Google AI Overviews cited self-promotional “best” listicles while excluding the brands behind them from recommendations in 69% of cases, according to a new analysis of B2B software queries by Lily Ray.
Brands have used self-serving listicles to influence AI search results, but Ray found Google often cited those pages while recommending competitors instead.
By the numbers. Ray analyzed 100 B2B “best [category] software” queries in Google AI Overviews across three dates: April 15, May 15, and June 8.
Of the 80 prompts that triggered an AI Overview, self-promotional listicles were cited 323 times.
In 224 cases, Google cited a brand’s own page but didn’t recommend that brand.
Competitors get recommended. Ray documented several cases where Google cited a brand’s “best” listicle while recommending better-known competitors.
For “best LMS for selling courses,” Google cited Oasis LMS but did not recommend it. Instead, it recommended Kajabi, Thinkific, LearnWorlds, and Teachable — all of which are named in the Oasis LMS article.
Similar patterns appeared in queries for help desk, task management, survey, CRM, and SEO software.
Stronger brands still appeared. Brands that already led their categories, were widely mentioned by third-party sources, and had stronger link profiles were more likely to appear in AI Overview recommendations, according to Ray.
The data showed a consistent split between citations and recommendations. A brand’s page could appear as a source while competitors received the recommendation.
Organic visibility fell. Ray also reported organic search declines for many sites that relied heavily on self-promotional listicles.
The declines began around Jan. 20, across dozens of sites she analyzed. Many also scaled other SEO- and GEO-focused content formats, including AI-generated articles, comparison pages, and large volumes of “best” pages ranking their own brand first.
Those declines continued and accelerated during Google’s May 2026 core update, according to Ray.
Review sites gained citations. Ray found Google relied heavily on third-party and user-generated-content sites for “best” queries, with Reddit citations increasing sharply in recent months.
Forbes, Reddit, and YouTube were among the most-cited domains in AI Overview responses containing “best.”
Why we care. A citation is not a recommendation. Your content can appear in an AI answer while helping competitors capture the visibility that matters most.
Search Engine Land also reported that the tactic may create legal risk under the FTC’s Consumer Review Rule when company-controlled content is presented as independent reviews, reviews are not based on real use, or material relationships are not clearly disclosed.
About the data. Using Ahrefs Brand Radar, Ray collected AI Overview answer text and cited sources for 100 B2B “best [category] software” queries at three checkpoints between April and June. The analysis measured two outcomes: whether a self-promotional listicle was cited and whether the brand behind it was recommended.
Content operations can run on instinct at a small scale. With a strong editorial team, a handful of trusted writers, and an understanding of voice, there’s usually enough discipline to keep the calendar moving.
But some businesses aren’t built that way. For media rollups, large affiliate networks, entertainment properties, sports brands, and other content-led businesses, publishing at triple-digit volumes per day makes sense.
In some cases, it’s necessary to survive because content is the operating model rather than a marketing function, as it is in many B2B organizations.
At that scale, content strategies don’t break because of content. More often, they break because economics, systems, and editorial judgment stop speaking to each other.
That B2B distinction is important. If you sell a niche manufacturing ERP, you simply don’t need that scale of content. There’s not enough to publish. You’d be burning cash and operating outside the market.
Some categories have the depth and audience appetite required to sustain hundreds of daily articles. Sports is an obvious example. There are games, trades, injuries, recaps, rankings, interviews, opinion pieces, explainers, storylines, and the list goes on.
A business like The Athletic can support significant publishing volume because audience demand is real, while the revenue model includes subscriptions, direct sales, programmatic display, affiliate revenue, and likely other sources under the hood.
In Q2 2025, The Athletic generated $54 million in revenue, according to its last standalone financial report. Of that, 64% came from subscriptions, 26% from advertising, and 10% from affiliate and licensing revenue.
When most revenue comes from people actively choosing to pay, editorial quality is no longer a judgment call. It’s the most important commercial requirement. Economics, systems, and editorial judgment are forced to speak the same language.
Other models are more fragile. The clearest example is when monetization is driven primarily by programmatic display measured by RPM (say, more than 70% of revenue), with content rewritten from existing coverage or produced around short-term search and social opportunities, where margins require high output and very low production costs.
The formula is simple:
Revenue = (Pageviews ÷ 1,000) × RPM
Profit = ((Pageviews ÷ 1,000) × RPM) − Production Cost
So if a website earns 4,000 pageviews per article at a $16 RPM, it generates $64 in revenue.
Subtract production costs. The margin gets thin fast.
