May 6 2026
The Whole Point Was The Mess via @sejournal, @pedrodias

Semrush put out an infographic last week. The kind built to be screenshotted into LinkedIn carousels and pasted into webinar decks. Four pillars. The fourth one is called “Technical GEO”: schema, structured data, clean architecture. The line that justifies it: “Ensures AI engines can parse and connect your content.”

Ensures.

See it live on X/Twitter. Image Credit: Pedro Dias

That is the entire piece in one word. The architecture of large language models is, by design, the opposite of ensured. And schema has nothing to do with whether an LLM can parse text. LLMs parse text by reading text.

Semrush is far from alone. Every SaaS vendor with skin in this game is running variations of the same play. SEO-era controllability, repackaged under a new acronym. The same percentages, pillars, and pyramids. All dressed for a system that was built specifically not to work this way.

I have made the strategic version of this case before, in “Your AI Strategy Isn’t a Strategy.” This piece is the technical floor underneath it.

Built To Read Whatever’s There

Language models exist because the web is a mess. Forums, Wikipedia stubs, blog posts written at 2 A.M., scraped product copy, machine-translated junk, code comments, half-formed sentences, typos, contradictions, every register from journal article to subreddit shitpost. Pre-training data is the public web, and the public web has never been structured.

The transformer architecture handles this by treating language as sequences of tokens. There is no parser inside the model looking for tags. There is no preference for FAQ markup. The model reads the words. That is the mechanism.

At inference time, the model generates more tokens conditioned on the input. None of that pipeline is reading microdata.

Schema.org has real jobs. It feeds rich results in classical search. It supports entity disambiguation in the knowledge graph. It helps voice assistants pull structured fields. These are well-defined functions inside specific systems. They are not the mechanism by which an LLM understands a sentence.

So when a vendor claims structured data “ensures AI engines can parse and connect your content,” there is nothing to ensure. The parsing layer they are imagining is not there. The model already parsed your sentence. It did so by reading the sentence.

One Trick, Three Brand Colors

Look at the biggest GEO and AEO explainers in the market right now, and you find the same SEO-era playbook with the acronym swapped.

Semrush is already covered. The fourth pillar of its “Technical GEO” presents schema and structured data as ensuring something that the architecture cannot ensure.

AirOps published a graphic titled “15 Ways to Get Cited by ChatGPT, Perplexity, & Google.” It is the most numbers-heavy specimen of the genre I have seen this year. Schema markup increases citation likelihood by 13%. Sequential H2 to H4 tags double your chances. Short paragraphs make content 49% more likely to appear in AI answers. Perplexity cites UGC in 91% of answers, versus Gemini’s 7. Read the source notes and the methodology trail comes home. The numbers in the graphic trace back to AirOps’s own “2026 State of AI Search Report.” AirOps is citing AirOps on the question of whether AirOps’s prescriptions work.

Peec AI does a more honest job in places. Its complete guide to GEO acknowledges the probabilistic nature of the system and concedes that foundation models are already trained, so optimization focuses on the retrieval layer. Then it lands the same prescriptions: heading hierarchy, bullet lists, FAQ markup, multiple schema types layered on each page, summaries at the top of sections – all built on the chunking claim that long paragraphs lose out because the engine extracts fragments rather than full articles.

Profound, citing Aleyda Solis’s checklist, is the most explicit in its piece: “Optimize for Chunk-Level Retrieval.” Each section, a standalone snippet. Each page, a buffet from which the engine takes what it wants. The engine, in this telling, is a polite guest who only takes what’s been laid out.

Three vendors. Same operating assumption: a controllable, prescriptive technical discipline sits between a publisher and a citation, and it occupies roughly the same shape as classical SEO. Schema, headings, structure, freshness, machine-readable formats. Familiar. Billable. Reportable up to a chief marketing officer.

What Schema Actually Does

Schema is not the target here. Schema has real, well-defined uses. Classical Google search uses it for rich results: prices, ratings, event times, the structured fields that drive search engine results page features. The knowledge graph uses it for entity disambiguation. Voice assistants pull structured fields out of it.

None of that goes away. If you’re responsible for technical SEO, keep implementing schema where it earns its keep.

Schema cannot reach into a transformer and improve its comprehension of your prose. The model isn’t architected to read schema as schema. It receives whatever text the engine fetched and chose to include, and processes that text as language tokens. The entire GEO/AEO marketing layer rests on conflating two distinct claims: that schema is useful in classical search, and that schema feeds the LLM. The first is true. The second is a category error.

Chunking Is Not Yours To Optimize

Image Credit: Pedro Dias

The chunking advice keeps reappearing because it sounds technical, sits neatly inside a flowchart, and gives a content team something concrete to do on Monday morning. It is also incoherent.

Chunking happens at retrieval time. Perplexity, ChatGPT, and Gemini each run a retriever over candidate documents, split them according to their own configurations (length, overlap, embedding model, sometimes semantic boundaries), and feed the top-k chunks into the model’s context. Those configurations belong to the engine. They get tuned differently across systems and retuned on schedules no publisher is privy to. The publisher’s view of the chunker is the publisher’s view of the model: black box, results only.

So when a vendor says “optimize for chunk-level retrieval,” what is actually being recommended is good writing. Short, self-contained paragraphs. Clear definitions near the top of sections. Internal logical structure. These are recognizable disciplines: information architecture, technical writing, readability. They have been recognizable disciplines since long before the transformer was invented. They are not a new technical layer.

A more honest version of the pitch would be: Hire someone competent at writing for the web. That sentence does not fit on a pricing page.

The Paper They Don’t Read

There is an actual academic paper called “GEO.” Aggarwal and co-authors, KDD 2024. It is the closest thing to a citable source the SaaS layer has when it sells generative engine optimization as a discipline. It is also, as papers go, easy to skim. Nine “optimization methods” are tested on a 10,000-query benchmark, with results.

What did the paper find worked?

Adding citations from credible sources. Adding quotations from relevant sources. Adding statistics. Improving fluency. Making prose easier to understand. The methods that produced the largest visibility lifts were essentially: write content with more evidence in cleaner prose.

What did the paper test and find did not work?

Keyword stuffing, the closest analogue in the paper to the SEO-era playbook the current GEO and AEO vendors have repackaged. Result: below baseline. The paper’s authors note in plain terms that techniques effective in search engines “may not translate to success in this new paradigm.”

Notice what is not in the list of nine methods. Schema. Structured data. FAQ markup. Heading hierarchy. Machine-readable formats. None of these are tested in the paper, because none of them are the optimization surface the paper studies. The paper is studying content-level interventions: what you put in the words, not metadata layered around the words.

The SaaS layer borrowed the acronym. The findings stayed in the paper. “Technical GEO” is the SEO playbook with different stickers on the same boxes, sold against research that points the other way.

The Assumption Smuggled In

The SaaS pitch only makes sense if you smuggle in one assumption: that the system you’re optimizing for has the same shape as the one that’s been billing SEO clients for a quarter-century. Inputs you control. Outputs that respond. A retrievable causal chain between the two.

That model was always a simplification of how search worked. It was close enough to keep the industry running, and close enough to keep the invoices going out.

None of that simplification survives contact with generative systems. The same prompt produces different answers across sessions, users, temperatures, model versions, and days. Observed behavior across the major engines, not a clean property of any single one. The retrieval layer in front of the model also moves: candidate sources shift, ranking shifts, freshness windows shift. No causal chain runs between “I added FAQ schema” and “the model cited my page.” What runs between them is a probability distribution, and the things you control affect that distribution in ways nobody can cleanly attribute. Not even the people who created these systems.

This is the established line on AI visibility tools, repeated here because it applies to the whole prescriptive layer. Statistically unverifiable data drawn from non-deterministic systems. A 13% citation lift, measured how, against what counterfactual, with what reproducibility? The methodological questions aren’t what those numbers are designed to answer. The numbers are the answer. They land in a graphic, get rendered as ROI in a board deck, and the conversation moves on.

