Tag: AI Overviews

How I Build a Powerful SEO Budget Case My CFO Can’t Ignore
If I walk into a budget meeting armed only with rankings, traffic, and keyword reports, I know I am making the wrong case. CFOs do not approve SEO budgets because channel metrics look encouraging. They approve investments that reduce business risk, improve commercial outcomes, and justify the way capital is allocated.

As AI reshapes search economics and customer acquisition costs continue to climb, I believe translating SEO into business risk is becoming as important as the search strategy itself. This is how I prepare for that conversation before I enter the room.

Why I see SEO budget conversations break down

A global enterprise software company recently shared a revealing example with us, and I keep returning to it because it captures the problem so clearly.

One of the company’s core product lines generated 291 inbound demo requests during a single month in 2008. In the corresponding month of 2026, it generated only 274. Nearly two decades later, and despite a digital marketing budget roughly eight times larger, the business was producing fewer qualified opportunities.

I do not see that as a simple search strategy problem. I see it as a structural problem—and the CFO had already noticed it.

The head of search entered the budget review with a 24-slide deck. Slide 3 documented ranking improvements. Slide 7 highlighted year-over-year organic traffic growth. Slide 12 outlined keyword opportunities.

Every number was accurate, but none of them answered the question that mattered to the CFO: Why was the company spending more each year to generate roughly the same number of qualified opportunities?

The CFO let the presentation continue. Then, at slide 19, she put down her pen and said, “This is all interesting. But I can’t see the connection to pipeline.”

The head of search began to explain. The CFO looked toward the CMO, and the meeting was effectively over.

The lesson I take from this is that many search leaders lose the CFO budget conversation before they enter the room. Their strategies may be sound, and their numbers may hold up, but they arrive speaking in sessions, rankings, and organic traffic share. That is not the language of financial decision-making.

When I prepare for this kind of meeting, I assume the CFO wants to discuss the P&L, risk, payback periods, and opportunity cost.

If I open with “organic traffic grew 23% year over year,” I risk telling the CFO, unintentionally, that I cannot connect my work to revenue. If the CFO has already seen cost per opportunity moving in the wrong direction, that disconnect does more than create skepticism. It creates a reason to cut the budget.

The structural shift I diagnose first

I start with the diagnosis before I discuss tactics. Without a clear diagnosis, everything else becomes a more polished way to lose the same argument.

In 2008, paid search behaved like an undersupplied monopoly channel: high intent, limited competition, and relatively linear returns. A dollar invested could produce a reasonably predictable return. There was no AI layer absorbing clicks before they happened, no army of comparison aggregators siphoning away high-intent traffic, and no group of competitors with 18 years to build organic authority in the category.

That environment is gone.

Today, I operate in a search landscape where organic authority is fiercely contested. AI Overviews can intercept high-intent queries before users reach paid ads, while attribution models designed for the old environment are still being used to defend budgets in the new one.

The message I bring to a CFO is not simply, “I need more budget,” or, “Our rankings are improving.” I explain that the structural conditions that once made search efficient have changed, show how those changes affect commercial performance, and present my plan for adapting.

Why I do not lead with channel metrics

I understand the temptation to showcase channel performance. After spending months building organic authority, improving rankings, and growing traffic, I naturally want that work to be visible. The problem is that presenting it without a commercial connection can undermine the very case I am trying to make.

CFOs have been burned by marketing attribution models before. They have seen enough ranking charts and organic traffic reports to know that neither metric connects directly to the P&L without additional evidence.

When I lead with channel metrics, I invite two immediate questions: “According to which model?” and “What does this mean for revenue?” Every slide that raises those questions before I have framed the argument spends some of my credibility.

How I handle the counterfactual problem

The deeper issue is the question I expect every CFO to bring into the room: “Would this revenue have happened anyway?”

I consider that the hardest question in marketing attribution, yet many budget presentations never answer it. They treat the connection between organic performance and commercial outcomes as self-evident. It is not. A CFO who has watched the marketing budget expand for a decade while blended customer acquisition cost drifts upward is right to challenge that assumption.

If I am asked, “How do we know those customers would not have found us anyway?” and I do not have a prepared answer, I have lost the thread. That is why I do not build my budget case on an attribution model I cannot defend under pressure. I build it around something much harder to dismiss: measurable business risk.

Dig deeper: Stop paying for traffic: The enterprise CMO’s guide to ROI-driven SEO

How I frame SEO as business risk

I think of CFOs primarily as risk managers, not channel optimizers. Their job is to protect the business from downside scenarios, allocate capital efficiently, and prevent unpleasant surprises in the P&L.

If I enter the room talking only about upside—what a larger budget might achieve—I am appealing to the wrong instinct.

Instead, I lead with downside and focus on three risks that a CFO can price, model, and act on.

1. Competitive displacement risk

I never treat organic search positions as permanent assets. They are contested positions in a live market. If I reduce investment, competitors do not pause and reduce theirs to match. They usually accelerate.

I also avoid saying only, “We will lose rankings.” Rankings are still a channel metric. I frame the risk in commercial terms:
- “A 30% budget reduction will not create a simple 30% reduction in output. I expect it to trigger a compounding decline over the next three to 18 months as competitor content accumulates, our positions erode, and the cost of recovering those positions exceeds the cost of maintaining them.”
I am presenting a deferred-liability argument, not merely a channel-performance argument. It gives the CFO a risk that can be modeled. For example, I can calculate how much a 20% decline in organic share of voice would add to CAC over 12 months if paid search had to compensate for the lost visibility.

When I show that calculation, I can move the conversation from “Can we afford this investment?” to “Can we afford the cost of withdrawing it?”

2. AI visibility risk

I see AI visibility as the newest and least understood risk in many boardrooms. That gives me an opportunity if I can explain it clearly and connect it to financial outcomes.

As AI Overviews and LLM citations become a primary discovery layer for high-intent queries, I no longer think of organic authority solely in terms of rankings. I also ask whether our brand appears in the AI-generated answer.

A paid campaign can often be restarted next quarter by adding budget. AI citation share is different. It depends on content depth, structured data, brand signals, and domain authority built over months or years. I cannot rebuild that visibility with a quick media buy; I need a content and authority program measured in quarters rather than weeks.

The commercial connection is crucial. If I lose AI visibility, I do not just lose traffic. The business may have to buy back those same high-intent users through paid search, often at CPCs inflated by competitors that continued investing and preserved their citation share.

I do not treat this as a distant concern. For many organizations, declining AI visibility can be the trigger for a broader CAC blowout, so I price the risk explicitly.

The framing I use with the CFO:
- “We currently hold strong AI citation share across our 10 most important commercial queries. I do not expect that position to maintain itself. Here is what it cost us to build, what I estimate it would cost to recover if we lost it, and the quarterly investment I recommend to defend it.”
Dig deeper: The bureaucracy tax: How disruptors are winning AI search visibility

3. CAC blowout risk

I find that this risk lands hardest because it is already materializing in many enterprise organizations.

As AI reshapes search, every glowing path to discovery carries commercial value—turning SEO investment into a conversation about pipeline, risk, and customer acquisition costs.

When I return to the enterprise software example, the year-over-year picture is even more revealing than the 18-year comparison:
- April 2025: Roughly $420,000 in Google spend, 681 inbound demo requests, and approximately $617 per opportunity.
- April 2026: Roughly $310,000 in Google spend, 418 inbound demo requests, and approximately $741 per opportunity.
Spend fell by 26%, qualified opportunities fell by 39%, and cost per opportunity increased by 20% in one year. The deterioration happened not simply despite the budget reduction, but partly because of it.

I expect a CFO to test a simpler explanation: Perhaps performance was already declining and the budget was cut in response. That is a reasonable hypothesis, but it does not fully fit the data. Cost per opportunity had started rising before the reduction. The cut did not create the original efficiency problem; it exposed a structural problem that already existed.

The search environment had changed, but the budget strategy had not. AI Overviews were absorbing high-intent category and solution queries before many of those searches became clicks.

At the same time, the organic authority that took years to build was generating fewer visits as zero-click search expanded. When paid spend fell, the organic foundation was not strong enough to carry the load. Together, the two effects caused more damage than either would have caused independently.

This is how I explain the CAC blowout mechanism: When organic visibility weakens and paid media has to compensate, blended CAC rises. If paid investment is then reduced before the organic gap is repaired, CAC can rise even further.

The CFO sees a negative trend and may conclude that search no longer works. I see a different problem: The structural relationship between paid and organic was never actively managed.

I do not consider this unique to enterprise software. It is a predictable outcome when paid and organic search are managed as separate budget lines with separate accountability, as they still are in many enterprise organizations.

The framing I use with the CFO: I show the relationship between organic share of voice and blended CAC across the previous 18 to 24 months. If organic visibility declined while paid CPCs rose, I have direct evidence of the risk.

If I have completed a cannibalization audit and redirected spend away from terms where paid ads competed with strong organic coverage, I also present that work. Moving the budget toward genuine demand gaps gives me a concrete example of the structural fix in action.

Why I brief the CMO before the meeting

One of the most valuable preparation steps I can take is briefing the CMO before I enter the budget meeting. I do this not simply to seek approval, but to stress-test my argument.

The CMO has usually participated in more CFO conversations than I have. They know which objections carry the most weight, which risks currently concern the CFO, and which parts of my case are likely to receive the greatest scrutiny. I cannot gain that perspective if I build the deck in isolation.

A CMO who has already challenged and strengthened my argument becomes an ally in the room. A CMO who hears the case for the first time alongside the CFO can become a liability. If the CMO hesitates over a number or qualifies a claim I presented with confidence, the CFO will notice.

That is why I brief the CMO and enter the meeting aligned. In my experience, much of the budget conversation is won or lost before anyone sits down.

How I prepare for three inevitable questions

Before I prepare the answers, I plan my opening move.

I do not spend the first 60 seconds summarizing last quarter’s performance, and I do not jump into risk without establishing common ground. Instead, I begin with the structural diagnosis.