To generate meaningful profit, the organization has little choice but to publish hundreds of articles per day while doing everything it can to maintain quality, discoverability, and audience trust.
That’s where these content strategies break.
A content model that breaks under its own weight
More content can look like more revenue. But the spreadsheet tells only a fraction of the story.
Numbers don’t show editorial quality, whether thinner work is being produced to feed the machine, or whether monetization decisions are inadvertently weakening the asset.
Data surfaces where that drift starts. Points captured within a CMS include:
Content types.
Categories.
Tags.
Author and editor attribution.
Cross-referenced with sessions, pageviews, pageviews per session, session duration, RPM, source/medium, and other metrics.
That lets analysts drill into content types by source, category, and tag, while providing visibility into top performers, opportunities to optimize the ad stack by content type, and more.
Here are some simple scenarios that highlight what that looks like in practice:
An analyst runs a pivot table on an entertainment property and notices higher pageviews from Google Discover per article among list content in the reality television category tagged to a specific show. Since traffic equals more revenue, the conclusion is to write more lists about that show.
An analyst notices RPM is lower on features than lists, even though average word counts are the same. The reason is that the ad stack serves programmatic display after each image, and features have four times fewer images than lists. Since images drive higher RPM, the conclusion is to increase the number of images in features or reduce the number of published features in favor of more lists.
Fairly simple stuff on the surface. However, this is where judgment becomes the difference between a healthy operation and one that’s quietly eating itself.
Scaling these operations past 100 writers is mainly a question of whether the business has the systems, data, and judgment required to keep the operation from collapsing under its own volume.
It’s worth noting that 100 writers is rarely just 100 writers. For many of these businesses, it’s 100 writers across a dozen properties, which is actually more than 1,000 writers when you account for the full footprint.
Independent publishers don’t typically hit that scale because the infrastructure requires a level of investment they most likely don’t have access to.
That infrastructure includes clearly defined communication structures for editors, project management ownership, and comprehensive guides covering writing, linking, imagery, social, and CMS usage.
Without them, standards can degrade unpredictably across properties, and editors lose the ability to diagnose why or quickly point people toward resources when putting out fires.
On the data side, granularity is a must. Without consistent tagging and categorization built into the CMS from the start, analytics can become too fuzzy to act on.
Performance needs to be attributable at every level, rolled up into a P&L for each property, and then rolled up again across the conglomerate.
Technical infrastructure is essential as well, often in ways editorial teams wouldn’t expect.
If you consider how to get images into Google Discover, for example, it requires CDN delivery within specific guidelines. That’s more of an engineering problem than an editorial one. User roles and permissions across CMS and revenue dashboards are another example, along with the development resources required to implement the CMS architecture needed for data capture and reporting in the first place.
Proprietary systems can also be beneficial depending on a business’s scale. If you’re a rollup with a dozen properties operating on one or two CMS templates, it’s much easier to make bulk optimizations or accelerate the integration of newly acquired properties.
Channel distribution isn’t static either. Platform value to publishers shifts. Think about when Facebook stopped sharing news links in Canada. It changes the economics of whether a platform is worth optimizing for. Consistent monitoring and testing need to be built in.
The judgment that keeps it from collapsing
The systems above create favorable conditions, but they don’t guarantee sound judgment.
Let’s revisit one of the examples above:
The ad stack serves programmatic display after each image. Editorial guidelines require one image per entry in a list. This generates higher RPM across Google Discover traffic for lists with 20 thin entries at 1,000 words than for a well-constructed feature.
If you’re looking only at the spreadsheet, you’d favor doing as much of that as possible. That’s tempting, especially if employers incentivize target RPMs or sessions per article as KPIs tied to bonus compensation.
However, thin content at volume isn’t ideal for organic visibility. Once readers and search engines encounter too much low-quality output, the traffic disappears.
You’d essentially optimize for short-term yield, reinforce that behavior through employee bonuses, and damage the asset in the process.
Or another example:
An editor notices that updating a datePublished timestamp drives a short-term bump in traffic. The conclusion is to roll out timestamp updates across hundreds of pages.
The problem is that doing it at scale without substantive edits and strict guidelines may create distrust. That’s the judgment call.
Three things need to be held in tension: economic logic, infrastructure and systems, and the judgment not to sacrifice long-term gains for short-term wins.
While that sounds like common sense, these responsibilities are often owned by different people who don’t speak the same language.
Finding a way to bridge that gap is the most important challenge in a scaled content operation. Diversified revenue streams like The Athletic’s help enforce that alignment.