Something To Say In The Meeting

Here is the part that the architecture argument and the methodology argument do not, on their own, explain. Why does the entire SaaS layer keep successfully selling this stuff to people who are not stupid?

The honest version of the answer goes something like: We are operating with reduced visibility into a system that does not expose its mechanics, that returns different outputs to different people for the same query, that is changing month by month, and that has folded a substantial chunk of the funnel into a black box. We can keep doing the work that has always been the work: writing well, being useful, building authority, maintaining the site. We can monitor what shows up where. The deterministic dashboard we used to have is not coming back.

That sentence is unsayable in a marketing meeting. It admits the lever is not connected. It tells leadership that the budget line they approved does not have a corresponding action. It gives the team nothing to put in next quarter’s plan.

So the SaaS layer fills the gap. It manufactures levers. Pillars, frameworks, percentage lifts, schema audits, chunking optimization, machine-readable formats. Reportable activity. Defensible expenditure. Something to say in the meeting. None of this gets you visibility. The engine decides that. What is on offer is the appearance of control, sold to people who would rather pay than concede that control left the room.

Once the lever is bought, it has to be operated. Schema audits get scheduled. Chunking checklists get reviewed. Citation likelihoods get tracked, refreshed, and compared. The dashboard the team paid for becomes the dashboard the team optimizes against, and the dashboard quietly replaces the actual problem with the part of the problem it can see. By the time anyone notices, the SaaS layer is writing the brief.

None of this is a moral failure on the buyer’s side. What you are watching is what happens when an industry has been organized for a quarter-century around the premise that you can pull a lever and watch the meter move, and the meter quietly disconnects from the lever. The vendors aren’t running a con. They are filling demand for the only thing the buyer can no longer afford to do without: an answer that fits in a slide.

Rank And Tank, All Over Again

I keep coming back to a phrase that fits this whole moment: dancing to the rank-and-tank tunes (I borrowed it from David McSweeney). The cycle goes: Vendor sells the controllable-discipline frame, agencies adopt it, content teams scale production around the prescriptions, AI-generated articles get pumped out at volume because the prescriptions are easy to template. Some of it ranks for a while. Most of it eventually tanks because the prescriptions were never the mechanism, and the engine adjusts, or the freshness window closes, or the system simply moves on.

The SEO industry has done this before. Spinning. Mass programmatic pages. Doorway content. Each cycle followed the same shape: a controllable input dressed as a discipline, sold at scale, briefly effective, eventually punished by the engine, replaced by the next controllable input dressed as a discipline.

GEO and AEO are the current cycle. The pillars and percentages and pyramids are this cycle’s templates. Underneath them, the strategies bifurcate.

One path is brand presence exploitation. Plant your name where the engines look. Reddit threads, top-X listicles, the same citation surfaces over and over. The cycle feeds itself: engines cite the surfaces, brands work the surfaces, surfaces feed the engines. I have written about this loop before; I called it the Ouroboros pattern. The short version is that the loop is less stable than the strategy assumes.

The other path is content at scale. Produce variations, pump out volume, treat the templated output as content that could earn a citation. I have written about this approach before, in the “Scaling Disappointment” piece. The short version is that uniqueness is not value, and at the pace these prescriptions enable, qualitative review stops being possible. The volume of AI-generated copy produced under this path is this cycle’s externality.

The next cycle will sell the cleanup.

Forget for a second whether your “Technical GEO” is set up correctly. Ask whether the thing you are putting on the page is worth reading. Large language models were designed to read whatever is there. If what is there is good, it will be read. If what is there is templated, low-utility content optimized against a chunking heuristic that does not exist, it will eventually be filtered out: by the engine, by the user, or by the next academic paper showing that retrieval quality is degraded by exactly this kind of slop.

The advantage, when it accrues, will accrue to the people who do not get distracted. Who do not subscribe to the dashboard. Who keep working on product-driven SEO and the foundations that have always connected content to people. There are early signs of this on the timelines I read. Practitioners openly questioning whether optimizing against a non-deterministic surface makes sense at all, and asking whether their attention belongs back on classical search; which, at the end of the chain, is what feeds these systems anyway.

The mess was always the point. The architecture handles it. The industry just needs to stop pretending the mess is the problem.

More Resources:

This post was originally published on The Inference.

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
May 6 2026
AI Just Handed PR Its Best Opportunity In SEO & Most Teams Are Missing It via @sejournal, @gregjarboe

A recent Linkedin post by Jim Yu flagged that BrightEdge’s AI Catalyst team analyzed citation and brand mention patterns  from prompts across Finance, Healthcare, Education, and B2B Tech in five AI search engines: ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews. The finding that mattered most was buried in the data. Despite wildly different source preferences, every engine tends to surface the same brands. Source overlap across engine pairs runs from 16% to 59%. Brand overlap lands in a much tighter band, 35% to 55%. The engines wander far on what they cite. They hold fast to who they recommend.

“Review sites, comparison content, trade press, retailer listings, and finance data are the sources AI most frequently reaches for. Investment in PR, trade coverage, review site visibility, and category comparison content translates into visibility across every engine, not just one.”

I sent that takeaway to Katie Delahaye Paine, as I have watched her track the collision points between data and communications longer than most people in this industry have been alive. She sent back a link to a press release that looks like a Yahoo Finance story with one question: “What do you think of this?”

In the link, Zen Media argued that AI tools are giving PR teams measurable citation data for the first time – a genuine breakthrough for a profession that has historically struggled to tie its work to business outcomes. I told her I thought PR had a new opportunity, if there were communications professionals brave enough to seize it. Unfortunately, too many are so service-oriented that they have become servile.

She responded, “Sad, but true.”

The Opportunity Is Real

The data backing this shift is not subtle. According to new Stacker research, earned media distribution can increase AI citations by a median lift of 239%. Brands with review profiles on platforms like Trustpilot, G2, and Capterra are three times more likely to be cited by ChatGPT than brands without them.

Lily Ray, while vice president of SEO Strategy & Research at Amsive, found that digital PR and YouTube optimization have become essential tactics for AI discovery. Amsive’s research showed ChatGPT most frequently cites Wikipedia, Perplexity leans on Reddit and YouTube, and Microsoft Copilot gravitates toward Forbes and Gartner. The implication is that being discussed in credible third-party sources, exactly what good PR has always produced, now feeds directly into the sources AI trusts most.

Research from Muck Rack’s Generative Pulse platform found that earned media still accounts for 25% of all AI citations. Press coverage, authoritative reviews, third-party writeups. The raw material of traditional PR. Being mentioned in a Wirecutter roundup or a TechCrunch feature, their team noted, does more for AI visibility than almost anything a brand publishes on its own site.

PR Has The Raw Material. It Lacks The Ambition

Here is the maddening part. Everything that matters for AI citation, third-party credibility, trade press coverage, review site presence, expert mentions,  is work that PR professionals are already positioned to do. They understand how to cultivate relationships with the publications and journalists that AI engines trust. They know how to place stories in the outlets that show up as authoritative sources. What they have lacked, historically, is a measurable link between that activity and business outcomes.

That link now exists. AI engines create a citation trail. Brand visibility in AI responses can be tracked, measured, and attributed. Katie has spent her career making the case that PR’s contribution to business value must be expressed in persuasion, trust, and credibility, which are all imminently measurable, she has argued for decades, if the profession would simply demand better tools. The tools now exist. The measurement imperative is sharper than ever.

So, why isn’t the initiative to combine SEO and PR coming from PR? Because far too many practitioners remain reactive. They wait to be briefed, execute campaigns, report outputs, and repeat. The organizations most likely to move first on this are the ones where someone outside the PR function, such as an SEO professional who understands earned media, a digital marketer watching their traffic erode from AI Overviews, a content strategist, or an entrepreneur tracking every conversion, recognizes that the citation graph and the PR strategy map are now the same document.

What A Unified Strategy Actually Looks Like

BrightEdge made the point clearly: Build for three source layers, not five LLM playbooks. Every AI engine draws from authoritative sources, commercial and editorial content, and user-generated content. They weigh the mix differently, Perplexity and Gemini lean toward authority, Google AI Overviews lean toward UGC, ChatGPT and AI Mode lean toward commercial content, but all three layers matter in every engine.

