Google-Agent: The Web’s New Visitor Just Got An Identity via @sejournal, @slobodanmanic

On March 20, 2026, Google quietly added a new entry to its official list of web fetchers. Not a crawler. Not a training bot. An agent.

Google-Agent is the user agent string for AI systems running on Google infrastructure that browse websites on behalf of users. When someone asks an AI assistant to research a product, fill out a form, or compare options across websites, Google-Agent is the thing that actually visits the page. Project Mariner, Google’s experimental AI browsing tool, is the first product using it.

This is not Googlebot. Googlebot crawls the web continuously, indexing pages for search. Google-Agent only shows up when a human asks it to. That distinction changes everything about how it operates.

Robots.txt Does Not Apply

Google classifies Google-Agent as a user-triggered fetcher. The category includes tools like Google Read Aloud (text-to-speech), NotebookLM (document analysis), and Feedfetcher (RSS). All of them share one property: a human initiated the request. Google’s position is that user-triggered fetchers “generally ignore robots.txt rules” because the fetch was requested by a person.

The logic: If you type a URL into Chrome, the browser fetches the page regardless of what robots.txt says. Google-Agent operates on the same principle. The agent is the user’s proxy, not an autonomous crawler.

This is a meaningful departure from how OpenAI and Anthropic handle similar traffic. ChatGPT-User and Claude-User both function as user-triggered fetchers, but they respect robots.txt directives. If you block ChatGPT-User in robots.txt, ChatGPT won’t fetch your page when a user asks it to browse. Google made a different call.

Website owners who relied on robots.txt as a universal access control mechanism now have a gap. If you need to restrict access from Google-Agent, you’ll need server-side authentication or access controls. The same tools you’d use to block a human visitor.

Cryptographic Identity: Web Bot Auth

The more significant development is buried in a single line of Google’s documentation: Google-Agent is experimenting with the web-bot-auth protocol using the identity https://agent.bot.goog.

Web Bot Auth is an IETF draft standard that works like a digital passport for bots. Each agent holds a private key, publishes its public key in a directory, and cryptographically signs every HTTP request. The website verifies the signature and knows, with cryptographic certainty, that the visitor is who it claims to be.

User agent strings can be spoofed by anyone. Web Bot Auth cannot. Google adopting this protocol, even experimentally, signals where agent identity is heading. Akamai, Cloudflare, and Amazon (AgentCore Browser) already support it. Google brings the critical mass.

This matters because the web is about to have an identity problem. As agent traffic increases, websites need to distinguish between legitimate AI agents acting on behalf of real users and scrapers pretending to be agents. IP verification helps, but cryptographic signatures scale better and are harder to fake.

What This Means For Your Website

Google-Agent creates a three-tier visitor model for the web:

  1. Human visitors browsing directly.
  2. Crawlers indexing content for search and training (Googlebot, GPTBot, Google-Extended).
  3. Agents acting on behalf of specific humans in real time (Google-Agent, ChatGPT-User, Claude-User).

Each tier has different access rules, different intentions, and different expectations. A crawler wants to index your content. An agent wants to complete a task. It might be reading a product page, comparing prices, filling out a contact form, or booking an appointment.

Here’s what to do now:

Monitor your logs. Google-Agent identifies itself with a user agent string containing compatible; Google-Agent. Google publishes IP ranges for verification. Start tracking how often agents visit, which pages they hit, and what they attempt to do.

Check your CDN and firewall rules. If your security tools aggressively block non-browser traffic, Google-Agent may be getting rejected before it reaches your server. Verify that Google’s published IP ranges are permitted.

Test your forms and flows. Google-Agent can submit forms and navigate multi-step processes. If your checkout, booking, or contact forms rely on JavaScript patterns that confuse automated systems, agent visitors will fail silently. Semantic HTML and clear labels remain the foundation.

Accept that robots.txt is no longer a complete access control tool. For content you genuinely need to restrict, use authentication. robots.txt was designed for crawlers. The agent era needs different boundaries.

The Hybrid Web Isn’t Coming. It’s Logged

A year ago, the idea that AI agents would browse websites alongside humans was a conference talk prediction. Today, it has a user agent string, published IP ranges, a cryptographic identity protocol, and an entry in Google’s official documentation.

The web didn’t split into human and machine. It merged. Every page you publish now serves both audiences simultaneously, and Google just made it possible to see exactly when the non-human audience shows up.

More Resources:


This post was originally published on No Hacks.


Featured Image: Summit Art Creations/Shutterstock

Stop Treating AI Visibility As One Problem. It’s Actually Three, On Three Different Layers via @sejournal, @DuaneForrester

When a brand stops appearing in ChatGPT, or when its share of voice in Perplexity drops by half over a quarter, the typical response from the marketing org is to write more content. Sometimes a lot more. The thinking goes that if AI systems aren’t surfacing the brand, the fix is to feed them more material to work with. That instinct is a misdiagnosis. It’s a retrieval-layer fix being applied to what is increasingly a different kind of problem entirely, and the cost shows up as wasted budget, missed quarters, and a creeping sense that the work isn’t connecting to the outcomes anymore.

The mistake is treating AI visibility as a single problem when it isn’t. There are three structurally different layers between your brand and the answer a user receives, each with its own failure modes, its own fixes, and increasingly its own organizational owner. Diagnose the wrong layer, and the fix doesn’t land.

Where Most Of The Conversation Has Been Living

The first layer is retrieval. This is where the AI search optimization conversation has spent most of the last two years. The mechanics are familiar in shape if not in detail. When a model needs to answer a question grounded in real-world content, it pulls relevant material from external sources and uses that material to construct the response. The technical name is retrieval-augmented generation, or RAG, and the layer it operates on is the gateway between your content and the model’s output.

This is where crawlability, parseability, and chunk-friendliness do their work. If your content can’t be retrieved cleanly, nothing downstream matters. The visibility tracking platforms most marketing teams have evaluated this year measure outcomes that depend on this layer functioning, which is why they tend to reward the same disciplines that produced good results in classical search: structured content, schema markup, self-contained answers, clean technical implementation.

But retrieval has a structural limit, and Microsoft Research has been unusually direct about it. Plain RAG, in their words, struggles to connect the dots. It retrieves chunks of text that look relevant to the question, but it cannot reason about how those chunks relate to each other. When the answer requires synthesizing information across multiple sources, or when the question is broad enough that the right answer depends on understanding patterns across an entire dataset, retrieval alone breaks down. The model gets the chunks and has to guess at the relationships, and guessing is where hallucinations enter.

The discipline question this layer asks is straightforward. Can the model retrieve our content at all, and is it retrieving the right content for the right query? Most marketing teams have some version of this work in flight already, even if the specific tactics have shifted from classical SEO. But retrieval is only the gateway. Even when a model retrieves your content correctly, what it does with it depends on whether you exist as a recognized thing in the layer above.

The State of AEO/GEO Report Conductor 2026

Where Entity Recognition Does The Real Work

The second layer is the relationship layer, and the dominant structure on it is the knowledge graph. The major search infrastructures all maintain one. Google’s Knowledge Graph, Microsoft’s Satori, and the open knowledge graph built on Wikidata and schema.org collectively define how your brand is represented as an entity, what category you sit in, and which other entities you’re connected to.

This is the layer that decides whether AI Overviews and large language model responses treat you as a recognized member of your category, or as one fuzzy candidate string among many. Brands that exist as clean, well-defined entities get cited consistently. Brands that exist as undifferentiated tokens scattered across the open web get pattern-matched against fifty other candidates and lose more often than they win.

