt their SEO thinking from driving clicks to earning selection.

That is the conversation SEO leaders should be having now.

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

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What repeated ChatGPT runs reveal about brand visibility

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What repeated ChatGPT runs reveal about brand visibility

We know AI responses are probabilistic – if you ask an AI the same question 10 times, you’ll get 10 different responses.

But how different are the responses?

That’s the question Rand Fishkin explored in some interesting research.

And it has big implications for how we should think about tracking AI visibility for brands.

In his research, he tested prompts asking for recommendations in all sorts of products and services, including everything from chef’s knives to cancer care hospitals and Volvo dealerships in Los Angeles.

Basically, he found that:

  • AIs rarely recommend the same list of brands in the same order twice.
  • For a given topic (e.g., running shoes), AIs recommend a certain handful of brands far more frequently than others.

For my research, as always, I’m focusing exclusively on B2B use cases. Plus, I’m building on Fishkin’s work by addressing these additional questions:

  • Does prompt complexity affect the consistency of AI recommendations?
  • Does the competitiveness of the category affect the consistency of recommendations?

Methodology

To explore those questions, I first designed 12 prompts:

  • Competitive vs. niche: Six of the prompts are about highly competitive B2B software categories (e.g., accounting software), and the other six are about less crowded categories (e.g., user entity behavior analytics (UEBA) software). I identified the categories using Contender’s database, which tracks how many brands ChatGPT associates with 1,775 different software categories.
  • Simple vs. nuanced prompts: Within both sets of “competitive” and “niche” prompts, half of the prompts are simple (“What’s the best accounting software?”) and the other half are nuanced prompts including a persona and use case (”For a Head of Finance focused on ensuring financial reporting accuracy and compliance, what’s the best accounting software?”)

I ran the 12 prompts 100 times, each, through the logged-out, free version of ChatGPT at chatgpt.com (i.e., not the API). I used a different IP address for each of the 1,200 interactions to simulate 1,200 different users starting new conversations.

Limitations: This research only covers responses from ChatGPT. But given the patterns in Fishkin’s results and the similar probabilistic nature of LLMs, you can probably generalize the directional (not absolute value) findings below to most/all AIs.

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Findings

So what happens when 100 different people submit the same prompt to ChatGPT, asking for product recommendations?

How many ‘open slots’ in ChatGPT responses are available to brands?

On average, ChatGPT will mention 44 brands across 100 different responses. But one of the response sets included as many as 95 brands – it really depends on the category.

How many brands does ChatGPT draw from, on average?

Competitive vs. niche categories

On that note, for prompts covering competitive categories, ChatGPT mentions about twice as many brands per 100 responses compared to the responses to prompts covering “niche” categories. (This lines up with the criteria I used to select the categories I studied.)

Simple vs. nuanced prompts

On average, ChatGPT mentioned slightly fewer brands in response to nuanced prompts. But this wasn’t a consistent pattern – for any given software category, sometimes nuanced questions ended up with more brands mentioned, and sometimes simple questions did.

This was a bit surprising, since I expected more specific requests (e.g., “For a SOC analyst needing to triage security alerts from endpoints efficiently, what’s the best EDR software?”) to consistently yield a narrower set of potential solutions from ChatGPT.

I think ChatGPT might not be better at tailoring a list of solutions to a specific use case because it doesn’t have a deep understanding of most brands. (More on this data in an upcoming note.)

Return of the ’10 blue links’

In each individual response, ChatGPT will, on average, mention only 10 brands.

There’s quite a range, though – a minimum of 6 brands per response and a maximum of 15 when averaging across response sets.

How many brands per response, on average?

But a single response typically names about 10 brands regardless of category or prompt type.

The big difference is in how much the pool of brands rotates across responses – competitive categories draw from a much deeper bench, even though each individual response names a similar count.

Everything old (in SEO) truly is new again (in GEO/AEO). It reminds me of trying to get a placement in one of Google’s “10 blue links”.

Dig deeper: How to measure your AI search brand visibility and prove business impact

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How consistent are ChatGPT’s brand recommendations?

When you ask ChatGPT for a B2B software recommendation 100 different times, there are only ~5 brands, on average, that it’ll mention 80%+ of the time.

To put it in context, that’s just 11% of all the 44 brands it’ll mention at all across those 100 responses.

ChatGPT knows ~44 brands in your category

So it’s quite competitive to become one of the brands ChatGPT consistently mentions whenever someone asks for recommendations in your category.

As you’d expect, these “dominant” brands tend to be big, established brands with strong recognition. For example, the dominant brands in the accounting software category are QuickBooks, Xero, Wave, FreshBooks, Zoho, and Sage.

If you’re not a big brand, you’re better off being in a niche category:

It's easier to get good AI visibility in niche categories

When you operate in a niche category, not only are you literally competing with fewer companies, but there are also more “open slots” available to you to become a dominant brand in ChatGPT’s responses.

In niche categories, 21% of all the brands ChatGPT mentions are dominant brands, getting mentioned 80%+ of the time.

Compare this to just 7% of all brands being dominant in competitive categories, where the majority of brands (72%) are languishing in the long tail, getting mentioned less than 20% of the time.