That means the practical work is, earn placement in trade press and analyst reports that are relevant to your category. Generate real customer reviews at scale. Produce comparison and category content that review aggregators and editorial sources want to reference. Get on the podcasts and YouTube channels that AI engines are already pulling from. None of this requires a new discipline. It requires PR and SEO professionals to stop treating their work as separate and start treating the citation graph as shared territory.

The brands that establish citation authority now are building something that compounds. Entity authority is slow to build and slow to decay. Early movers in AI visibility are capturing ground that late movers will find increasingly expensive to reclaim.

AI has handed PR the measurement framework it never had and the strategic mandate it always deserved. The question is whether the profession will recognize the moment, or wait for someone else in the organization to seize it first.

More Resources:

Featured Image: Red Vector/Shutterstock

Ecommerce MGMT 0 Comments
May 5 2026
Google Is Testing New Bot Authorization Standard via @sejournal, @martinibuster

Google is testing Web Bot Auth, an experimental protocol designed to help websites verify that automated traffic is really coming from the bot or service it claims to represent. The new protocol could give site owners a dependable way to separate legitimate automated traffic from bots that hide or misrepresent who they are.

A new developer support page was published provide information on how to verify requests with the Web Bot Auth protocol, which is currently in an experimental phase.

What Google’s Web Bot Auth Is Based On

The new protocol is technically called the HTTP Message Signatures Directory. It’s a proposed technical standard designed to automate trust between web services. It helps websites recognize verified automated services without requiring each side to manually exchange security keys beforehand.

The basic idea is similar to giving verified automated services a standardized way to present credentials. Instead of relying only on names, user-agent strings, or private setup between companies, the protocol gives websites a repeatable way to check whether an automated request can be verified. That matters because many bots can claim to be something they are not. Web Bot Auth does not decide whether a bot is good or bad, but it can give site owners a stronger signal about whether the bot is really the service it claims to be.

A Reliable Way To Identify Bots

The cryptographic part is important because it makes identity harder to fake. Today, a rogue bot can claim to be a legitimate crawler by copying a name or user-agent string. Web Bot Auth is designed to move beyond that kind of self-identification by giving websites a way to check whether an automated request matches the service’s cryptographic credentials.

Under this protocol, a bot would need more than a label saying who it is. It would need to prove that identity in a way that a website can validate. That could give site owners a secure basis for allowing verified automated services while blocking bots that cannot prove who they are. The protocol does not automatically decide which bots should be allowed or blocked, but it could give websites a more dependable signal for making that decision.

Cryptographic verification is what makes Web Bot Auth better than current bot identification methods. Instead of relying on signals that can be misrepresented, it gives websites a way to verify automated requests. That means recognition is based less on what a bot says about itself and more on whether its identity can be confirmed by cryptographic credentials.

Caveat: It’s In An Experimental Phase

The proposed protocol will make it possible to distinguish between rogue bots that are impersonating trusted crawlers from the genuine bots from trusted services. This protocol is like a whitelist of what’s allowed which may make it easier to isolate untrusted crawlers.

However, because this is an experimental phase, the “whitelist” currently only applies to a subset of traffic, such as the Google-Agent . Google is “not yet signing every request,” so a missing signature does not automatically mean a bot is rogue. Site owners are advised to continue using IP addresses and reverse DNS alongside the protocol to avoid accidentally blocking legitimate traffic that hasn’t migrated yet.

What It Does

The new standard replaces manual setup between websites and bots, crawlers, and other automated services with a three-step discovery process:

  • Standardized Key Files:
    Keys are stored in a common format, JSON Web Key Set (JWKS), that all servers can read.
  • Well-Known Addresses:
    It defines a specific “home” on a website (/.well-known/) where these keys are always kept.
  • Self-Identifying Requests:
    It adds a new header, Signature-Agent, to HTTP requests that acts like a digital business card, pointing the receiver directly to the sender’s key directory.

Benefits For Automated Services And Websites

Web Bot Auth could make bot verification easier to scale by reducing the need for manual setup between each website and automated service. It also gives automated services a more consistent way to stay recognizable when their security details change, which can help avoid broken verification over time.

Web Bot Auth Is Experimental

Google stresses that users should continue using existing standards such as user-agent IP-based bot verification, stressing that the standard itself is a proposal that is subject to change.

The new documentation provides the following warning:

“The experimental status means that:

Not all Google user agents are using Web Bot Auth.

Google is not yet signing every request of agents using the protocol.

We recommend that in addition to Web Bot Auth you continue relying on IP addresses, reverse DNS, and user-agent strings as we gradually roll out signed traffic.

If you’re a developer or system administrator looking to allowlist our experimental AI agents, you can implement verification through the Web Bot Auth protocol:

  • Using a product or service that supports Web Bot Auth
  • Verifying requests yourself”

Nevertheless, the standard does aim to simplify bot identification and controlling bot traffic by using a cryptographic protocol that a rogue agent can’t spoof, provide insights into how bots are interacting with your traffic, and to build a better way to control the currently out of control situation with bot crawling.

Google encourages users interested in the protocol to contact their web hosting providers to see if they intend to support the experimental protocol, keep up to date with the latest changes published by the Web Bot Auth Working Group and to send feedback through Google’s official Web Bot Auth feedback form.

Read Google’s new documentation:

Authenticate requests with Web Bot Auth (experimental)

Featured Image by Shutterstock/Efkaysim

Ecommerce MGMT 0 Comments
May 5 2026
AI Changed My Work. And Yours, Too via @sejournal, @Kevin_Indig

Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!

Am I still an advisor? Or a builder? I’m having an existential moment.

My work has forever changed in a way I’m still trying to understand. Six months ago, agentic vibe coding crossed a threshold. Since then, I have used AI to raise my impact by a magnitude.

  • I designed landing pages end-to-end for a major travel brand that made it into production.
  • I automated topic prioritization, SEO testing, and SEO reporting for my clients with full-blown apps.
  • I built an array of useful applications for myself, from automating the SSI (SEO Site Index found in the bimonthly Growth Intelligence Briefs) to Openclaw agents that help me with research and charts.

The work I shipped improved, while it also became harder to define. But when the cost of building collapses due to AI, judgment is the only thing that doesn’t compress. Meanwhile, most operators are still hiring, budgeting, and measuring as if execution is the constraint.

A screenshot of the keyword universe I built for my clients. One example of several tools I built to make my work more efficient. (Image Credit: Kevin Indig)

I’m not alone: AI companies are reaching $100 million ARR faster than ever, in large part because they’re AI native. Their whole product development philosophy is fundamentally different. Heck, Anthropic went from $9 to $30 billion USD in six months and is now worth about as much as Starbucks, Mastercard, or McDonald’s.

And I have my feelings about Matt Schumer’s essay “Something Big Is Happening,” but with reportedly 80 million views, it clearly hit a nerve.

AI companies grow faster than anything before (source: Bain) – Image Credit: Kevin Indig

So, I want to take a beat from publishing research this week and take measure of how agentic coding changes software, distribution, and people.

The Effect On Software

In 2024, I made a bold prediction that AI agents would hit 100 million users in 2025. I was off by about a year. Agents didn’t hit 100 million users in 2025, but they did hit production in 2026, and the gains are measurable:

  • METR found 1.5 to 13x (!) time savings when technical staff used Claude Code.
  • A 40% reduction in cost and 60% reduction in time from agentic AI is not unrealistic.
  • Bain & Co estimates a 30-50% gain in productivity from deploying AI agents and automation.
Time savings factor results chart
Study from METR showing time savings between ~1.5x and ~13x (Image Credit: Kevin Indig)

What happens to software when non-engineers can ship code?

After the iShares software ETF (IGV) cratered 24% in Q1 2026 (steepest quarterly drop since Q4 2008), you could sense a panic in the air that AI would make software companies redundant. But software is more than code.