Otherwise, your content strategy will probably fail when you scale past 100 writers. And the examples above are just two of hundreds of scenarios where the spreadsheet points one way, and the right decision points another.
A year ago, 82% of consumers said AI-powered search was more helpful than traditional search. By 2026, that number had dropped to 54%, a 28-point decline in sentiment over 12 months.
Consumers aren’t giving up on AI search, though. Seventy percent say they’re using AI tools for search more than they did last year.
How should search marketers adapt their GEO strategies? Where are we going wrong as we bring AI deeper into our workflows?
To find out, Fractl partnered with Search Engine Land to expand our 2025 research, surveying 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are evolving in the age of AI. (Disclosure: I’m the co-founder of Fractl.)
Here’s what the data means for your 2026 search strategy.
Consumers are using AI more and trusting it less
1. Usage is saturated. The growth story is over.
Seventy percent of consumers report increased use of AI tools for search over the past year. Just 3% say it’s decreased.
Surprisingly, baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically love AI and older generations are lagging behind. In reality, early adopters are signaling that while usage may be rising, trust still has to be earned.
That matters because the remaining competitive battle isn’t about adoption. It’s about trust, quality, and which brands consumers find credible when AI surfaces answers.
2. The trust erosion is faster than anyone projected.
In 2025, the AI skeptic camp (consumers who found AI less helpful than traditional search) represented just 3% of respondents. In 2026, that segment grew to 17%, nearly six times larger than the year before.
The 54% who still find AI helpful are mostly hedging: 37% say it’s “somewhat more helpful,” compared with 17% who say it’s “much more helpful.” Enthusiasm has declined rapidly as hallucinations have become a more widely recognized challenge.
3. AI content volume is now a brand trust liability
In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%.
For search marketers, the implication is significant. Scaling content output with AI is no longer a neutral operational decision.
Consumers are paying attention, and a substantial portion of your audience has an opinion about it. Publishing without disclosure, or publishing at scale without clear quality signals, is now a reputational variable.
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%).
The audience most likely to engage deeply with your brand online, share your content, and drive long-term organic visibility is also the audience with the lowest tolerance for AI-generated filler. Quality isn’t optional if Gen Z matters to your brand.
5. Disclosure is no longer a nice-to-have. It’s a near-universal consumer expectation.
Across every 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%. The percentage of respondents who strongly agree exceeds 50% in every category.
This isn’t a soft preference. It’s close to a mandate, and as we’ll cover in Part 2, most brands are nowhere near meeting it.
6. Consumers believe AI will dominate search. They just don’t love what it currently delivers.
Sixty-four percent of consumers agree AI will replace traditional search engines within five years, essentially unchanged from 66% in 2025. The belief that AI will eventually dominate search remains intact, even as satisfaction scores decline.
What this tells search marketers is that the channel isn’t going away. But being present in AI results and being trusted in AI results are increasingly separate challenges. Optimize for both.
Google still leads on trust, but the map is getting more complex
7. For purchase-intent queries, Google leads AI roughly 3-to-1
When consumers are making purchase decisions, 39% turn to Google first. Reddit comes in second at 15%, just ahead of AI tools at 14%. Review sites and friends and family each come in at 11%.
The trust consumers have built in Google hasn’t automatically extended to AI.
8. Platform preference varies by query type. Optimize accordingly.
Google dominates five of six major search categories. For local businesses (74%), product research (58%), travel planning (57%), and health questions (55%), it’s the default first stop. However, YouTube overtakes Google for how-to content at 50%.
ChatGPT has become the second-most-used destination for health questions at 26%. It also ranks second or third for product research (19%), travel planning (18%), and how-to content (17%).
There’s no single AI search platform to optimize for. Each query category has its own preferred platform. Map your content strategy to where your audience actually goes for each topic.
9. Consumers use 2.4 platforms before making a purchase decision
Before making a purchase decision, the average consumer checks 2.4 platforms, and that behavior is consistent across generations: Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2.
Google remains the default authority for product recommendations, while Reddit and AI tools reinforce confidence.
In 2026, search optimization is no longer limited to page rankings. It’s built around cohesive content strategies that strengthen your entity authority while helping people learn, engage, and convert across multiple platforms.
A brand that appears in Google results but nowhere else loses to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has review content on third-party sites.
How AI is changing marketing operations (and where the gaps are)
10. AI integration in marketing teams has crossed the majority threshold
AI now touches 53% of marketing work on average, up from 38% in 2025. The equivalent of one full workday per week has shifted to AI-assisted workflows in 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it’s involved in three-quarters or more.