The responses to nuanced prompts are harded to dominate

A nuanced prompt doesn’t dramatically change the long tail of little-seen brands (with <20% visibility), but it does change the “winner’s circle.” Adding persona context to a prompt makes it a bit more difficult to reach the dominant tier – you can see the steeper “cliff” a brand has to climb in the “nuanced prompts” graph above.

This makes intuitive sense: when someone asks “best accounting software for a Head of Finance,” ChatGPT has a more specific answer in mind and commits a bit more strongly to fewer top picks.

Still, it’s worth noting that the overall pool doesn’t shrink much – ChatGPT mentions ~42 brands in 100 responses to nuanced prompts, just a handful fewer than the ~46 mentioned in response to simple prompts. If nuanced prompts make the winner’s circle a bit more exclusive, why don’t they also narrow the total field?

Partly, it could be that the “nuanced” questions we fed it weren’t meaningfully more narrow and specific than what was implied in the simple questions we asked.

But, based on other data I’m seeing, I think this is partly about ChatGPT not knowing enough about most brands to be more selective. I’ll share more on this in an upcoming note.

Dig deeper: 7 hard truths about measuring AI visibility and GEO performance

What does this mean for B2B marketers?

If you’re not a dominant brand, pick your battles – niche down

It’s never been more important to differentiate. 21% of mentioned brands reach dominant status in niche categories vs. 7% in competitive ones.

Without time and a lot of money for brand marketing, an upstart tech company isn’t going to become a dominant brand in a broad, established category like accounting software.

But the field is less competitive when you lean into your unique, differentiating strengths. ChatGPT is more likely to treat you like a dominant brand if you work to make your product known as “the best accounting software for commercial real estate companies in North America.”

Most AI visibility tracking tools are grossly misleading

Given the inconsistency of ChatGPT’s recommendations, a single spot-check for any given prompt is nearly meaningless. Unfortunately, checking each prompt just once per time period is exactly what most AI visibility tracking tools do.

If you want anything approaching a statistically-significant visibility score for any given prompt, you need to run the prompt at least dozens of times, even 100+ times, depending on how precise you need the data to be.

But that’s obviously not practical for most people, so my suggestion is: For the key, bottom-of-funnel prompts you’re tracking, run them each ~5 times whenever you pull data.

That’ll at least give you a reasonable sense of whether your brand tends to show up most of the time, some of the time, or never.

Your goal should be to have a confident sense of whether your brand is in the little-seen long tail, the visible middle, or the dominant top-tier for any given prompt. Whether you use my tiers of ‘under 20%’, ‘20–80%’, and ‘80%+’, or your own thresholds, this is the approach that follows the data and common sense.

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What’s next?

In future newsletters and LinkedIn posts, I’m going to build on these findings with new research:

  • How does ChatGPT talk about the brands it consistently recommends? Is it indicative of how much ChatGPT “knows” about brands?
  • Do different prompts with the same search intent tend to produce the same set of recommendations?
  • How consistent is “rank” in the responses? Do dominant brands tend to get mentioned first?

This article was originally published on Visible on beehiiv (as Most AI visibility tracking is misleading (here’s my new data)) and is republished with permission.

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Reddit says 80 million people now use its search weekly

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Reddit search

Eighty million people use Reddit search every week, Reddit said on its Q4 2025 earnings call last week. The increase followed a major change: Reddit merged its core search with its AI-powered Reddit Answers and began positioning the platform as a place where users can start — and finish — their searches.

  • Executives framed the move as a response to changing behavior. People are increasingly researching products and making decisions by asking questions within communities rather than relying solely on traditional search engines.
  • Reddit is betting it can keep more of that intent on-platform, rather than acting mainly as a source of links for elsewhere.

Why we care. Reddit is becoming a place where people start — and complete — their searches without ever touching Google. For brands, that means visibility on Reddit now matters as much as ranking in traditional and AI search for many buying decisions.

Reddit’s search ambitions. CEO Steve Huffman said Reddit made “significant progress” in Q4 by unifying keyword search with Reddit Answers, its AI-driven Q&A experience. Users can now move between standard search results and AI answers in a single interface, with Answers also appearing directly inside search results.

  • “Reddit is already where people go to find things,” Huffman said, adding the company wants to become an “end-to-end search destination.”
  • More than 80 million people searched Reddit weekly in Q4, up from 60 million a year earlier, as users increasingly come to the platform to research topics — not just scroll feeds or click through from Google.

Reddit Answers is growing. Reddit Answers is driving much of that growth. Huffman said Answers queries jumped from about 1 million a year ago to 15 million in Q4, while overall search usage rose sharply in parallel.

  • He said Answers performs best for open-ended questions—what to buy, watch, or try—where people want multiple perspectives instead of a single factual answer. Those queries align naturally with Reddit’s community-driven discussions.
  • Reddit is also expanding Answers beyond text. Huffman said the company is piloting “dynamic agentic search results” that include media formats, signaling a more interactive and immersive search experience ahead.

Search is a ‘big one’ for Reddit. Huffman said the company is testing new app layouts that give search prominent placement, including versions with a large, always-visible search bar at the top of the home screen.

  • COO Jennifer Wong said search and Answers represent a major opportunity, even though monetization remains early on some surfaces.
  • Wong described Reddit search behavior as “incremental and additive” to existing engagement and often tied to high-intent moments, such as researching purchases or comparing options.