Enterprise software has strong guards against AI redundancy. Anyone who has ever purchased a CRM or migrated to another vendor knows how hard this is and how much is involved.

Enterprise software is more than code. It’s code plus integration, security, uptime, sales, and support … all wrapped up in procurement cycles, IT review, and legal sign-off.

AI can chip away at any one of those pieces. For example, an agent can handle an integration, run a security audit, even book a demo. But no agent shows up to get sued when a mission-critical system goes down at 3 A.M. Accountability is the part that doesn’t unbundle. Enterprise companies don’t replace this stack; they build their own agents and AI workflows on top of it.

Self-serve software is a different beast. Anyone can now spin up a simple task tracker in a Kanban format. I personally would rather pay a few dollars a month and spare myself the hassle of bug fixing, but it’s possible and quick. Self-serve products need to move upmarket. The playbook shows up in Notion’s, Figma’s, and Canva’s moves into enterprise.

In this shift, two archetypes stand out:

  1. Data providers.
  2. System of records.

1. Data providers provide value by making data that the market could not otherwise access. These companies lose leverage from their user interfaces but gain it from their data. For example, let’s say a data provider gives you app store rankings. The user interface for that company is slowly turning into friction as more people can code their own dashboards. But their data becomes much more interesting. The durable levers for APIs/MCPs in this world are data completeness, uniqueness, stability, and cost. The logical move is to shift to a headless experience for early adopters and keep the user interface for legacy users.

2. Systems of record (SOR) are the canonical place where a company’s own data lives. Salesforce, Workday, or Coupa are the bane of existence for many people, but they’re billion-dollar companies because they’re extremely hard to replace. The moat is the tangle of permissions, audit trails, integrations, compliance posture, and decades of workflow conventions built around that data. An agent can generate a CRM in an afternoon; replacing Salesforce at a Fortune 500 is a multi-year change-management project. These companies have already started and will continue to use AI more to provide better user experiences. But their levers are depth of integrations, compliance and audit posture, switching cost, and the quality of their agents. The winners in the SOR space are the ones whose agents make the existing system of record more useful, not the ones trying to replace it.

The Effect On Distribution

Distribution is more important than product, or so the saying goes, but getting it in 2026 is hard. Platforms are closing (by reducing clickouts and keeping users inside), and they’re taking opportunities away to convert or build direct relationships with visitors outside of the platform.

  • AI Overviews and AI Mode make more than 50% of clicks redundant and keep users on the Google platform.
  • AI chatbots send a tiny fraction of traffic out.
  • Social is flooded, word of mouth is uncontrollable, and paid gets more expensive.

From The Brand Tax:

Cost per visit climbed 9.4% in 2025 alone, adding to a 30% cumulative increase over 3 years. Conversion rates fell 5.1%.

How do you get distribution in this AI-first world? Two levers compound:

  1. Velocity.
  2. Product.

1. Velocity means you execute faster (and better) than your competitors. When all distribution channels decline, and no alternatives open up, the only way to grow is to leverage them better. Play the game better than the competition. Fast shipping speed becomes table stakes, and ideas + compute become the differentiators.

PwC found AI speeds content production up by 3-10x. In plain words, we need to automate more. But not at the cost of trust. When you lose trust, you lose the game.

2. Product is the marketing now, with two distinct effects:

  • AI sees through marketing gloss. Agents can read ingredient lists, parse reviews, compare specs. “We’re the best X in the world” doesn’t survive an agent that actually checks. But strong products get chosen consistently.
  • Free product is the new top of the funnel. Standalone tools that solve a real problem are easier to build than ever, and they acquire better than ads. Ramp Sheets routes users toward Ramp’s core product without a marketing budget.

When product is the marketing, the emphasis shifts to product growth: onboarding, engagement, retention. The fastest-growing products these days all have product-led growth motions. So, marketing and product development melt together.

The Effect On People

AI capability is racing ahead, but human cognition … isn’t. Until we reach AGI (God knows when; I hope not any time soon), human cognition is what limits AI productivity. We can only ship as much as we can review.

AI tools can take in more input than ever before, while our own human attention span is declining: AI’s context windows grew 3,906x (!) over the last 10 years, from 512 to 2 million tokens, while human attention has shrunk. We’re outsourcing thinking faster than we’re learning to check it.

Refer to caption
Image Credit: Kevin Indig

Two cost curves are racing each other: the Cost to Automate (exponential decay) versus the Cost to Verify (biologically bottlenecked). In “Some Simple Economics of AI,” Catalini et al., argues that tasks with a verifiable output will be automated the fastest. Work that requires a human to check it compounds slower, so we’ll automate work that’s easy to measure faster. I feel it whenever I’m running four terminal windows at once: the focus drain is as high as the throughput. At scale, what holds us back is how much we can proofread and direct.

When anyone can build anything, the ways we’re limited change: Skill and tools matter less. But judgment, ideas, and time decide whether you run in the right direction or in circles. It’s very easy to get distracted with AI because the cost to build is now so low.

Judgment is the part that doesn’t compress. I can ask Claude Cowork for a contract review, but I have to know what it missed. Claude will happily write me a Q4 plan, but it’s only as good as my read on which market to attack and what my competitors are about to do.

Over the last six months, I implemented more agentic and automated systems than I’ve done hands-on work. My clients now have access to unique software they can’t get anywhere else that solves their unique problems.

Three things are now compressing toward zero: the cost to build software, the cost to produce content, the cost to spin up a tool. But another cost is trending far away from zero: The cost to know whether any of it is right.

I’m not directly “doing” the work anymore in a traditional sense. I’m now building the thing that does the work, then checking it. The work that matters now is the part I can’t hand to an agent … knowing what to build, what to kill, and what the agent missed. And I’m here to figure out what that means – with you.

More Resources:

Featured Image: Fit Ztudio/Shutterstock; Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments
May 5 2026
Google Says A New Wave Of AI Users Is Transforming Search via @sejournal, @martinibuster

Google’s Martin Splitt and Nikola Todorovic discussed the impact of AI on search, revealing that there’s a new wave of people that are doing things with Google search that is markedly different than in the past and that this is an upward trend.

Martin Splitt noted that AI in search is not new and that it had always been there behind the scenes assisting in the organic search results. It’s only recently that it’s been moved to the forefront where it is now assisting users with increasingly complex multimodal search queries. Funny thing about AI search is that whereas AI plays a role in the background of organic, organic search plays a role in the background of AI search.

Martin asked if AI Search is evolutionary or a revolutionary change:

“Yeah, because I think everyone is talking about AI in search as if it’s a new thing, but it has been there behind the scenes, so to speak, before that.

  • So what makes these AI features that people are using now and that are progressively enhancing the search experience for them so different from the features we had before?
  • Would you consider these new features revolutionary and completely different from what we’ve been doing so far?
  • Or is it more like an evolution of what we have been doing in the past?”

Google’s Nikola Todorovic, Director of Software Engineering at Google Search, answered that it’s revolutionary and that search today is very different from what it was ten years ago. he also noted that current AI-driven search behavior is changing because users are becoming increasingly confident about the kinds of questions that Google is able to answer.

Todorovic replied:

“I think the way they are being used, and I think it is a revolution that they’re speaking of right now. But clearly in the whole process, there’s like small steps. But if you compare search now and search 10 years ago, it’s a very different product. So I would say yes, this is like a big step change and it is absolutely changing the way the users are searching.

So if you think about it, any feature is changing in some way. For example, if you bring like more images, videos, etc, then it is bringing this kind of experience. So people are going more to image search. For example, when we added what we call the image universal blocks on the main page. Now that this new wave is also changing the way the users are searching because they are uncovering that search can actually answer to more complex questions.

And for that reason, we do see that user queries or you call them prompts now, so they’re getting longer. They become more detailed and the average query length is growing.

So we do see the new traffic and this new wave of traffic is a consequence of users being able to see, aha, there is something new I can do over here. That’s from that perspective, it is revolution, but it is obviously a bunch of steps in between that happened and have been improving search all the time.”

