AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator. 

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm. 

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization. 

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it: 

• “If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.” 

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines: 

• What the review process for AI-created content looks like. 
• When and how to disclose AI involvement in content creation. 
• How to protect proprietary information (not uploading confidential or client information into AI tools).
• Which AI tools are approved for use, and how to request access to new ones.
• How to log or report problems.

Logically, the policy will evolve as the technology and regulations change. 

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well. 

LLMs are great at predicting the next best sentence and making it sound convincing. 

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value. 

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem. 

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

• “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” 

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

• Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
• Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
• Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources. 
• Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
• Create detailed author bios: Include:
  • Relevant qualifications, certifications, awards, and experience.
  • Links to social media, academic papers (if relevant), or other authoritative works.
• Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
• Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.

Dig deeper: Writing people-first content: A process and template

Train the LLM 

LLMs are trained on vast amounts of data – but they’re not trained on your data. 

Put in the work to train the LLM, and you can get better results and more efficient workflows. 

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

• Audience personas.
• Voice traits that matter.
• Reading level, if applicable.
• The do’s and don’ts of phrases and language to use. 
• Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc. 

You can refresh this as needed and use it to further train the model over time. 

Build a prompt kit  

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with: 

• The style guide
  • This covers everything from the audience personas to the voice style and formatting.
  • If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time. 
• A content brief template
  • This can be an editable document that’s filled in for each content project and includes things like:
    • The goal of the content.
    • The specific audience.
    • The style of the content (news, listicle, feature article, how-to).
    • The role (who the LLM is writing as).
    • The desired action or outcome.
• Content examples
  • Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more. 
  • If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic. 
• Sources
  • Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research. 
  • For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.  
  • As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
• SEO prompts
  • Consider building SEO into the structure of the content from the outset.  
  • Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

• Crafting a direct answer in the first one to two sentences, then expanding with context.
• Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
• Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
• Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
• Provide clear citations and semantic richness (synonyms, related entities) throughout. 

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG 

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules. 

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base. 

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement. 

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

• “Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.) 
• “Is the tone and voice completely aligned with the style guide?” 

Have an established editing process 

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.  

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality. 

This might include:

• Identifying the parts of the content creation workflow that are best suited for LLM assistance.
• Conducting an editorial meeting to sign off on topics and outlines. 
• Drafting the content.
• Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
• Getting sign-off from stakeholders.  

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

• Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
• All facts are traceable to credible, approved sources.
• Outdated statistics (more than two years) are replaced with fresh insights. 
• Draft meets the style guide’s voice guidelines and tone definitions. 
• Content adds valuable, expert insights rather than being vague or generic.
• For thought leadership, ensure the author’s perspective is woven throughout.
• Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI. 
• Draft aligns with brand values and meets internal publication standards.
• Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search. 

Dig deeper: An AI-assisted content process that outperforms human-only copy

The conversation around artificial intelligence (AI) has been dominated by “replacement theory” headlines. From front-line service roles to white-collar knowledge work, there’s a growing narrative that human capital is under threat.

Economic anxiety has fueled research and debate, but many of the arguments remain narrow in scope.

• Stanford’s Digital Economy Lab found that since generative AI became widespread, early-career workers in the most exposed jobs have seen a 13% decline in employment.
• This fear has spread into higher-paid sectors as well, with hedge fund managers and CEOs predicting large-scale restructuring of white-collar roles over the next decade.

However, much of this narrative is steeped in speculation rather than the fundamental, evolving dynamics of skilled work.

Yes, we’ve seen layoffs, hiring slowdowns, and stories of AI automating tasks. But this is happening against the backdrop of high interest rates, shifts in global trade, and post-pandemic over-hiring.

As the global talent thought-leader Josh Bersin argues, claims of mass job destruction are “vastly over-hyped.” Many roles will transform, not vanish. 

What this means for SEO

For the SEO discipline, the familiar refrain “SEO is dead” is just as overstated.

Yes, the nature of the SEO specialist is changing. We’ve seen fewer leadership roles, a contraction in content and technical positions, and cautious hiring. But the function itself is far from disappearing.

In fact, SEO job listings remain resilient in 2025 and mid-level roles still comprise nearly 60% of open positions. Rather than declining, the field is being reshaped by new skill demands.

Don’t ask, “Will AI replace me?” Ask instead, “How can I use AI to multiply my impact?”

Think of AI not as the jackhammer replacing the hammer but as the jackhammer amplifying its effect. SEOs who can harness AI through agents, automation, and intelligent systems will deliver faster, more impactful results than ever before.

• “AI is a tool. We can make it or teach it to do whatever we want…Life will go on, economies will continue to be driven by emotion, and our businesses will continue to be fueled by human ideas, emotion, grit, and hard work,” Bersin said.

Rewriting the SEO narrative

As an industry, it’s time to change the language we use to describe SEO’s evolution.

Too much of our conversation still revolves around loss. We focus on lost clicks, lost visibility, lost control, and loss of num=100.

That narrative doesn’t serve us anymore.

We should be speaking the language of amplification and revenue generation. SEO has evolved from “optimizing for rankings” to driving measurable business growth through organic discovery, whether that happens through traditional search, AI Overviews, or the emerging layer of Generative Engine Optimization (GEO).

AI isn’t the villain of SEO; it’s the force multiplier.

When harnessed effectively, AI scales insight, accelerates experimentation, and ties our work more directly to outcomes that matter:

• Pipeline.
• Conversions.
• Revenue.

We don’t need to fight the dystopian idea that AI will replace us. We need to prove that AI-empowered SEOs can help businesses grow faster than ever before.

The new language of SEO isn’t about survival, it’s about impact.

The team landscape has already shifted

For years, marketing and SEO teams grew headcount to scale output.

Today, the opposite is true. Hiring freezes, leaner budgets, and uncertainty around the role of SEO in an AI-driven world have forced leaders to rethink team design.

A recent Search Engine Land report noted that remote SEO roles dropped to 34% of listings in early 2025, while content-focused SEO positions declined by 28%. A separate LinkedIn survey found a 37% drop in SEO job postings in Q1 compared to the previous year.

This signals two key shifts:

• Specialized roles are disappearing. “SEO writers” and “link builders” are being replaced by versatile strategists who blend technical, analytical, and creative skill sets.
• Leadership is demanding higher ROI per role. Headcount is no longer the metric of success – capability is.

What it means for SEO leadership

If your org chart still looks like a pyramid, you’re behind. 

The new landscape demands flexibility, speed, and cross-functional integration with analytics, UX, paid media, and content.

It’s time to design teams around capabilities, not titles.

Rethinking SEO Talent

The best SEO leaders aren’t hiring specialists, they’re hiring aptitude. Modern SEO organizations value people who can think across disciplines, not just operate within one.

The strongest hires we’re seeing aren’t traditional technical SEOs focused on crawl analysis or schema. They’re problem solvers – marketers who understand how search connects to the broader growth engine and who have experience scaling impact across content, data, and product.

Progressive leaders are also rethinking resourcing. The old model of a technical SEO paired with engineering support is giving way to tech SEOs working alongside AI product managers and, in many cases, vibe coding solutions. This model moves faster, tests bolder, and builds systems that drive real results.

For SEO leaders, rethinking team architecture is critical. The right question isn’t “Who should I hire next?” It’s “What critical capability must we master to stay competitive?”

Once that’s clear, structure your people and your agents around that need. The companies that get this right during the AI transition will be the ones writing the playbook for the next generation of search leadership.

The new human-led, agent-empowered team

The future of SEO teams will be defined by collaboration between humans and agents.

• These agents are AI-enabled systems like automated content refreshers, site-health bots, or citation-validation agents that work alongside human experts.
• The human role? To define, train, monitor, and QA their output.

Why this matters

• Agents handle high-volume, repeatable tasks (e.g., content generation, basic auditing, link-score filtering) so humans can focus on strategy, insight, and business impact.
• The cost of building AI agents can range from $20,000 to $150,000, depending on the complexity of the system, integrations, and the specialized work required across data science, engineering, and human QA teams, according to RTS Labs.
• A single human manager might oversee 10-20 agents, shifting the traditional pyramid and echoing the “short pyramid” or “rocket ship” structure explored by Tomasz Tunguz.

The future: teams built around agents and empowered humans.