AI answers make Reddit more important. Huffman also linked Reddit’s search push to its partnerships with Google and OpenAI. He said Reddit content is now the most-cited source in AI-generated answers, highlighting the platform’s growing influence on how people find information.

  • Reddit sees AI summaries as an opportunity — to move users from AI answers into Reddit communities, where they can read discussions, ask follow-up questions, and participate.
  • If someone asks “what the best speaker is,” he said, Reddit wants users to discover not just a summary, but the community where real people are actively debating the topic.

Reddit earnings. Reddit Reports Fourth Quarter and Full Year 2025 Results; Announces $1 Billion Share Repurchase Program

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OpenAI starts testing ChatGPT ads

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OpenAI confirmed today that it’s rolling out its first live test of ads in ChatGPT, showing sponsored messages directly inside the app for select users.

The details. The ads will appear in a clearly labeled section beneath the chat interface, not inside responses, keeping them visually separate from ChatGPT’s answers.

  • OpenAI will show ads to logged-in users on the free tier and its lower-cost Go subscription.
  • Advertisers won’t see user conversations or influence ChatGPT’s responses, even though ads will be tailored based on what OpenAI believes will be helpful to each user, the company said.

How ads are selected. During the test, OpenAI matches ads to conversation topics, past chats, and prior ad interactions.

  • For example: A user researching recipes might see ads for meal kits or grocery delivery. If multiple advertisers qualify, OpenAI shows the most relevant option first.

User controls. Users get granular controls over the experience. They can dismiss ads, view and delete separate ad history and interest data, and toggle personalization on or off.

  • Turning personalization off limits ads to the current chat.
  • Free users can also opt out of ads in exchange for fewer daily messages or upgrade to a paid plan.

Why we care. ChatGPT is one of the world’s largest consumer AI platforms. Even a limited ad rollout could mark a major shift in how conversational AI gets monetized — and how brands reach users.

Bottom line. OpenAI is officially moving into ads inside ChatGPT, testing how sponsored content can coexist with conversational AI at massive scale.

OpenAI’s announcement.Testing ads in ChatGPT (OpenAI)

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Google AI Mode doesn’t favor above-the-fold content: Study

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AI Mode depth doesn't matter

Google’s AI Mode isn’t more likely to cite content that appears “above the fold,” according to a study from SALT.agency, a technical SEO and content agency.

  • After analyzing more than 2,000 URLs cited in AI Mode responses, researchers found no correlation between how high text appears on a page and whether Google’s AI selects it for citation.

Pixel depth doesn’t matter. AI Mode cited text from across entire pages, including content buried thousands of pixels down.

  • Citation depth showed no meaningful relationship to visibility.
  • Average depth varied by vertical, from about 2,400 pixels in travel to 4,600 pixels in SaaS, with many citations far below the traditional “above the fold” area.

Page layout affects depth, not visibility. Templates and design choices influenced how far down the cited text appeared, but not whether it was cited.

  • Pages with large hero images or narrative layouts pushed cited text deeper, while simpler blog or FAQ-style pages surfaced citations earlier.
  • No layout type showed a visibility advantage in AI Mode.

Descriptive subheadings matter. One consistent pattern emerged: AI Mode frequently highlighted a subheading and the sentence that followed it.

  • This suggests Google uses heading structures to navigate content, then samples opening lines to assess relevance, behavior consistent with long-standing search practices, according to SALT.

What Google is likely doing. SALT believes AI Mode relies on the same fragment indexing technology Google has used for years. Pages are broken into sections, and the most relevant fragment is retrieved regardless of where it appears on the page.

What they’re saying. While the study examined only one structural factor and one AI model, the takeaway is clear: there’s no magic formula for AI Mode visibility. Dan Taylor, partner and head of innovation (organic and AI) at SALT.agency, said:

  • “Our study confirms that there is no magic template or formula for increased visibility in AI Mode responses – and that AI Mode is not more likely to cite text from ‘above the fold.’ Instead, the best approach mirrors what’s worked in search for years: create well-structured, authoritative content that genuinely addresses the needs of your ideal customers.
  • “…the data clearly debunks the idea that where the information sits within a page has an impact on whether it will be cited.”

Why we care. The findings challenge the idea that AI-specific templates or rigid page structures drive better AI Mode visibility. Chasing “AI-optimized” layouts may distract from work that actually matters.

About the research. SALT analyzed 2,318 unique URLs cited in AI Mode responses for high-value queries across travel, ecommerce, and SaaS. Using a Chrome bookmarklet and a 1920×1080 viewport, researchers recorded the vertical pixel position of the first highlighted character in each AI-cited fragment. They also cataloged layouts and elements, such as hero sections, FAQs, accordions, and tables of contents.

The study. Research: Does Structuring Your Content Improve the Chances of AI Mode Surfacing?

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AI Content Generation for SEO: Pros, Cons & How to Use It

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AI content generation for SEO can be a game-changer if you use it the right way.

AI tools help increase the speed of your content production, from brainstorming to drafting. And yes, we’ve built our own AI writer into Ubersuggest to make that process easier.