Key insights about search behavior today:

  • User queries are becoming longer and more detailed.
  • Users are discovering new things they can do with search.

That last one is important and may partially explain where some of the traffic is going. People are doing more complex searches, plus as noted in a podcast interview of Liz Reid, people are using multiple AI chat services.

While some SEOs say that AI Search is longtail now, that’s not really what’s happening behind the scenes because Classic Search is still happening behind the scenes because the AI is splitting complex queries into simpler fan-out queries. “Keyword-ese” queries are still happening to a certain extent but now they’re components of a larger query that itself is longtail.

Takeaways

  • AI in search is not new, but AI at the front of the search experience is changing how people use Google.
  • Google says search today is a different product than it was ten years ago because users are asking longer, more detailed, and more complex questions.
  • Users are discovering that search can handle questions they may not have tried before, which is creating new search behavior.
  • AI Search may look like longtail search on the surface, but Google can break complex prompts into simpler fan-out queries behind the scenes.
  • Classic Search still matters because AI Search depends on retrieval. Organic search has not disappeared. It has moved into the background of the AI experience.
  • Keywords are not dead. They may now function as smaller pieces inside larger prompts and more complex search sessions.
  • Content has to work at two levels: retrievable for classic search and useful for more complex AI Search behavior.

The important insight is not that users are writing longer queries, but that users are learning what search can do now. As AI Search solves more complex queries, SEO begins to feel more uncertain. It may be useful to consider that simpler fan-out queries are what is being optimized for. But also see the insights about Browsy Queries.

Listen to Search Off The Record

Featured Image by Shutterstock/takasu

Ecommerce MGMT 0 Comments
May 5 2026
Google: AI Makes Human Experience More Important For Content via @sejournal, @martinibuster

A recent Search Off The Record episode featuring Martin Splitt and Nikola Todorovic, Director of Software Engineering at Google Search, explored the revolutionary aspect of AI and how a new wave of users are crafting longer conversational queries. They pointed out that while AI has democratized access to information it has made experience-based insights more valuable, implying that this is a key to standing out in the AI search.

AI Makes Human Experience And Opinions More Important

While AI is making information more accessible, it’s making basic information less important because it’s something that AI can do. Something like the specs of Texas Instruments OPA1656 op-amps is something that is provided by Texas Instruments and data sheets available from sites like electronics warehouses like DigiKey and Mouser.

What AI can’t provide are opinions and experience with those electronic parts, like what is the sonic difference between using an OPA1656 and something else that is six times more expensive? This is something that an AI can’t provide and as a consequence human experience and opinion is the thing that is variously referred to as the “value” that makes one site useful and another site not useful.

Martin Splitt made this case in talking about how AI can bridge human experience and the basic type of information that’s found “on the box.”

Splitt explained:

“Some people have misunderstood whatever it was that they’re trying to accomplish or to provide to be these cumbersome bits and only these cumbersome bits, right?

But eventually that turned into…, how do I put this nicely, putting words around spec sheets from manufacturers. And that wasn’t really the value that I was looking for. I’m not interested in knowing how many gigahertz a certain new processor has because I can read that basically on the box. It says it on the box. You don’t have to tell me that this is now a 3 gigahertz processor. It says it on the box.

And I had a key moment when I was buying a joystick back in the days for a computer game. And I didn’t know what force feedback was. And that’s effectively you have a different resistance. And it might move and vibrate the device if there’s any shaking happening in the surroundings. And I didn’t know what that was. And it said on the box, it has force feedback.

And so I went to someone who worked at the shop, and I anticipated them to be like an expert on the topic. So I’m like, so this says force feedback. What does that mean? And he literally said to me, that means that this joystick has force feedback.

And this is funny, but I’m seeing this a lot in articles and on websites that they’re effectively not giving me any context. They’re just explaining what I can kind of glimpse and gather from the information that is right in front of me. And I think AI makes that easier. You don’t have to spend as much time to rattle off the spec sheets into a more readable human conversational form. But chat bots do that.”

Splitt followed up by saying that it’s no longer necessary for websites to focus on providing commonly available information. That’s still important but there is a higher level of information that based on human experience that websites can provide, even if it’s something as small as explaining what “force feedback” on a gaming joystick is.

Paradoxically, while information is now more widely available than at any point in human history, it’s also made human judgement and opinion more valuable because that’s something that an AI system cannot do.  And while there are many ways to approach content, it’s the subjective information that can be said to be the value add.

Splitt explained:

“So I think there is still enough space online for different outlets and people and opinions and experiences, but I think we have to increase the level of our content to be useful and interesting for humans, from humans to humans. And I don’t think AI is going to take that away. I think AI is going to bridge that.”

Martin Splitt insists that basic content is no substitute for expertise. He suggests that judgment and insights earned through experience are superior to surface-level content that can be found anywhere. Human experience is a key ingredient of high-value content.”

Content that only repeats widely available facts now has a weaker claim on attention because AI can make that same baseline information easier to reach. The stronger opportunity is content built from what a person notices, tests, prefers, questions, compares, and learns through use. That is where experience becomes editorial value, not as a decorative personal angle but as the part of the page that changes what the reader understands.

  • Facts explain commonly known information.
  • Experience explains what it means to a human.
  • What it means turns information into guidance.
  • Guidance is the value-add that makes a web page worth visiting.

What this means for SEO is that these kinds of considerations can be used for evaluating content and identifying reasons why it’s not being indexed, why it’s underperforming in search. And I know that for beginners a step-by-step approach feels useful but in real-life, optimizing for search engines, a checklist approach to optimizing only gets you to a shallow level of content and not to the higher standards necessary to stand out.

Listen to Search Off The Record here:

Featured Image by Shutterstock/ra2 studio

Ecommerce MGMT 0 Comments
May 5 2026
Google Says Search Is Fine, AI Insiders Say the Median Person Has No Future via @sejournal, @gregjarboe

On April 29, 2026, Sundar Pichai stood before Alphabet’s investors and delivered a masterclass in optimism. Google Cloud revenue crossed $20 billion for the first time. AI Overviews are driving Search queries to all-time highs. Gemini Enterprise paid users grew 40% quarter over quarter. “A terrific start to the year.”

One day later, Jasmine Sun published a guest essay in The New York Times called “Silicon Valley Is Bracing for a Permanent Underclass.” Her opening line: “Most people I know in the A.I. industry think the median person is screwed, and they have no idea what to do about it.”

Same industry, same week, two completely different stories. Both true.

That’s the uncomfortable reality SEO professionals, content creators, digital marketers, and entrepreneurs need to sit with. The gap between the investor deck and the off-the-record conversation has never been wider, and navigating it requires more than following the headlines.

What Pichai Is Telling Investors

Pichai’s Q1 2026 remarks were a triumph of the quantifiable. Search revenue grew 19%. AI Overviews are bringing people back to Search, not away from it. The company’s first-party AI models now process 16 billion tokens per minute, up from 10 billion last quarter. Personal Intelligence is now live in the Gemini app, AI Mode, and Gemini in Chrome. Search latency is down more than 35% over five years, and the cost of AI-powered responses dropped more than 30% since Google upgraded to Gemini 3. For anyone who has spent two years worrying that AI would hollow out organic search, the message was: calm down, Search is fine, and we’re winning everywhere.

What Sun Is Telling Everyone Else

Sun’s essay draws on conversations with engineers, venture capitalists, and economists who tend to be more candid off the record than on it. OpenAI’s Tejal Patwardhan, who leads frontier evaluations, told the Times that GDPVal now shows ‘over an 80 percent win rate compared to human professionals,’ a figure that exceeds OpenAI’s highest published benchmark result of 70.9%. The AI Productivity Index evaluates frontier models against investment banking associates, Big Law attorneys, and management consultants, not arbitrarily, but because those benchmarks signal where development energy is being aimed.

Sun also surfaces something that should concern anyone in knowledge work. She reported that “Anthropic researchers found that junior engineers who relied on A.I. coding agents not only didn’t complete tasks much faster; they also understood their work less when quizzed about it afterward”. If that dynamic extends to content creation, marketing strategy, and SEO analysis, it has practical implications for anyone whose career depends on accumulating expertise through practice. 