Real-world archetypes

• SaaS companies: Develop a bespoke “onboarding agent” that reads product data, builds landing pages, and runs first-pass SEO audits, human strategist refines output.
• Marketplace brands (e.g., upcoming seasonal trend): Use an “Audience Discovery Agent” that taps customer and marketplace data, but the human team writes the narrative and guides the vertical direction.
• Enterprise content hubs: deploy “Content Refresh Agents” that identify high-value pages, suggest optimizations, and push drafts that editors review and finalise.

Integration is key

These new teams succeed when they don’t live in silos. The SEO/GEO squad must partner with paid search, analytics, revenue ops, and UX – not just serve them.

Agents create capacity; humans create alignment and amplification.

A call to SEO practitioners

Building the SEO community of the future will require change.

The pace of transformation has never been faster and it’s created a dangerous dependence on third-party “AI tools” as the answer to what is unknown.

But the true AI story doesn’t begin with a subscription. It begins inside your team.

If the only AI in your workflow is someone else’s product, you’re giving up your competitive edge. The future belongs to teams that build, not just buy.

Here’s how to start:

• Build your own agent frameworks, designed with human-in-the-loop oversight to ensure accuracy, adaptability, and brand alignment.
• Partner with experts who co-create, not just deliver. The most successful collaborations help your team learn how to manage and scale agents themselves.
• Evolve your team structure, move beyond the pyramid mentality, and embrace a “rocket ship” model where humans and agents work in tandem to multiply output, insights, and results.

The future of SEO starts with building smarter teams. It’s humans working with agents. It’s capability uplift. And if you lead that charge, you’ll not only adapt to the next generation of search, you’ll be the ones designing it.

Google added Query groups to the Search Console Insights report. Query groups groups similar search queries together so you can quickly see the main topics your audience searches for.

What Google said. Google wrote, “We are excited to announce Query groups, a powerful Search Console Insights feature that groups similar search queries.”

“Query groups solve this problem by grouping similar queries. Instead of a long, cluttered list of individual queries, you will now see lists of queries representing the main groups that interest your audience. The groups are computed using AI; they may evolve and change over time. They are designed for providing a better high level perspective of your queries and don’t affect ranking,” Google added.

What it looks like. Here is a sample screenshot of this new Query groups report:

You can see that Google is lumping together “search engine optimization, seo optimization, seo website, seo optimierung, search engine optimization (seo), search …” into the “seo” query group in the second line. This shows the site overall is getting 9% fewer clicks on SEO related queries than it did previously.

Availability. Google said query groups will be rolling out gradually over the coming weeks. It is a new card in the Search Console Insights report. Plus, query groups are available only to properties that have a large volume of queries, as the need to group queries is less relevant for sites with fewer queries.

Why we care. Many SEOs have been grouping these queries into these clusters manually or through their own tools. Now, Google will do it for you, making it easier for more novie SEOs and beginner SEOs to understand.

More details will be posted in this help document soon.

Every year, Search Engine Land is delighted to celebrate the best of search marketing by rewarding the agencies, in-house teams, and individuals worldwide for delivering exceptional results.

Today, I’m excited to announce all 18 winners of the 11th annual Search Engine Land Awards.

The 2025 Search Engine Land Awards winners

Best Use Of AI Technology In Search Marketing

• 15x ROAS with AI: How CAMP Digital Redefined Paid Search for Home Services

Best Overall PPC Initiative – Small Business

• Anchor Rides – Post-Hurricane PPC Comeback (AIMCLEAR)

Best Overall PPC Initiative – Enterprise

• ATRA & Jason Stone Injury Lawyers – Leveraging CRM Data to Scale Case Volume

Best Commerce Search Marketing Initiative – PPC

• Adwise & Azerty – 126% uplift in profit from paid advertising & 1 percent point net margin business uplift by advanced cross-channel bucketing

Best Local Search Marketing Initiative – PPC

• How We Crushed Belron’s Lead Target by 238% With an AI-Powered Local Strategy (Adviso)

Best B2B Search Marketing Initiative – PPC

• Blackbird PPC and Customer.io: Advanced Data Integration to Drive 239% Revenue Increase with 12% Greater Lead Efficiency, with MMM Future-Proofing 2025 Growth

Best Integration Of Search Into Omnichannel Marketing

• How NBC used search to drive +2,573 accounts in a Full-Funnel Media Push (Adviso)

Best Overall SEO Initiative – Small Business

• Digital Hitmen & Elite Tune: The Toyota Shift That Delivered 678% SEO ROI

Best Overall SEO Initiative – Enterprise

• 825 Million Clicks, Zero Content Edits: How Amsive Engineered MSN’s Technical SEO Turnaround

Best Commerce Search Marketing Initiative – SEO

• Scaling Non-Branded SEO for Assouline to Drive +26% Organic Revenue Uplift (Block & Tam)

Best Local Search Marketing Initiative – SEO

• Building an Unbeatable Foundation for Success: Using Hyperlocal SEO to Build Exceptional ROI (Digital Hitmen)

Best B2B Search Marketing Initiative – SEO

• Page One, Pipeline Won: The B2B SEO Playbook That Turned 320 Visitors into $10.75M in Pipeline (LeadCoverage)

Agency Of The Year – PPC

• Driving Growth Where Search Happens: Stella Rising’s Paid Search Transformation

Agency Of The Year – SEO

• How Amsive Rescued MSN’s Global Visibility Through Enterprise Technical SEO at Scale

In-House Team Of The Year – SEO

• How the American Cancer Society’s Lean SEO Team Drove Enterprise-Wide Consolidation and AI Search Visibility Gains for Cancer.org

Search Marketer Of The Year

• Mike King, founder and CEO of iPullRank

Small Agency Of The Year – PPC

• ATRA & Jason Stone Injury Lawyers – Leveraging CRM Data to Scale Case Volume

Small Agency Of The Year – SEO

• From Zero to Top of the Leaderboard: Bloom Digital Drives Big Growth With Small SEO Budgets

“I’m going to SMX Next!”

Select winners of the 2025 Search Engine Land Awards will be invited to speak live at SMX Next during our two ask-me-anything-style sessions. Bring your burning SEO and PPC questions to ask this award-winning panel of search marketers!

Register here for SMX Next (it’s free) if you haven’t yet.

Congrats again to all the winners. And huge thank yous to everyone who entered the 2025 Search Engine Land Awards, the finalists, and our fantastic panel of judges for this year’s awards.

Many PPC advertisers obsess over click-through rates, using them as a quick measure of ad performance.

But CTR alone doesn’t tell the whole story – what matters most is what happens after the click. That’s where many campaigns go wrong.

The problem with chasing high CTRs

Most advertisers think the ad with the highest CTR is often the best. It should have a high Quality Score and attract lots of clicks.

However, in most cases, lower CTR ads usually outperform higher CTR ads in terms of total conversions and revenue.

If all I cared about was CTR, then I could write an ad:

• “Free money.”
• “Claim your free money today.”
• “No strings attached.”

That ad would get an impressive CTR for many keywords, and I’d go out of business pretty quickly, giving away free money. 

When creating ads, we must consider:

• Type of searchers we want to attract.
• Ensure the users are qualified.
• Set expectations for the landing page.

I can take my free money ad and refine it:

• “Claim your free money.”
• “Explore college scholarships.”
• “Download your free guide.”

I’ve now:

• Told searchers they can get free money for college through scholarships if they download a guide.
• Narrowed down my audience to people who are willing to apply for scholarships and willing to download a guide, presumably in exchange for some information.

If you focus solely on CTR and don’t consider attracting the right audience, your advertising will suffer. 

While this sentiment applies to both B2C and B2B companies, B2B companies must be exceptionally aware of how their ads appear to consumers versus business searchers. 

B2B companies must pre-qualify searchers

If you are advertising for a B2B company, you’ll often notice that CTR and conversion rates have an inverse relationship. As CTR increases, conversion rates decrease.

The most common reason for this phenomenon is that consumers and businesses can search for many B2B keywords. 

B2B companies must try to show that their products are for businesses, not consumers.

For instance, “safety gates” is a common search term. 

The majority of people looking to buy a safety gate are consumers who want to keep pets or babies out of rooms or away from stairs. 

However, safety gates and railings are important for businesses with factories, plants, or industrial sites. 

These two ads are both for companies that sell safety gates. The first ad’s headlines for Uline could be for a consumer or a business. 

It’s not until you look at the description that you realize this is for mezzanines and catwalks, which is something consumers don’t have in their homes. 

As many searchers do not read descriptions, this ad will attract both B2B and B2C searchers. 

The second ad mentions Industrial in the headline and follows that up with a mention of OSHA compliance in the description and the sitelinks. 