But here’s the thing: AI isn’t a shortcut to rankings. Without the right prompts and a human touch, AI content can actually hurt your traffic. Google’s recent updates and the rise of AI Overviews in search show just how important quality and clarity are.

So no, AI-generated content isn’t bad, but you need a strategy. Otherwise, it’s just more noise.

Key Takeaways

  • LLMs won’t cite your content unless it’s structured, trustworthy, and answers real user questions.
  • AI content generation for SEO works, but only with the right strategy and human oversight.
  • AI can speed up all stages of content production, but publishing without reviewing will tank your results.
  • Prompts matter. Clear direction on content structure and audience and strong keyword targeting separate ranking content from noise.
  • Human elements like originality, firsthand insights, and strong E-E-A-T signals are still non-negotiable.

AI VS Humans: Pros & Cons

With AI, we found that you can’t just publish the content it generates and go off to the races.

It still takes time to use AI.

From modifying the content to putting it in your CMS to adjusting the format, creating content takes time whether you use AI or not.

Here’s how long it takes to create content using AI versus a human.

When using AI we found that you can write content, post it into a CMS, and publish it all within 16 minutes.

Humans on the other hand took an average of 69 minutes.

But there are some issues that most people don’t talk about.

The first is AI takes what’s on the web and “regurgitates” the same old info.

People want to read something new…

The second is we found that 94.12% of the time human written content outranked AI-created content.

With that said, there is still a role for AI-generated content in an SEO strategy.

<h2>Does AI-Generated Content Support SEO? </h2>

Our findings aren’t all “doom and gloom” for AI, especially as platforms and LLMs evolve. It can absolutely support your SEO strategy, especially when it comes to scaling content or repurposing existing assets, but AI needs direction. If you feed it a vague prompt like “write a blog post about SEO,” you’ll get generic, surface-level content that won’t rank or convert.

Your prompt is essential in making AI-generated content SEO-friendly. You need to tell the tool exactly what keywords to target, what questions to answer, what structure to follow, and who the audience is. Doing that requires real marketing experience.

This is where human input and oversight still matter. You need to choose the right keywords and guide the AI to meet quality standards. AI is just guessing without that input, and that rarely ends well for SEO.

It’s also worth noting that while AI can help draft content, it won’t replace human editing. You still need someone to review for tone and voice accuracy, and depth. 

<h3>Does AI-Generated Content Help with LLM Presence? </h3>

AI content won’t magically get picked up by LLMs. But with smart prompting and a clear optimization strategy, it can absolutely improve your chances.

Large language models (LLMs) like ChatGPT and Gemini pull from indexed content to generate answers. This process is known as retrieval augmented generation (RAG)

A ChatGPT answer about passive income.

If your content is well-structured and authoritative, it has a better shot of getting cited or referenced in those answers, but generic content won’t cut it. These models are picky.

To actually earn LLM visibility, you need to create content that matches how LLMs surface information. That means answering specific questions, using structured data where it makes sense, and writing in a way that’s clear, concise, and trustworthy.

AI tools can help here, but again, prompting is key. If your AI-generated content isn’t shaped around real user questions or lacks structure that aligns with LLM output patterns, it’s unlikely to perform.

Digging deeper and learning more about LLM SEO and LLM optimization is a great way to improve your skills in this area. By understanding these concepts, you’ll learn exactly what to include in your content and how to use AI to get there.

Integrating AI Into Your Content Approach (The Right Way)

Used well, AI can help you move faster but it’s the human touches that drive results. You need to start thinking of AI as a starting point, not the whole process.

We ran an experiment across 68 sites, publishing 744 articles—half written by humans, half by AI. Five months in, the average AI article brought in 52 visitors a month.
Human-written articles? 283.

Now, sure, you could scale faster with AI, but pumping out a ton of mediocre content does more harm than good. In fact, when we pruned low-quality posts, we saw an 11 to 12 percent traffic lift.

If you’re going to use a GenAI tool to do your writing, do it with intention:

  • Start with smart prompts. Include keyword targets and content goals.
  • Feed the tool solid references like existing content, credible sources, or structured outlines.
  • Don’t just hit publish. Run a full human review: fact-check, rewrite weak sections, fix tone issues, and make sure it aligns with your brand.

And here’s the secret sauce: add manual value. Include firsthand insights via screenshots or updated data. Layer in trust-building elements like personal experience or expert sourcing. That’s how you build E-E-A-T—Google’s framework for judging helpful, credible content.

FAQs

Is AI-generated content good for SEO?

It can be, if you do it right. AI can help you scale content creation, but you still need a human touch to make sure it’s high-quality and helpful. Google rewards useful content, not mass-produced fluff.

Does AI-generated content affect SEO?

Yes, but how it affects your SEO depends on what you publish. If your AI content adds value and matches search intent, it can help you rank. If it’s generic or purely written for keywords, it’ll likely hurt you.

Will Google penalize SEO content generated by AI?

Google will not penalize you for using AI alone. Google doesn’t care how content is made as long as it’s useful and trustworthy. But if the content is spammy or misleading, that’s where penalties come in.

Case Study: How We Use AI

AI’s biggest impact on our content writing process isn’t even the writing part.

It’s the research part.

For example, at NP Digital, we used AI to help UTI boost its traffic.