Why This Is Specifically an SEO Problem

The gap between what AI company executives say publicly and what their researchers say privately is a version of a problem SEO professionals already know well: the distance between what platform owners announce and what practitioners observe in the field.

Google has spent years telling advertisers that its systems reward quality and intent. SEO practitioners have spent years measuring what actually moves rankings. Sometimes those accounts align. Often, they don’t completely, and the discrepancy only resolves through direct testing.

The AI era is creating a similar dynamic at a much larger scale. Pichai tells investors that AI Overviews are driving more queries. Sun reports that recent college graduates are applying to hundreds of jobs without a single interview. Both can be simultaneously accurate. Neither tells you what to do Monday morning. 

Ground Truthing in the AI Era

The phrase “ground truthing” comes from cartography. Before you trust what a satellite image appears to show, you send someone to the actual location to verify. You gather objective, empirical data through direct observation.

That discipline is what the AI era demands from marketing professionals. Not faith in the bullish investor narrative, not paralysis in the face of the bearish cultural one, but a methodical commitment to measuring what is actually happening in your specific market with your specific tools.

What is your organic click-through rate doing as AI Overviews expand? Are conversion rates from AI-assisted search traffic different from traditional organic? If you have started using AI for content production, what is happening to time-on-page, return visits, and brand sentiment? Are junior team members building expertise or outsourcing the thinking?

These are answerable questions, and the answers will tell you far more than either a Q1 earnings call or a New York Times opinion essay.

Confident claims about what AI means for your business will keep coming. Some from people with financial incentives to sound optimistic. Some from people whose job is to surface uncomfortable truths. Your job is to test both against observable reality and update accordingly. That’s not pessimism. It’s just good measurement practice, which has always been the foundation of effective SEO.

More Resources:

Featured Image: Kateryna Onyshchuk/Shutterstock

Ecommerce MGMT 0 Comments
May 5 2026
Why AI Search Skips Your Content (And How to Diagnose Where It’s Failing) via @sejournal, @jeffrey_coyle

This post was sponsored by Siteimprove. The opinions expressed in this article are the sponsor’s own. 

Why does my content get crawled but never cited in ChatGPT or Perplexity?

How do I tell if my AI visibility problem is technical or content-quality related?

What actually decides whether AI picks my page over a competitor’s?

The gap between appearing in an AI answer and being retrieved by an AI system is where the actual AI search strategy lives.

This article breaks down that AI search strategy process:

  1. How AI search systems retrieve and select content.
  2. Why eligibility alone doesn’t win.
  3. How to diagnose whether your content is failing at the retrieval layer or the quality layer.

The fix is different for each, and most teams are solving the wrong problem.

How AI Search Crawls Your Site & What Just Changed

AI search systems still rely on crawlers. If your pages block crawl access, depend on unexecuted JavaScript rendering, or bury content behind authentication walls, nothing downstream matters.

Semantic HTML, proper heading hierarchy, and descriptive markup remain the cost of entry. But the stakes are higher now: these aren’t just accessibility compliance items anymore. They’re the structural signals AI systems use to parse and chunk your content for retrieval.

Platforms like Siteimprove.ai that audit accessibility and content quality natively can surface these issues before they become retrieval problems. If you’re already running accessibility audits, you’re closer to AI search readiness than you might think.

What has changed is what happens after the system accesses your content.

Why You’re Now Competing Paragraph-by-Paragraph, Not Page-by-Page

AI systems don’t ingest a page as a single unit. They break it into passages: discrete chunks of text that get indexed independently.

This is where most traditional SEO thinking falls short. You’re no longer competing at the page level. You’re competing at the passage level.

A 3,000-word guide might contain 15 to 20 individually indexed passages. Some of those will be clear, self-contained, and directly responsive to a query. Others will be vague transitions or filler paragraphs that contribute nothing to retrieval.

Every passage is either a retrieval candidate or a wasted one. A page can rank well in traditional search while performing poorly in AI search, because its best passages are buried inside paragraphs the system can’t cleanly extract.

How to audit passages manually:

  1. Copy one important page into a plain document. Break it into individual paragraphs or short sections, then read each passage on its own without the surrounding page context.  
  2. Ask one question per passage. For each paragraph, write the query it actually answers. If you cannot name a clear query, that passage probably is not strong retrieval material.  
  3. Rewrite weak passages to stand alone. Lead with the answer, add specific context, and remove vague transitions that only make sense when someone reads the full page from top to bottom. 

      How AI Picks Which Passages Make It Into an Answer

      When a user asks an AI system a question, the system doesn’t read the web in real time. It queries a pre-built index, retrieves the most relevant passages from potentially millions of candidates, and scores them for relevance and quality.

      But the system rarely stops at the literal query. It expands the question into a network of related sub-questions (follow-ups, edge cases, adjacent concerns) and retrieves passages for each. This is query fan-out, and it fundamentally changes what “ranking” means.

      Your content isn’t just competing against pages that target your exact keyword. It’s competing against everything the system retrieves across that entire network of related queries.

      A page that answers one narrow question well might get retrieved for that specific sub-query. But a page that anticipates the follow-ups, the “what about” variations, and the context a user would need next gets retrieved across multiple nodes in the fan-out. That’s a fundamentally different kind of competitive advantage.

      Citation happens after all of this. The system attributes its synthesized answer to the sources that contributed the most useful material. Chasing citations without understanding retrieval is working backwards.

      How to map a simulated query fan-out manually:

      1. Start with one target question. Write down the main query your audience would ask, then list the follow-up questions they would naturally ask next.  
      2. Group those questions by intent. Separate beginner questions, implementation questions, comparison questions, edge cases, and decision-making questions.  
      3. Match each question to existing content. If a question does not map to a clear passage on your site, that is a retrieval gap. If it maps to a vague or buried passage, that is a passage-quality gap. 

      Why Being Indexed Doesn’t Mean You’ll Get Cited

      Here’s where most AI visibility strategies stall.

      Teams invest heavily in technical optimization (fixing crawl issues, improving page speed, adding structured data) and assume the rest will follow. They treat retrieval readiness as the destination instead of the starting line.

      Being indexed by an AI system means your content can be retrieved. It doesn’t mean it will be.

      Consider a practical example. Two sites publish guides on international SEO for e-commerce. Site A has strong domain authority, clean technical SEO, and a 4,000-word guide that covers the topic broadly but generically. Site B is a smaller consultancy with a 1,500-word page focused specifically on hreflang implementation for Shopify stores with three or more language variants.

      When an AI system receives a query about multilingual e-commerce SEO, it fans out into sub-questions. For the specific sub-query about hreflang configuration on Shopify, Site B’s focused passage gets retrieved and cited. Site A’s guide technically covers hreflang, but its relevant passage is buried in paragraph 37 of a general overview, sandwiched between topics that dilute its signal.

      Site A is retrieval-ready. Site B is answer-worthy. That distinction is the core tension of AI search optimization, and it requires a completely different audit than most teams are running.

      How to test this manually:

      1. Run the same query across multiple AI search experiences. Use a small set of high-value questions and record which sources are cited or referenced.  
      2. Compare the cited source to your page. Do not compare the full articles. Compare the exact section or passage that appears to answer the query.  
      3. Look for the selection difference. Ask whether the cited passage is more specific, more direct, more current, or more practical than yours. That usually reveals why it won. 

      The Two Signals That Decide AI Search Passage Selection

      The hreflang example illustrates a broader pattern. Once your content clears the technical gates, competition shifts entirely to quality. And “quality” in AI retrieval means something more specific than most content strategies account for.

      Information Gain Is A Very Important Signal

      An important factor in passage selection is whether your content contributes something the system can’t assemble from other sources.

      This is information gain: original data, proprietary research, first-person case studies, or novel frameworks that don’t exist elsewhere in the index. When every other passage in the candidate pool says roughly the same thing, the passage that introduces a new data point or a genuinely different perspective has a structural advantage.