While both ads promote similar products, the second one will achieve a better conversion rate because it speaks to a single audience. 

We have a client who specializes in factory parts, and when we graph their conversion rates by Quality Score, we can see that as their Quality Score increases, their conversion rates decrease. 

They will review their keywords and ads whenever they have a 5+ Quality Score on any B2B or B2C terms. 

This same logic does not apply to B2B search terms. 

Those terms often contain more jargon or qualifying statements when looking for B2B services and products. 

B2B advertisers don’t have to use characters to weed out B2C consumers and can focus their ads only on B2B searchers.

How to balance CTR and conversion rates

As you are testing various ads to find your best pre-qualifying statements, it can be tricky to examine the metrics. Which one of these would be your best ad?

• 15% CTR, 3% conversion rate.
• 10% CT, 7% conversion rate.
• 5% CTR, 11% conversion rate.

When examining mixed metrics, CTR and conversion rates, we can use additional metrics to define our best ads. My favorite two are:

• Conversion per impression (CPI): This is a simple formula dividing your conversion by the number of impressions (conversions/impressions). 
• Revenue per impression (RPI): If you have variable checkout amounts, you can instead use your revenue metrics to decide your best ads by dividing your revenue by your impressions (revenue/impressions).

You can also multiply the results by 1,000 to make the numbers easier to digest instead of working with many decimal points. So, we might write: 

• CPI = (conversions/impressions) x 1,000 

By using impression metrics, you can find the opportunity for a given set of impressions. 

By doing some simple math, we can see that option 2, with a 10% CTR and a 7% conversion rate, gives us the most total conversions.

Dig deeper: CRO for PPC: Key areas to optimize beyond landing pages

Focus on your ideal customers

A good CTR helps bring more people to your website, improves your audience size, and can influence your Quality Scores.

However, high CTR ads can easily attract the wrong audience, leading you to waste your budget.

As you are creating headlines, consider your audience. 

• Who are they? 
• Do non-audience people search for your keywords?
  • How do you dissuade users who don’t fit your audience from clicking on your ads? 
• How do you attract your qualified audience?
• Are your ads setting proper landing page expectations?

By considering each of these questions as you create ads, you can find ads that speak to the type of users you want to attract to your site. 

These ads are rarely your best CTRs. These ads balance the appeal of high CTRs with pre-qualifying statements that ensure the clicks you receive have the potential to turn into your next customer. 

The web’s purpose is shifting. Once a link graph – a network of pages for users and crawlers to navigate – it’s rapidly becoming a queryable knowledge graph. 

For technical SEOs, that means the goal has evolved from optimizing for clicks to optimizing for visibility and even direct machine interaction.

Enter NLWeb – Microsoft’s open-source bridge to the agentic web

At the forefront of this evolution is NLWeb (Natural Language Web), an open-source project developed by Microsoft. 

NLWeb simplifies the creation of natural language interfaces for any website, allowing publishers to transform existing sites into AI-powered applications where users and intelligent agents can query content conversationally – much like interacting with an AI assistant.

Developers suggest NLWeb could play a role similar to HTML in the emerging agentic web. 

Its open-source, standards-based design makes it technology-agnostic, ensuring compatibility across vendors and large language models (LLMs). 

This positions NLWeb as a foundational framework for long-term digital visibility.

Schema.org is your knowledge API: Why data quality is the NLWeb foundation

NLWeb proves that structured data isn’t just an SEO best practice for rich results – it’s the foundation of AI readiness. 

Its architecture is designed to convert a site’s existing structured data into a semantic, actionable interface for AI systems. 

In the age of NLWeb, a website is no longer just a destination. It’s a source of information that AI agents can query programmatically.

The NLWeb data pipeline

The technical requirements confirm that a high-quality schema.org implementation is the primary key to entry.

Data ingestion and format

The NLWeb toolkit begins by crawling the site and extracting the schema markup. 

The schema.org JSON-LD format is the preferred and most effective input for the system. 

This means the protocol consumes every detail, relationship, and property defined in your schema, from product types to organization entities. 

For any data not in JSON-LD, such as RSS feeds, NLWeb is engineered to convert it into schema.org types for effective use.

Semantic storage

Once collected, this structured data is stored in a vector database. This element is critical because it moves the interaction beyond traditional keyword matching. 

Vector databases represent text as mathematical vectors, allowing the AI to search based on semantic similarity and meaning. 

For example, the system can understand that a query using the term “structured data” is conceptually the same as content marked up with “schema markup.” 

This capacity for conceptual understanding is absolutely essential for enabling authentic conversational functionality.

Protocol connectivity

The final layer is the connectivity provided by the Model Context Protocol (MCP). 

Every NLWeb instance operates as an MCP server, an emerging standard for packaging and consistently exchanging data between various AI systems and agents. 

MCP is currently the most promising path forward for ensuring interoperability in the highly fragmented AI ecosystem.

The ultimate test of schema quality

Since NLWeb relies entirely on crawling and extracting schema markup, the precision, completeness, and interconnectedness of your site’s content knowledge graph determine success.

The key challenge for SEO teams is addressing technical debt. 

Custom, in-house solutions to manage AI ingestion are often high-cost, slow to adopt, and create systems that are difficult to scale or incompatible with future standards like MCP. 

NLWeb addresses the protocol’s complexity, but it cannot fix faulty data. 

If your structured data is poorly maintained, inaccurate, or missing critical entity relationships, the resulting vector database will store flawed semantic information. 

This leads inevitably to suboptimal outputs, potentially resulting in inaccurate conversational responses or “hallucinations” by the AI interface.

Robust, entity-first schema optimization is no longer just a way to win a rich result; it is the fundamental barrier to entry for the agentic web. 

By leveraging the structured data you already have, NLWeb allows you to unlock new value without starting from scratch, thereby future-proofing your digital strategy.

NLWeb vs. llms.txt: Protocol for action vs. static guidance

The need for AI crawlers to process web content efficiently has led to multiple proposed standards. 

A comparison between NLWeb and the proposed llms.txt file illustrates a clear divergence between dynamic interaction and passive guidance.

The llms.txt file is a proposed static standard designed to improve the efficiency of AI crawlers by:

• Providing a curated, prioritized list of a website’s most important content – typically formatted in markdown.
• Attempting to solve the legitimate technical problems of complex, JavaScript-loaded websites and the inherent limitations of an LLM’s context window.

In sharp contrast, NLWeb is a dynamic protocol that establishes a conversational API endpoint. 

Its purpose is not just to point to content, but to actively receive natural language queries, process the site’s knowledge graph, and return structured JSON responses using schema.org. 

NLWeb fundamentally changes the relationship from “AI reads the site” to “AI queries the site.”

The market’s preference for dynamic utility is clear. Despite addressing a real technical challenge for crawlers, llms.txt has failed to gain traction so far. 

NLWeb’s functional superiority stems from its ability to enable richer, transactional AI interactions.

It allows AI agents to dynamically reason about and execute complex data queries using structured schema output.

The strategic imperative: Mandating a high-quality schema audit

While NLWeb is still an emerging open standard, its value is clear. 

It maximizes the utility and discoverability of specialized content that often sits deep in archives or databases. 

This value is realized through operational efficiency and stronger brand authority, rather than immediate traffic metrics.

Several organizations are already exploring how NLWeb could let users ask complex questions and receive intelligent answers that synthesize information from multiple resources – something traditional search struggles to deliver. 

The ROI comes from reducing user friction and reinforcing the brand as an authoritative, queryable knowledge source.

For website owners and digital marketing professionals, the path forward is undeniable: mandate an entity-first schema audit. 

Because NLWeb depends on schema markup, technical SEO teams must prioritize auditing existing JSON-LD for integrity, completeness, and interconnectedness. 

Minimalist schema is no longer enough – optimization must be entity-first.

Publishers should ensure their schema accurately reflects the relationships among all entities, products, services, locations, and personnel to provide the context necessary for precise semantic querying. 

The transition to the agentic web is already underway, and NLWeb offers the most viable open-source path to long-term visibility and utility. 

It’s a strategic necessity to ensure your organization can communicate effectively as AI agents and LLMs begin integrating conversational protocols for third-party content interaction.

Nearly 90% of businesses are worried about losing organic visibility as AI transforms how people find information, according to a new survey by Ann Smarty.

Why we care. The shift from search results to AI-generated answers seems to be happening faster than many expected, threatening the foundation of how companies are found online and drive sales. AI is changing the customer journey and forcing an SEO evolution.