For SEO and content teams, this means your competitors are producing at a higher velocity. Volume advantages are increasingly commoditized. Accuracy, original insight, and brand credibility aren’t.
11. SEO and analytics teams are under the highest AI adoption pressure
We’re in an operational pressure cooker: 55% of marketing roles report a 7:10 level of pressure to adopt AI. SEO and analytics roles feel the greatest pressure, but PR sits at 5.8. As AI commoditizes generic content, the advantage shifts to what AI can’t automate: human judgment, relationships, and trust.
12. We’re buying production speed at the cost of quality
Only 26% say AI made their work faster and better. Nearly half admit it made their work faster, but more generic. Seven percent report an outright decline in quality.
This is where your competitive advantage lives. If your peers are scaling AI slop while your team invests in original data, expert quotes, and earned brand mentions, you’re building assets that make your brand more visible, credible, and retrievable across search engines, social platforms, and LLMs.
How you apply AI to your workflows will separate the brands that scale entity authority and brand visibility from those that scale slop and fade into a sea of sameness.
13. Nearly half of AI-generated content doesn’t go through governance processes
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 a legal or compliance review. Only 27% evaluate content for bias.
Nearly half of AI-generated content is entering the market without fact-checking, legal review, or plagiarism checks. Instead, most marketers are focusing on subjective, surface-level editorial review: Does it sound right? Is the tone appropriate? Are there typos?
In a year when consumers are already primed to distrust AI slop, your brand’s AI governance process is one of the cheapest gaps to close and one of the most expensive to ignore.
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, and the compliance gap is significant.
For search marketers producing content at scale with AI, this is an emerging trust and brand risk, not just an ethical concern. The takeaway isn’t to abandon AI. It’s to stop treating governance as optional. Every AI workflow needs clear checks for accuracy, transparency, and human review before content reaches your audience.
14. AI hallucinations about your brand are already a PR problem, and most teams don’t have a process to catch them
A year ago, only about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved, reaching 24%.
Meanwhile, 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 people. If AI is summarizing your category, comparing your product, or explaining your brand incorrectly, that’s not just an SEO issue. It’s a reputation risk, a revenue risk, and a PR issue waiting to become a headline.
When AI misrepresents your brand, fixing the source matters more than disputing the output. Reach out to the publisher for an update, update owned profiles, and publish a correction page tied to your brand.
Where visibility is being won and lost
15. Organic traffic is under pressure, not in freefall
So yes, 50% of the marketers we surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI.
This is a prime example of traffic diversification. The real shift isn’t from Google to ChatGPT. It’s from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across multiple platforms and communities.
The same marketers reporting organic losses are often finding new ground elsewhere:
57% report visibility growth from social platforms (TikTok, Reddit, and YouTube).
40% see growth from AI assistants (ChatGPT, Gemini, and Perplexity), early evidence that GEO investment is generating returns.
31% see growth in direct or branded traffic.
Only 10% report no visibility growth anywhere.
Your 2026 brand visibility strategy now depends on how effectively you build brand mentions and entity authority across platforms, not just on individual page rankings in Google.
16. Most teams are doubling down on the easiest tactics
Which strategies are marketers prioritizing to hedge against AI’s impact?
The good news is that teams are moving toward the right categories: community building, earned authority, owned audiences, expert content, and traffic diversification.
The most prioritized strategies for maintaining visibility in the AI era include building brand presence on social platforms (59%), GEO/AEO optimization (54%), and creating authoritative expert content (44%).
The least prioritized strategy is investing in original research and data, at 15%.
That’s a strategic inversion. Original, proprietary research is one of the hardest content assets for AI to replicate, synthesize, or commoditize. It generates citations, earns links, and builds topical authority in ways that FAQ pages and generic thought leadership can’t.
Teams investing here are building durable moats. Others are investing in areas where AI makes competition easier.
17. In GEO, the popular tactic is also the least defensible
When we drilled into the specific GEO tactics marketers were using, most were content-led and easily replicated by AI systems. Long-tail FAQs matter for AI Overviews, but they’re easy to replicate. Schema helps, but it doesn’t build credibility.
Entity authority creates the strongest moat: proprietary data, expert perspectives, topical authority, and third-party validation. These brands create the source material that journalists, communities, search engines, and AI systems rely on.
18. GEO measurement is lagging execution by a wide margin
It’s no surprise that only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results.