Knowledge graphs have been around long enough that the discipline is reasonably mature. Schema markup on owned properties, consistent naming and identifiers across the open web, structured presence on the high-trust nodes like Wikidata entries and review platforms, and the slow accumulation of brand mentions in contexts that the graph treats as authoritative. This is where the unlinked brand mentions conversation lives, because consistent contextual mentions strengthen the entity even without a hyperlink attached. The fix at this layer is structural rather than volume-based. Writing more content does almost nothing if the entity definition underneath it is fuzzy.

The discipline question here is harder than the retrieval-layer question. Are we a clean, defensible entity in our category, or are we still being pattern-matched against fifty other candidate strings? A brand that can’t answer that question affirmatively is going to lose ground in AI search, regardless of how much content it produces, because the second layer is where the model decides what your content is actually about.

The knowledge graph tells the model what your brand is. But increasingly, your brand has to function inside a third layer that most marketing teams haven’t met yet, where the model isn’t just understanding you, it’s being asked to reason about you on behalf of someone making a decision.

The Layer Enterprise Companies Are Quietly Building Right Now

The third layer is the context graph, and this one needs a careful introduction because most of the marketing conversation hasn’t reached it yet.

A context graph has the same structural shape as a knowledge graph, with entities, relationships, and typed connections, but it’s grounded differently. A knowledge graph models the world. It tells you what things are and how they relate in general. A context graph models a specific organization’s data, decisions, policies, and operational reality. The cleanest framing I’ve seen calls a knowledge graph the library and a context graph the operating manual written by the people who actually run the place. The library tells you what exists. The operating manual tells you what’s relevant, what’s authorized, and what to do about it right now. The library is read-only semantic infrastructure. The operating manual is a living operational layer that grows every time a business process executes.

What separates a context graph from anything that came before it is that governance lives inside the graph rather than alongside it. Policies, permissions, validity windows, and authorization rules are nodes the graph itself queries, not external documentation applied at the edges. When an agent retrieves something from a context graph, the result has already been filtered through what’s currently authorized, currently valid, and currently applicable. The graph is also continuously evolving, so what it knows about you this week is not necessarily what it knew last quarter. That’s where the word “governed” comes from when people in this space talk about governed retrieval. It isn’t a frame, but rather the architecture.

That architecture used to be invisible to anyone outside the organization that built it, which is why marketers haven’t had to think about it. That changed at Google Cloud Next ’26, when Google introduced the Knowledge Catalog inside its new Agentic Data Cloud. Google’s own description of the product, written in their own first-party blog content, says the Knowledge Catalog constructs a unified, dynamic context graph of your entire business, enabling you to ground agents in all of your business data and semantics. That sentence is the moment the term left the data-engineering blogs and entered enterprise procurement vocabulary.

The reason this matters for marketing is that context graphs are what’s going to power the next generation of agents inside your enterprise customers. Gartner projects that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. Procurement agents, competitive intelligence agents, content strategy agents, vendor evaluation agents. These agents won’t be reasoning about your brand from the open web. They’ll be reasoning about your brand from inside their company’s context graph, and what that graph says about you depends on what got ingested into it.

That ingestion is where the work for marketing lives. The brand that arrives at the context graph fragmented arrives weak. If your category positioning is inconsistent across owned and earned media, the graph picks up the contradictions and represents you ambiguously. If your entity data is fuzzy on the second layer, it stays fuzzy when it gets pulled into the third. If your third-party signal is thin or contradictory, the graph has nothing solid to anchor to. The work is upstream of the graph, but the consequences land downstream of it, inside an agent’s reasoning process that you’ll never see directly.

I think of this discipline as governed visibility. The practice of making sure your brand arrives at the context graph in a state that holds up under governed retrieval. Clean entity definition, consistent third-party representation, reliable structured data, and a category position that doesn’t fall apart when an agent traverses the relationships around it. Governed visibility isn’t a new tactic stack. It’s the result of doing the second-layer work well enough that the third layer has something solid to ingest.

The discipline question at this layer is the one most marketing teams haven’t started asking yet. When an agent inside our customer’s company is reasoning about us, what does it find, and is the version of us it finds the version we’d want it to act on?

Three layers, three different problems, three different fixes. But also three different responsibility zones, and that’s where most teams are quietly losing ground.

The Reason Most Teams Will Lose This Even Though They’re Working Hard

Each layer maps to a different organizational responsibility, and most marketing teams only own one of the three cleanly.

  • The retrieval layer is shared with web, dev, and sometimes IT. Marketing influences what gets published, but the infrastructure that makes content retrievable sits in someone else’s domain.
  • The knowledge graph layer is genuinely marketing’s territory. Schema discipline, entity definition, third-party signal, brand consistency, the slow structural work that compounds over years.
  • The context graph layer is where IT owns the infrastructure inside the customer’s organization, but marketing has to influence what gets ingested. The work is upstream, and the consequences land downstream, often invisibly.

The teams that win in 2026 are the ones that figured out how to operate across all three responsibility zones rather than perfecting their work on just one. Most teams I see are still optimizing their owned content, which is the retrieval layer, while losing ground on entity definition, which is the knowledge graph layer, and remaining completely absent from the context graph conversation, which is the layer where some enterprise businesses are quietly standing up right now.

The work isn’t writing more content. The work is figuring out which layer the problem actually lives on, and building the disciplines to operate on all three. Governed visibility is the third-layer discipline that marketing is going to have to develop, whether or not the term sticks. The brands that build it now will look prepared in eighteen months. The brands that don’t will be wondering why their content investments stopped producing the visibility they used to.

If any of this lands or contradicts what you’re seeing inside your own teams, I want to hear about it. Drop a comment about which layer your work has been concentrated on, where you’re seeing the gaps, or where the responsibility zones break down inside your organization. The patterns are still forming, and the conversations in the comments tend to be fresher than anything else.

A lot of the measurement frameworks for this kind of work sit in The Machine Layer, which expands the original 12 KPIs for the GenAI era into something teams can actually run against.

The State of AEO/GEO Report Conductor 2026

More Resources:


This was originally published on Duane Forrester Decodes.


Featured Image: Master1305/Shutterstock; Paulo Bobita/Search Engine Journal

Lessons Learned From Adobe’s 2026 Q2 AI Traffic Report via @sejournal, @slobodanmanic

The sign on AI-referred traffic conversion flipped. I’m not sure if enough of us have noticed.

Twelve months ago, visitors arriving at U.S. retailers from AI assistants converted at roughly half the rate of visitors from other channels. In March 2026, they converted 42% better. Same channel. Same stores. Different year.

Adobe Analytics published the 2026 Q2 AI Traffic Report on April 16 (Adobe’s fiscal Q2 covers calendar Q1 2026). The growth numbers land first: AI-referred traffic to U.S. retailers grew 393% year-over-year in Q1 2026, peaking at 1,151% YoY in December. Engagement up 12%, time spent up 48%, pages per visit up 13%, revenue per visit up 37%. All measured against non-AI traffic in March 2026, using Adobe’s own analytics data from retailers running on the Adobe platform.

The real story is the conversion sign flip. The channel went from worst-performing in U.S. retail to best-performing. In 12 months.

If you run or optimize a website, this changes which number actually matters to you.

One caveat worth naming up front. Adobe publishes this report alongside Adobe LLM Optimizer, a product they sell for making websites more visible to AI assistants. The research and the product roll out together, and the link sits inside the report itself. The underlying numbers are Adobe’s own, self-reported from their analytics platform, and the kind of data that would be hard to fake and easy to challenge if it weren’t accurate. But the framing should be read knowing the vendor also sells the tool that addresses the problem the report describes. Thanks to Els Aerts for flagging this.