By the numbers. Most prefer to keep the “SEO” label – with “SEO for AI” (49%) and “GEO” (41%) emerging as leading terms for this new discipline.

• 87.8% of businesses said they’re worried about their online findability in the AI era.
• 85.7% are already investing or plan to invest in AI/LLM optimization.
• 61.2% plan to increase their SEO budgets due to AI.

Brand over clicks. Three in four businesses (75.5%) said their top priority is brand visibility in AI-generated answers – even when there’s no link back to their site.

• Just 14.3% prioritize being cited as a source (which could drive traffic).
• A small group said they need both.

Top concerns. “Not being able to get my business found online” ranked as the biggest fear, followed by the total loss of organic search and loss of traffic attribution.

About the survey. Smarty surveyed 300+ in-house marketers and business owners, mostly from medium and enterprise companies, with nearly half representing ecommerce brands.

Yes, but. While AI search is booming, multiple studies suggest that ChatGPT and LLM referrals convert worse than Google Search – and AI systems won’t have parity with organic search within the next year.

The survey. SEO for AI (GEO) Statistics: 90% of Businesses Are Worried About the Future of SEO and Organic Findability Due to AI / LLMs

The death of an ad, like the end of the world, doesn’t happen with a bang but with a whimper. 

If you’re paying attention, you’ll notice the warning signs: click-through rate (CTR) slips, engagement falls, and cost-per-click (CPC) creeps up. 

If you’re not, one day your former top performer is suddenly costing you money.

Creative fatigue – the decline in ad performance caused by overexposure or audience saturation – is often the culprit. 

It’s been around as long as advertising itself, but in an era where platforms control targeting, bidding, and even creative testing, it’s become one of the few variables marketers can still influence.

This article explains how to spot early signs of fatigue across PPC platforms before your ROI turns sour, and how to manually refresh your creative in the age of AI-driven optimization. 

We’ll look at four key factors: 

• Ad quality.
• Creative lifecycle.
• Audience saturation.
• Platform dynamics.

1. Ad quality

Low-quality ads burn out much faster than high-quality ones. 

To stand the test of time, your creative needs to be both relevant and resonant – it has to connect with the viewer. 

But it’s important to remember that creative fatigue isn’t the same as bad creative. Even a brilliant ad will wear out if it’s shown too often or for too long. 

Think of it like a joke – no matter how good it is, it stops landing once the audience has heard it a dozen times.

The data behind ad quality

To track ad quality, monitor how your key metrics trend over time – especially CTR, CPC, and conversion rate (CVR). 

A high initial CTR followed by a gradual decline usually signals a strong performer reaching the end of its natural run.

Because every campaign operates in a different context, it’s best to compare an ad’s results against your own historical benchmarks rather than rigid KPI targets. 

Factor in elements like seasonality and placement to avoid overgeneralizing performance trends. 

And to read the data accurately, make sure you’re analyzing results by creative ID, not just by campaign or ad set.

Dig deeper: How Google Ads’ AI tools fix creative bottlenecks, streamline asset creation

2. Creative lifecycle 

Every ad has a natural lifespan – and every platform its own life expectancy. 

No matter how timely or novel your ad was at launch, your audience will eventually acclimate to its visuals or message. 

Keeping your creative fresh helps reset the clock on fatigue.

Refreshing doesn’t have to mean reinventing.

Sometimes a new headline, a different opening shot, or an updated call to action is enough to restore performance. (See the table below for rule-of-thumb refresh guidelines by platform.)

The data behind creative lifecycle

To distinguish a normal lifecycle from an accelerated one that signals deeper issues, track declining performance metrics like CTR and frequency – how many times a user sees your ad. 

A high-performing ad typically follows a predictable curve.

Engagement drops about 20-30% week over week as it nears the end of its run. Any faster, and something else needs fixing.

Your refresh rate should also match your spend. Bigger budgets drive higher frequency, which naturally shortens a creative’s lifespan.

3. Audience saturation

You’ve got your “cool ad” – engaging visuals, a catchy hook, and a refresh cadence all mapped out. 

You put a big budget behind it, only to watch performance drop like a stone after a single day. Ouch.

You’re likely running into the third factor of creative fatigue: audience saturation – when the same people see your ad again and again, driving performance steadily downward. 

Failing to balance budget and audience size leads even the strongest creative to overexposure and a shorter lifespan.

The data behind audience saturation

To spot early signs of saturation, track frequency, and reach together. 

Frequency measures how many times each person sees your ad, while reach counts the number of unique people who’ve seen it. 

When frequency rises but reach plateaus, your ad hits the same people repeatedly instead of expanding to new audiences. 

Ideally, both numbers should climb in tandem.

Some platforms – including Google, Microsoft, LinkedIn, and DSP providers – offer frequency caps to control exposure. 

Others, like Meta, Amazon, and TikTok, don’t.

Dig deeper: How to beat audience saturation in PPC: KPIs, methodology and case studies

4. Platform dynamics

These days, algorithms don’t just reflect performance – they shape it. 

Once an ad starts to underperform, a feedback loop kicks in.

Automated systems reduce delivery, which further hurts performance, which leads to even less delivery.

How each platform evaluates creative health – and how quickly you respond before your ad is demoted – is the fourth and final factor in understanding creative fatigue.

The data behind platform dynamics

Every platform has its own system for grading creative performance, but the clearest sign of algorithmic demotion is declining impressions or spend despite stable budgets and targeting.

The tricky part is that this kind of underdelivery can look a lot like normal lifecycle decline or audience saturation. In reality, it’s often a machine-level penalty. 

To spot it, monitor impression share and spend velocity week over week, at the creative level (not by campaign or ad set).

What to do when the algorithm punishes you

When impressions or spend drop despite a stable budget and consistent targeting, your ad has likely been demoted by the platform. 

That doesn’t necessarily mean it’s poor quality. 

This usually means the algorithm has lost “confidence” in its ability to achieve your chosen goal, such as engagement or conversions.

Here’s how to recover:

• Check your performance metrics: Sharp declines in CTR, engagement, or conversions can trigger a penalty. Compare the trend line to earlier in the campaign.
• Assess audience saturation: If frequency exceeds 3 for prospecting or 5 for retargeting, your audience may be too small for the budget. Broaden targeting or reduce spend.
• Refresh the creative: Launch new or updated versions under new ad IDs so the system re-enters its learning phase.
• Don’t make drastic edits: Frequent budget, bid, or targeting changes reset learning and slow recovery.

When the algorithm cools your ad, don’t panic. 

Act quickly to identify whether the issue lies in quality, freshness, audience, or budget – and make deliberate adjustments, not hasty ones.

Turning creative fatigue into a performance signal

Creative fatigue, like death and taxes, is inevitable. Every ad has a beginning, middle, and end. 

The key is recognizing those stages early through vigilant data monitoring, so you can extend performance instead of waiting for the crash.

While automation may be taking over much of marketing, ad creative, and copy remain one arena where humans still outperform machines. 

Great marketers today don’t just make good ads. They know how to sustain them through smart refreshes, rotations, and timely retirements.

Because when you can see the whimper coming, you can make sure your next ad lands with a bang.

Dig deeper: 7 best AI ad creative tools, for beginners to pros

Q4 is here – and for ecommerce brands, that means the biggest sales opportunities of the year are just ahead.

Black Friday, Cyber Monday, Christmas – the biggest sales events are just around the corner. To hit your targets, preparation is key. It’s not too late to act, and the opportunities ahead are huge.

Use this checklist to get up to speed quickly and set your account up for success.

Website and UX

Review site speed 

Start with a website audit to identify any red flags. Tools like PageSpeed Insights can help diagnose technical issues. 

Encourage clients to review key pages and the checkout process on multiple devices to ensure there are no bottlenecks. 

If resources allow, use heatmap or session analysis tools such as Microsoft Clarity or Hotjar to better understand user behavior and improve the on-site experience.

Confirm tracking setup

Double-check that all tracking is configured correctly across platforms. 

Don’t just verify that tags are firing – make sure all events are set up to their fullest potential. 

For example, confirm high match rates in Meta and ensure Enhanced Conversions is fully configured.

Add VIP sign-ups/pop-ups

Before the sales period begins, encourage users to join a VIP list for Black Friday or holiday promotions. 

This can give them early access or exclusive deals. Set up a separate automated email flow to follow up with these subscribers.

Launch sale page early

Publish your sale page as soon as possible so Google can crawl and index it for SEO. 

The page doesn’t need to be accessible from your site navigation or populated with products right away – the key is to get it live early. 