While that’s normal for a new channel, GEO is becoming a serious function. Visibility tracking, citation monitoring, and branded search lift need more attention. Building measurement infrastructure for AI search visibility is a competitive advantage. Teams that can prove GEO ROI can defend and grow investment.
19. The main obstacle to AI adoption isn’t budget or buy-in. It’s skills.
The top barrier to deeper AI integration in marketing is team training and skill gaps (26%). Tool fragmentation comes second at 20%, followed by budget constraints (19%), unclear ROI (12%), and legal and compliance concerns (12%).
Leadership buy-in stands at just 2%, indicating that executive support is largely in place. The gap is execution capability. For search marketing teams specifically, investing in AI literacy, prompt strategy, content quality control, and GEO measurement skills is more valuable right now than adding new tools.
What this means for your 2026 search strategy
The data across both consumers and marketers tells a coherent story. Users are adopting AI search faster than they’re developing trust in it. Marketers are deploying AI faster than they’re governing it. For search professionals, both gaps create specific, actionable opportunities.
Audit your brand’s AI footprint before someone else does
Brands have already been misrepresented in AI responses. Query your brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews. Document what’s accurate, what’s missing, and what’s wrong. Build a monitoring cadence before you’re in damage-control mode.
Invest in entity authority and original research
AI can’t generate proprietary survey data, original research, named expert perspectives, or verified brand facts. Marketers prioritizing original research are building assets that will become even more valuable as AI systems get better at rewarding genuine authority over generic content.
Distribute your visibility across multiple platforms
Consumers are checking 2.4 platforms before buying, and they’re doing it consistently across every generation. Google organic is necessary, but it isn’t sufficient.
Your brand needs a coherent, consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media. If a consumer asks ChatGPT about your category and you’re not mentioned, or you’re mentioned inaccurately, you’ve lost that decision before they ever reach a search results page.
Build AI content governance, not just AI content workflows
Consumer demand for AI disclosure ranges from 84% to 91% across formats. Only 20% of brands always disclose. This disconnect is a reputational liability and, increasingly, a legal and regulatory one. Establish disclosure policies, fact-checking checkpoints, bias reviews, and hallucination escalation processes as operating standards.
Close the GEO measurement gap
If you can build attribution frameworks that connect AI-assisted search mentions to traffic, lead quality, and revenue, you’ll be able to prove ROI at a time when most teams can’t. That’s a budget and strategy advantage that compounds.
Double down on what AI can’t replicate
Proprietary data. Named experts. Human-verified claims. Transparent sourcing. Consistent brand voice at high quality. The brands that treat quality as a strategic differentiator in 2026 are the ones whose names will come up when consumers and AI systems go looking for answers.
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 spanned 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.
“Ultimate guides” were the undisputed heavyweight champions of SEO. They were built specifically to align with how Google’s algorithm measured content value.
But the web moved on. Search intent shifted toward fast answers, AI saturation destroyed length as a credibility signal, and Google’s systems began penalizing the one thing ultimate guides were engineered to produce: zero information gain.
So, what now?
The new content constraint is extractability, and it changes every structural decision downstream, from brief to publication.
Your content has a word limit: the grounding budget
AI engines like Gemini allocate approximately 380 words per webpage for query grounding, regardless of the article’s total length. It’s a retrieval constraint you have to adapt to.
The extraction data is precise:
Pages under 5,000 characters: 66% AI extraction rate.
Pages over 20,000 characters: 12% AI extraction rate.
Generative systems now answer many queries without requiring a click. The traffic those pages once captured no longer exists to be captured. The 4,000-word ultimate guide content marketing approach actively destroys generative search visibility.
What replaces the informational library is something structurally different and considerably more demanding to produce. Every sentence must earn its place by naming an entity, stating a relationship, preserving a condition, or making a citable claim.
Traditional keyword targeting asked one question: “What are people searching for?”
Problem-first positioning asks a harder one: “What situation has produced this search, and what does a genuinely useful answer look like inside that situation?”
That’s where the padlock principle becomes useful. Your business is a lock that opens for multiple combinations, each representing a distinct problem for a distinct person.
For example, a car insurance provider targeting “car insurance” is a category. The same provider building separate pages for “an 18-year-old new driver declined by standard insurers” and “a courier using a vehicle for commercial work” is a solution.
The distinction sounds philosophical until you realize it affects every downstream structural decision. Andrew Holland is right: AI killed low-grade informational SEO. Here’s some tactical advice to shift your content approach.