Instead of relying on AI to write extensive content, we leveraged it to create select drafts (which then undergo our human editing process) and assist us in conducting research for all the cities in which UTI has campuses.

This allowed us to scale the creation of their local pages and ensure high quality by leveraging our human content staff to incorporate other elements that would be useful for someone performing a local search.

We even won an award for this work at the Drum Awards.

Conclusion

AI can be used to help you, the issue is most marketers are relying on it to fully create their content for them.

AI is great, but it’s not there yet to just do everything for you.

And even if AI was perfect, if it doesn’t talk about something new that people haven’t seen before it won’t produce the results you are looking for.

So, are you using AI to create your content?

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Digital Marketing Trends & Predictions 2026

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If 2025 taught us anything, it’s that AI is no longer just a side tool. It’s the engine running campaigns and reshaping how people discover brands.  

At the same time, platforms have declared war on the “click.” We’re seeing an aggressive push for native conversions, where the goal isn’t to drive traffic to the website but to close the deal right in the feed. 

That shift toward “frictionless” experiences, combined with the saturation of AI-generated noise, has forced another major change. Content with deep educational value is starting to outperform the high-volume, “101-level” content that simply fills space. 

As we get deeper into the new year, those shifts are accelerating. 

The top digital marketing trends for 2026 reflect this reality: Automation handles execution, while human elements like strategy and storytelling set the winners apart.  

If you want to stay relevant, abandon the old metrics of “rankings” and “reach.” They no longer guarantee relevance. Here’s what’s actually moving the needle in 2026 (and how the best digital marketers are keeping up). 

Key Takeaways

  • With the rise of agentic AI, machines can now handle the lifecycle and campaigns, but human oversight is essential. 
  • User discovery spans platforms like TikTok, Reddit, YouTube, and Meta. Each one requires unique formats, signals, and intent-based optimization. 
  • Funnels are no longer static. AI personalizes journeys in real time based on user behavior, replacing manual segmentation and drip campaigns. 
  • Chat assistants recommend brands based on trust and content relevance. Consistency and large language model optimization (LLMO) are key to inclusion. 
  • Google’s traditional and AI systems (PMax, AI Overviews, Demand Gen, and Search) now operate as one. Aligning creative and goals across all touchpoints boosts results. 

AI Agents Take Over Execution

We’re already seeing AI streamline much of a marketing team’s content production. But the new flex is agentic AI. We’re talking about autonomous “team members” that can now handle your entire campaign workflow.  

According to PwC, nearly 80 percent of organizations have already adopted AI agents to some degree. And most plan to expand use as these systems move from experimentation into day-to-day operations. 

 AI agent adoption levels across organizations, with most reporting broad or limited adoption. 

This goes far beyond production and publishing. Large language models (LLMs) have advanced to the point that they can manage the full lifecycle. We’re talking about agents embedded into tools that can help: 

  • Manage your customer relationship management (CRM) data 
  • Analyze data performance 
  • Provide campaign insights 
  • Adjust ad bids for paid campaigns in real time 

This year, AI is going from writing your content to autonomous operations. It handles the execution while you focus on strategy and oversight. 

Search Everywhere Optimization Becomes Mandatory

For the last few years, “search everywhere” has been a catchy conference buzzword. In 2026, it’s a baseline for survival. 

The era of the “Google-default” mindset is over. Discovery now happens across platforms, feeds, and AI systems. Today’s SEO is drifting more and more toward search everywhere optimization and less search engine optimization. 

Your audience isn’t just “Googling it” anymore. They’re asking questions and validating purchases on the platforms they trust most. And each has its own algorithm, formats, and user behavior.  

For example: 

  • A TikTok viewer wants quick, visual tips.  
  • A Reddit user wants deep, authentic discussion.  
  • Pinterest needs eye-catching visuals with keyword-rich descriptions.  
  • YouTube demands longer, high-value content with tight intros and strong engagement. 

The most disruptive shift, however, is happening outside traditional feeds. Voice assistants like Alexa and Siri, and generative chat tools like ChatGPT, Gemini, or Claude are increasingly acting as answer engines.  

The numbers show where we’re headed. Nearly 1 in 5 people use voice search, and Statista predicts 36 percent of the global population will be searching via AI by 2028.  

Example of an AI chat assistant returning a summarized product recommendation list, showing how search increasingly happens inside answer engines.

Prompt-Driven Campaigns and Product Development

Digital marketers no longer need full engineering cycles to test new ideas.  

Prompt-driven tools now make it possible to prototype calculators, quizzes, internal tools, and campaign utilities in hours instead of weeks. 

Tools like Cursor and Replit let marketers translate plain-language instructions into working interfaces, lowering the barrier to experimentation. You still need engineering for production-scale products, but prompts now handle much of the early build and validation work. 

Base44 is another example of a “vibe coding” platform that can turn your detailed descriptions into functional tools, reinforcing the same idea: Prompts are becoming a new control layer.  

Everyone’s an engineer now. Look out, Silicon Valley!  

The game has changed. You can now test fast, learn faster, and skip the bottlenecks that used to slow everything down. 

Funnels Become Dynamic and Self-Optimizing

Static funnels are out. In 2026, customer journeys are becoming shorter and increasingly influenced in real time by AI systems. 