If possible, reuse the same URL from previous years to build on existing SEO equity. 

You can also add a data capture form to collect VIP sign-ups until the page goes live with products.

Display cutoffs clearly

If shipping cutoff dates aren’t clear, many users won’t risk placing an order close to the deadline. 

Clearly display both standard and express delivery cutoff dates on your website.

Highlight sales sitewide with banners

Don’t rely solely on a homepage carousel to promote your sale. 

Add a banner or header across all pages so users know a sale is happening, no matter where they land.

Dig deeper: Holiday ecommerce to hit record $253 billion – here’s what’s driving it

Creative and messaging

Run pre-sale lead gen ads

As mentioned with pop-ups, supplementing that strategy with lead generation ads can help grow your email list and build early buzz around your upcoming sale.

Launch simple, clear primary sale ads

These will be your Black Friday or holiday sale ads running for most of the campaign. 

Keep the messaging and promotion straightforward. Any confusion in a crowded feed will make users scroll past. 

Use strong branding, put the offer front and center, and include a clear CTA. On Meta, this often works best as a simple image ad.

Create Cyber Monday-specific ads

Many brands simply extend their Black Friday sale rather than creating Cyber Monday-specific ads and web banners. 

Take advantage of the opportunity to give your campaign a fresh angle – both in messaging and offer. 

Since it’s often the final day of your sale, you can go bigger on discounts for one day or add a free gift with purchases over a certain amount. 

It’s also a great way to move slower-selling inventory left over from Black Friday.

Refresh primary ads with ‘last days’ urgency

Add urgency to your messaging as the sale nears its end by including countdowns or end dates. 

This tactic works especially well for longer campaigns where ad fatigue can set in.

Finalize all creative assets early

November and December are busy months for ad builds and platform reviews. 

Make sure all sale assets are ready several weeks before launch to avoid rushed builds and delays from longer approval times.

Advertising and data

Audit product feeds

Make sure item disapprovals and limited products are kept to a minimum. Double-check that your setup is current. 

For example, if your return window has changed, update that information in Google Merchant Center.

Refresh first-party data and remarketing lists

Update any lists you plan to use this season. 

If you don’t have direct integrations, upload new or revised lists manually. 

Review your integrations and confirm that data is flowing correctly.

Build lookalike and custom audiences early

Start building audiences as soon as your first-party and remarketing lists are refreshed. 

Create Meta Lookalike Audiences, Performance Max audience signals, and Custom Audiences. 

If you run into volume issues, you’ll have time to adjust or explore alternatives.

Finalize budget by week, not just month

Agree on budgets early so you know your spending limits. Don’t plan just by month. Map out weekly spend, too. 

You’ll likely want to invest more heavily in the final week of November than in the first.

Use title and description extensions or ad customizers

Updating search ad copy can be tedious and time-consuming. 

These tools let you control and update copy dynamically without editing every RSA manually – saving hours in campaign builds.

Use ad assets, promo sitelinks, and GMC promotions

Enable sale-related sitelinks, callouts, and promotion extensions across search campaigns so your offers appear everywhere. 

In Shopping, set up Google Merchant Center promotions to highlight deals and incentives in your Shopping ad annotations.

Apply countdown features

Add a dynamic countdown timer to search ads to show exactly when your sale ends. 

This feature helps your ads stand out and adds urgency as the sale nears its close.

Launch search remarketing activity

Bid on generic keywords you wouldn’t normally target, but limit them to remarketing or first-party data audiences. 

For example, people searching for “Black Friday deals” who have purchased from your site in the past 30 days already know your brand and are primed to buy again.

Apply seasonality adjustments

If you use Google Ads or Microsoft Ads with a target ROAS strategy, apply seasonality adjustments to prepare the algorithm for higher conversion rates during the sale period. 

Remember to apply a negative adjustment once the sale ends to prevent unnecessary spend spikes.

Dig deeper: Seasonal PPC: Your guide to boosting holiday ad performance

Focus on what matters most for Q4 success

Not every tactic will fit your business or resources – and that’s OK. 

The key is to focus on what will have the biggest impact on your store. 

By addressing most of the points in this checklist, you’ll build a solid foundation for a strong Q4 and set yourself up to capture more sales during the busiest shopping season of the year.

Preparation is everything. The earlier you audit, test, and launch, the smoother your campaigns will run when traffic – and competition – start to surge.

Google Search Console’s performance report is stuck and has not shown an update in the main report since Sunday, October 19th. Google confirmed the issue and said it will catch up.

What it looks like. As I said on the Search Engine Roundtable, before Google confirmed the issue, the performance reports for all Search Console profiles are stuck on Sunday. Here is a sample chart:

More details. The weird thing is that when you dive in to 24 hour data, you do get recent data. So it does seem like the data is being collected and stored but it just isn’t being rendered in most of the reporting.

In addition, when you click on the by date breakdown under the chart, Google is only showing data as recent as this past Sunday.

Again, I really think the data is not lost and will soon show up in the main reporting charts soon.

What Google said. Daniel Waisberg from the Google Search Central team who works with Search Console said on X, “We’re catching up.”

Here is that post:

Why we care. If you’ve been looking to run reports for clients or stakeholders, you may have to wait a few more days for this report to catch up. It is not a bug just for your site, but for all sites in Google Search Console and it should be fixed soon.

On episode 329 of PPC Live The Podcast I speak to Jack Hepp, founder of industrious Marketing LLC, discussing the fallout and what it took to bring him back to feet after he got fired from his first PPC job.

The big f-up: fired from his first agency job

Jack’s defining career moment came just a year and a half into his first agency role. Despite being eager to learn, he was still new to digital marketing when a major mistake occurred.

“We dramatically underspent a client’s ad budget for the month — by almost 50%,” Jack recalls.

That underspend had serious consequences. The client relied heavily on Google Ads for online sales, and the missed spend translated directly into lost revenue. As the main point of contact, the blame fell on Jack.

“I was fired,” he says plainly. “It was my first job ever, and it crushed me.”

Communication breakdown: the real mistake

Looking back, Jack believes the root issue wasn’t just the budget underspend — it was poor communication.

“There were other agencies, account reps, and multiple layers of management involved,” he explains. “But I didn’t communicate clearly enough. I was afraid to admit that something was going wrong.”

That fear of being honest about mistakes is common in early careers. I note, “If people hear that one mistake could get them fired, they’ll stop taking risks. That’s not how innovation happens.”

Jack now emphasizes transparency as a key professional value: “If I had communicated better, maybe things would’ve played out differently.”

The role of leadership and training

As the conversation unfolds, it becomes clear that Jack’s situation reflected deeper organizational issues. He received no formal training before being handed digital responsibilities.

“It was very much like, ‘You’re young, you know how the internet works,’” Jack laughs. “But I didn’t know what I didn’t know.”

He and I agree that poor management and lack of mentorship can lead to preventable failures. “That agency set you up to fail,” I remark. “Good leadership means helping juniors understand what to do — and why.”

Rising again: the power of networks

After being let go, Jack initially swore off marketing altogether. “I thought I was done. I even started applying for jobs in banking,” he says.

But three months later, his professional network changed everything. A local agency called him out of the blue — thanks to someone who had vouched for him.

“They told the agency, ‘You need to talk to Jack. He’s great — don’t judge him by that one mistake.’”

That opportunity reignited his marketing career, proving the power of community connections.

“Your network can be your safety net,” I said. “Join Slack groups, PPC chats, LinkedIn communities. Those relationships can literally save your career.”

Lessons learned: transparency and ownership

Since that early setback, Jack has gone on to manage countless campaigns and clients. And yes — he’s made other mistakes, including the classic daily budget typo (“$1,000 instead of $100”).

But this time, he handled it differently:

“I went straight to my manager and owned it. We told the client, fixed the pacing, and moved on. Being transparent builds trust.”

Jack’s biggest takeaway?

• “Communicate openly, even when it’s uncomfortable. People are usually understanding if you’re honest and proactive about fixing the issue.”

On AI and the future of PPC

In the latter part of the discussion, Jack and I explore how AI is changing the paid search landscape — and why experience still matters.

Jack cautions against over-reliance on automation:

• “AI makes it easier to manage accounts, but harder to manage them well. You still need human judgment.”

He’s noticed a worrying trend where brands “take the human out” of campaign management, trusting AI to do everything. “That’s when you start seeing nonsense ad copy or mismatched targeting,” he warns.

I add, “AI should be a tool, not a crutch. You still need people who understand the fundamentals of marketing — audience, message, and intent.”