2026 Adobe Report Suggests AI Traffic Converts Better Than Non-AI Traffic

This is not something slowly getting better. This is something that’s gone from pretty much broken to kind of working.

Maturation would look like half the non-AI rate to 25% worse to 10% worse to break-even to slight edge. Three, four years of grind. Slow curve. Predictable report cycles. That’s what maturation normally looks like for a new channel. Paid search did that. Mobile did that. Social did that. AI-referred traffic is not doing that. Two measurement checkpoints twelve months apart, sign flipped. Different kind of event.

The playbooks calibrated to “AI traffic is early, optimize gradually, the channel isn’t mature yet” are calibrated to the wrong curve. Any agency, consultant, or vendor still saying “early stage” or “not ready” about AI retail traffic hasn’t read this month’s numbers. The tell is in the timeline they propose. If the pitch is “let’s learn what works over the next year,” they missed the flip.

They’re working from a brief that’s twelve months out of date.

Why AI Agents Fail To Parse Non-Readable Retail Websites

Adobe’s report dedicates an entire section to what they call Citation Readability: how well a page can be understood, parsed, and surfaced by AI systems. The gap between top and bottom performers is brutal. Homepages from top-AI-visit-share retailers score 62% higher than the bottom. Search results pages, 32% higher. Blog and editorial content, 30% higher.

Read that as an operator’s diagnostic. Adobe is telling you why the growth is uneven.

The 393% aggregate is what’s getting through despite readability gaps. Retailers whose pages AI models can actually parse and cite are pulling the average up. Retailers whose pages AI can’t read reliably are dragging it down.

Most website owners don’t even know their website isn’t entirely readable by machines.

Not “we know we’re behind on AI.” Not “we’re testing.” Website owners who run their analytics every morning, review conversion rates every week, argue about CRO every quarter, have no visibility into what a GPTBot, ClaudeBot, or PerplexityBot sees when it crawls their product page. Their dashboards don’t show when an AI indexer fetched a shell. Their session recordings don’t capture bots. Their attribution rarely tags AI referrals cleanly.

The real conversion lift on websites that are actually machine-readable is higher than the aggregate suggests. The average is being held down by everyone else.

Comparing Dell’s Internal Data Vs. Adobe’s AI Traffic Trends

Eight days before Adobe published this data, Dell’s head of global consumer revenue programs told Digital Commerce 360 that agentic shopping is delivering “nothing to the point that is earth-shaking” yet.

Both things are true at the same time.

There’s a chance Dell’s website is bad. It’s not that the entire industry of AI-assisted shopping is wrong. Dell was measuring one website. Adobe was measuring aggregate traffic across many retailers. Dell looked at their own conversion data, saw flat numbers, published the number. Adobe looked at the set of websites AI models can read and cite, saw a channel inversion, published that.

If your conversion numbers look like Dell’s, don’t wait for the channel to mature. Audit the website. Dell’s admission is a diagnostic about dell.com. Adobe’s data is about where the channel is going. Don’t confuse them.

How AI-Assisted Research Shortens The Purchase Funnel

Traffic growth the way we were trained to think about it in the last 30 years, that doesn’t matter at all anymore.

Impressions. Sessions. Unique visitors. Page views. The vocabulary that defined SEO and CRO practice from 1998 to 2024. All of it assumed traffic meant humans arriving to decide. You grew top-of-funnel, so more humans entered deliberation. You optimized the funnel so more of them converted. That was the arithmetic.

AI-referred traffic doesn’t work like that.

When someone clicks through from ChatGPT, Perplexity, or Gemini, they’ve already done their research inside the assistant. They compared options. They asked follow-up questions. They landed on a shortlist. The click to your website is the last step in a decision, not the first. Adobe’s numbers reflect this: 12% higher engagement, 48% longer time per visit, 37% higher revenue per visit. That’s not a better funnel. It’s a shorter funnel. Most of the consideration happened off your website.

If you’re optimizing for volume (more impressions, more sessions, more referrals), you’re optimizing for the old economy. The retailers winning this 393% growth are the ones the AI assistants actually cite, link to, and send pre-qualified buyers to. That’s a legibility problem, not a visibility one.

Technical Audit For AI Crawlers And JavaScript Readability

Two things you can verify this weekend, without tools, without a team, without budget.

Disable JavaScript. Fresh browser profile, JavaScript off, reload a product page. Is the price there in the HTML? The name? The stock status? The buy button? Most AI crawlers that index pages for citation don’t execute JavaScript, or execute it inconsistently. If the critical facts need JavaScript to render, the AI can’t cite what it can’t see, and your page won’t surface as a reference in the assistant’s answer.

Check the answer-first test. Does your product page lead with what the thing is, what it costs, and whether it’s available? Or does it lead with brand nav, hero imagery, lifestyle copy, and a carousel? AI models retrieving and summarizing your page pick up the first dense, structured facts they find. Humans tolerate brand theater. AI indexers don’t scroll past it to find the price.

If both check out, flat AI numbers are a distribution problem. You’re not being referred. Work on that separately. If either fails, it’s an architecture problem. The 393% is passing you by.

Legibility Vs. Optimization For AI Referral Traffic

AI-referred traffic doesn’t reward optimization. It rewards legibility. Those are not the same thing.

More Resources:


This post was originally published on No Hacks.


Featured Image: Thefirst7/Shutterstock

How To Measure SERP Visibility When Rankings Aren’t Enough [Webinar] via @sejournal, @lorenbaker

Rank #1 and still invisible?

It happens more than you’d think.

That’s why this SEO webinar is key.

Organic Visibility Isn’t What It Used to Be

SERP features, local packs, knowledge panels, featured snippets, shopping ads, now dominate significant portions of the page.

For certain intents and verticals, even the top organic result sits below the fold for most users. That means your rank doesn’t tell you whether searchers are actually seeing your brand.

STAT’s Sr. Search Scientist Tom Capper has been working through something genuinely different: pixel height data from a large-scale analysis of search results. Instead of asking “where do you rank,” he’s asking “how many pixels from the top of the SERP does your result appear — and what’s already taken up all the space above it?”

Join This SEO Webinar & Learn

About the Speaker

Tom Capper is Sr. Search Scientist at STAT, where he leads large-scale research into search result behavior and organic performance. His work is grounded in data analysis at a scale most SEO teams don’t have access to, and this session is a direct look at his findings.

Scaling AI Content Is The #1 Enterprise Priority: How Do You Scale Without Penalty? via @sejournal, @theshelleywalsh

Scaling AI content generation is the number one content strategy for enterprise organizations optimizing for AI search visibility. According to Conductor’s 2026 State of AEO/GEO CMO Investment Report, which surveyed over 250 executives and digital leaders across 12 industries, it ranked above structured data, above authoritative long-form guides, and above original research. Across every maturity level surveyed, from organizations venturing into AI visibility to those with enterprise-wide adoption, it was the top answer.

However, this may also be where the problem starts.

The State of AEO/GEO Report Conductor 2026

AI Content Scaling Is Failing

Inside the report, Aleyda Solis acknowledged the strategic intent but raised a concern: “Although it’s possible to leverage AI for content, a personalized editorial and optimization workflow is required to ensure quality, originality, and expertise by integrating unique brand insights and first-party data, which is exactly what AI platforms are likely to cite.”