3 tactical rewrites for problem-first positioning
Replace categorical identity with problem identity
Before: “We are an insurance provider.”
After: “We solve the underwriting problem for first-time drivers under 25 who are declined by standard insurers.”
Rewrite titles as outcomes, not labels
Before: “Car Insurance | BrandName”
After: “Car insurance for new drivers under 25 declined by most providers”
Lean into constraints rather than suppressing them
Acknowledging that your solution works for teams of 100 or more but not for solo operators signals to a retrieval system that your content can be cited with confidence. Generic advice is the content AI already generates for free.
Constraint-aware, condition-specific guidance is what AI cannot replicate and therefore must source.
This logic collapses one of the most entrenched distinctions in digital marketing. The traditional separation between informational content and commercial landing pages was always somewhat artificial, but AI retrieval has made it structurally unsustainable.
What replaces the previous distinction is a fundamentally different content architecture: Every page is a document that knows exactly who it is for, states the problem it solves in the first sentence, and earns its keep by delivering a resolution specific enough to be cited but human enough to convert.
Marketers should start injecting problem-positioned, AI-readable answers directly into commercial pages rather than blogs. Low-grade information recaps like the “best tools for X” roundup and the “how-to” guide that adds nothing to existing knowledge have been absorbed by generative systems that now answer those queries without a click.
Every sentence must be self-contained and able to survive alone. AI retrieval systems do not read your article the way a human does: sequentially, with accumulating context.
Instead, an LLM will lift sentences in a “send this to someone without context” type of way by extracting passages and evaluating sentences as independent semantic units.
If a sentence requires its neighbors to make sense, it cannot be extracted and evaluated as an independent semantic unit (i.e., it’s neither easily understood nor useful for a machine).
The three failure patterns and their fixes:
Failure
Example
Fix
Unresolved pronoun
“It also includes unlimited storage.”
“The Dropbox Business Standard Plan includes 5TB of encrypted cloud storage.”
Stripped condition
“The price has dropped significantly.”
“The Asana Enterprise Plan costs $24.99 per user per month, down from $30.49 in Q1 2024.”
Vague claim
“Our platform makes team management easier.”
“The Asana Enterprise Plan streamlines cross-functional project tracking for teams of over 100 people.”
If you want to write LLM-friendly content, no matter what content format you are creating, here’s my advice: look into semantic triples.
Because AI systems evaluate content using identical retrieval infrastructure regardless of page type, the semantic triples (subject, predicate, object, conditions preserved) apply equally to blog articles, product descriptions, and pricing pages.
Here’s a concrete application of semantic triples: Make your heading more explicit. Explicit headings placed directly above their corresponding paragraphs add mathematical relevance (i.e., they improve cosine similarity scores), which means that an AI is 17.54% more likely to select that passage if it has a good headline.
The citation bait formula
How do you keep content fresh in the age of AI?
First, accept that you’re optimizing paragraphs, not pages.
The citation-bait formula defines how to structure the paragraph blocks that sentences belong to.
Step 1: Direct declarative opening (40 to 60 words)
No preamble. No “in this section we will explore.” The answer first, always. This block is what generative systems extract.
Step 2: Context (one to two sentences maximum)
Expand without burying. Every additional sentence beyond two reduces the density of what came before.
Step 3: Structured evidence
A table, a numbered list, or a comparison. Something extractable in its own right, independent of the surrounding prose.
Step 4: Self-contained heading
The H2 or H3 that follows must name the topic, intent, and scope of what just appeared. Not “Key takeaways.” Not “Overview.”
The heading must make complete sense when read entirely out of context, because in generative retrieval, it frequently will be.
Managing the tension between AI-readable structure and human persuasion is difficult. Like Shrek’s onion analogy, LLM-friendly content has more layers than most people realize. You don’t have to choose between the two. You have to layer them.
The AI inverted pyramid places machine-readable answer blocks at the opening of each section. Human storytelling — the anecdote, the constraint, the actual number/stat/finding — belongs immediately after, connected by a natural transition that moves the reader from optimized structure and into earned narrative.
Jessica Foster identified Dove’s “Real Beauty Stories” as a great example of this type of copywriting. Dove opens with structured how-tos that satisfy intent-driven retrieval, then anchors those tutorials to the lived experiences of real customers.
The machine gets a citable answer at the top of the block. The human gets a reason to believe it in the body. Neither layer compromises the other because they occupy different positions in the document.
Casey Nifong has a great audit workflow for existing content:
Identify the main question each section answers.
Find the clearest direct answer buried in the paragraphs and move it to the top.