Advice for managers and new marketers

For managers training juniors in the AI era, Jack offers simple guidance:

• “Even if AI can automate tasks, make sure your team understands why those tasks matter. Teach the reasoning, not just the process.”

This builds critical thinking — the kind that prevents mistakes before they happen.

Final thoughts: The PPC Sequel

If Jack’s PPC career were a movie, what would it be called?

• “The PPC Sequel: I thought it was over, but I came back for part two.”

It’s the perfect metaphor for his journey — from fired beginner to respected thought leader.

I closes the conversation with a reminder:

“No matter how low things seem, things can always turn around.”

Following OpenAI’s launch of ChatGPT Atlas, software company Search Atlas flagged a major issue: the AI-powered browser can interact with websites in a way that looks indistinguishable from real human users, including clicking on paid advertisements.

Why we care. Businesses running digital ad campaigns could unknowingly pay for clicks generated by AI agents, not real prospects. The technology also threatens to corrupt analytics data, making it harder to measure genuine traffic and user behavior.

The problem. ChatGPT Atlas is built on Google Chrome, so ad networks and websites perceive its activity as a legitimate user.

• Every AI click on sponsored posts can trigger ad spend just like a real visitor.
• Website metrics may become unreliable, affecting marketing decisions and ROI.
• Most platforms ban bot traffic, but current detection methods cannot flag AI agents like ChatGPT Atlas.

What to do. Look for unusual traffic spikes or patterns in your analytics, as well as odd click behavior or declining conversion rates. If irregular activity is detected, inform marketing teams and ad providers.

Industry implications. Search Atlas founder Manick Bhan warned this could push platforms like Google and Meta to develop new standards for distinguishing human traffic from AI agents. He predicts the rise of AI-driven agents operating in the background will make separating human and AI activity critical for accurate measurement and protecting ad budgets.

Bottom line. As AI browsers gain adoption, brands may face hidden costs and unreliable data unless new detection tools and standards are developed – signaling both risk and opportunity for innovation in ad measurement and traffic verification.

Google today celebrated the 25th anniversary of its ad platform, first launched as AdWords in 2000. Now known as Google Ads, the platform remains the backbone of the company’s revenue engine.

Over two and a half decades, Google Ads has evolved from a manually managed system to a sophisticated AI-driven advertising platform spanning Search, YouTube, Play, and the wider web.

What Google is saying. Google is celebrating, with Vidhya Srinivasan, VP/GM of Ads & Commerce, sharing how AI is empowering us to rethink what’s possible’ and create a future of smarter, more helpful advertising.

• “Generative AI is transforming digital marketing, and I am so proud to see us leading the way with agentic capabilities, tools that automate and optimize campaigns and cutting-edge creative generation”

She emphasizes that the best ads provide answers and opportunities for inspiration, especially on Search and YouTube. She finally thanks its teams, advertisers, publishers, and creators for 25 years of collaboration, looking forward to the next 25.

Lookback. On this quarter-century milestone, Google has been moving fast on AI updates in the past few years and has really sped up the frequency of update announcements, with many of them coming from industry experts instead of Google itself.

Let’s look at some significant Google Ads stories in the past couple of years:

New products at Google Marketing Live 2024

• Google PMax upgrade allows mass AI creative asset production
• Google rolls out immersive, AI-powered Shopping Ads
• Google visual storytelling advances for YouTube, Discover, Gmail 
• Google starts testing ads in AI overviews
• Google’s first-party data unification Ads Data Manager available to all
• Google gives merchants new brand profiles, AI branding tools
• Google tests AI-powered ads for complex purchases

New products at Google Marketing Live 2025

• Google expands ads in AI Overviews, AI Mode to desktop
• New Google AI tools transform Creative Ads, Video Campaigns, Brand Listings
• Google unveils Smart Bidding Exploration
• Google unveils new tools for testing, measurement, data
• Google is putting agentic tools in Ads and Analytics
• Google Marketing Advisor, an AI sidekick for advertisers, coming soon to Chrome

Performance Max

• Advertisers gained control to customise elements like logos, colours and fonts within PMax campaigns for better brand alignment.
• Google acknowledged that API-based placement exclusions do work for PMax campaigns (contrary to earlier guidance) giving more granular control.
• PMax campaigns can now include “Message” assets (chat-based interaction) allowing direct messaging from ads, increasing engagement options.
• Advertisers gained visibility into which search terms triggered PMax ads and can now add negative keywords directly from that report—boosting transparency.
• PMax campaigns can now include or exclude specific device types (desktop, mobile, tablet, TV) offering more precision in device-level targeting.
• Advertisers can now add up to 50 “Search Themes” (signals guiding PMax toward search intent) — up from earlier limits (10→25→50).
• The negative keyword cap in PMax campaigns was raised from 100 to 10,000 per campaign — giving much greater control over exclusions.
• Advertisers can now view cross-campaign channel performance for all PMax campaigns at the account level instead of individual campaigns only.
• PMax campaigns now support 9:16 vertical image formats (e.g., for mobile/Shorts-type placements) — enabling mobile-first creative flexibility.

Audience.

• Google broadened “Audience Signal Targeting” (previously only for Performance Max) to App campaigns — giving advertisers more direct audience signals in app-install/engagement campaigns.
• Advertisers can now build remarketing lists from viewers of specific YouTube creator videos (not just own channel) — expanding the audience-targeting toolkit.
• Due to consent issues, Google disabled key conversion tracking & remarketing audience building for EU traffic if Consent Mode v2 isn’t used — impacting audience signal quality.
• Google introduced a limit on how long a user can remain in a Customer Match list (540 days max) to improve data freshness and privacy compliance.
• GA4 audiences (including predictive ones) can now be created and used directly in Google Ads, giving more segmentation and activation capability.

RSAs.

• For developers/advertisers: via API you can remove automatically created assets linked to RSAs, giving more control.
• Google’s AI now dynamically assembles above- and below-the-fold assets: omitted descriptions, headlines used as sitelinks, etc., increasing flexibility of RSAs.
• Data for each RSA headline (clicks & conversions) is now available — big step for creative optimisation in RSAs.

New AI Tools.

Google officially announced AI Max for Search campaigns — enabling automation of keywords, creative and landing pages. In the following months advertisers kept noticing even more capabilities for this new functionality:

• Additional insights on rollout, keyword-less targeting, geo controls and reporting enhancements.
• Brand inclusion/exclusion controls integrated into the AI Max settings panel for new campaigns.
• AI Max appears as its own match type in reporting dashboards — giving advertisers visibility into automated matches.
• Reporting now allows viewing search terms, ad headlines and landing pages together for AI Max campaigns.
• Google introduced a new experiment type for testing AI Max within campaigns (50/50 split control-variant).
• New “Sources” column shows where AI Max traffic comes from (landing page inclusion, expanded matches etc.).

Privacy concerns continue.

• In October 2025, after industry concerns of Google’s privacy solution and Google saying they will no longer be removing third party cookies from Chrome, Google officially shut down its Privacy Sandbox initiative, which aimed to replace third-party cookies with privacy-preserving ad technologies.
• In February 2025, Google introduced a 540-day cap on Customer Match list durations across its advertising platforms.
• In May 2024, Google began rolling out changes to comply with new state privacy laws and user opt-out preferences across its ads and analytics products.
• In January 2024, Google updated its data privacy policies for targeted ads in Europe to comply with the Digital Markets Act (DMA).

In the early days of SEO, ranking algorithms were easy to game with simple tactics that became known as “black hat” SEO – white text on a white background, hidden links, keyword stuffing, and paid link farms. 

Early algorithms weren’t sophisticated enough to detect these schemes, and sites that used them often ranked higher. 

Today, large language models power the next generation of search, and a new wave of black hat techniques are emerging to manipulate rankings and prompt results for advantage.

The AI content boom – and the temptation to cut corners

Up to 21% of U.S. users access AI tools like ChatGPT, Claude, Gemini, Copilot, Perplexity, and DeepSeek more than 10 times per month, according to SparkToro. 

Overall adoption has jumped from 8% in 2023 to 38% in 2025. 

It’s no surprise that brands are chasing visibility – especially while standards and best practices are still taking shape.

One clear sign of this shift is the surge in AI-generated content. Graphite.io and Axios report that the share of articles written by AI has now surpassed those created by humans.

Two years ago, Sports Illustrated was caught publishing AI-generated articles under fake writer profiles – a well-intentioned shortcut that backfired. 