Eli Schwartz predicted that the current AI content scaling trend “will change in 2026 as Google and other LLMs push back against low-quality content” with what he described as an AI version of Google’s Helpful Content Update. He also flagged that the leaders he speaks with are “somewhat skeptical about the effectiveness of mass amounts of AI content, but are afraid of being left behind if they don’t do this.”

Fear of missing out is not a basis for an effective content strategy.

Lily Ray, who is known for her in-depth analysis, said earlier this year: “Interesting, but not surprising, to see people on LinkedIn sharing their stories of losing all search visibility (sometimes overnight) after an aggressive AI content strategy.” She added: “Just because it’s easy doesn’t mean it’s a good idea.”

I strongly echo that if something is easy, it’s easy for everyone and not competitive.

Pedro Dias documented that in June 2025, Google began issuing manual actions specifically for scaled content abuse, targeting sites that had been mass-publishing AI-generated content. Sites across the UK, US, and EU received Search Console notifications citing “aggressive spam techniques, such as large-scale content abuse.”

Dan Taylor recently wrote about the mechanics of this failure in granular detail, sharing traffic graphs that illustrate what Glenn Gabe calls the “Mt. AI” effect, an initial spike when new content floods the index, followed by a cliff edge as Google’s quality threshold assessment kicks in. What Taylor identifies as the real problem isn’t AI content itself, but the absence of any genuine content strategy underneath it. “The real problem lies in the fact that scaling content production, regardless of the method, often introduces a raft of quality control issues,” he writes. The freshness boost that new URLs receive masks those issues temporarily. Then it doesn’t.

I write, read, and edit a lot of content, and I can clearly see when AI has been used to supplement writing. Some writers can do this well and have input enough of their expertise to get reasonable results. Others not so much, where they are leaning on AI to supplement their lack of knowledge or expertise. For myself, I can get astounding results from Claude when I input quality, unique research, but I do have to invest a huge amount of guidance to get anything worth publishing.

To be clear, I’m not anti-AI usage. Like Google, I’m focused on good quality content and writing.

That gap between what AI produces by default and what’s actually publishable is precisely where the opportunity still lives for writers who know their subject. Exceptional human-guided content isn’t a compromise. Right now, it’s the competitive advantage.

Google Is Consistent About AI Content

Google’s position on the use of AI content and quality content has been consistent.

Danny Sullivan spoke at the Google Search Central event in Toronto in April 2026 about the concept of commodity versus non-commodity content.

Commodity content is everything an AI can produce from publicly available information. Non-commodity content requires you to have actually done something, know something from direct experience, or hold an opinion grounded in genuine expertise. And this is what Google considers your competitive strength going into the AI era.

John Mueller framed AI content abuse in the context of Google’s Quality Rater Guidelines update, which now explicitly groups AI-generated content in a section about content created with little effort or originality. Quality raters are instructed to apply the lowest rating to pages where all or almost all of the content is auto- or AI-generated with little to no effort, originality, or added value, regardless of production method. Google’s guidelines are explicit that AI tools alone don’t determine the rating, effort, originality, and value do.

This all aligns with the foundations of what Google wants to surface – quality content that demonstrates first-hand experience.

We Have Seen This Before

Lily Ray ran a test by asking Perplexity for SEO news and received a confident report about the “September 2025 Perspective Core Algorithm Update,” a Google update that had never happened. The citations Perplexity provided pointed to AI-generated posts on SEO agency blogs. Sites that had run a content pipeline, hallucinated an update, and published it as reporting. Perplexity read this and treated it as source material, and served it back to her as fact.

There’s a historical parallel here that some older SEOs will recognize.

Early digital PR/link building efforts involved seeding stories or content into lower-tier publications because top-tier journalists used them as source material, and it generated implied credibility of multiple citations. Journalists then began to cite what was published by other sites, and published sites cited and referenced them in the same citation cycle.

Another example I saw recently involved several articles [incorrectly] reporting that Jeremy Clarkson and his partner Lisa Hogan (from the top Amazon UK show Clarkson’s Farm) were spending time apart and ending their relationship. What Clarkson had actually said was that they deliberately go their separate ways during the day so they have something interesting to talk about in the evening. This might be a low-stakes example, but it perfectly illustrates how quickly misinformation spirals.

Screenshot from search for [have jeremy clarkson and lisa hogan split up], Google UK, May 2026

Content Scale Is Strategy And Challenge

The highest-maturity organizations in the Conductor report (organizations where AEO/GEO is a core digital priority) have already arrived at the right conclusion, and they are the only group in the study that prioritized original research based on first-party data as a content strategy. They understand that first-party data and genuine research cannot be replicated by running an AI content operation and exclusivity is the point.

The Conductor report’s headline finding is that 94% of enterprise organizations plan to increase AEO/GEO investment in 2026, and that AEO/GEO has become the number one marketing priority, above paid media and paid search. The report also surfaces that generating AI-optimized content at scale is not only the top stated strategy, but also the top stated challenge. Brands know what they want to do, but they don’t know how to get there.

How Enterprise Brands Can Scale And Win

Industries that already operate on programmatic content models (travel, ecommerce, large product catalog sites) have been producing content at scale for years. A hotel comparison site generating location pages, a retailer producing thousands of product descriptions, a marketplace creating structured listings are all legitimate use cases where AI can effectively accelerate something that was already happening.

But, to have real brand differentiation, investing in a unique voice and approach to how they write these listings can set them apart and be a competitive advantage.

Alongside their programmatic content, enterprise brands should also be finding ways they can produce content that is genuinely difficult to replicate. Experience-driven, data-grounded, editorially considered, and specific in ways that only a real subject matter expert would know.

For an enterprise brand to win at scaling content, my recommendation is to wrap AI usage around subject-matter experts and editors. The power of AI is how it can turn experts into super producers and allow them to produce more. Enterprise brands should invest in finding these super producers and then use AI to exponentially scale their ability, not try and replace them.

AI Amplifies What’s Already There

The most useful frame for AI in content production is as an amplifier of whatever you bring to it. If you have genuine subject matter knowledge, proprietary data, and the editorial discipline to maintain quality, AI can meaningfully accelerate your output. It helps you produce more of what you’re already good at, faster.

But if you don’t have those things, AI produces more of what you don’t have, faster. The content output has structure, length, and the right vocabulary, but it contains nothing that an LLM can’t generate from publicly available information. Nothing that differentiates you from every other brand trying to scale with AI in the same way.

As I said earlier, I have produced in-depth content for years, and for me, AI is a creative amplifier and an exciting tool that augments what I know. It doesn’t replace me, and it certainly can’t do what I can by itself. On that basis, I see subject-expert editors as being the new information gatekeepers.

For enterprise brands who want to scale their content they should start with understanding that good content is not about including everything; it’s about knowing what not to include.

The State of AEO/GEO Report Conductor 2026

The full Conductor 2026 State of AEO/GEO CMO Investment Report is available here.

More Resources:


Featured Image: ImageFlow/Shutterstock

The Consensus Gap via @sejournal, @Kevin_Indig

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

Most teams talk about “AI visibility” like it’s one thing. New data on 3.7 million citations across ChatGPT, Perplexity, and Google AI Overviews suggests it isn’t. And the gap between the three engines is wider (and more strategically important) than your dashboard likely admits.

Today’s memo breaks down:

  • Why a blended AEO score hides the only finding that matters.
  • Which page types and domains actually travel across engines.
  • The shift from measuring AI presence to measuring portability.

One of the biggest differences between AEO and SEO is that AEO plays on more platforms.

Omnia data shows across multiple samples that only 2.35% to 2.45% of cited URLs appeared in ChatGPT, Perplexity, and Google AI Overviews for the same prompt. 91% of citations appeared in only one engine.