I might say:
- “Before I walk through the data, I want to explain why we are having this conversation. The search environment has changed materially over the past three years. I want to show how that change is affecting our cost per opportunity and what I recommend we do about it.”
From there, I present the evidence, explain the risks, and prepare for the questions. These questions are not hypothetical. Search leaders hear them repeatedly, so I want my answers ready before I enter the room.

“What happens if we cut this by 30%?”

I do not respond by declaring the cut unacceptable or catastrophic. A CFO asking this question may be testing how well I understand the program’s efficiency curve rather than announcing an actual reduction. If I become defensive, I signal that I have not modeled the scenario.

I prepare a specific answer in advance:
- “A 30% reduction applied evenly across the program would cost us approximately [X] in organic traffic within six months. At our current organic conversion rate, that represents [Y] in pipeline impact. If we need to remove 30%, I would make these specific cuts to minimize commercial damage. This is the threshold below which I believe the program becomes structurally unsustainable and the cost of recovery exceeds the savings.”
With that answer, I demonstrate P&L literacy, anticipate the follow-up questions, and shift the meeting from budget defense to business problem-solving. I am not protecting a line item; I am helping the CFO make a better capital allocation decision.

“How do we know these conversions would not have happened anyway?”

I do not try to defend an attribution model as if it were indisputable. I am unlikely to win that argument, and fighting it can damage the credibility of everything else I have presented.

Instead, I acknowledge the attribution problem and pivot to incrementality:
- “I agree that last-click attribution overstates organic search’s contribution, so I do not use it as my primary evidence. Instead, I track periods when organic visibility declined across our most important commercial queries and paid CAC increased as paid search compensated. I consider that our most defensible proxy for organic search’s incremental contribution, and I have deliberately kept the estimate conservative.”
I find that intellectual honesty about attribution limitations builds credibility with a financially trained audience. CFOs have seen too many models that appear designed to prove whatever the presenter wants to prove.

By acknowledging the limitation first and offering a conservative proxy, I can earn more trust than I would by making an aggressive ROI claim.

“What is the payback period?”

I avoid answering with a broad argument about long-term brand equity or compounding authority. CFOs working within quarterly reporting cycles are unlikely to approve capital based only on a three-year organic growth narrative. If I lead with that answer, I suggest that I do not understand how the allocation decision is being made.

I separate the investment into two components with different payback profiles.

Maintenance spend covers the work required to preserve existing positions, keep content current, and maintain technical health. I frame its payback as immediate because it protects value the business has already created. The relevant comparison is the future cost of recovering the positions if they are lost.

Growth spend covers new content, category expansion, and authority building. For content aimed at existing demand with known search volume, I model the payback across six to 12 months. I make the assumptions visible, including query volume, conversion rate, and revenue per conversion.

I show my work. If the CFO stress-tests my assumptions and challenges specific numbers, I consider that constructive engagement with the model. It is a better outcome than polite agreement followed by a budget cut because my methodology failed to inspire confidence.

The data I leave behind—and the data I bring

Before I build the deck, I decide what to remove. Most search budget presentations do not fail because they lack useful data. They fail because the valuable evidence is buried beneath metrics that erode credibility before the important numbers appear.

What I leave behind
- Keyword rankings in isolation: Unless I can connect a specific ranking movement to pipeline impact, I treat it as another channel metric that invites the counterfactual question.
- Organic sessions without market context: If my traffic grew by 15% while the market grew by 40%, I lost ground. Without an external benchmark, year-over-year traffic growth gives the CFO little basis for evaluation.
- Metrics that require a glossary: If I have to explain what a metric means before I can explain why it matters, I leave it out of the meeting. Every definition delays the commercial argument.
- Long-term brand equity arguments: I do not reject these arguments, but I recognize that they are difficult to act on within a quarterly budget cycle. Leading with them creates a mismatch between my timeline and the CFO’s.
What I bring

Before I finish the deck, I decide what deserves the most important slide. I do not choose a generic traffic graph or ranking summary. I begin with a commercially meaningful statement such as:
- “I estimate that organic search offset $[X] in paid-search dependency this quarter.”
I lead with the money the program saved the business, expressed in language the CFO already uses. The supporting evidence follows:
- Blended CAC across the previous 18 to 24 months, segmented by channel. I use this chart to expose the relationship between paid and organic performance and connect search investment to the P&L.
- Organic share of voice compared with the three leading competitors over time. I use this to make competitive displacement measurable. If a competitor gained ground while our investment remained flat, I show it.
- Pipeline contribution by channel under a conservative, clearly labeled attribution model. I state whether the model is last-touch, position-based, or something else. I find that transparent disclosure builds more credibility than an optimistic number that invites a methodological dispute.
- A pre-modeled 30% reduction scenario with specific commercial consequences. I consider this the most powerful analysis I can bring because it answers the likely budget question before it is asked.
- AI Overview citation share across the 10 most important commercial queries. I use our own data to ground the AI visibility argument. It demonstrates that I understand the changing discovery landscape without relying on vague industry generalizations.
How I turn the meeting into a capital allocation conversation

I do not consider the enterprise software company in this example an outlier. I see the same pattern across enterprise search: budgets rise, efficiency declines, and CFO skepticism grows as AI Overviews absorb intent, paid and organic remain disconnected, and reporting continues to reward channel metrics instead of commercial outcomes.

I have learned that winning this conversation does not depend only on having the best search strategy. It depends on translating SEO into business risk in language a CFO can evaluate and act on.

Before I enter the room, I brief the CMO, model the commercial effect of a budget cut, prepare a conservative answer to the attribution question, and separate maintenance investment from growth investment. That preparation is within my control, even though the structural shift in search—and the CFO’s skepticism—are not.

Ultimately, I choose which conversation I am ready to have. I can defend a collection of channel metrics, or I can help the CFO make a capital allocation decision. Only one of those approaches gives my SEO budget a compelling business case.

Inspired by this post on Search Engine Land.
July 10, 2026
Credibility-First Link Building: How We Earn Lasting Authority
At Resolve, we define link building for legitimacy as earning authoritative backlinks, brand mentions, and media coverage that demonstrate trust, expertise, and credibility to search engines and AI systems. Instead of chasing link volume, we use digital PR, original research, thought leadership, and journalist relationships to earn genuine editorial citations. These are the authority signals behind Google’s E-E-A-T framework, and they can help us appear in AI Overviews, earn citations from large language models, and build visibility that survives ranking swings.

We believe a little competition is healthy. It challenges us, sharpens our thinking, and pushes us to pursue bigger and better results.

However, today’s search environment is changing faster than ever. Large language models, AI-generated answers, and frequent algorithm updates are reshaping how people find information, making it increasingly difficult for us to rely on yesterday’s playbook.

The metrics we once used to keep brands afloat — traffic, domain authority increases, and keyword rankings — no longer define SEO success on their own. We can reach the top of a search results page and still see very few conversions.

If we continue chasing those numbers in isolation, we risk being left behind. We have to adapt.

We now widen our view to the outcomes that matter most: trust and brand authority. Unlike a temporary ranking or traffic spike, trust and authority are not earned quickly or easily.

We need time to spread the word about our brand, and we need even more time to prove that people can rely on us. Once we establish that trust, however, it becomes much harder to dislodge.

An algorithm update can cut our traffic overnight. It cannot erase genuine trust overnight.

Our challenge is learning how to build trust and strengthen our brand while every competitor is trying to do the same. We also need meaningful ways to measure concepts that can initially seem difficult to quantify.

We have found that the answers involve some nuance, but they are simpler than they appear. The process begins with a shift in perspective.

Why we see link building as more than rankings

For years, we treated link building like a popularity contest. The site that collected the most votes, in the form of backlinks, was often rewarded with a prominent position in the search results.

Relevance and trust beat volume: 86% of journalists use at least some PR-pitched stories, yet 54% seldom respond, while 61% of Gen Z turn to generative AI instead of Google.

Over time, Google and other search engines updated their algorithms to improve the search experience. With each change, Google cracked down on more sites that tried to manipulate the system with backlink volume instead of earning links with real editorial and audience value. Countless sites lost traffic, and many still feel the effects.

Today, we see Google place more emphasis on relevance, industry trust, and authority. That helps explain why a familiar brand can attract more searchers than a smaller competitor even when both publish similar content and target similar keywords.

Large language models and Google’s AI Overviews have widened this divide. These systems can use retrieval-augmented generation, or RAG, to retrieve relevant sources, often favoring authoritative publications and proprietary information. If we merely repeat a statistic already cited by a top-tier publication, an AI system may choose the better-known source to reduce the risk of spreading inaccurate information.

We also see younger searchers moving toward AI tools. In a 2025 Resolve study, 61% of Gen Z respondents said they used generative AI instead of Google.

None of this means every form of link building looks like spam to Google or an LLM. It means we need backlinks to work alongside a broader set of authority signals.

When publications and journalists cite our brand, they signal authority. When we publish original content and proprietary data, we signal authority. When we create useful graphics and informative videos, we signal authority again.

Once Google and AI systems recognize these signals, the backlinks supporting them become meaningful votes of confidence. Our site may then be more likely to rank prominently, appear in AI Overviews, and receive citations in LLM-generated answers.

How we use E-E-A-T in a competitive search environment

In 2018, Google updated its quality-rater guidance to place greater focus on expertise, authoritativeness, and trustworthiness, commonly shortened to E-A-T. In 2022, Google added another E for experience. Together, these qualities provide a framework for understanding how Google considers credibility and legitimacy.
- Experience: We demonstrate that an author has personally engaged with the subject. Examples include a forum where people describe testing a product or a gardener documenting firsthand pest-prevention trials.
- Expertise: We show that the author has relevant knowledge, qualifications, or credentials supporting the information and advice.
- Authoritativeness: We earn recognition from credible sources and industry voices that cite or link to our work, helping establish us as a respected participant in the field.
- Trustworthiness: We remain transparent, accurate, and honest. We avoid deceiving readers or using manipulative link-building practices.
We apply E-E-A-T both on and off the page. Author biographies can demonstrate expertise, while accurate sourcing can demonstrate trustworthiness. Off the page, we strengthen E-E-A-T signals through the quality of the sites that link to us and the journalists who rely on us as a source. Both dimensions influence how search engines assess whether our information is useful, accurate, and credible.