The move damaged the brand’s credibility without driving additional traffic. 

Its authoritativeness, one of the pillars of Google’s E-E-A-T (experience, expertise, authoritativeness, and trustworthiness) framework, was compromised.

While Google continues to emphasize E-E-A-T as the North Star for quality, some brands are testing the limits. 

With powerful AI tools now able to execute these tactics faster and at scale, a new wave of black hat practices is emerging.

The new black hat GEO playbook

As black hat GEO gains traction, several distinct tactics are emerging – each designed to exploit how AI models interpret and rank content.

Mass AI-generated spam

LLMs are being used to automatically produce thousands of low-quality, keyword-stuffed articles, blog posts, or entire websites – often to build private blog networks (PBNs). 

The goal is sheer volume, which artificially boosts link authority and keyword rankings without human oversight or original insight.

Fake E-E-A-T signals

Search engines still prioritize experience, expertise, authoritativeness, and trustworthiness. 

Black hat GEO now fabricates these signals using AI to:

• Create synthetic author personas with generated headshots and fake credentials.
• Mass-produce fake reviews and testimonials.
• Generate content that appears comprehensive but lacks genuine, human-validated experience.

LLM cloaking and manipulation

A more advanced form of cloaking, this tactic serves one version of content to AI crawlers – packed with hidden prompts, keywords, or deceptive schema markup – and another to human users. 

The goal is to trick the AI into citing or ranking the content more prominently.

Schema misuse for AI Overviews

Structured data helps AI understand context, but black hat users can inject misleading or irrelevant schema to misrepresent the page’s true purpose, forcing it into AI-generated answers or rich snippets for unrelated, high-value searches.

SERP poisoning with misinformation

AI can quickly generate high volumes of misleading or harmful content targeting competitor brands or industry terms. 

The aim is to damage reputations, manipulate rankings, and push legitimate content down in search results.

Dig deeper: Hidden prompt injection: The black hat trick AI outgrew

The real risks of black hat GEO

Even Google surfaces YouTube videos that explain how these tactics work. But just because they’re easy to find doesn’t mean they’re worth trying. 

The risks of engaging in – or being targeted by – black hat GEO are significant and far-reaching, threatening a brand’s visibility, revenue, and reputation.

Severe search engine penalties

Search engines like Google are deploying increasingly advanced AI-powered detection systems (such as SpamBrain) to identify and penalize these tactics.

• De-indexing: The most severe penalty is the complete removal of your website from search results, making you invisible to organic traffic.
• Manual actions: Human reviewers can issue manual penalties that lead to a sudden and drastic drop in rankings, requiring months of costly, intensive work to recover.
• Algorithmic downgrading: The site’s ranking for targeted keywords can be significantly suppressed, leading to a massive loss of traffic and potential customers.

Reputation and trust damage 

Black hat tactics inherently prioritize manipulation over user value, leading to poor user experience, spammy content, and deceptive practices.

• Loss of credibility: When users encounter irrelevant, incoherent, or keyword-stuffed content – or find that an AI-cited answer is baseless – it damages the perception of the brand’s expertise and honesty.
• Erosion of E-E-A-T: Since AI relies on E-E-A-T signals for authoritative responses, being caught fabricating these signals can permanently erode the brand’s trustworthiness in the eyes of the algorithm and the public.
• Malware distribution: In some extreme cases, cybercriminals use black hat SEO to poison search results, redirecting users to sites that install malware or exploit user data. If a brand’s site is compromised and used for such purposes, the damage is catastrophic.

AI changes the game – not the rules

The growth of AI-driven platforms is remarkable – but history tends to repeat itself. 

Black hat SEO in the age of LLMs is no different. 

While the tools have evolved, the principle remains the same: best practices win. 

Google has made that clear, and brands that stay focused on quality and authenticity will continue to rise above the noise.

ChatGPT referral traffic converts worse than Google search, email and affiliate links, trailing on both conversion rate and revenue per session, according to a new analysis of 973 ecommerce sites.

Why we care. AI search platforms are starting to refer meaningful traffic to retailers – but not yet sales. For now, Google (paid organic) search still wins on conversion and revenue per session.

By the numbers. The dataset consisted of 12 months (Augusut 2024 to July 2025), 973 ecommerce sites, and $20 billion combined revenue.

• ChatGPT referral traffic was ~0.2% of total sessions – ~200× smaller than Google organic.
• >90% of LLM-originating ecommerce traffic came from ChatGPT (Perplexity, Gemini, Copilot, etc., are were negligible).
• Affiliate (+86%) and organic search (+13%) conversion rates were higher than ChatGPT; only paid social converted worse than ChatGPT.
• ChatGPT trailed paid and organic search on revenue per session, but beat paid social.
• ChatGPT referrals had lower bounce rates than most channels, but organic/paid search was still best on bounce rate. Session depth was generally lower than most channels.

Trendline. Conversion rate and revenue per session from ChatGPT improved, while average order value declined.

• Model projections suggested continued gains but no parity with organic search within the next year.

Between the lines. Authors suggested early-stage friction – trust and verification behavior – may push shoppers to confirm elsewhere before buying, shifting last-click credit to traditional channels.

Yes, but. Findings reflect last-click attribution and an emerging channel. If ChatGPT (and other LLMs) reshape customer journeys or make it easier to buy directly, its impact on sales could become more visible in the data.

Bottom line. Despite the hype, the data suggests AI assistants haven’t disrupted Google Search – and won’t at least in the next year. However, the trajectory for AI assistants is up and to the right. Now is the time to test, learn, and iterate to be ready when LLM shopping matures.

About the research. The study analyzed 12 months of first-party Google Analytics data from 973 ecommerce websites generating $20 billion in combined revenue. Researchers compared more than 50,000 ChatGPT-driven transactions with 164 million from traditional digital channels, using regression models that accounted for data sparsity, site effects, and device differences to evaluate conversion, order value, and engagement metrics.

Recent studies echo the same pattern. LLM traffic may be rising, but it’s weaker on engagement and conversion.

• A SALT.agency analysis found organic search visitors were more likely to engage or complete key actions across most sectors, with LLMs performing better only in a few niches like health and careers.
• Another study by Amsive showed no significant conversion difference between LLM and organic traffic, and that LLM referrals made up less than 1% of total sessions.
• All of this research points to a consistent takeaway: AI-driven referrals are growing, but still lag traditional search in both scale and purchase intent.

The working paper. ChatGPT Referrals to E-Commerce Websites: Do LLMs Outperform Traditional Channels?

Dig deeper:

• ChatGPT Shopping is here – and it’s changing ecommerce SEO rules
• Optimizing for ChatGPT Shopping: How product feeds power GEO

Looking to take the next step in your search marketing career?

Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.

Newest SEO Jobs

(Provided to Search Engine Land by SEOjobs.com)