      Generic coverage that restates widely available information is the easiest content for an AI system to replace with any other source. Original expertise is the hardest. If your content strategy doesn’t have a plan for producing material that is uniquely yours, you’re filling the index with passages any competitor could displace.

      How to identify information gain manually: 

      1. Review the top competing pages for the same topic. Look for repeated claims, definitions, examples, and recommendations that appear across nearly every source.  
      2. Mark anything your page says that competitors do not. This could include proprietary data, internal benchmarks, customer examples, expert commentary, original frameworks, or lessons from implementation.  
      3. Strengthen the unique material. Move original insights higher on the page, give them clearer headings, and support them with concrete examples instead of burying them in generic explanation. 

      How Topic Depth Gets More of Your Pages Into the Candidate Pool

      Information increases the likelihood that gain gets your best passages selected. Depth and coverage determine how many passages you have in the candidate pool to begin with.

      AI systems exploring a subject pull from multiple passages across multiple pages. If your site covers a topic comprehensively, with dedicated pages for subtopics, related concepts, and adjacent questions, you create more opportunities to be retrieved across the full query fan-out.

      This works at two levels. Across your site, topic clusters with focused pages for each subtopic outperform a single pillar page surrounded by thin supporting content. Within a single page, going three layers deep on a subject (the basics, the edge cases, and the practitioner-level tradeoffs) gives the system more high-quality passages to select from.

      A domain with strong general authority but shallow coverage of a specific subject will lose passage-level retrieval to a smaller site that covers that subject exhaustively. AI systems evaluate authority at the topic level, not just the domain level.

      How to assess topic depth manually:

      1. Create a simple topic map. Put your main topic in the center, then list the subtopics, adjacent questions, use cases, objections, comparisons, and technical details a buyer or practitioner would need.  
      2. Assign each subtopic to a URL. If several important subtopics are crammed into one broad guide, they may need dedicated pages or stronger sections.  
      3. Look for thin or missing coverage. Prioritize gaps where competitors have specific, useful content and your site only has a passing mention. 

      How to Diagnose Why Your Content Isn’t Getting Cited In AI Answers

      When AI visibility underperforms, the instinct is to produce more content. That’s often the wrong move.

      The first diagnostic question is simpler: is this a retrieval problem or a quality problem? Each has different symptoms, different causes, and different fixes.

      Signs Your Content Never Reaches the AI’s Candidate Pool

      If your content isn’t appearing in AI responses at all, even for queries where you have relevant, published material, the issue is upstream. The content isn’t reaching the candidate pool.

      Audit for these signals:

      • Crawl access restrictions or rendering failures preventing indexing.
      • Missing or broken semantic structure: heading hierarchy, section markers, descriptive markup.
      • Passages that are too long, too short, or too loosely structured to be extracted cleanly.
      • Content buried inside tabs, accordions, or interactive elements that don’t render for crawlers.

      In practice, this looks like a page that performs reasonably in traditional search but generates zero AI citations. The content might be strong. The system just can’t access or parse it at the passage level.

      Retrieval failures are technical. They’re also the fastest to fix, because the content itself may already be competitive. It just needs to reach the candidate pool.

      Signs You’re in the AI Search Citation Pool but Losing to Competitors

      If your content is being retrieved but not selected, or selected less often than competitors for the same queries, the issue is downstream. The system can see your content. It’s choosing something else.

      Audit for these signals:

      • Passages that are vague, indirect, or take too long to reach the point.
      • Coverage gaps where competitors address sub-questions your content ignores.
      • Lack of original data, examples, or practitioner-level specificity.
      • Generic treatment of a topic that other sources cover with equal or greater depth.

      The telltale sign is finding competitor citations for queries your content should own. When you compare the retrieved passages side by side, the competitor’s passage answers the question more directly, with more specificity, in fewer words.

      Quality failures require content investment. They can’t be solved with technical fixes alone.

      Fix This First, Then Move to Quality

      Start with retrieval. Technical fixes are lower effort and unlock everything downstream. A page that isn’t being crawled or chunked properly can’t benefit from content improvements at any level.

      Once retrieval is confirmed, shift to passage-level quality. Identify the specific queries where competitors are winning selection, compare the actual passages head-to-head, and close the gap at the individual passage level rather than rewriting entire pages.

      The highest-ROI work sits at the intersection: passages that are already being retrieved but aren’t winning selection. They’re close. They just need to be more direct, more specific, or more useful than the alternatives.

      How to prioritize fixes manually:

      1. Create a simple two-column audit. Label each issue as either “retrieval” or “quality.” Retrieval issues include crawl blocks, broken structure, hidden content, and poor extractability. Quality issues include vague answers, missing examples, shallow coverage, and weak differentiation.  
      2. Fix retrieval blockers first. There is no point improving a passage that systems cannot access, parse, or associate with the right topic.  
      3. Then improve near-miss passages. Focus on pages that already rank, receive impressions, or cover the right topic but lose citations to more specific competitor content. 

      What to Track Instead of Citation Screenshots

      If the old metrics (mention counts, citation screenshots, brand-name tracking) don’t tell the full story, what does?

      Track retrieval presence separately from citation selection. Retrieval presence asks whether your content appears anywhere in the system’s candidate set for a given query cluster. Citation selection asks whether it was chosen for the final synthesized answer.

      A page with high retrieval presence but low citation selection has a quality problem. A page with low retrieval presence for queries it should match has a technical problem. That distinction tells you exactly where to invest.

      The challenge is that most teams piece this together across disconnected tools: one for accessibility auditing, another for content analytics, a third for search performance. By the time you’ve correlated the data, you’ve lost the thread between cause and effect.

      This is where Siteimprove’s approach matters. Because accessibility auditing, content quality scoring, and search analytics live in one platform with native analytics, you can trace a retrieval failure back to its structural cause without jumping between tools or reconciling data sets. A broken heading hierarchy flagged in an accessibility audit connects directly to the search performance data showing that page’s declining AI visibility. A content quality score on a specific page maps to its passage-level competitiveness for the queries you’re targeting.

      That closed loop between accessibility, content, and search performance is what turns the retrieval-vs-quality framework from a diagnostic concept into an operational workflow.

      How to track AI visibility manually:

      1. Build a query-tracking spreadsheet. Include the query, topic cluster, your best-matching URL, whether your brand appeared, whether you were cited, which competitors appeared, and what type of issue you suspect.  
      2. Track patterns, not one-off screenshots. AI answers can vary, so look for repeated behavior across multiple prompts, systems, and dates.  
      3. Separate visibility from selection. A page that appears in related answers but rarely gets cited likely has a quality problem. A page that never appears for relevant prompts likely has a retrieval or coverage problem. 

      What It Takes to Get AI to Pick You

      The question brands should be asking isn’t “Can AI find us?” It’s “Does AI find us useful?”

      That shift reframes content strategy entirely — from visibility tracking to retrieval mechanics, from page-level optimization to passage-level precision, and from generic authority-building to topic-specific depth.

      Three principles hold across every AI search system operating today.

      First, treat technical accessibility as non-negotiable infrastructure. It doesn’t differentiate you, but its absence disqualifies you.

      Second, build content for the query network, not the individual keyword. AI systems resolve clusters of related questions simultaneously. Your content architecture should map to that same structure.

      Third, prioritize information gain. Original research, proprietary data, and first-person expertise are the hardest assets for an AI system to source elsewhere — and a strong signal that your content deserves selection.

      The brands that win in AI search won’t be the ones that figured out how to get mentioned. They’ll be the ones whose content was too useful to leave out.

      FIND THE GAPS IN YOUR CONTENT SYSTEM

      Image Credits

      Featured Image: Image by Siteimprove. Used with permission.

      Ecommerce MGMT 0 Comments
      May 5 2026
      Google On Keyword Fragmentation And User Needs In AI Search via @sejournal, @martinibuster

      Google’s Liz Reid explained on the Bloomberg Odd Lots podcast how AI Mode and AI Overviews are enabling detailed, need-based query patterns that create new challenges for Google. This points to a consequential change in search behavior that directly impacts how to approach SEO.