If we consistently earn backlinks from dozens of irrelevant websites, that pattern can look like a low-quality or manufactured signal. If several respected journalists mention our brand because we published a valuable study, those mentions are much more likely to function as genuine votes of confidence.

Move beyond fragile SEO numbers. This side-by-side graphic shows how earned media, branded searches, industry citations and conversions build authority that can survive algorithm updates.

For us, link quantity is no longer a reliable proxy for legitimacy. We look for backlinks that demonstrate real relevance and value.

We cannot earn those links half-heartedly. We need a coordinated strategy that strengthens credibility both on and off our site.

The off-page SEO tactics we use to demonstrate value

When we ask how to earn links that search engines and LLMs will treat as signs of trust, we do not look for a single outreach tactic. Strong links usually emerge from several activities that we sustain over time.

We create genuinely linkable assets

To prove that people genuinely want to reference our site, we first create content worth referencing. If we are accustomed to quick and easy links, this may require a larger investment in content than we have made before. A routine how-to article or listicle is rarely enough by itself.

We define linkable content as something journalists, publishers, and readers find distinctive and useful — something they have not already encountered dozens of times. We often draw from the following content formats.
- Original data and proprietary research: We publish information people cannot find elsewhere. In a crowded search environment, that means conducting original research rather than recycling familiar statistics. When a journalist needs a statistic and our site is the primary source, we can earn a natural backlink.
- Thought leadership and expert commentary: We share an original perspective from a credible expert within our organization, giving publishers a useful idea or quotation they may cite in future coverage.
- Authoritative long-form guides: We answer the main question thoroughly and anticipate the follow-up questions a reader is likely to ask. This depth can help us earn links as audiences move further into their research.
- Engaging visuals and infographics: We invest in visual assets that make complex information easier to understand and share. Ahrefs found that YouTube mentions strongly correlated with inclusion in AI Overviews. Videos can be especially valuable, but informative infographics also give publishers a useful visual for their own audiences.
These formats demand more time, effort, and money, but we have found that they are often more sustainable than disposable content. They help us earn credible editorial citations and build industry authority that is more resilient to algorithm updates.

We connect link building with digital PR

We place digital PR at the center of authority building because it connects brand development with link acquisition. It helps us earn coverage, attract links, and introduce our organization to new audiences through credible journalists. Those are precisely the kinds of signals search engines can consider when assessing legitimacy.

Unlike traditional PR, our digital PR work focuses on online coverage and backlinks from news organizations and media outlets. We create useful assets or proprietary data, identify the journalists most likely to care, and pitch stories that fit their established beats.

Many of these publications carry significant influence and reach large audiences that can introduce our brand to more people. When a highly authoritative outlet covers our story, other journalists may discover and cite it organically. Syndication can amplify the effect further when a media group republishes an article across its network, potentially producing many relevant links from one story.

Our strongest digital PR campaigns typically use one or more of the following approaches.

Build lasting brand authority in five steps: target trusted publications, create citation-worthy assets, launch digital PR, nurture journalist relationships, then measure and refine your approach.
- Data-led PR campaigns: We begin with what journalists and their readers care about, not simply what we find interesting. We review local news, Google News, and current coverage to understand which subjects are gaining attention. By considering journalist intent from the start, we improve our chances of receiving responses and earning placements.
- Newsjacking or reactive PR: When we can move quickly, we contribute expert opinions, data, or commentary to breaking stories that relate to our brand. This gives journalists material they can use while the topic is still timely.
- Proactive PR: We anticipate trends before they break and prepare insights around recurring news cycles, holidays, and other relevant media moments.
- Contributed content and guest features: We place useful content written by our experts in relevant publications, allowing us to speak directly to established audiences and earn recognition.
When we combine these tactics effectively, we can elevate our brand to a level that competitors cannot reproduce with a batch of low-value links.

We build relationships with journalists and publishers

We know that even fascinating proprietary data, packaged in an expertly designed analysis, can fail if our journalist outreach is poorly targeted.

Journalists receive an enormous number of PR pitches, and those messages can either support or obstruct their work. According to a 2026 Muck Rack study, nearly nine in 10 journalists said at least some of their stories originated with PR pitches.

The same survey found that 54% of journalists seldom or never responded to most pitches. Relevance was a central problem: nearly half said a genuinely relevant pitch was rare.

If we send a journalist at an economics publication a pitch about music-listening habits, we should expect a rejection because the subject may matter to only a small part of that publication’s audience. We do not take that response personally. Journalists build their careers around particular topics and beats, and our job is to support that work rather than distract from it.

We therefore approach outreach as relationship building: a two-way exchange that should benefit everyone involved. Above all, we remember that there is a real person on the other side of every email.
- We personalize our emails and explain why a story fits the journalist’s audience.
- We respond graciously when a journalist says no because our next idea may be a better fit.
- We share relevant work from journalists and publications through social media.
- We contribute thoughtful comments when we have something useful to add.
- We cite journalists’ reporting in future content when it genuinely supports our work.
As we strengthen these relationships, journalists become more likely to consider future opportunities. A thoughtful follow-up or second pitch can receive a warmer response when a reporter already knows that we provide reliable data and useful commentary.

PR relationships grow over time. Even when our first pitch does not fit a journalist’s beat, we remain willing to return with a better story or a new set of relevant data.

How we measure real brand authority

We recognize that authority, trust, and legitimacy feel less concrete than traffic or keyword position. Yet they have become more important. A traffic surge may look encouraging while reflecting temporary attention, weak intent, or an advantage that disappears after an algorithm update.

Authority and legitimacy are more durable. We can also measure the impact of credibility-focused work through several meaningful indicators.

EZ Contacts’ six-month digital PR campaign delivered 1,000+ media placements, raised Domain Rating from 40 to 43, and doubled visibility across ChatGPT and Google AI Overviews.
- Earned media placements: We track the publications that cover our brand, including coverage containing an unlinked mention. These placements help us assess brand credibility.
- Branded search volume: We monitor whether more people search for our company or products after discovering us through media coverage.
- Industry coverage: We look for the point at which publications we have not contacted begin citing our work. That organic pickup is a valuable sign that our authority is spreading.
- Conversions: We measure whether greater credibility leads more people to trust our organization, products, or services and ultimately take meaningful action.
- Organic ranking improvements for target keywords: We still review rankings, but we treat them as one indicator within a broader picture. Sustained movement can show that search engines increasingly view us as a credible result relative to competing pages.
We do not expect these indicators to appear overnight.
- We invest real effort in creating proprietary data.
- We build trust with journalists through repeated, useful interactions.
- We grow authority through sustained work over time.
Our advice is simple: we stay patient, keep improving, and allow credible results to compound.

How we build a credibility-focused link strategy

Knowing the principles of SEO authority is one thing; building an entire campaign around them is another. We use the following five-step process to turn those principles into consistent action.
1. Step 1 — We define our target publications: We identify five to 10 publications that our audience trusts and that search engines are likely to recognize as authoritative within our field. These become our priority coverage targets.
2. Step 2 — We develop linkable assets: We create at least two content or media assets designed around the interests of those publications. We may use original survey data, visual guides, proprietary analysis, or expert thought leadership.
3. Step 3 — We launch a digital PR campaign: We proactively pitch our assets to relevant publications. We can also use platforms such as Connectively or Muck Rack to identify ongoing opportunities with writers covering subjects related to our research.
4. Step 4 — We nurture relationships: We treat every positive media interaction as the beginning of a longer relationship. We follow up with useful information, engage with published coverage, and build the kind of rapport a journalist can rely on.
5. Step 5 — We measure and iterate: We review our authority indicators each quarter, learn from the response to our campaigns, and adjust our content and outreach accordingly.
We know this process can consume a team’s time, particularly when resources or specialized expertise are limited.

In those situations, we may benefit from working with a link-building and digital PR specialist who can expand our capacity and keep pace with search changes. The right support can help us establish sustainable visibility without allowing every minor ranking fluctuation to pull us off course.

How we build authority that lasts at Resolve

We know that quality usually stands the test of time better than quantity. The difficult part is maintaining that focus when competitors appear to be winning with sudden traffic spikes or eye-catching vanity metrics.

We do not let temporary numbers distract us from the larger goal. We focus on lasting authority and legitimacy earned through sustained content creation, thoughtful PR outreach, and genuine relationship building.

When an internal team lacks the time or patience required to maintain that effort, we can step in.

At Resolve, we work with brands to build credibility-focused SEO campaigns through linkable content, data-led digital PR, and hands-on link building. Our goal is sustainable organic growth, not a burst of visibility that disappears after the next algorithm update.

We have seen this approach pay off. In a recent data-led campaign for EZ Contacts, we earned more than 1,000 placements in outlets including the New York Post and Yahoo. As the coverage grew, the brand’s visibility in ChatGPT and Google’s AI Overviews doubled. That is the kind of durable growth we want to build — growth that extends beyond the next algorithm update.

A bold Google logo sits atop layered, colorful digital documents, evoking the fast-moving world of search marketing, ad formats, campaign assets, and platform updates.

When we are ready to build links that last, we can visit growresolve.com to learn more.

Our answers to common link-building questions

How do we distinguish link building from digital PR?

We see considerable overlap between link building and digital PR, but we do not treat them as identical. Link building is the broader practice of acquiring backlinks from other websites to improve search authority. Digital PR is a particular approach within that practice, focused on earning links through media coverage, journalist relationships, and placements in credible publications rather than relying on directory submissions, guest-post exchanges, or other lower-authority tactics.

We often use digital PR to pursue the strongest editorial backlinks because reputable outlets have real audiences and established review standards. At the same time, this work builds brand visibility and consumer trust in ways that many conventional link-building methods do not.

How long do we wait for meaningful results?