• Organic Growth Strategist ~ Omniscient Digital ~ $80k-100k ~ Remote (US)
  October 27, 2025
  Omniscient Digital is an organic growth agency that partners with ambitious B2B SaaS companies like SAP, Adobe, Loom, and Hotjar to turn SEO and content into growth engines. We pride ourselves on being lean, agile, and experimental. Our team thrives on R&D and innovation, always exploring the smartest ways to deliver exceptional results. We believe […]
• SEO Specialist ~ UMass Global ~ $56,000 – $76,000 annual ~ Remote (USA)
  October 27, 2025
  Please Note: Internal Employees, please access the Jobs Hub app on the Workday Dashboard homepage to apply for the position. The University of Massachusetts Global (UMass Global) is a private, nonprofit affiliate of the University of Massachusetts. Accredited by WASC (Western Association of Schools and Colleges), the university offers undergraduate, graduate, credential, and certificate programs […]
• (Contract) Senior SEO Strategist ~ CSP Agency ~ $80,000 – $95,000 ~ Remote (US)
  October 26, 2025
  About CSP Agency: We’re an established SEO, GEO, Content and Link-Building agency recognized nationally for our thought leadership and 13 years of driving measurable results. Our team is small by design; a group of dedicated SEO professionals who thrive on collaboration, creative problem-solving, and pushing the boundaries of what’s possible in search. We partner with clients […]
• Local SEO Director ~ Rankings.io ~ $120k annually ~ Remote (USA)
  October 25, 2025
  Description The Director of Local SEO & LSA will serve as the strategic leader driving Rankings.io’s local visibility across all client accounts — blending deep expertise in Local SEO with advanced knowledge of Google Local Services Ads (LSA) to help law firms dominate local search results and generate high-quality leads. This role will lead the […]
• Senior SEO Analyst ~ Lower ~ $140,000 – $160,000 ~ Remote (US)
  October 24, 2025
  Here at Lower, we believe homeownership is the key to building wealth, and we’re making it easier and more accessible than ever. As a mission-driven fintech, we simplify the home-buying process through cutting-edge technology and a seamless customer experience. With tens of billions in funded home loans and top ratings on Trustpilot (4.8), Google (4.9), […]
• SEO Data Analytics Specialist ~ New Media Advisors ~ $70,000 to $75,000 USD ~ Remote (US)
  October 24, 2025
  At New Media Advisors, we don’t just deliver SEO and content strategies—we empower in-house marketing teams to build and sustain digital growth. As ex-in-house leaders with 125+ years of combined experience, we partner with mid-market and enterprise brands to solve real problems, drive organic performance, and upskill internal teams. We’re not an agency. We’re strategic […]
• Staff Full-Stack Engineer, Front-end (SEO Engineering) ~ NerdWallet ~ $152k – $282k ~ Remote (USA)
  October 24, 2025
  At NerdWallet, we’re on a mission to bring clarity to all of life’s financial decisions and every great mission needs a team of exceptional Nerds. We’ve built an inclusive, flexible, and candid culture where you’re empowered to grow, take smart risks, and be unapologetically yourself (cape optional). Whether remote or in-office, we support how you […]
• SEO Specialist ~ Pella Corporation ~ 80,000-100,000 ~ Remote (US)
  October 23, 2025
  Pella is seeking a strategic, data-driven, and curious SEO specialist to lead and scale our organic search strategy. This role will partner with internal and external stakeholders to manage and deliver cutting edge digital experiences. As an SEO Specialist, this position will be responsible for driving the development and execution of comprehensive SEO strategies, enhancing visibility […]
• SEO Content Specialist (PT) ~ Response Mine Interactive ~ 25-30 per hour ~ Remote (USA)
  October 23, 2025
  SEO Content Specialist Role Description This is a part-time position, offering up to 20 hours per week of work. While this Position relies on traditional SEO skills to achieve results in the Marketplace, the SEO Specialist will primarily be creating content and working within CMS (WordPress) daily to achieve desired Rankings. The SEO Specialist position […]
• SEO Technical Automation Marketer ~ Tallo ~ $57,519.75-$120,000 ~ Remote (USA)
  October 23, 2025
  Job Description Most young people aren’t asking “How do I find a job?” They’re asking, “Who can I become?” That’s the question traditional hiring infrastructure wasn’t built to answer. Career platforms treat people like résumés. Educational systems prepare students for pathways that may not fit. Employers struggle to find talent that’s been there all along. […]

Newest PPC and paid media jobs

(Provided to Search Engine Land by PPCjobs.com)

• SEM Account Manager ~ Accelerated Digital Media ~ $60,000 – 78,000 ~ Remote (USA/CAN)
  October 27, 2025
  Accelerated Digital Media stands as a self-funded, employee-owned digital marketing agency with a focus on performance media management across paid search and paid social channels. Our culture prioritizes our team and values high standards. We welcome fresh perspectives, champion collaboration, and cultivate individual accountability to meet our ambitious growth objectives. Specializing in direct-to-consumer brands, we […]
• Paid Social Video Editor (Part-Time, Remote) ~ Much Better Adventures ~ £30,000 – £45,000 per annum ~ Remote (UK/EU)
  October 26, 2025
  Description We’re looking for a brilliant Part-Time Paid Social Video Editor on an initial 6-month fixed-term contract to create bold, results-driven social ads that bring our adventures to life. Read more about working at Much Better Adventures The Role Can you craft bold, engaging ad creatives that drive growth across Meta, TikTok, and YouTube? This […]
• Paid Media Strategy Director ~ Obility ~ $90,000 – $120,000 per year ~ Remote (USA)
  October 25, 2025
  Job Description Job Description The Paid Search Strategy Director is a standout expert in the field of B2B paid media (search, display, social, retargeting, etc.). They are the lead day-to-day subject matter expert (SME) for their assigned B2B clients, providing strategic recommendations, analysis, and reporting as well as responding to ad-hoc requests. They have a […]
• PPC Manager ~ Pitchup.com ~ £40,000-£50,000 per year ~ Remote (UK/EU)
  October 24, 2025
  Want to work with an award-winning and quickly expanding company? We’re a travel site making it a doddle to book camping, glamping and caravan sites all over the world, with over 6,000 to choose from. Pitchup.com receives 40m annual visits and up to 9,173 bookings per day – so far we’ve booked £500m worth of […]
• Paid Media Manager ~ Porch Group ~ $67,500- $90,000 annual ~ Remote (USA)
  October 24, 2025
  Porch Group is a leading vertical software and insurance platform and is positioned to be the best partner to help homebuyers move, maintain, and fully protect their homes. We offer differentiated products and services, with homeowners insurance at the center of this relationship. We differentiate and look to win in the massive and growing homeowners insurance […]

Other roles you may be interested in

SEM Manager, U.S. News & World Report (Remote)

• Salary: $95,000 – $120,000
• Work on high impact, strategic, user acquisition-focused projects for paid search engine marketing, developing new growth levers, scaling paid search traffic and growing performance marketing revenue and margin at US News.
• Be a channel expert for vertical teams as marketing needs scale

Senior Manager, SEO, Pressed Juicery (Remote)

• Salary: $135,000 – $150,000
• Develop and execute a content roadmap focused on high-value topics (e.g., juice cleanses, gut health, immunity).
• Optimize Google Business Profiles and store location pages for “near me” and local intent searches.

Search Engine Optimization Manager, The Hiring Advisors (Hybrid, Boston, MA)

• Salary: $130,000 – $160,000
• Build and execute a full-funnel SEO strategy across technical, content, and off-site initiatives
• Define KPIs that tie organic growth directly to revenue

SEO & Generative Search Manager, Scale Media (Remote)

• Salary: $80,000 – $100,000
• Develop and execute SEO strategies across multiple DTC health and wellness brands
• Spearhead Generative Search Optimization (GEO) initiatives to secure brand visibility inside AI-generated answers

Head of PPC (Remote), Puffy (Remote)

• Salary: $165,000 – $175,000
• Full P&L responsibility for all paid search channels.
• Campaign architecture and scaling strategy from the ground up.
• Mastery and optimization of Performance Max and Shopping campaigns.

Marketing Lead, The Cake (Remote)

• Salary: $180,000
• Lead Generation: Design and execute digital campaigns that build measurable pipeline — not just impressions.
• LinkedIn Dominance: Create and grow presence with daily content, thought leadership, and engagement.

Search Engine Optimization Specialist, Harnham (Remote)

• Salary: $120,000 – $140,000
• Develop and manage holistic SEO strategies that drive organic growth and align with client business goals
• Conduct keyword research, competitive and technical audits, and backlink analyses

Senior Growth Product Manager, Reku (Remote)

• Salary: $180,000 – $220,000
• Lead our Product-Led Growth (PLG) strategy and roadmap
• Build viral loops, retention drivers, and onboarding magic
• Run experiments, crunch funnels, and live in the data

Growth Marketing Manager, Storm2 (Hybrid, New Jersey, US)

• Salary: $100,000 – $130,000
• Develop and execute data-driven growth marketing strategies.
• Manage multi-channel campaigns (digital, social, influencer, partnerships).

Senior SEO Manager, The Hired Guns (Remote)

• Salary: $56,000 – $84,000
• You’ve worked inside WordPress and other CMS platforms, conducted full-scale technical audits, and can wield tools like Ahrefs, SEMrush, Moz, Screaming Frog, or similar like a pro.
• You can explain technical work in plain terms—whether you’re coaching a team member, presenting to a client, or reporting to leadership.

Senior Manager, Performance Marketing – Paid Social, Uber (New York, NY)

• Salary: $203,000 – $225,500
• Own the global strategy for paid social growth campaigns, with accountability for performance across regions and lines of business.
• Architect automation and optimization frameworks in collaboration with Ad Tech and Product partners-using tools like Meta APIs, Smartly, and custom-built solutions to scale operations.

Director of Growth, Havas Media Network (Hybrid, New York City Metropolitan Area)

• Salary: $135,000 – $145,000
• Lead outbound prospecting for Havas Play and Havas Market, generating and qualifying new business opportunities with net-new clients.
• Develop tailored outreach strategies that resonate with brand marketers, e-commerce leaders, and cultural partners.

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