Strip conversational lead-ins that delay the core answer.
Run both the isolation test and the disambiguation test on every mid-page sentence.
Leave stories, examples, and brand voice intact below the answer block, connected by natural transitions.
The missing angle: Your workflow doesn’t exist yet
You now know good content no longer looks like a 4,000-word-long ultimate guide. Now it’s time to figure out what workflow produces said new good content.
Most articles on Search Engine Land describe the destination, not the road. That’s because you’re responsible for the journey. You need to build your editorial checklist, prompt structure (if you’re using LLMs to restructure existing content), and grounding budget calculation.
Go beyond theory and build an editorial system that consistently produces LLM-friendly content without sacrificing the human specificity no model can replicate.
Meta launched AI Mode in Facebook Search. AI Mode gives users AI-generated answers based on public content from Facebook Groups, Reels, and other Meta apps.
Instead of showing a standard list of search results, AI Mode uses Meta AI to answer questions directly within Facebook. Meta said responses are grounded in what people are publicly saying across its apps, including real experiences and recommendations.
AI answers in search. AI Mode supports both broad discovery and specific questions. Users can search or explore their Feed and receive responses from Meta AI within Facebook. This gives Facebook a new way to surface public social content.
Groups and Reels could become part of how Meta answers questions about products, places, hobbies, and everyday advice.
Source selection is unclear. Meta said the feature delivers “real answers from real people.” But it didn’t explain how AI Mode selects which public posts, Groups, or Reels appear in responses. It also didn’t say whether brands, creators, or publishers will be able to see when their content is used.
Why we care. Facebook search is moving toward an AI answer experience built on public social content. That could change how people discover recommendations, local information, and brand-related conversations across Meta’s apps.
A familiar name. Obviously, Meta’s new AI Mode feature shares its name with Google’s AI Mode. Meta gets no points for creativity.
What Meta is saying. AI Mode is powered by Meta AI and Muse Spark. Meta didn’t explain how Muse Spark influences search ranking, source selection, or answer generation.
The search update was part of a broader Facebook AI rollout that also included new creative tools for photos, videos, profile pictures, and Stories.
Much of the GEO conversation focuses on how AI systems discover, extract, cite, and recommend content. That work matters. But visibility also depends on what the content contains once it’s found.
Next-question intent is a way to test whether a page provides enough information to support the user’s next decision, not just the initial query.
The first search is often only the starting point. Real decisions happen in the follow-up questions, comparisons, constraints, and objections that come next.
Content that helps answer those questions gives AI systems more useful material to summarize, compare, cite, and recommend.
From results to narratives: Traditional search vs. AI search
Traditional search was built around a results page: a ranked set of links users could scan, compare, and interpret for themselves. AI search is increasingly built around a synthesized answer drawn from multiple sources.
That changes what content must do. A page can rank, index, and appear technically sound, yet still fail to provide the information needed to support an AI-generated answer. That’s where next-question intent matters.
Search intent asks, “What is this user trying to do?”
Next-question intent asks, “What will the user need to know next before they can trust, compare, choose, buy, book, or move on?”
That question is becoming increasingly important because AI systems don’t simply match queries to pages. They assemble answers, comparisons, qualifications, and recommendations.
In that environment, content must support the full answer path, not just the first query.
A user’s first search is often broad, incomplete, or simply exploratory. It signals a direction. Real value appears in what comes next: the follow-up, the objection, the comparison, the constraint, the “practical anxiety,” the “Yes, but what about my very specific situation?” moment.
As the simplest example, someone searches “best CRM software for small business.” The first query becomes a doorway. But the actual buying process begins with the follow-up questions.
Which platform is easiest for a two-person team?
Which integrates best with QuickBooks?
Which one works for a business without a formal sales department?
Which one is best for a local service company rather than a software startup?
Which one won’t make an owner, office manager, or intern quietly resent tech?
These queries aren’t add-on or side questions. They’re the actual decision path.
Otherwise competent content fails at this stage. It answers the query, but doesn’t help complete the conversation. A page can define the category, mention benefits, include a few keywords, and still omit information buyers need to make decisions.
In traditional search, the user might click a few results and assemble context manually. In AI search, the system will assemble it for them. If your content lacks that useful context, it gives the system less to work with and may appear less visible.
Next-question intent is not just a writing exercise
The risk with any new content framework is that it becomes a fresh label for familiar advice. Next-question intent should do more than remind you to “write better content.” It should help you test whether a page contains enough context to support the next step in a user’s decision.