We do not expect authoritative backlinks or earned media coverage to produce results overnight. That is an honest trade-off when we choose a credibility-focused approach instead of more aggressive tactics. Most brands can begin seeing meaningful domain-authority gains and early ranking movement after three to six months of consistent execution, while highly competitive keywords and top-tier placements may require more time.

The advantage is that our results can compound. Links from credible publications tend to endure, strong journalist relationships can create repeat opportunities, and the authority generated through consistent coverage can keep delivering value long after the initial campaign.

What do we mean by an authority backlink?

We define an authority backlink as a link from a source that search engines and its audience regard as credible and trustworthy. These sources typically have genuine readers, clear editorial processes, established authority, and topical relevance to our industry.

A regular backlink can come from any website willing to link to us, regardless of its relevance, quality, or editorial standards. That distinction matters because search engines do not evaluate every link equally. One editorial link from a respected industry publication can be more valuable than dozens of links from low-authority sites, while also supporting the kind of E-E-A-T credibility that bulk link acquisition cannot reproduce.

Do we value brand mentions without hyperlinks?

Yes. We recognize that Google can associate brand mentions with entities even when a publication does not include a hyperlink. Relevant, unlinked mentions in credible coverage can still contribute to the wider authority signals surrounding our brand.

That is why we consider digital PR valuable even when every placement does not produce a direct link. A credibility-focused off-page strategy should not be reduced to backlink acquisition alone. Our larger objective is to build a brand that respected publications genuinely want to mention, cite, and cover.

What risks do we face with outdated link-building tactics?

We see risks ranging from wasted effort to serious search penalties. Link buying, reciprocal-link schemes, private blog networks, and manipulative anchor-text optimization can violate Google’s spam policies. These tactics may trigger manual actions or algorithmic suppression that substantially reduces our search visibility.

Even when outdated tactics do not produce an immediate penalty, they can lose their value as search systems become better at identifying manufactured signals. Recovering from a link-related penalty can be slow and expensive. We would rather invest in credible link building from the beginning than repair the damage caused by shortcuts later.

Inspired by this post on Search Engine Land.
July 10, 2026
How I Turn Search Console Data Into SEO Wins With AI

I rely on Google Search Console because it is excellent at collecting search data. The challenge is that it still does not make interpretation easy.

When I open almost any property, I usually find thousands of queries, landing pages, impressions, clicks, rankings, and click-through rates. That volume is useful, but it can quickly become overwhelming when I am trying to answer one simple question: what should I do next?

For years, my workflow was familiar: export the data into Excel or Google Sheets, build pivot tables, apply filters, and start digging for patterns. That approach works, but it is slow. More often than not, I am searching for insights without knowing exactly what I am looking for.

That is where AI makes the workflow more useful. I use it to speed up the hardest part of Search Console analysis: finding meaningful patterns hidden across thousands of rows of search data.

I think of Google Search Console as my source of truth and AI, whether ChatGPT or Claude, as the analyst sitting beside me. GSC shows me what happened. AI helps me explore why it happened, uncover opportunities I might miss, and organize messy data into decisions I can act on.

A quick note on regex

Most of the examples I use start in the same place inside Google Search Console: Performance → Queries → + Add Filter → Query → Custom (regex).

From there, I enter a regular expression to filter query data before exporting it for analysis.

The useful part is that I no longer need to memorize regex syntax. I can ask ChatGPT to write it for me. For example, I might prompt: Create a regex for Google Search Console that matches queries beginning with question words.

ChatGPT may return something like (?i)^(who|what|why|how|can|does|will|should)b.

If I need something more specific, I simply describe the pattern I want. I might ask for a regex that matches queries containing five or more words, identifies comparison searches, or finds branded queries that include product names.

The better I describe the pattern, the better the regex usually becomes.

Here are seven practical ways I combine Google Search Console with AI so I can spend less time sifting through data and more time making decisions.

1. I stop looking only at queries and start looking at intent

Most Search Console analysis still happens at the keyword level. The problem is that people do not really search by keyword. They search with intent.

Instead of reviewing thousands of individual queries one by one, I use regex to isolate investigation-focused searches before exporting the data.

One useful regex is (?i)^(best|top|vs|review|reviews|compare|comparison).

After exporting the filtered query data, I ask Claude or ChatGPT to classify intent. My prompt is usually something like: Categorize these queries into informational, navigational, investigation, transactional, and local intent. Return a CSV with classifications and confidence scores.

This helps me spot patterns that are difficult to see keyword by keyword. Informational traffic may be growing while commercial investigation queries are declining. Transactional queries may rank well but earn weak click-through rates. Comparison searches may be driving impressions without having dedicated content to support them.

When I segment by intent, the next steps become much clearer.

2. I discover questions my audience is already asking

Question-based keyword research is not new, but AI helps me identify themes across hundreds of question-oriented searches much faster.

I start with a regex like (?i)^(who|what|where|when|why|how|can|does|should|will)b.

Then I export the results and ask Claude or ChatGPT: Group these questions into common themes and identify unanswered topics.

A Google Search Console query filter highlights how regex can narrow SEO performance data, helping marketers turn thousands of search terms into focused insights.

Instead of manually reviewing hundreds of questions, I can quickly see broader patterns around pricing concerns, product comparisons, implementation challenges, and industry-specific use cases.

This becomes more than a content exercise. I can use these themes to improve FAQs, support resources, sales enablement materials, and AI Overview optimization.

The best opportunities are often not hidden in one query. They are hidden in clusters of related questions.

3. I find queries likely to trigger AI Overviews

Google does not give me a filter for queries likely to trigger AI Overviews, but I can build a useful approximation.

I start by isolating common informational and comparison patterns with a regex like (?i)^(what is|how to|best|vs|difference between|guide to).

Then I export the matching queries and ask Claude or ChatGPT: Review these queries and group them by the content format needed to answer them effectively.

The themes often fall into definitions, tutorials, comparisons, or expert recommendations.

This helps me see where my content may need to shift from simply ranking for keywords to becoming the best available answer. Increasingly, those are not always the same thing.

4. I track emerging trends earlier

Traditional keyword research can be reactive. By the time a trend is obvious in keyword tools, competitors may already be building content around it.

Google Search Console can help me identify shifts earlier, as long as I know how to look for them.

Instead of searching for individual keywords, I use ChatGPT to build regex around broader concepts. For example, I might prompt: Create a Google Search Console regex to identify searches related to AI agents, copilots, assistants, automation, and autonomous workflows.

The output may look like (?i)(ai agent|agentic|copilot|assistant|automation).

This same approach works for new technologies, product categories, competitors, industry buzzwords, and changing customer concerns.

Once I filter and export the data, I let AI look for emerging themes. A prompt I like is: Review these queries and identify emerging themes, new terminology, and shifts in search behavior. Highlight which topics appear to be gaining traction, recommend whether they deserve a new content asset or an update to an existing page, and identify any patterns that could influence our content strategy.

Instead of only confirming that a trend exists, AI helps me decide whether the trend is meaningful enough to act on and what the next move should be.

5. I surface conversion intent inside informational traffic

One of the most overlooked opportunities in Search Console is finding bottom-of-funnel signals inside queries that appear informational at first glance.

I might ask ChatGPT: Create a regex for searches that indicate evaluation, comparison, pricing, alternatives, migration, implementation, or vendor selection intent.

An example output is (?i)(cost|pricing|price|vs|alternative|compare|implementation|migration).

I apply that regex to the query report, export the filtered data, and then ask Claude or ChatGPT to analyze it.

My prompt usually looks like this: Review these Google Search Console queries and identify recurring buying signals. Group them into themes such as pricing, comparisons, implementation, and vendor evaluation. Recommend which existing pages should better address this intent, and identify opportunities to improve content through stronger CTAs, internal links, comparison tables, FAQs, or supporting resources.

A visual metaphor for AI turning messy Google Search Console queries into clear SEO decisions, separating qualified intent from irrelevant traffic signals.

I often find that pages created for top-of-funnel education are already attracting visitors who are evaluating solutions. In that case, the best opportunity may not be creating a new page. It may be improving the page that already earns the visit, so users can take the next step without breaking the informational experience.

Sometimes the biggest content opportunity is recognizing the conversion intent already reaching the pages I have.

6. I find audience-specific opportunities

One of my favorite ways to uncover new content opportunities is filtering queries by industry, audience, or customer segment. It quickly shows me whether my content is resonating with the audiences I intended to reach or revealing opportunities I had not considered.

I start by asking ChatGPT to create a regex based on the audience segments that matter most to the business.

For example, I might prompt: Create a Google Search Console regex that identifies queries related to healthcare, manufacturing, retail, education, financial services, government, and nonprofit organizations.

An example output is (?i)(healthcare|hospital|medical|manufacturing|factory|retail|education|school|financial|bank|government|public sector|nonprofit).

After applying the filter and exporting the results, I ask Claude or ChatGPT: Analyze these queries and group them by audience segment. Identify which industries show the strongest search demand, what recurring questions or pain points each audience has, and recommend opportunities for new content, landing pages, case studies, or internal linking that would better serve those audiences.

The differences can be valuable. Healthcare searches may consistently focus on compliance, while manufacturing queries may revolve around implementation. Retail searches may reveal entirely different use cases than financial services searches.

7. I uncover striking-distance opportunities at scale

Every SEO knows the classic advice: look at keywords ranking in positions 5-15 to identify opportunities within striking distance.

The challenge is doing that at scale. A report with hundreds of queries where a site is close to stronger rankings can become overwhelming fast.

I take the regex patterns above a step further. I apply the filters that match my goals, then narrow the report to positions 5-15 before exporting the queries.

Then I ask my AI analyst: Identify recurring themes across these queries and recommend page-level optimizations rather than keyword-level optimizations.

Instead of getting tiny recommendations for individual keywords, I often uncover larger opportunities. A page may be missing subtopics, comparison details, stronger internal links, or use cases that would make it more complete.

The result is usually fewer optimizations, but more meaningful ones.