Conclusion: AI visibility is not a single leaderboard. Instead, it’s three different distribution systems that sometimes overlap and usually do not.

Only 2% Of URLs Get Cited By All 3 Engines

Most people would guess that if a URL gets cited by one major AI engine, it has a reasonable shot at appearing in the others.

But the 20,000 prompt sample shows only 2.37% of cited URLs show up across all three engines for the same prompt.

Meanwhile, 91.07% show up in only one. Those two numbers belong next to each other because they explain each other. The remaining ~7% overlap in pairs, which means engines are drawing from largely disjoint pools rather than ranking the same pool differently.

Image Credit: Kevin Indig

For AEO/SEO teams, that means a single composite visibility score is the wrong unit of measurement. Averaged AEO scores hide this. A brand can look strong in aggregate and be invisible in 2 of 3 engines. Teams chasing one blended AI visibility number are compressing three ranking systems into one metric and calling it strategy.

The 2% Holds Across Every Cut

The ~2% overlap rate and ~91% exclusive rate stay almost perfectly flat across four samples.

Image Credit: Kevin Indig

That consistency matters more than the exact decimal point. The consensus gap is not an artifact of one query set or one time window. It looks structural.

In Q3 2025, universal overlap was 2.2%. In Q4 2025 and Q1 2026, it rose to 2.7%. Engine-exclusive citations fell from 90.1% to about 88%. So yes, a small amount of convergence. But even after that shift, fragmentation still dominates.

Commercial Prompts Don’t Converge Either

The intent split is one of the quietest but most useful parts of the dataset. You could argue that commercial queries should produce more consensus. When someone searches for [the best CRM], [best running shoes], or [best project management software], the pool of acceptable sources feels narrower than it does for broad informational prompts.

Surprisingly, the data does not support a big difference.

Image Credit: Kevin Indig

Commercial prompts show 2.4% universal overlap. Informational prompts show 2.0%. Even when the query should narrow the answer set, the engines still choose different sources most of the time.

That pushes against a common instinct in SEO and content strategy. Teams often assume high-intent queries are where shared authority will show up. The opposite looks closer to true. Even in commercial territory, each engine’s own retrieval logic, what sources it trusts, what formats it prefers, is doing most of the work.

Guides Beat Homepages By 2x

The page type breakdown below shows guides and tutorials have the highest cross-engine overlap at 2.3%, followed by blogs at 1.8%, category pages at 1.6%, product pages at 1.2%, and homepages at 1.1%.

Image Credit: Kevin Indig

Two lessons:

  1. First, explanatory content travels better than brand or transactional assets. If you want the best shot at showing up across engines, the strongest candidate is not the homepage and not the product page. It is the page that helps, explains, compares, or teaches, but keep in mind that these are also content formats that AIs can answer directly well.
  2. Second, even the best page types perform badly in absolute terms. Guides are not winning across engines in any meaningful sense. The right read on this is not “publish more guides and you will win everywhere.” It’s simpler than that: Helpful content travels better than brand content.

Visibility Is Not The Same As Portability

One of the easiest mistakes in this space is to confuse citation frequency with citation portability. Wikipedia is the cleanest example. It appears 16,073 times in the dataset, but only 1.3% of those appearances are universal across engines. Reddit appears 14,267 times, but only 0.1% are universal. Reuters shows up 1,202 times and still lands at 0.0% universal overlap.

Image Credit: Kevin Indig

That is why an important metric is portability. A domain can show up all over one engine and barely travel, which means a brand looking dominant in an aggregate dashboard may be one platform’s habit away from invisibility. Presence tells you whether you are visible. Portability tells you whether that visibility is resilient.

What This Means For Operators

The practical implication is simple: Stop treating AI visibility as one thing. Examine the comprehensive visibility of your domain by measuring:

1. Presence, the % of your tracked prompts where your domain appears in any engine. Presence tells you whether you’re visible.

2. Portability, the % of your cited URLs that appear in all three engines. Portability tells you whether that visibility is resilient.

3. Concentration, the % of your citations that come from a single engine. Concentration tells you which engine your current dashboard is secretly built on.

If the overlap between engines is this low, a single AEO strategy is too abstract to be useful.

When we approach AI visibility from a holistic perspective, it forces sharper questions:

  • Which engine matters most for us?
  • Which of our assets travel across engines, and which only work in one?
  • Are we measuring presence when we should be measuring portability?

This also changes how brand teams should think about diagnostics. A weak homepage across engines may not be a homepage problem. It is a symptom of something broader: Engines favor utility over brand centrality. In that world, visibility comes less from being the official source and more from being the useful source.

The strategic question is no longer, “How do we rank in AI?” We should instead be asking ourselves, “How do we build assets that survive different engine preferences?” That is a narrower question. It is also a better one.

Methodology

There are a few caveats to this analysis:

  • The dataset is skewed toward Omnia’s customer base.
  • The intent and page-type cuts rely on regex classification, which is useful for directional analysis but not perfect taxonomy work.

Those caveats do not weaken the main finding much. The biggest signal is not precision at the edges. It is consistency at the center. No matter how the cuts change, the same pattern resurfaces: very little overlap, very high engine-specificity, and only modest differences by time, intent, or page type.

Dataset Size And Time Window

The analysis draws on four prompt samples. Three cohorts of 5,000 prompts each, tracked from Jan. 1, 2025; July 1, 2025; and Jan. 1, 2026. A separate 20,000-prompt random sample underpins the headline 2.37% and 91.07% figures. The time-view cut spans Q3 2025 through Q1 2026 (to date) and covers 3.7 million URL citations in total. Commercial/Informational/Other intent splits are drawn from roughly 2.6 million URLs across the combined sample. Page-type splits span 4.1 million URL appearances.

How Prompts Were Selected

The 20,000 prompts are drawn as a random sample from Omnia’s live prompt monitoring pool. The pool reflects what real marketing teams chose to track, weighted toward Omnia’s customer geography (Spain-heavy, plus UK, Nordics, and other EU markets). Each prompt runs in its country’s primary language, so Spanish is overrepresented versus a U.S.-only dataset. Industry mix is fintech/insurtech, travel, SaaS, B2B services. Treat findings as directional for European AI search.

Engine Coverage

The study covers three engines: ChatGPT, Perplexity, and Google AI Overviews. Each fires the same prompt concurrently within the same minute, twice a day, with country localization, and each engine queried in its default web-enabled, unauthenticated state. Perplexity tracking runs on Sonar, while ChatGPT and Google AI Overviews use each vendor’s default production model for logged-out web browsing (which neither OpenAI nor Google pins publicly to a specific version).

Classification Methodology

Intent and page type are assigned by regex. Intent buckets are Commercial, Informational, and Other. Page-type buckets are Guide/tutorial, Article/blog, Category page, Product page, Homepage, Wikipedia, and Other. The rules are keyword- and URL-pattern-based, which makes them fast enough for a multi-million-URL dataset but coarse at the edges. Edge cases fall into Other, which is why Other carries a high share in both the intent and page-type tables. Treat the regex cuts as directional, not authoritative.

More Resources:


Featured Image: FGC/Shutterstock; Paulo Bobita/Search Engine Journal

The 90-Day AI Search Sprint: How To Rebuild Your Marketing For 2026 Visibility via @sejournal, @hethr_campbell

AI visibility is a learnable, measurable skill: The 90-day playbook to build it is here.

When a potential customer asks Gemini or Claude to recommend a solution like yours, does your brand get mentioned?

Do you have a plan to restructure your growth engine around AI visibility, or are you still waiting to see how it plays out?