It may seem shocking at first, but it makes sense when you zoom out and think about it. We are no longer pushing users through a pre-set funnel. We’re letting AI agents build the funnel around the user in real time. 

In the early days of Google (and online shopping), a customer would have to visit several sites to research and read reviews—and, eventually, make a purchase. This is the classic marketing funnel we’re all familiar with. There’s a clearly defined top-of-funnel, mid-funnel, and bottom-of-funnel. 

With generative AI tools now offering in-platform purchases, that funnel shrinks significantly. Your typical user can now research, build trust, and make a purchase all within an LLM like ChatGPT.  

We’ve even begun to see major retailers like Walmart and Amazon move toward this model.  

Walmart Sparky can answer user queries and pull in product recommendations to answer deeper questions. It even leads you to check out when you’re ready to purchase.  

Walmart interface showing its AI shopping assistant answering product questions, comparing options, summarizing reviews, and guiding users toward checkout within a single on-platform experience. 

(Image Source) 

The same setup applies to Amazon Rufus, enabling customers to get details, get suggestions, get help, and get inspiration (and ultimately get stuff) all within one platform.  

Amazon’s Rufus AI assistant helping users research products, get recommendations, and shop without leaving Amazon

(Image Source) 

The result is higher engagement and faster conversions with way less manual work. These tools provide a hyper-personalized shopping experience faster than ever before. Platforms like Shopify and Etsy have also partnered with ChatGPT to purchase products directly in the LLM. 

AI Attribution Connects Content to Revenue

Attribution isn’t new, but it’s getting more accurate. AI-powered attribution now connects every touchpoint—from the first video view to the final click—with real revenue outcomes. 

Platforms like Wicked Reports are enabling marketers to tie initial ad clicks to lifetime purchases and provide “first click” and “time decay” tools to help you pinpoint the most successful starting point for your customers’ buying journeys. This app also provides revenue forecasting to help B2C and e-commerce businesses reliably predict and scale their growth. 

Marketing analytics dashboard showing AI-driven measurement, signal correction, and performance insights used to connect campaigns to real revenue outcomes. 

(Image Source) 

Your latest blog post may not have converted immediately, but it made the visitor trust you enough to subscribe for email updates. That email is the next stop in their journey, pushing them to check out your pricing page. AI sees it all and assigns value accordingly. 

With these new insights, you finally know which content moves the needle.  

And it’s having a real financial impact. Teams using AI-driven marketing analytics report return on investment (ROI) improvements of roughly 300 percent and customer acquisition costs dropping by more than 30 percent. 

Chat Assistants Reshape Discovery

We mentioned earlier how people’s search has evolved into asking AI chat tools like ChatGPT, Gemini, and Perplexity to answer their product questions. These platforms now include brand recommendations built right into the response, as well as the ability to shop for Shopify and Etsy products. 

This is the same dynamic powering tools like Walmart Sparky and Amazon Rufus, where research and recommendations happen within a single AI experience.  

These assistants don’t list 10 “sponsored” links, a la Google. They summarize what they trust. If they don’t mention your brand, you’re invisible in this new layer of discovery. 

AI answer engine Perplexity showing summarized recommendations for ‘best email marketing tools for SaaS,’ with brands cited directly in the response instead of traditional search links. 

It takes more than gaming keywords to show up on these platforms. It’s all about relevance and consistency.  

The more helpful, high-quality content you create around a topic, the more citations you’ll receive from users sharing it across the internet. Signals like structured content, schema markup, and consistent third-party validation help AI systems interpret your authority and decide when your brand is worth referencing. 

This shift has given rise to large language model optimization (LLMO), a new branch of SEO focused on training AI to recognize and recommend your brand. If you’re not already thinking about LLMO, it’s time to get caught up. 

The big takeaway here is that usefulness matters more than volume as discovery moves into AI systems. Provide enough high-quality answers to your audience’s questions, and the bots will start to bring your name up first. 

Content Structure Becomes Even More Important

Old-school SEO was all about keywords. In 2026, performance increasingly comes from covering topics in depth and structuring content so both people and machines can understand it. 

As we mentioned in the last section, search engines and AI assistants care more about how well you answer a question than how many times you use a keyword. That means your content needs to be thorough and easy to interpret at a glance, no matter who (or what) is doing the glancing. 

NerdWallet does this well by organizing credit card content into a clear hub, then breaking it into tightly related subtopics that cover a ton of topical ground. It’s no longer a game of relying on individual keyword pages. Notably, Nerdwallet is one of the most frequently cited websites in LLMs. 

NerdWallet credit cards hub showing a structured topic cluster with subcategories like travel, cash back, balance transfer, and student cards organized under a single pillar. 

So, switch your strategy mindset from pages to topic clusters. Cover a topic from every angle across multiple assets. Use headers, FAQs, schema markup, and internal links to connect the dots.  

The better you structure your content, the easier it is for AI to find and promote it. 

Your target audience is searching across multiple channels in today’s environment. Focusing on individual keywords leaves a lot of opportunity on the table.  

Today’s rising search platforms, like social media apps and LLMs, revolve around semantic queries. 

People talk to these tools naturally and conversationally (some of them even use ChatGPT’s voice functionality). This means you can’t hone in on a specific keyword. Using a keyword cluster that covers the most popular phrasings customers may use is a much better way to make sure you’re covering what people are asking, increasing your probability of being found.  