Turning Search Console data into decisions

As an SEO, I do not have a data shortage. I have a prioritization problem.

Google Search Console remains one of the richest sources of insight into how people discover a business. The difficult part is turning thousands of rows into something actionable.

That is where AI fits into my workflow. It helps me uncover patterns, organize information, and surface opportunities I might otherwise miss. It is not a replacement for SEO strategy, experience, or critical thinking.

The real advantage is not writing better regex or exporting cleaner spreadsheets. It is spending less time searching for insights and more time acting on them.

Because data does not improve SEO. Better decisions do.

Inspired by this post on Search Engine Land.

July 9, 2026
Submit Your SMX Next Pitch and Share Bold Search Ideas

SMX Next returns online Nov. 18, and I’m excited to help shape a program focused on today’s complex search landscape and the tactics that will define success in 2027 and beyond.

Search marketing isn’t just changing. From my perspective, it has become an entirely new kind of challenge, and that is exactly why fresh voices and practical expertise matter so much right now.

In SEO, I’m seeing the field shift toward AI Overviews, search everywhere optimization, and the rise of autonomous AI agents that browse on behalf of users. Trustworthiness, digital authority, and precise alignment with user intent are no longer nice-to-have ideas. They are becoming essential.

On the PPC side, generative AI and deep automation are creating new levels of personalization. At the same time, they are raising urgent questions for marketers: How do we keep strategic control, protect data privacy, and avoid wasted spend?

If you’re an enthusiastic search marketer with a passion for sharing what you know, I hope you’ll consider submitting a session pitch for SMX Next. I’m looking for subject matter experts who can share insights, strategies, and tactics that help SEO and PPC marketers thrive in 2027.

Whether you’ve been speaking for years or you’re a practitioner ready to share something new you’ve developed, I want to hear from you. I’m especially interested in new speakers with diverse points of view and real-world experience.

The deadline for SMX Next pitches is Aug. 7.

When I review session proposals, I’m looking for ideas that feel original, specific, and useful. Advanced, forward-thinking topics or unique frameworks that aren’t already common at other search events will stand out.

I also want to see actionability. Be clear about what attendees will be able to do better, faster, or differently after your session.

Bring the data whenever you can. A case study, concrete example, or tested approach makes your pitch stronger, especially when you explain how the lesson can scale across different types of organizations.

Keep the scope focused. A 30-minute session works best when it goes deep on a narrow or specialized topic instead of trying to cover too much at once.

Most importantly, give attendees something tangible to take with them. I’m looking for sessions that leave people with a clear action plan, framework, or process they can put to work right away.

Visit this page for more details on how to submit a session idea, or go directly to this page to create your profile and submit your pitch.

If you have questions, feel free to contact me directly at kathy.bushman@semrush.com. I’m looking forward to reading your proposals!

Inspired by this post on Search Engine Land.

July 3, 2026
Why I’m Making TikTok Part of My SEO Strategy

I see TikTok becoming harder to ignore in SEO because discovery no longer happens in one clean path. Someone might find a restaurant on TikTok, verify it through Google Reviews, check Reddit for honest opinions, scan the menu on the business website, and then book a table. Someone else might take those same steps in a completely different order.

Nearly half of U.S. consumers used TikTok as a search engine in 2026, up from 41% in 2024, according to Adobe survey data. What stands out to me is why people search there: short-form video, storytelling, interactivity, tutorials, product reviews, personal stories, and influencer recommendations all make the platform feel more immediate than a traditional results page.

I also think TikTok recent updates show how seriously the platform wants to be part of the search journey. Many purchase decisions are visual, social, emotional, and trust-driven, which is exactly where TikTok has strength. With Local Feed, AI summaries, creator reviews, and shopping features, TikTok is trying to meet people at the moment they are exploring, comparing, and deciding.

So instead of asking whether TikTok is a traditional search engine, I ask a more useful question: how do I make sure people can find, understand, trust, and choose a brand wherever their search journey begins? More often than many marketers want to admit, that starting point may be TikTok.

TikTok SEO Is More Than Hashtags Now

I think of TikTok SEO much like traditional SEO: it is the work of making a business, place, product, service, or experience easier to discover. As TikTok has evolved, the discovery surfaces have expanded far beyond captions and hashtags.

In the past, I mostly associated TikTok optimization with captions, hashtags, trending sounds, posting times, and the hope that a video would land on the For You feed. Those pieces still matter, but they are no longer the full picture.

Today, I have to think about TikTok Search, recommendations, Local Feed, Places, reviews, comments, creator content, visual cues, product signals, and AI-assisted discovery. A stronger TikTok SEO strategy now includes search query relevance, spoken topic clarity, on-screen text, captions, hashtags, location context, creator reviews, comments, product visuals, and the searches people make after seeing a video.

TikTok documentation says search results can be shaped by how well content matches a query, along with hashtags, sounds, user interactions, language, and location. The For You feed also weighs user interactions, content information, user information, and watch behavior, which means usefulness and engagement both matter.

Local Feed Creates a New Discovery Surface

TikTok launched Local Feed in the U.S. on Feb. 11 as a home-screen tab for nearby content related to travel, events, restaurants, shopping, small businesses, and local creators. TikTok says posts can appear based on location, topic, and when the content was published.

I see Local Feed as another organic discovery touchpoint, especially for local businesses. A restaurant can appear while someone is deciding where to eat nearby. A wellness club can show up when someone is looking for weekend plans. A venue can answer practical before-you-go questions before a guest ever reaches the box office.

There are limits I would keep in mind. TikTok precise location setting is optional, off by default, available only for users 18 and older, and still rolling out across the U.S. TikTok also says private accounts, accounts for users under 18, and posts limited to Friends or Only You will not appear in Local Feed.

Local Explorer Shows TikTok Is Investing in Places

TikTok Local Explorer Program is one of the clearest signs I have seen that the platform wants to build stronger place-based discovery. The program encourages people to submit location-based reviews and rewards participation with experience points, levels, badges, community access, and other perks.

I would not assume every market has the same access or level of activity, because availability has been limited and uneven by region. Still, the direction matters: TikTok is building more ways for users to evaluate places inside the app.

I have also seen TikTok incentivize reviews for places that do not already have TikTok reviews. In one example, a coffee shop had no TikTok reviews, and I was offered a $1 Promote coupon to leave one.

When a place does not have native TikTok reviews, I have seen TikTok pull reviews from TripAdvisor and, in some cases, Google. That makes the Places tab a useful comparison surface where people can evaluate reviews, videos, and comments before deciding whether to visit a local business.

Visual Search Links Matter More Than Exact Keywords

TikTok increasingly adds automated search links and related query prompts beneath videos. I pay attention to these because they show how TikTok can connect a video to a broader topic, place, or product discovery path.

For example, a video about a place like Glen Ivy may show a search bar at the bottom that lets users explore more related content. Those search bars can appear even when a creator has not overloaded the description with exact-match keywords, which tells me TikTok is reading more than just captions.

TikTok Shop Turns Discovery Into Buying

With TikTok Shop, someone can see a product in a video, search for it, compare it through comments and creator content, and buy it without leaving the app. That makes TikTok more than a discovery channel for ecommerce brands; it can become part of the full purchase path.

I would optimize TikTok Shop content around the information TikTok needs to understand a product. Search relies heavily on how well a shopper query matches product information such as titles, categories, attributes, and content context.

TikTok Shop has also released Shoppable Photos in beta for select sellers. Eligible sellers can create image-based posts, include multiple photos, and tag products directly in the post. These posts may appear in the For You feed, Search, and the Shop tab, giving sellers a simpler way to showcase inventory without producing a full video.

AI Is Becoming Part of TikTok Discovery

I am also watching TikTok AI-assisted discovery features closely, even though availability varies by market, account, and test. Features such as Tako, AI Overviews, Quick Highlights, AI summaries, and Content Studio all point in the same direction: TikTok wants to help users search, summarize, and create faster.

Tako is TikTok chatbot, and it lets users search in a way that feels similar to using the app search bar. It can surface relevant TikTok videos and external sources, including articles.

TikTok also now offers AI Overviews for some searches. When users search a topic, they may see an AI-generated summary of the results. If they click a visual search bar, they may also see Quick Highlights that summarize that search experience.

The Places tab includes AI summaries too, and users can see how many posts were used to generate a place summary. For local businesses, that makes the quality and clarity of creator posts, customer videos, and reviews even more important.

On the creator and seller side, TikTok AI tools can help generate captions, hashtags, and even videos. I would treat these tools as helpful support, not a substitute for real strategy, because features like Content Studio are still not available to everyone and remain in testing.

How I Would Improve Visibility on TikTok

On TikTok, visibility comes from what people search for, what TikTok can understand, and what the camera actually shows. That means I would focus less on cleverness and more on showing people what they need to see before they choose a business, product, or place.

For restaurants, I would show menu items, exterior signage, the dining room, takeout packaging, seasonal dishes, and neighborhood cues. Those visuals help both users and TikTok understand what the place offers and where it fits.

For retail, I would show product displays, packaging, try-ons, shelf layout, gift ideas, and the storefront. The more clearly a video communicates what is available, who it is for, and where someone can get it, the stronger the discovery signal becomes.

I would also build simple habits into every TikTok content workflow: use location context naturally, show products clearly, show the storefront or interior when relevant, mention the city or neighborhood when it helps, create timely content around local moments, tag the physical location when appropriate, and work with creators who already understand discovery-driven content.

Keyword Research

I would start TikTok keyword research inside the app because that is where the search behavior is happening. Seed topics might include best brunch, World Cup outfits, things to do in [location], wedding inspiration, or gluten-free bakery.

From there, I would search each phrase on TikTok, document autocomplete suggestions, review suggested filters, look for Others searched for prompts, study top videos, and pay close attention to comment themes. I would also test city and neighborhood modifiers, then compare TikTok findings with Google Search Console, Google autocomplete, Reddit, YouTube, and site search data.

TikTok Creator Search Insights can add another useful layer by showing personalized information about search topics, content gaps, and how content tied to searched topics is performing.