👆 Get the 90-day AI visibility playbook. Unlock the recording, above .

Learn:

How To Transform Your SEO Strategy In 90 Days, Like Google, & Headspace

AI Overviews, ChatGPT search, and Perplexity are where your buyers are going now. This on-demand session gives you the signals that drive AI discoverability, a phased 90-day framework, and a look at how funded teams are restructuring to stay ahead.

Jason Shafton, Founder & CEO of Winston Francois, shared battle-tested strategies from scaling growth at Google, Headspace, Kajabi, and 10+ funded startups. to help you restructure your marketing for AI-era discovery and stay visible where your buyers are actually searching.

You’ll Learn:

  • Current AI Search Signals: Which factors drive citation and discoverability in AI search.
  • The 90-Day Visibility Framework: A phased plan to audit your baseline, run AI-native experiments, and scale what’s working.
  • How Growth Teams Are Restructuring: What funded startups are cutting, doubling down on, and handing to AI, so you can benchmark your own approach.

Register above to get actionable, practitioner-tested frameworks for winning AI visibility, built from real experience.

🎬 Unlock the on-demand replay above to watch the full recording on your schedule.

The Tech SEO Audit for the AI Search Era: How to Maximize Your AI Visibility via @sejournal, @JetOctopus

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

How do I optimize my site for ChatGPT and Perplexity, not just Google?

How do I know if AI bots are actually crawling my site?

How should my technical SEO strategy change for AI Search?

A significant portion of your site’s search impressions in 2026 are generated by machines researching on behalf of humans.

Those machines don’t care about your keyword rankings. They care whether your:

  • HTML loads cleanly in under 200 milliseconds
  • Product detail page is reachable in fewer than four clicks
  • Content answers a specific, nine-word question that has never appeared in any keyword research tool in your career.

This isn’t speculation. It’s what our server log data across hundreds of enterprise websites is showing us, consistently, since mid-2025.

What’s Actually Happening On Your Site

My colleague, Stan, flagged a pattern in a Slack message: query lengths were growing at rates that didn’t correlate with human behavior.

A 161% growth rate in 10-word queries year-over-year is not driven by users who suddenly got more verbose. It’s driven by AI agents decomposing a single user prompt into dozens of parallel sub-queries, a process researchers now call “fan-out.”

Query Length Growth in 2025

Image created by JetOctopus, Aggregated GSC data across hundreds of enterprise properties, 2025

The gradient is the tell. Human search behavior doesn’t scale this cleanly by word count. Machines do. By October 2025, 7-plus-word queries reached nearly 1% of total query volume, roughly triple their historical share.

More revealing than the volume is the CTR. While impression counts for 10-word queries spiked 161%, click-through rate collapsed to 2.26%, down from 8–11% in 2023.

The AI reads your page, extracts the answer, synthesizes it for the user. Your site never gets the visit.

We call these “phantom impressions.” They’re real signals that your content is being evaluated inside AI reasoning chains. If you’re filtering them out of your reporting because they don’t drive traffic, you are flying blind.

The Three Bots Visiting Your Site & Their Impact On SERP Visibility

Not all AI crawlers are equal, and treating them as a single category is the first mistake most technical SEOs make.

Training bots crawl broadly and ignore click depth. A training visit means the AI knows your content exists, not that users will ever see it.

AI search bots drop off quickly beyond two or three clicks from the homepage and typically visit each page only once a month.

AI user bots are initiated when a real person asks a question in ChatGPT, Perplexity, or Claude, and the AI researches the answer on their behalf. These are the only visits that translate to actual AI visibility.

Bot Type What Triggers It Crawl Depth Impact on AI Visibility
Training bots Model education cycles Deep — ignores click distance None directly. Awareness only.
AI search bots New URL discovery & fresh content Shallow — ~1 visit/month beyond 2–3 clicks Critical gatekeeper. If it misses a page, user bots won’t find it either.
AI user bots Real user query in ChatGPT / Claude / Perplexity Selective — driven by speed and structure High. Closest proxy to an AI impression.

Your site can receive heavy crawling from training and search bots and still be completely absent from AI-generated answers. If you’re not segmenting AI bot traffic by type in your log analysis, you have no idea which third of the iceberg you’re measuring.

Which SEO Signals Do LLMs Respect?

Robots.txt is your primary lever.

Most major AI platforms (ChatGPT, Claude, Gemini) follow robots.txt directives. Perplexity is a partial exception: PerplexityBot respects robots.txt, but Perplexity-User, the user-triggered bot, does not. Cloudflare confirmed this in an investigation. Most sites haven’t audited their robots.txt with AI access in mind. Do it.

Sitemaps are broadly supported.

ChatGPT, Claude, and PerplexityBot all use XML sitemaps for URL discovery. Keep them accurate.

Signals Best Saved For SEO & Ranking Efforts

These signals below don’t appear to impact AI visibility, but are still key for ranking for queries that still trigger traditional SERPs.

Canonical tags and noindex directives do nothing for AI bots.

AI crawlers don’t build a search index, so they have no use for these meta-signals. Content hidden from Google using noindex is fully visible to ChatGPT’s crawler.

LLM.txt does nothing.

Our log data shows major AI bots don’t read this file. Don’t invest time here.

JavaScript rendering is a critical blind spot.

Most AI crawlers (ChatGPT, Claude, Perplexity) don’t render JavaScript. If your product pages load key content client-side, those agents read an empty shell. Server-side rendering is the only architecture that works universally. The exception is Google Gemini, which uses the same Web Rendering Service as Googlebot.

How To Make Sure ChatGPT, Perplexity & LLMs Can Reach Your Content

AI search bots visit deep pages roughly once a month and drop off sharply beyond three clicks from the homepage. The pages with the most specific, answerable information are often the hardest for agents to reach.

The fix: Elevate your most valuable deep pages through internal linking, ensuring they’re reachable within four clicks.

Pages crawled by training bots but never reached by user bots are your highest-priority targets. Pages AI user bots visit frequently are telling you what to scale: more content covering the same topic cluster and depth.

Optimize Content For Longer, Fan-Out Queries

95% of the queries driving AI citations have zero monthly search volume. They’re synthetic sub-queries generated by AI models. But they show up in GSC: impressions, no clicks, query lengths you’d never target voluntarily.

How To Find Fan Out Query Opportunities

To surface fan out queries that are worth chasing, connect your GSC API to JetOctopus (to bypass the 1,000-row UI limit) and filter for: query length greater than 7 words, impressions under 50, clicks at 0, over the last 3 months. That’s your Fan-Out Opportunity Matrix, the exact questions AI agents are asking about your content.

Prompt Types That Fan Out Most

Image created by JetOctopus, 2025

If your content isn’t structured to answer list and comparison queries, with explicit rankings, pros/cons, and side-by-side specs, you’re leaving the highest fan-out surface area unoptimized.

“Product review” intent queries surged from 239 in June 2025 to over 40,000 by September 2025. That 16,000% increase was AI agents systematically harvesting structured opinion data. If your product pages lack this depth, you’re invisible to that harvest.

The Technical Audit: Where to Start

Step 1: Identify AI User Bot Traffic In Logs

Pull raw server logs (Apache/Nginx) and export all lines containing these user agents: OAI-SearchBot and ChatGPT-User, PerplexityBot and Perplexity-User, Claude-SearchBot and Claude-User. Then manually group hits by user-agent patterns and endpoints in a spreadsheet. To distinguish training bots from user bots, you’ll need to maintain your own classification list — one that changes often and isn’t standardized.