This query within Perplexity demonstrates how people interact with search tools. They’re not always typing keywords. They’re asking full, conversational questions and expecting a clear answer. 

AI answer engine responding to a conversational question, ‘Which is better for a headache, Tylenol or ibuprofen?,’ with a summarized comparison pulled from multiple medical sources. 

You also have to consider that many users never click through to your site. Zero-click searches are growing fast, which means your content needs to deliver value right in the SERP—or immediately on platforms like social, LLMs, and voice. 

If you’re still chasing individual keywords, you’re missing the bigger opportunity: becoming the trusted source on your topic. 

Brand Trust Is Measured in Citations and Sentiment

AI doesn’t care how loud you are. It cares how often others talk about you, and what they say when they do. 

Large language models prioritize brands with consistent, credible citations across the web. That includes mentions in blog posts, news articles, podcasts, reviews, and Reddit threads. The more quality signals you earn, the more likely AI is to recommend you.  

But the mentions are just the beginning. Your performance in 2026 really boils down to your audience’s perception of you. Sentiment analysis now plays a big role in ranking. Positive discussions boost your chances of surfacing in AI results, while negativity can drag you down. 

Until recently, this layer of discovery was almost impossible to measure. Traditional analytics don’t show when your brand is cited inside AI-generated answers. But a new class of AI visibility tools now tracks where and how often brands appear across platforms like ChatGPT, Perplexity, Claude, and Google’s AI Overviews (along with the surrounding context). But what types of brands are succeeding using this strategy? 

Brands like Patagonia and TOMS are shining examples of this. These companies leverage philanthropy to increase their goodwill and, in turn, their customers’ positive sentiment toward them.  

Leveraging elements like philanthropy the right way switches these brands’ audiences from customers to loyal supporters. 

Patagonia webpage outlining causes the company funds and does not fund, illustrating clear brand values and consistent public positioning. 

This shift rewards brands that build goodwill rather than just backlinks. If your strategy still centers on shouting the loudest, you’ll get buried by brands that are being talked about, and for the right reasons. 

A ChatGPT result talking about TOMS philanthropy efforts.

Trust is now your most important ranking factor. Earn it or fade out. 

Blogs Influence AI Models, Not Just Traffic

If you think blogs don’t “work” like they used to, you’re missing the bigger picture. They still do heavy lifting behind the scenes to shape AI output and position your brand as a go-to source. 

In modern search, everything you publish helps shape how AI models understand your brand. When you consistently cover a topic with depth and clarity, models start to associate your name with that subject.  

This new reality turns your blogs from content assets into signals of authority. 

Even if search traffic dips due to zero-click results or AI summaries, the long-term payoff is still there. The more high-quality content you create, the more likely your brand is to be cited by the higher-profile AI channels and included in trusted content roundups. 

Social Platforms Function as Search Engines 

As the search everywhere trend shows us, search behavior is spreading. And, according to Statista, nearly a quarter of U.S. adults treat social media as their starting point for search. 

People are searching TikTok to see how something works or whether a restaurant’s worth trying.  

TikTok search results for ‘best places to eat in Las Vegas,’ showing short videos answering a local restaurant query instead of traditional search links. 

They’re using YouTube to learn how to install software or compare skincare brands. Considering that this is the largest search engine after Google, it’s a great platform to focus efforts on. 

This matters because social search runs on a different logic than traditional SEO or AI answer engines. These platforms reward relevance through engagement. 

Each platform has its own discovery logic. TikTok rewards watch time and velocity. YouTube favors relevance and retention. Instagram leans on recency and interaction. 

Without optimizing for these platforms, you’re missing a huge part of the search pie. You should be treating social platforms like search engines, because your audience already does. 

This is where more traditional on-page SEO comes into play. That means digging into the types of questions your audience is asking and focusing on tried-and-true tactics like using clear, searchable titles and engaging hooks to “stop the scroll” and get your viewers’ attention in the first three seconds. 

Content Quality Outperforms Quantity Across Channels

Publishing more content won’t save you in 2026. 

Social platforms are flooded, and search is competitive. On top of that, AI is getting better every day at filtering out thin, repetitive, or regurgitated content.  

Consequently, original insights and pieces that actually teach something are rising to the top. 

We see this in emerging trends. For starters, the average number of posts per day among brands has decreased to 9.5. Engagement is moving in the opposite direction, with inbound interactions increasing by roughly 20 percent year over year.  

Instead of posting five times a day, focus on publishing things worth reading and sharing, even if it’s only one well-structured piece of content per week.  

A thoughtful video or long-form LinkedIn breakdown that sparks conversation will do much better than 100 pieces of AI-generated blogs that barely scratch the surface of a topic. 

Take National Geographic, for example. Rather than posting constantly, it focuses on educational storytelling. Check out its TikTok grid. 

National Geographic’s TikTok profile showcasing educational, documentary-style videos that prioritize learning and storytelling over high-volume posting. 

Content creators are experiencing the benefits of this strategy in real time.  

A recent survey finds that 35 percent of creators say they’re seeing higher potential ROI from longer-form content formats, with 39 percent saying they’re seeing better engagement. And almost half (49 percent) say that the choice to produce longer-form content is helping them reach a wider audience.  