Keyword Placement

I would place the core topic where TikTok and viewers can recognize it quickly: in the first few seconds of the video, the first text overlay, the opening of the caption, relevant hashtags, location tags, pinned comments, reply videos, the profile bio, playlist names, and creator briefs.

Comments and Reviews

I would treat comments and reviews as visibility assets, not afterthoughts. That means pinning genuinely helpful comments, replying to repeated questions with videos, correcting misinformation when trust is at stake, watching for recurring objections, and turning repeated questions into FAQs, landing page content, Google Business Profile posts, and future videos.

A creator saying that a bakery is the best gluten-free option in Portland because it takes cross-contamination seriously may be more useful than a generic five-star review. That kind of specific language can shape website copy, FAQ strategy, and customer messaging.

Referral Traffic and Branded Search

I would track TikTok referral traffic and monitor branded searches over time. When a TikTok post performs well, I would annotate it and compare branded search trends against a baseline.

I would look for directional movement in branded clicks, branded impressions, TikTok referral traffic, Google Business Profile actions, and engagement on related pages. At the same time, I would avoid giving TikTok credit for every increase without considering PR, paid campaigns, email, promotions, seasonality, and other marketing activity.

Attribution may never be perfect, but imperfect measurement does not make TikTok influence meaningless. I would rather measure directional impact than ignore a channel that is clearly shaping discovery behavior.

I Would Explore TikTok Instead of Ignoring It

Someone may find a business on TikTok before they ever search for its name on Google or ChatGPT. Someone else may turn to TikTok midway through the journey to decide whether the business is worth the trip, the purchase, or the recommendation.

Either way, I believe TikTok has earned a meaningful role in modern SEO strategy. Between Local Feed, Places, Tako, AI summaries, creator reviews, and TikTok Shop, the platform keeps adding new ways for businesses to be discovered, and many of those opportunities are still underused.

Inspired by this post on Search Engine Land.

July 3, 2026
How I Use Google Query Expansion to Boost Visibility

LLMs have changed how people search and how Google responds. The SERP has not been limited to 10 blue links for a long time, but traditional search has usually centered on one core intent: the thing someone is trying to find.

Now, AI Overviews can create a full answer directly in the SERP. They do more than respond to the original query. They also bring in related terms, contextual refinements, and supporting information that help searchers make better decisions.

That is why I pay close attention to Google query expansion. When I understand how Google connects related searches, I can find visibility opportunities that competitors may miss.

What is Google query expansion?

I think of Google query expansion as Google broadening a searcher’s query so it can return more accurate results, especially for long-tail searches that might otherwise produce weak or limited results.

This can happen through synonyms. For example, Google may connect “budget” with “affordable” when the intent is similar.

It can also happen through intent expansion. Google may understand what my audience means even when they do not type the exact words I expected.

Related topic expansion matters too. Google can use similar searches and connected topics to surface content that supports the searcher’s broader need.

I do not use this as an excuse to stuff keywords into a page. Instead, I use query expansion as a research signal. When I see related searches that make sense, I can add useful supporting information and help my content rank for a wider range of relevant queries.

Here is a simple example. If I have an article about backyard chicken care and someone searches “What’s the average lifespan of a chicken?”, my page might appear even if I never used the word “lifespan.”

In that case, Google has decided the article is semantically relevant. Once I know Google has made that connection, I can add a helpful section about chicken lifespan. That gives the page a stronger chance to rank for the term and attract more traffic.

It can also improve the odds that my content appears in relevant AI Overviews.

The difference between Google query expansion and query fan-outs

Google query expansion and query fan-outs are related, but I do not treat them as the same thing.

Query expansion is part of traditional search. Google broadens a query with synonyms, related terms, and intent signals before results are generated. Because of that, my content can rank for searches I did not directly target.

Query fan-outs are part of AI Mode. They break a query into multiple related subqueries while the AI response is being generated. Because of that, my content can be retrieved as a source for an AI-generated answer.

So why does traditional query expansion still matter in a search world shaped by LLMs and AI Overviews?

Because the same semantic relationships that help Google expand a query can also influence which content AI systems retrieve during query fan-outs.

How I find query expansion opportunities

The first place I look is Google Search Console. It is one of the clearest ways to confirm whether query expansion is already happening for my site and my content.

My workflow is straightforward. I go to Performance > Search results, filter by a specific page, pull the full query list, and sort by impressions.

From there, I look for queries I never intentionally targeted. I pay attention to synonyms with meaningful impressions, question-based searches that may be especially useful for AI visibility, and broader keywords that are not currently addressed on the page.

I do not assume every discovered query deserves a content update. Sometimes a page appears for terms that are not truly relevant. When that happens, I audit the page and make sure the content is not drifting into unrelated topics that fail to match the promise of the SERP result.

How I plan better content with query expansion

Once I understand which expanded queries Google is connecting to my content, I use that data to strengthen the page instead of chasing isolated keywords.

I write for topic coverage

For a long time, strong SEO has been less about exact keywords and more about semantic relevance. I try to build coverage around subtopics, related questions, and adjacent ideas because that gives Google more context than a page built around one keyword alone.

I answer questions adjacent to the main topic

For example, if I am working on content for a company that sells chicken feed, I would not only explain the feed itself. I would also consider why the right balance matters and how the right feed can support chicken health.

I can find those adjacent questions by reviewing query expansion data in Google Search Console, checking tools like Ahrefs, and studying the SERP to see what supporting information Google is already surfacing for the topic.

I use expansion data to find content gaps

If Google Search Console shows that Google is pulling my page for a query I have not planned for, and that query is genuinely relevant, I treat it as a signal that the page may need more complete coverage.

Sometimes query expansion data includes odd or unrelated searches. I ignore those. But when I find adjacent queries that clearly strengthen the topic, I add them to the page in a useful and natural way.

I also revisit content regularly, usually at least once a quarter. New queries can appear, while others fade away. Since I am already keeping content fresh for the SERP, query expansion gives me another practical way to make each topic stronger.

How I use query expansion to improve AI Overviews visibility

AI Overviews often pull from ranking pages on a topic to build a more complete answer. Those answers can include semantic connections and supporting subtopics, not just the exact phrase someone searched.

That is why I cross-reference my query expansion data with the main keyword in the SERP. If an AI Overview includes supporting topics that are relevant to my page, I consider adding those topics to the content.

For example, I followed this process for a blog post titled “Tandem vs. Spread Axles in Trucking.” After filtering by impressions, I found that the page appeared for “tandem truck meaning,” even though that exact phrase was not specifically included in the content.

The page ranked first, but it was not included in the AI Overview for that specific query. That told me there was an opportunity.

Because the page already ranked well, I could use the expanded query and the supporting information in the SERP to create a section that better addressed both the query expansion term and the query fan-out patterns behind the AI Overview.

That is the value of this process. Query expansions can reveal supporting topics that strengthen traditional search visibility and improve the chances of being included in AI-driven results.

How query expansion helps my SEO strategy evolve

I use query expansion as a practical way to identify supporting topics and expand content coverage across search experiences.

As clicks become harder to earn, I want my content to appear across more relevant search moments. Broader visibility can strengthen brand awareness, support AI visibility, and keep my content in front of the people most likely to need it.

Inspired by this post on Search Engine Land.

July 3, 2026
Why B2B Brands Rank But Vanish From AI Overviews

I’m seeing a sharp disconnect in B2B search visibility: many brands still rank for thousands of Google keywords, but they appear in only about 3% of AI-generated answers, according to Walker Sands’ B2B AI Search Visibility Benchmark of 828 enterprise companies. (Disclosure: I’m the director of SEO and GEO at Walker Sands.)

For this benchmark, I looked at more than 45 million search queries from March across 828 enterprise B2B companies in 14 industries. The analysis evaluated each domain across four core metrics: keyword coverage, keywords with AI Overviews, AI Overview incidence, and citation inclusion rate.

Keyword coverage measures how many keywords a company ranks for in Google. Keywords with AI Overviews shows how many of those ranking keywords trigger AI-generated responses. AI Overview incidence captures the percentage of ranking keywords where AI Overviews appear. Citation inclusion rate measures how often a company’s domain is cited inside those AI-generated answers.

Together, these metrics give me a baseline for understanding how often AI Overviews show up and how often B2B brands actually earn visibility within them.

A baseline for B2B AI search visibility

The benchmark shows a meaningful gap between traditional ranking visibility and AI citation visibility. AI Overviews appear in about 50% of search results where enterprise B2B brands rank, yet the median enterprise B2B brand is cited in just 3% of relevant AI Overviews.

I also found that 4.6% of enterprise B2B companies are not cited in AI Overviews for any of their relevant keywords. That may sound like a small share of the market, but it points to a serious visibility problem for brands that still appear in Google’s organic results while disappearing from the AI-generated answers buyers increasingly see first.

The typical enterprise B2B company ranks organically for about 9,700 search queries, and AI Overviews appear in nearly half of those searches. But across all those opportunities, the median brand earns citations in only 3% of AI Overviews.

In other words, I’m seeing B2B brands present in the search results that AI Overviews summarize, but largely absent from the summaries themselves.

When a brand has few or no citations, I often see deeper issues underneath: limited topical authority, unstructured or inaccessible content, and too little content that directly answers the questions buyers are asking.

Addressing those gaps is becoming essential for visibility in AI-driven search experiences.

The narrowing funnel from ranking to citation

I think of AI search performance as a funnel with four layers, and the value lost at each step is where the story gets clearer.

It starts with keyword coverage, or the number of keywords where a brand ranks in Google’s top 100 organic results. On that measure, many leaders still look strong. The median company ranks for about 9,700 keywords, while top-quartile brands rank for more than 37,000.

The next layer is keywords with AI Overviews. These are ranking keywords that trigger an AI Overview. The median company has roughly 4,500 of them, which is already less than half of its ranking footprint.

The third layer is AI Overview incidence, which measures how often AI-generated answers appear across a brand’s relevant searches. The median is 48.8%, meaning AI now intercepts roughly half the queries where these companies compete. Top-quartile brands operate in even more AI-heavy environments, with an incidence rate of 61.7%.