In JetOctopus Log Analyzer, this segmentation is built in: filter by bot type (training, search, and user) in a few clicks and immediately see which pages AI user bots visit (your AI-visible content, ready to scale) versus pages training bots hit but user bots never reach (your highest-priority fix targets).

Step 2: Audit Technical Accessibility Of Deep Pages

Pick a sample of deep URLs and check HTML payload size, confirm key content isn’t injected via JavaScript by viewing raw HTML, simulate crawl depth by counting clicks from the homepage, and test load time in Chrome DevTools or Lighthouse. Also check whether important content sits behind accordions or “View More” elements — these require JavaScript execution that AI bots skip entirely. For large sites with thousands of deep pages, this sampling approach misses a lot. AI agents don’t click. If information only appears after user interaction, it doesn’t exist for these crawlers.

Step 3: Clean Up Your Robots.txt

Open your robots.txt and review all Disallow and Allow directives for every user-agent line by line. AI bots follow Disallow rules, so make sure you’re not accidentally blocking important URLs. Manually test key URLs to confirm they aren’t blocked. A 30-minute audit here can prevent you from blocking crawlers you want in, or exposing content you’d rather keep out.

Step 4: Map Your Phantom Impressions

Export data from GSC Performance reports filtered by impressions with zero clicks. Because of the 1,000-row UI limit, you’ll need to use the GSC API or export in chunks by date and query, then merge datasets in spreadsheets or BigQuery. Also factor in query frequency: long queries appearing daily are likely not fan-outs.

Connect your GSC API to JetOctopus to bypass the row limit and build your Fan-Out Opportunity Matrix automatically — the exact questions AI agents are asking about your content, ready to act on.

Step 5: Monitor The Changes

Set up a recurring export process — pull GSC data monthly and compare impressions over time, re-run log analysis scripts and diff bot activity, track Core Web Vitals separately in PageSpeed Insights or CrUX. You’ll end up stitching together multiple data sources with no unified alerting, making it hard to catch regressions early.

JetOctopus Alerts covers exactly this: unified notifications for changes in AI bot activity alongside Googlebot behavior, Core Web Vitals, on-page SEO issues, and SERP efficiency drops, so you catch regressions before they compound.

The New KPI: Technical Accessibility

SEO in 2026 is restructuring around one constraint: can an AI agent crawl, reach, and extract a fact from your 50,000th product page in under 200 milliseconds?

If the answer is no, your rankings, backlinks, and content quality become irrelevant for a growing share of search interactions. The machines are searching. The question is how quickly you can see what’s actually happening.

Start with your logs. Everything else follows from there.

Want to see exactly how AI bots are interacting with your site: which pages they reach, which they skip, and where your fan-out opportunities are hiding? Book a live walkthrough of the JetOctopus platform. We’ll pull your actual log data and show you what your GSC reports aren’t telling you.

Image Credits

Featured Image: Image by JetOctopus. Used with permission.

How We Use AI To Run A 90-Day Growth Audit

Most growth audits are a performance. Someone shows up with a slide deck, interviews a few stakeholders, and delivers a 40-page PDF that lives in a drawer. The team feels busy for three weeks, and nothing changes. I’ve been on both sides of that transaction, and I got tired of it.

At my growth consultancy, we run 90-day growth sprints for venture-backed and private equity (PE)-backed companies. The audit is the first phase. It used to take two to three weeks of manual work just to get a clear picture of what was happening inside a company’s marketing organization. Now, with AI woven into every step, we compress that discovery into days and spend the remaining time actually fixing things.

Here’s exactly how we do it.

Why Traditional Growth Audits Fail

The classic consulting audit has a structural problem. The people conducting it are incentivized to find complexity because complexity justifies a bigger engagement. So the deliverable becomes a laundry list of everything that could be improved, ranked by nothing in particular, with no connection to what the business actually needs in the next quarter.

I ran marketing at companies ranging from Fortune 200 to early-stage startups before starting my own firm. At one company, a 30-minute meeting with the CEO required two or three pre-meetings just to polish the deck. The decision was made in minutes. The deck went into a drawer. All those hours, gone.

That experience shaped how I think about audits. The output has to be a working document that becomes the blueprint for what happens next. Not a souvenir.

The AI-Assisted Audit Framework

Our audit covers three areas: the marketing org itself, the tech stack, and what I call AI readiness. That last one didn’t exist two years ago. Now it’s arguably the most important piece, because it determines how much of the roadmap a company can actually execute without hiring five more people.

Each area follows a specific process, and AI shows up differently in each one.

Phase 1: Intake And Context Building

Before we talk to anyone on the client’s team, we feed everything we can get our hands on into Claude. Investor decks. Board presentations. The company’s public marketing. Competitor creative. Job postings from the last six months. Glassdoor reviews. Product screenshots. Pricing pages.

Two years ago, synthesizing all of that required a senior strategist spending a full week reading, annotating, and building a briefing document. Now, we build a comprehensive context package in a day. Claude processes the raw material and produces a structured brief that includes the company’s positioning gaps, messaging inconsistencies across channels, competitive white space, and the questions we should be asking in stakeholder interviews.

The output isn’t a summary. It’s a diagnostic framework tailored to that specific company. We review it, challenge it, add our own operator instincts, and walk into discovery calls with a point of view instead of a blank notepad. That changes the conversation immediately. Clients notice when you’ve done the homework.

Phase 2: Tech Stack And Workflow Mapping

This is where things get specific. We pull a full inventory of all of the tools the marketing team uses. Customer relationship management (CRM). Email platform. Analytics. Attribution. Ad platforms. Content management. Design tools. Project management. The average mid-stage startup has between 15 and 30 marketing tools, and in almost every audit, at least a third of them overlap or go mostly unused.

We document every workflow: how a campaign goes from idea to live, how leads get routed, how reporting happens, who touches what, and when. Then we map each workflow against what’s now possible with AI-native alternatives.

A real example: One client had three people spending a combined 40 hours per week on creative production for paid social. Briefing a designer. Waiting for rounds of revisions. Resizing for different placements. Exporting. Uploading. We replaced that workflow with a combination of AI creative tools and a custom automation that handled asset generation, versioning, and platform-specific formatting. The same volume of creative now takes roughly eight hours of human time per week, and most of that is strategic review rather than production.

Tools like HeyGen and ElevenLabs handle video and audio production that used to require a studio. Custom AI agents built on open-source AI harnesses like OpenClaw and Hermes automate research, competitive monitoring, and content drafts. The point isn’t to name-drop software. It’s that the landscape of what can be automated has expanded dramatically in the last 18 months, and most marketing teams haven’t caught up.

Phase 3: AI Readiness Assessment

This phase is the one that surprises clients the most, because it’s less about technology and more about people.

We evaluate three things. First, does the team have the curiosity and willingness to adopt AI tools? Some teams are eager. Some are terrified. Knowing where people stand before you start pushing new workflows prevents the kind of resistance that kills transformation projects. I spoke about AI readiness to a group of senior marketers at a hyper-growth consumer app, and the first question asked was: “Isn’t the magic in our human work and interactions?” They were afraid.

Second, does the company’s data infrastructure actually support AI-driven optimization? If your CRM is a mess, your attribution is broken, and your analytics are built on vanity metrics, no AI tool is going to save you. Garbage in, garbage out still applies. We flag the data hygiene issues that need to be fixed before any AI implementation will produce reliable results. And the audit acknowledges the data gaps and how (and why) to fix them.