      Keyword Fragmentation In AI Search

      Liz Reid explained that users have always wanted to express longer natural language queries but were forced to narrow them down to keywords like “best restaurants in New York” even though what they really wanted may have been more specific like a restaurant with vegan options and an opening for a party of five.

      For as long as I’ve been in SEO, and I’m near 30 years in the business, keyword research has been the foundation of digital marketing. You pick the keywords you want to rank for then create the content in a way that is optimized for that keyword. The problem with optimizing for a short keyword phrase is that there are hidden meanings within that keyword and that’s always been the case.

      The way Google used the issue of latent meanings within keywords is to use things like clicks to better understand what users meant when they typed ambiguous keyword phrases like “restaurants in New York.” Some SEOs believe that the clicks were used for ranking websites but another use for clicks is understanding what people mean when they type ambiguous phrases. What Google has done for quite awhile now is to rank the most popular meaning of the keyword phrase first and no matter how many links a page received, if the content aligned with a less popular meaning the page wouldn’t rank.

      Liz Reid said that people who use AI-based search are using longer queries that articulate what the problem or information need is, making it easier for Google fetch the information they’re looking for. That change gets to the heart of the problem with organic search that AI search is solving and the implications for SEO are profound.

      Liz Reid begins:

      “We have seen with AI overviews meaningfully longer queries. We see more natural language queries, but it’s also not even something as basic as that.

      It can also be like you were searching for restaurants. We used to laugh about the like before I worked on search, I worked on maps and local, some of the intersection with search, and people would just be like, “restaurants New York.”

      And you’re like, what do you want me to do with that query? Like, okay, the best restaurants in New York are going to take three months and 99.9% of the population can’t afford to go to them.

      Okay, but like, are you picking 10 random ones, etc.?

      But like, part of why people would do that is they had a much more complex– I want a restaurant in this location for five people. It can’t be too pricey. I have a vegan member. I also have kids. That was the question they had in their mind.

      And in the old world of keyword-ese, that information would be spread throughout the web. And so you wouldn’t feel confident you could just put in the question.

      And now with AI Overviews and AI Mode, you can start to actually, and you see people do this, they tell you the real problem, right?

      They don’t take their need and translate it to what the computer understands. They try to give the computer their actual need and expect us to do the translation.”

      The big ideas to unpack there are:

      • A typical complex question asked in AI Search may not be solved by one web page.
      • Complex questions may be one-off and rarely, if ever, repeated, which in many cases may lower the value of optimizing for those phrases, because the time used for crafting them could be more profitably spent doing something else.
      • Given that a site will likely share the AI Overviews (AIO) space with another site it increases the need to optimize other factors such as brand icons that stand out in a positive way, use of images that are relevant, and even the use of videos to claim as much AIO space as possible.
      • And yet, perhaps the bigger takeaway is that it’s not all longtail because Google breaks down the longtail phrases into smaller highly specific keyword phrases that reflect a portion of the information need, query fan-out, and fires those off to classic search. Google’s AI then picks from among the top three for each query and uses that to synthesize an answer.

      So it’s not really that SEOs should optimize for long-tail queries because query fan-out uses Classic Search, bringing it all back to the specific queries that web pages are relevant and optimized for.

      Addressing Real Needs

      Reid didn’t go into detail about this point but it’s interesting anyway because she said that the process of breaking a complex natural language query into smaller queries becomes a quality issue. One of the problems with AI Search is that people aren’t searching with the same keyword phrases which means that Google can’t cache similar queries in the same way it can with organic search.

      She explained:

      “I think it means you have to do, it’s a harder job on quality, right?

      You have to take this question, there’s many parts, and you have to figure out how you break it apart. And you have to do work to think about things like latency, because you can’t just, you know, if everyone uses the same keyword and it’s not personalized, then you can cache it all. If all of a sudden the queries get much more diverse, you know, it has consequences there.

      But I think we just see that it’s very empowering people, right? That it takes some of the work out of searching.

      A few years ago, they said, What more can you do with Google search? But if you actually ask them, Okay, when was the last time you spent 20 minutes searching when you would have preferred to spend 2? It’s actually not that hard for me. … And so it’s been kind of exciting to just… make people’s lives easier by helping them address their real need.”

      On the surface, the idea of addressing user’s real needs sounds like one of those unhelpful “be awesome” or “content is king” type slogans. But it’s actually a way that every SEO should be auditing web pages. Rather than limiting their scope to keywords, headings, technical issues, take a look at how it’s filling some kind of need.

      Someone today asked me to look at their website that was having trouble getting indexed. They suspected that it might be a technical issue. My response is that yeah, everyone hopes it’s a technical issue but in many cases, especially for this one I was looking at, the problem becomes apparent when looked at through the lens of asking, “what need is this page filling?” as well as by asking, “How is this not just different from some other page but different and better?”

      Watch the Liz Reid interview here:

      Google’s Liz Reid on Who Will Own Search in a World of AI

      Featured Image by Shutterstock/TierneyMJ

      Ecommerce MGMT 0 Comments
      May 5 2026
      Chrome Extensions for GenAI Visibility

      There is no clear, step-by-step process for optimizing visibility in generative AI models. We’re left with testing and experimenting to reveal the priorities of platforms such as ChatGPT, Gemini, Claude, and others.

      I rely on multiple tools for that testing to ensure a diversity of views.

      Here are my three go-to Chrome extensions for improving genAI visibility. All provide helpful recommendations, though none are definitive owing to the lack of ranking signals, unlike traditional search engines.

      Chrome Extensions for GenAI

      GEO Auditor

      GEO Auditor scores the AI optimization of any page based on three factors:

      • “Semantic coverage” refers to whether the content addresses all related concepts and entities. The extension then lists missing topics to add. (I’ve addressed tools for semantic relevance.)
      • “Extractability” tracks the ease of obtaining answers from your content. This is akin to search optimizer Daniel Shashko’s “atomic facts,” which I’ve covered.
      • “Answerability” measures the density of verifiable facts and E-E-A-T signals (Experience, Expertise, Authority, Trustworthiness) as AI is more likely to cite pages with authority.

      The extension also analyzes (i) crawlability from a domain’s robots.txt file and meta robot tags and (ii) domain authority based on backlinks and citations, though it doesn’t cite the source.

      The extension then recommends optimization improvements per page. For my Smarty Marketing site, the tool suggested:

      • Adding Schema.org markup for an article (or blog post) and the author.
      • Enriching with quantitative data and sources.
      • Addressing missing topics, including Reddit advertising, influencer marketing, and content strategy.
      Screenshot of GEO Auditor's scorecard page.

      GEO Auditor scores any page based on its optimization for AI bots. Click image to enlarge.

      AI SEO Extension

      AI SEO Extension by RadarKit assigns a score to each page’s “AI friendliness.”

      • “AI visibility” provides an overview of the page. AI bots usually access server-rendered HTML first. Some AI crawlers may miss text if injected by JavaScript. This section reveals content that’s readable or not without JavaScript.
      • “AI Access” checks whether leading AI bots can crawl a page based on the robots.txt file and meta tag directions.
      • “Content” provides readability scores, though I’m unaware of any definitive correlation between readability and AI visibility. Many tools and practitioners claim otherwise.
      • Finally, the “Schema” and “LLM txt” sections recommend structured markup. I’ve seen no proof that such markup improves AI visibility, but it won’t hurt.
      Screenshot of a score page for AI SEO Extension.

      AI SEO Extension assigns a score to each page’s “AI friendliness.” Click image to enlarge.

      All in One SEO Analyzer

      All in One SEO Analyzer by AIOSEO focuses on traditional search engines, but it provides a detailed and helpful analysis of H1-H6 headings, which likely drive AI citations.

      Screenshot of the HTML headings analysis on All in One SEO Analyzer.

      All in One SEO Analyzer provides a helpful analysis of H1-H6 headings. Click image to enlarge.

      Ecommerce MGMT 0 Comments