The final layer is the one that matters most, and it is where almost everyone loses ground: citation inclusion rate. This measures how often a brand is cited as a source within an AI Overview. The median is 3.0%. Even the top quartile reaches only 4.5%, while the bottom quartile sits at 1.7%.

Viewed from top to bottom, the funnel is unforgiving. Tens of thousands of ranking keywords compress into a single-digit share of AI citations. Much of the visibility B2B brands have built through organic search does not carry into the layer of search that is shaping buyers’ first impressions of a category.

Ranking breadth does not guarantee AI citations

The most important takeaway is also the most counterintuitive: ranking breadth alone does not predict AI citation rates.

I found that some companies rank for thousands of keywords but rarely surface in AI-generated answers. The strengths that helped brands win traditional SERP visibility, including page volume, broad keyword targeting, and years of accumulated domain authority, do not automatically make a brand the source an AI system chooses to cite.

That creates a real challenge for B2B SEO teams. If a dashboard only tracks ranking keywords and estimated organic traffic, it may tell a flattering story about a layer of search that is losing influence while saying little about the AI layer that is gaining it.

The brands that are consistently cited in AI-generated answers tend to share three traits: deep topical authority across related content areas, clear and structured explanations that directly answer buyer questions, and consistent coverage across multiple relevant pages.

The common thread is specificity. Generative systems appear to reward content that resolves a buyer’s question clearly and demonstrates sustained expertise on a topic, instead of content that simply ranks for a query.

That changes the work. Optimizing for AI citations looks less like chasing keyword volume and more like building genuine, well-structured subject-matter depth.

Some industries are far more exposed than others

AI search visibility is not distributed evenly across B2B technology. The industry breakdown shows very different competitive dynamics depending on the category.

Cybersecurity leads on both fronts. AI Overviews appear in a median of 59.9% of cybersecurity-related searches, and cybersecurity brands earn the highest median citation rate in the study at 4.2%. Enterprise software, with 55.3% AI Overview incidence, and martech, with 56.3%, also see AI-generated answers in well over half of relevant queries.

At the other end, professional services and distribution and logistics trail in citations, both with a median rate of just 2.1%. Distribution and logistics also has the lowest AI Overview incidence at 29.6%, meaning buyers in that category encounter AI-generated summaries far less often than buyers in cybersecurity.

These differences create both risks and opportunities. In categories where AI-generated answers are already common, such as cybersecurity, the cost of being invisible is immediate. Buyers are forming impressions inside AI summaries right now.

In categories where citation rates are low and few brands have figured out the new mechanics, I see a real first-mover opportunity. Brands that learn how to earn citations before competitors do can help shape how an entire category is framed in AI-generated answers, much like early SEO adopters captured outsized organic visibility.

The brands that have gone completely dark

The most striking number in the report is that 4.6% of enterprise B2B companies are not cited at all in AI-generated answers for their relevant keywords.

These are not small, unknown operations. They are companies with $100 million or more in revenue that, in many cases, still rank well in traditional search. They are present in the index but absent from the answer.

Near-zero citation rates usually point to deeper structural issues: thin topical authority, content that is difficult for systems to parse, and a lack of material that directly answers the questions buyers are asking.

For a small but meaningful slice of the market, AI search is not just a place where they are losing share. It is a place where they barely exist.

What this means for B2B search teams

The benchmark gives me a baseline, but the strategic implications for SEO, GEO, and marketing teams are already clear.

First, measurement has to evolve. Citation inclusion rate is now a distinct KPI from ranking. Teams that cannot see whether their content is being cited in AI-generated answers are missing visibility into one of the fastest-growing parts of the funnel. Knowing your own citation rate, and comparing it with the 3% median and 4.5% top-quartile benchmarks, is a practical starting point.

Second, the content mandate is shifting from breadth to depth. The drivers point toward consolidating authority around the topics buyers care about, structuring content so machines can interpret it, and answering real questions directly instead of producing content volume for its own sake.

Third, the window is open but closing. Generative AI is expected to influence more than 75% of B2B search queries within the next one to two years. If that projection is even close, the median 3% citation rate is not a stable endpoint. It is a snapshot of an early, contested market that rewards brands that move now.

The uncomfortable truth is that much of the SEO equity B2B brands have built is being summarized by AI systems that do not cite the companies that created it. For most enterprise brands, I no longer see the central question as whether they rank. The question is whether they are in the answer at all.

The full H1 2026 B2B AI Search Visibility Benchmark is available from Walker Sands.

Inspired by this post on Search Engine Land.

July 3, 2026
AI Slop Accountability: Why Businesses Should Worry

The best and worst part of the web, in my view, is that I can share an opinion freely even when that opinion is not technically accurate.

But I keep wondering what happens when that freedom comes with real accountability, not only for what I say online, but also for whether the words came from me or from AI.

A recent report makes that question feel a lot less theoretical. A German court held Google accountable for AI Overview content, treating those AI-generated summaries as Google’s own content and rejecting the idea that users alone were responsible for fact-checking the results.

View embedded content

I want to unpack what that could mean for businesses, SEOs, and individuals who are leaning harder on AI every day.

The ‘disclaimer’ defense is cracking

For the last few years, I have seen nearly every AI platform rely on some version of the same warning: AI can make mistakes, so users should verify important information.

Most of us accepted that as the price of using these tools.

But the German court essentially said that a warning about possible errors does not automatically erase responsibility when those errors cause harm. If a system creates new claims that were never in the source material, those claims are no longer just someone else’s words. They become the platform’s words.

I think that shift is bigger than many people realize. This is where legal AI ramifications start to become very real.

Why? Because the conversation moves away from whether AI is useful and toward who owns the consequences when AI gets something wrong.

What this means for businesses

I see many companies rapidly adopting AI across content creation, customer service, product descriptions, reporting, legal reviews, hiring, and internal communications. In many cases, they are blindly trusting the output because the efficiency gains are so tempting.

Most of the conversation still centers on speed and cost. Can we create content faster? Can we answer support tickets more cheaply? Can we automate this process?

Those are fair questions. I ask them too.

But this ruling adds a more important question: Who is responsible when the output is wrong?

What happens if an AI-generated support response gives a customer inaccurate guidance? What happens if an AI-written article damages a competitor’s reputation? What happens if an AI-generated report includes fabricated information that influences a business decision?

I do not think the “AI wrote it” defense will age well. In my own experience, it darn near cost me 20 million.

The more we position AI as a trusted source of information, the harder it becomes to argue that we should not be accountable for what it says.

The situation is kinda funny…

The irony is that most AI vendors already know this.

That is why nearly every platform includes warnings, disclaimers, and usage policies.

At the same time, those same companies market AI as smarter, faster, more capable, and increasingly reliable.

I do not think you can tell users to trust the answer while also arguing that nobody should trust the answer.

At some point, those positions collide. We are already starting to see Google’s solution: an option to opt out of AI.

Germany may simply be one of the first courts willing to force Google, or any other LLM business, to take clearer responsibility for the systems it puts in front of users.

What SEOs should be paying attention to

Ironically, I think this ruling could end up benefiting everyone.

Right now, the debate is focused on whether AI companies should be responsible for the content their systems generate. But I can see accountability expanding well beyond AI.

The internet has spent decades creating distance between actions and consequences. Anonymous accounts, fake profiles, throwaway emails, and now AI-generated content all make it easier for people to say things without owning them.

That is why I find this ruling so interesting.

It is not just about Google. It is about the idea that “I did not write it” may no longer be enough.

The image below shows a real email that Russell and Nina Westbrook received. A real person sat behind a keyboard and sent a message hoping they would die in a car crash.

That is not free speech. It is hate speech.

The internet, especially now that AI is layered into it, needs more confidence that content is accurate and that the people and companies creating it can be held accountable.

I do not believe we get to claim the productivity gains when AI is right and then blame the algorithm when it is wrong.

This post first appeared on the author’s website and is republished here with permission.

Inspired by this post on Search Engine Land.

July 3, 2026
AI Search Trust Is Falling: What Marketers Must Fix

A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.

That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.

That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.

To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.

Here is what I believe the data means for 2026 search strategy.

Consumers are using AI more, but trusting it less

AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.

One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.

In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.

I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.

AI content volume has become a brand trust risk

In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.

If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.

Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.

That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.

Disclosure is now a consumer expectation

Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.

I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.

Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.

Google still leads on trust, especially for buying decisions

When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.

Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.

That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.

Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.

A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.

AI is changing marketing operations quickly

AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.

For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.

Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.

The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.

That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.

AI governance is still too weak

About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.

That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?

In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.

The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.

The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.

AI hallucinations are already a brand problem

A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.

More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.

If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.

When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.

Organic traffic is under pressure, not in freefall

Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.

The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.

The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.

That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.

Marketers are prioritizing the easiest tactics

Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.

Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.

But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.

Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.

In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.

As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.

The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.

GEO measurement is lagging behind execution

Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.

Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.

I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.

The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.

For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.

Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.

What I would do for a 2026 search strategy

First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.

Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.

Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.

Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.

Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.

Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.

Methodology

Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.

Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.

Inspired by this post on Search Engine Land.

July 3, 2026
AI and SEO Explained: What Marketers Need to Know Now
If it feels like the whole internet woke up and decided every sentence needed to start with “AI,” I get it. I feel that fatigue too.

As marketers, we are getting hit every day with LinkedIn hot takes, rushed prompt hacks, and promises that ChatGPT will either 10x our productivity or replace us completely.

And right in the middle of all of that is the digital marketer trying to figure out whether AI is just another buzzword cycle or the start of a major rewrite of how we handle content, SEO, PPC, reporting, and almost everything else.

So I want to break it down in plain English.

Think of this as my AI starting guide for marketers who are tired of needing someone younger to translate every new acronym, the same way many of us once had to help our parents get online or open an AOL chat window.

Defining AI and LLMs, and why they matter

I am not asking “what is AI” just to chase keyword density. I want to start with a shared definition, because a lot of these terms get used interchangeably, and not always correctly.