If your strategy is still built around churning out content to “stay active,” it’s time to shift. Fewer pieces. Bigger impact. Better outcomes. 

That’s what wins in 2026. 

Conversion Happens On-Platform, Not On-Site 

The platforms people use every day are getting very good at keeping them there.  

Think about it: Nearly every social platform has lead forms and lets you shop inside the app. The goal of these features is to help you convert without ever leaving their platform. 

Instagram and TikTok, for example, have fully integrated shopping experiences. And it’s working. Sales through social media channels are forecasted to reach nearly 21 percent in 2026. 

Google’s even testing AI-generated product recommendations with built-in checkout links, like Etsy and ChatGPT. The whole point is to remove friction and keep the experience seamless. 

That shift changes what a “landing page” even means. In many cases, it’s a native form, a product card, or an in-app checkout flow that closes the deal on the spot. 

Your website still matters, but forcing every conversion to happen there can introduce unnecessary drop-off. When users are ready to act, the simplest path usually wins. 

This shift is giving rise to what some teams now call checkout optimization, and it’s getting some pretty serious results. E-commerce brands with 1,000 to 2,000 orders per month are implementing checkout optimization and seeing measurable gains in shipping revenue and order total.  

Comparison of e-commerce checkout flows before and after optimization, showing fewer steps, clearer shipping options, and reduced friction at checkout. 

(Image Source) 

When you meet users where they are, you lower the barrier to action. No load times. No messy redirects. Just a quick tap or swipe to buy, book, or sign up. 

Video Becomes a Primary Search and AI Input 

Video is increasingly becoming more than just a distribution format. It’s now a primary way people search—and a growing input for AI systems. 

Search engines and AI platforms now index video much like they do written content, pulling from structural signals to generate results. If those signals aren’t there, the video might as well not exist. 

ChatGPT interface responding to the prompt ‘Hit me with some funny cat videos’ by embedding a YouTube video thumbnail of a cat sitting in a plastic container in water. 

What do those signals look like in practice? 

Well, because search engines and AI platforms can’t watch your videos, they instead rely on clean transcripts, keyword-rich titles and descriptions, and clear segmentation. Think chapters, not rambles. Structure is what makes video searchable. 

This video from Neil Patel uses chapters, summaries, and clear topic segmentation, making it easier for search engines and AI systems to interpret and reference specific sections. 

The more structured and searchable your video content, the more likely it is to be cited by AI assistants. 

Text still matters. But if video isn’t part of your SEO and discovery strategy, you’re leaving serious visibility on the table. 

Paid Media Shifts to AI-Led Campaigns

We’ve seen AI-driven paid media campaigns for some time now, but platforms like Google’s Performance Max and Meta’s Advantage+ are refining and elevating how it’s done. We’re seeing these platforms automatically testing creative and placements to hit performance goals, and even testing the benefits of AI-powered segmentation or ad bidding. 

The result is less manual control and more system-led optimization, which is a benefit for many marketers. Retail marketers, for example, have seen a 10 percent to 25 percent lift in their return on ad spend (ROAS) by implementing AI-powered campaign elements.  

But “hands-off” doesn’t mean “set it and forget it.” 

In this model, your role shifts from managing campaigns to training the system. The better your inputs—creative variety, first-party data, and clear conversion signals—the better your results.  

Lazy targeting and generic ads just get ignored. 

Want to lower customer acquisition cost (CAC) or increase return on ad spend (ROAS)? Focus on refining your creative and uploading strong first-party data. AI will handle testing and optimization, but it can’t fix bad inputs. 

Savvy marketers are shifting their roles from campaign operators to strategy leads. They’re spending less time on dashboards and more time building assets that actually convert, such as a robust content library or unique, impactful insights from proprietary data. 

It all comes down to this: AI runs the ads, but you train it. If you’re not giving the algorithm something great to work with, you’re not going to like what it gives back. 

FAQs

What are the digital marketing trends for 2026?

In 2026, AI is running full campaigns, dynamic funnels are replacing traditional static ones, and users are increasingly discovering brands across platforms. Chat assistants like ChatGPT now also recommend brands, and SEO is more about structured topics than keywords. Quality content outperforms quantity, and conversion often happens off your site. 

How can businesses stay updated on marketing trends?

Follow trusted industry blogs (like NeilPatel.com), subscribe to marketing newsletters, and keep an eye on platform updates from the big players (Google, Meta, and TikTok). Tools like Ubersuggest can also help spot shifts in search behavior. But more than anything, continue testing and tracking, and stay close to what your audience responds to. 

Conclusion

Many experts say that marketing is changing, but the fact is that it’s already changed.  

AI now drives the full spectrum of content marketing. Platforms prioritize native conversion. Content shapes how machines and people see your brand. If you’re still playing by old rules—keyword-centric strategy, manual funnels, or high-volume posting—you’re going to get left behind. 

Winning in 2026 means adapting quickly to emerging digital marketing trends by thinking strategically and building trust across every touchpoint. 

If you’re not sure where to start, check out my guide on search engine trends to see how modern discovery actually works today. 

The marketers who move first always get the advantage. So, make your move. 

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