In practical terms, next-question intent means asking whether the content is answer-ready.
Answer-ready content addresses the user’s initial need, anticipates the next layer of decision-making, and provides specific, verifiable, and contextual information to support a synthesized answer.
This distinction matters because AI search visibility isn’t exclusively about rankings. It’s also about citations, mentions, recommendations, and whether a brand is recognized as a trusted answer in a given context.
Those outcomes require something more than volume. They depend on whether the brand’s content provides the system with enough substance to understand what the brand does, who it serves, when it’s useful, why it’s trustworthy, and how it compares to alternatives.
Where good content goes thin
Most brands have decent content that’s accurate, readable, and optimized around a keyword. There may even be an FAQ section, like a useful but decorative basket of afterthoughts.
In AI search, decent may not be enough.
AI systems need extractable clarity, but they also need context. They must understand what something is, who it’s for, when it’s useful (and when it’s not), what evidence supports the claim, and what the user should consider next.
This level of context is where many pages go thin.
As an example, a service page says, “We offer customized marketing strategies.” But what does customized mean?
A real strategy?
A lightly personalized template?
A monthly call where everyone nods at a dashboard no one has time to interpret?
The product page says “safe for families.” Safe for whom?
Babies?
Pets?
People with health issues?
A software page says, “built for small businesses.” What business?
A solo bookkeeper?
A nonprofit?
A 40-person heating and cooling company?
A founder doing payroll late at night after working all day?
Broad claims offer humans little to trust and AI systems little to use. Specific, structured, evidence-backed content offers something better.
A next-question audit looks beyond keyword coverage and asks whether a page contains the information needed to support the next step in the user’s journey.
For every important page, you should ask:
What’s the primary question this page answers?
What would a serious buyer, reader, or researcher ask next?
What objection would stop them from acting?
What comparisons would help them understand the category?
What proof would make this answer trustworthy?
What detail would make this financially, technically, locally, or personally relevant?
Where are we applying broad language because we haven’t done the harder thinking?
The best inputs for the audit often come from inside the business, not from keyword tools alone. Customer reviews, comparison queries, demo questions, sales calls, support tickets, chat logs, internal site search, and objection patterns can all reveal the questions real people ask when making decisions.
That information is often closer to the buyer’s actual path than a neat spreadsheet of keywords.
Examples of next-question content across industries
For a local service business, next-question content might involve service areas, prices, appointment windows, insurance, reviews, emergency availability, or what happens after someone books.
B2B software may invest in next-question content that involves integrations, user roles, implementation times, costs for switching, security, support, or whether a lower-tier plan is useful.
For higher-trust categories like medical, financial, and legal, next-question content involves scope, credentials, risk, regulation, evidence, or when to speak with a qualified professional.
The point isn’t to stuff pages with every possible question. It’s to build content around how people actually decide.
AI search rewards content that completes the answer
Next-question intent helps you avoid one of the least useful responses to AI search: publishing more content because visibility feels uncertain. The better move is more specific, decision-ready content.
If your page says, “I/we help small businesses grow,” explain which small businesses, what kind of growth, what constraints, what proof, what trade-offs, and what alternatives.
For example:
“We help local service businesses without in-house marketing teams improve search visibility and generate more qualified appointment requests by clarifying their website content, answering the questions clients actually ask, and building pages that support both traditional and AI-generated search. This is best for businesses looking for durable visibility rather than a quick paid-ad spike.”
In that same line of thought, if a page says “We’re eco-friendly,” explain the materials, sources, use cases, certifications, limitations, disposal issues, and even circumstances where that claim doesn’t apply.
If a page says “This is AI-powered,” explain what that AI tool actually does, what it automates, what remains human-led, what data it uses, and where users will still need judgment.
This isn’t writing for bots. It’s writing for real people whose decisions are increasingly being mediated by AI-generated answers. The goal is to make your expertise, relevance, and trustworthiness easier to understand and use.
Traditional SEO asked whether a page could rank. AI search asks whether a page can contribute to the answer.
Any page can be indexed, optimized, and technically sound, yet still fail if it lacks substance. It might answer the initial query, but ignore the information users need to make a decision.
The opportunity isn’t to chase every new acronym or rebrand every content plan as a new discipline. It’s to build answer-ready content.
That means clearer definitions, stronger examples, honest comparisons, better proof, more precise positioning, and direct answers to the questions customers ask every day.
In traditional search, visibility belonged to the page that best matched the query. In AI search, it increasingly belongs to the content that helps people move forward.
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