At its core, artificial intelligence refers to machines performing tasks that usually require human intelligence, such as understanding language, recognizing patterns, making decisions, and generating content.

The kind of AI getting the most attention right now is generative AI: models that can create text, images, code, video, and other outputs based on patterns learned from huge datasets.

Tools like ChatGPT, Gemini, and Claude do not “think” the way people do. They predict the next most likely word, phrase, or response based on what they have been trained on.

That matters because AI is not a magic shortcut to instant wealth, overnight automation, or effortless headcount reduction. I see it more as large-scale data aggregation and pattern recognition.

Large language models, or LLMs, are not creating net-new truth from nothing. They process massive amounts of existing information and produce answers based on patterns, probabilities, and what looks like internet consensus.

For content creators and marketers, that is a major shift. I am no longer thinking only about optimizing for a traditional search engine click. I also have to think about whether machines can understand, summarize, cite, and reuse my content.

The biggest implication is the rise of zero-click search. AI systems can answer users directly through experiences like Google AI Overviews or ChatGPT responses, often without sending that user to the original website.

That changes SEO from a pure traffic game into an authority, visibility, and data-ingestion game.

That is why I think marketers need to understand what AI does well, what it struggles with, and where it actually belongs in a broader marketing strategy.

AI jargon I think marketers need to know

Before going deeper, I want to separate a few terms that often get mashed together: AI, machine learning, NLP, generative AI, LLMs, and AI agents. They are related, but they are not the same thing.

Understanding the difference helps me make better decisions about which tools to use, where to trust them, and where human judgment still matters most.

Artificial intelligence (AI)

Artificial intelligence is the broad umbrella term for machines performing tasks that usually require human intelligence. That includes problem-solving, learning, speech recognition, language understanding, and decision-making.

In marketing and search, AI has become a catch-all phrase. But in practice, most of the tools I use fall into more specific categories.

Example of AI: Siri and Google Assistant use AI to interpret voice commands and respond in context.

Machine learning (ML)

Machine learning is a subset of AI. Instead of giving a system explicit instructions for every possible situation, we feed it data so it can identify patterns and make predictions.

In marketing, machine learning powers ad targeting, customer segmentation, recommendations, predictive analytics, and plenty of optimization systems we already rely on.

Example of machine learning: Netflix uses machine learning to recommend shows based on viewing history.

Natural language processing (NLP)

Natural language processing helps machines understand, interpret, and generate human language.

NLP is why ChatGPT can carry on a conversation and why Google can understand that “cheap running shoes” and “affordable sneakers” are closely related searches.

Example of natural language processing: Google Translate uses NLP to understand and convert language in real time.

Generative AI

When people casually say “AI,” they often mean generative AI, which is a branch of artificial intelligence that creates content instead of only analyzing existing data.

Generative AI models are trained on massive datasets to learn patterns in language, images, audio, code, or video. Then they use those patterns to produce something new.

But I always remind myself that these systems are still predicting likely outputs. They are not thinking, reasoning, or understanding the world like a person.

That is also why generative AI can go off track. When a model confidently makes something up, we call it a hallucination.

Some of the most infamous hallucination examples include AI answers suggesting people eat small rocks or use glue to keep cheese on pizza. Funny in hindsight, but a serious reminder that fact-checking is not optional.
- ChatGPT can draft articles, emails, and outlines.
- Midjourney and DALL·E can create images.
- Claude can help write and refine code.
- Sora can generate video from prompts.
Large language models (LLMs)

Large language models are a specialized type of generative AI trained on huge amounts of text, including books, websites, code, and other online sources, to generate human-like responses.

I think of LLMs as the engine behind many chatbot experiences. They are the part that interprets what I type and produces a response.

When I use an LLM effectively, I do not treat it like a replacement for my brain. I give it context, examples, constraints, and direction. It can help refine a draft, suggest wording, or organize messy thoughts, but I still own the strategy and final judgment.

In short, LLMs react to input. They do not act independently unless they are connected to tools and workflows that let them take action.
- GPT models from OpenAI, used in ChatGPT.
- Claude models from Anthropic.
- LLaMA models from Meta.
AI agents

AI agents go beyond responding to prompts. They can work through multi-step tasks, use tools, navigate websites, fill out forms, call APIs, analyze files, and complete workflows with less hand-holding.

They are still powered by LLMs under the hood, but the key difference is that they have goals, tools, and a degree of autonomy.

That is why AI agents feel more consequential for marketers. They are not just talking; they are beginning to do the work.
- ChatGPT can search the web, analyze files, and review code.
- Google Gemini in Workspace can summarize email threads and suggest replies.
- Microsoft Copilot can assist across Microsoft 365 workflows.
How I see AI affecting marketing today

Once the terminology is clearer, the marketing impact becomes easier to see. AI is changing how people search, how content is produced, how visibility is measured, and how stakeholders talk about growth.

People have been saying SEO is dying for years. I do not think SEO is dead, but I do think “SEO is changing” undersells the size of the shift.

We are in the middle of a major industry pivot, and AI is at the center of it.

Organic traffic is being cannibalized

AI Overviews are Google’s automated summaries that appear at the top of some search results, often pulling from multiple sources.

I think of them like Featured Snippets turned up several notches. They do not simply quote one source and send the click back. They blend sources, rewrite information in Google’s voice, and may push attribution lower on the page.

For broad informational queries, that means the first thing a user sees may be Google’s answer instead of my blue link. The likely result is a lower click-through rate and fewer visits to publisher and brand websites.

Before AI Overviews, informational queries were often useful for introducing a brand early in the research journey. Now, more of that attention and trust can stay with Google.

Claim: AI Overviews only appear for fluffy queries, so my traffic is safe.

Reality: Google is testing and expanding AI Overviews across more serious query types, including YMYL, product, and B2B searches.

What I would do next: Stop chasing every possible click, measure visibility and influence alongside conversions, and build enough topical authority that my brand becomes a source AI systems can confidently cite.

Content creation is exploding, and so is the noise

Generative AI has removed one of content marketing’s biggest bottlenecks: production time. Work that used to take a team a month can now be drafted by one marketer in a week.

That is not automatically bad. The problem is that when everyone can publish “good enough” content quickly, the internet gets louder and less useful.

Claim: More content means more traffic.

Reality: That was already questionable before AI. Now, search systems are increasingly tuned to reduce the visibility of generic, low-value, quickly produced content.

Google’s Helpful Content updates, Bing’s spam improvements, and social platform feed changes all point in the same direction: thin content is easier to produce, but it is also easier to ignore.

What I would do next: Focus on authority-driven content such as case studies, original data, expert analysis, and proprietary insights. I would publish less, promote more, and use AI for research, outlining, repurposing, and refreshing instead of simply flooding the web.

Search results are becoming deeply personalized

Traditional SEO has dealt with personalization for years through local results, logged-in history, and device context. LLM-powered tools like ChatGPT, Perplexity, and Gemini take that much further.

The same question can produce different answers depending on the user, their prompt, their past interactions, available data, and the model being used.

For example, if someone asks, “What is the outlook for Tesla?” a financial analyst may get an answer focused on stock performance and filings, while a new driver may see information about models, battery life, and charging infrastructure.

A Semrush Enterprise AI source analysis view tracks how often domains are cited in ChatGPT results, revealing shifting visibility trends across LinkedIn, Wikipedia, Semrush and industry publishers.

Claim: I will just optimize for the top answer in ChatGPT the way I optimize for position one in Google.

Reality: The idea of one universal top answer is breaking down. Personalization makes it harder to define, track, and reverse-engineer a single ranking position.

What I would do next: Track visibility across search engines and LLMs, build a recognizable brand entity, invest in multiple content formats, use structured data, and create clear, citable answers that machines can understand.

Attribution is breaking

When Google, Bing, Perplexity, or another AI-driven platform answers a question directly, users may never visit the website that influenced the answer. Even when they do visit, their journey may start in an AI tool, move through another search, and only later reach the site.

That breaks the clean channel → click → conversion model marketers have relied on for years.

Claim: I will measure traffic from LLMs directly in analytics.

Reality: That assumes users are clicking through from AI answers. In many cases, they are not.

A Semrush-style AI sentiment dashboard visualizes how brands appear across AI search, with mention trends, sentiment mix, and a competitive leaderboard.

What I would do next: Move beyond last-click attribution, pay more attention to assisted conversions, and track broader demand signals such as direct traffic, branded search volume, brand mentions, sentiment, and “How did you hear about us?” responses.

I would also budget for influence that is hard to perfectly track, including podcasts, PR, thought leadership, community visibility, and media coverage.

Clients and bosses expect magic

Because AI hype is everywhere, stakeholders often expect it to make everything faster, cheaper, and better without understanding the risks, learning curve, or human oversight involved.

Claim: We can replace our SEO or content team with AI tools and get the same results.

Reality: AI can accelerate tasks, but it does not replace strategy, judgment, subject-matter expertise, or a real understanding of customer needs.

What I would do next: Set expectations early. AI can make some work faster and cheaper, but it is not a push-button strategy. I would show stakeholders the hidden work behind good AI output, including prompt refinement, editing, fact-checking, compliance, and final review.

The best use of AI is not to remove human thinking. It is to free up more human time for the strategic work that actually moves the business forward.

Search is evolving

I am not interested in getting stuck in a debate over Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or any other acronym. The important point is simpler: search today is not what it was yesterday.

Organic visibility is no longer only about ranking in Google. Search now includes AI answers, YouTube, Reddit, newsletters, communities, social platforms, and every place people go to discover, compare, and validate information.

If I am only thinking about the traditional search bar, I am already behind. The better path is to build authority, create content worth citing, understand how AI systems interpret information, and measure visibility across the full discovery journey.

AI is not the end of SEO. It is a major shift in how search works, how content is consumed, and how brands earn trust. The marketers who adapt will be the ones who separate useful strategy from the noise.

Inspired by this post on Search Engine Land.
July 3, 2026