Third, where are the highest-leverage automation opportunities? Not everything should be automated. Creative strategy still requires human judgment. Brand decisions still need a human with taste and context. The audit identifies which workflows will benefit most from AI and which ones need a human firmly in the loop. AI readiness is not about replacing all humans with AI tools and agents.

What The Deliverable Actually Looks Like

We don’t hand over a deck. We produce a shared document with four sections: current state diagnosis, prioritized opportunity map, 90-day implementation roadmap, and a tool-by-tool recommendation list with estimated time and cost savings.

The roadmap breaks the 90 days into three phases. The first month focuses on quick wins, the workflows where AI can be plugged in with minimal disruption and immediate impact. Month two tackles the structural changes, things like rebuilding attribution models or redesigning the content production pipeline. Month three is about training and handoffs, ensuring the team can run the new systems independently.

The document is collaborative. Clients can comment, push back, and reprioritize. It becomes the working blueprint for the engagement, not a PDF that gets emailed and forgotten.

Where The Real Savings Show Up

The savings are rarely where people expect them. Most founders assume AI will cut their ad spend or reduce their agency fees. Sometimes it does. But the bigger wins tend to be in time recaptured.

A marketing team that was spending 60% of its week on production and reporting and 40% on strategy gets those numbers flipped. Humans focus on the work that actually requires taste, judgment, and relationship-building. The AI handles the repetitive execution that was eating their calendars.

One engagement reduced a client’s creative production cycle from three weeks to four days. Another automated their weekly reporting entirely, freeing up a senior analyst to focus on actual analysis instead of pulling numbers into slides. A third rebuilt their email lifecycle from scratch using AI-generated segmentation and content, which cut their cost per acquisition by 30% in the first 60 days.

None of those outcomes required firing anyone. They required moving people from low-leverage tasks to high-leverage tasks. That’s the part of the AI conversation that gets lost in the layoff headlines.

What I’d Tell Any Marketing Leader Reading This

You don’t need to hire a firm to start. Pick one workflow on your team that is repetitive, time-consuming, and doesn’t require deep creative judgment. Map it out step by step. Then ask whether an AI tool could handle any of those steps today.

Begin by tackling reporting. Next, focus on competitive research. Consider first-draft content production as an early win. Finally, initiate the process wherever the pain is loudest and the risk is lowest. Get a win. Show the team what’s possible. Then expand.

The companies that will struggle are the ones waiting for someone to hand them a playbook. The companies that will win are the ones running their own experiments right now, even clumsy ones, and learning what works inside their specific context.

The audit is just a structured way to do what every marketing team should already be doing: looking honestly at how time gets spent and asking whether there’s a better way. AI just made “better” a lot more accessible than it was 18 months ago.

More Resources:


Featured Image: Tetiana Yurchenko/Shutterstock

I Helped Build Google’s Keyword System. Here’s Why It’s Becoming Obsolete via @sejournal, @siliconvallaeys

If you’ve been running Google Ads for more than a few years, your job description has changed without your consent. Match types that once signaled precision now target “related intent”; a 2023 rebuild made Broad Match competitive again; and Smart Bidding shifted the focus from keywords to outcomes like return on ad spend (ROAS) and cost-per-action (CPA). Now, with AI Max, keywords are becoming optional in Search campaigns altogether.

I joined Google in 2002 as one of its first few hundred employees and spent a decade as the first AdWords Evangelist. Back then, the keyword was the undisputed foundation of paid search. After 24 years in the industry, my conclusion is simple: Keywords are dead.

This isn’t a slogan. It is a technical reality. The core system is being replaced, even if the legacy interface remains. As users shift from search queries to conversational prompts, the “synthetic keyword” – a distillation of complex intent – is replacing the legacy keyword. We are moving toward an auction that runs on pure intent, with no keyword abstraction required. We aren’t there yet, but if you still define PPC as “picking the right keywords,” the ground is shifting under you.

Here is what we are losing, and gaining, as this transition plays out.

The Original Deal

For most of Google Ads’ history, keywords worked like a contract.

You agreed to put in the work to research relevant keywords for your business so that Google could show useful ads to searchers. You structured your account around them. You wrote ads that spoke to their intent. In return, Google agreed to only show your ads when matching queries, based on the match type you chose, lit up in the auction.

  • Exact meant exact.
  • Phrase meant phrase.
  • Broad was the wildcard for advertisers willing to trade precision for reach.

That arrangement gave us something valuable: diagnosability. When a campaign underperformed, you opened the search terms report and saw, line by line, exactly what you were paying for. Bad queries got negatived. Good queries got promoted. Match type was the main lever we had, and we used it carefully.

That’s the world I helped build. It worked for a long time because the tech underpinning the search experience was limited and couldn’t realistically do anything useful with more precise keywords that exceeded the max of 10 words.

What Changed, One Product Decision At A Time

The deal didn’t break in a single moment. It came apart over a decade of decisions that often raised advertisers’ blood pressure and brought us to this moment.

Close variants came first. Exact started including misspellings, then plurals, then function-word variations. By the mid-2010s, “exact match” was already a misnomer. The match type hadn’t changed, but the definition of a match had.

Smart Bidding shifted the center of gravity. Once bids were being set against conversion probability, the question of which keyword triggered the auction mattered less than the question of whether this user would convert. Match type became a throttle for how aggressively the system could explore new queries.

The 2023 Broad Match overhaul changed the narrative. Google invested real engineering into making Broad the semantically intelligent match type – and publicly reported ~25% more conversions in Target CPA campaigns. Advertisers who’d spent 15 years feeling Broad was a money pit were now being told Broad was the future.

AI Max is where the synthetic keyword shows up. Give Google your URL, your assets, and your business data, and the system finds the intent. From the advertiser’s side, keywords become optional. But the auction itself still runs on a keyword substrate. What’s changed is who picks the keywords that continue to underpin the ads auction and how visible those are to advertisers. Instead of you declaring a keyword list, Google now generates intent matches on the fly from the user’s prompt and your business signals.

And it isn’t just Google.

At Optmyzr, we recently started placing ads on ChatGPT. On OpenAI’s ad surface, keywords are optional from day one. You feed the system signals about your business, and it matches your ad to the shape of the user’s question rather than a phrase you pre-declared.

When the company that defined keyword advertising and the company reinventing search both land on keyword-optional intent matching, that’s a pretty clear signal that intent itself has outgrown the keyword as the unit of targeting. The signals now live in your pages, products, prompts, and context, not in a list you typed into an ad group.

What We’re Losing

I’m not going to pretend this transition is costless. Three things are being taken away.

Granular diagnosability is the first casualty. When a keyword-less campaign underperforms, the old debugging playbook of reading the search terms report, finding the bad queries, adding negatives, and tightening match type only half works. Negative keywords still exist and still matter. But the intent-matching engine is harder to reason about. “Why did my ad show here?” has a fuzzier answer in 2026 than it did in 2016.

The craft of account structure is second. For two decades, one of the hallmarks of a good PPC manager was the ability to architect a campaign. Tight ad groups. Themed structures. Clean branded-versus-non-branded separation. A lot of that structure was a proxy for control. Once the system handles more of the targeting, the strategic value of elaborate structure drops. Some of it was always over-engineering. But the practice of thinking carefully about how intent maps to campaigns was real craft, and it’s at risk of atrophying.

Training is the third. Junior PPC analysts used to build their intuition inside the search terms report. You’d watch queries for a week and start to understand how users phrase problems, how language drifts, how seasonal variations leak into the data. That was a masterclass in consumer psychology. A system that abstracts the keyword away also abstracts away one of the best teaching tools this discipline has ever had. And it removes our ability as marketers to detect shifts in consumer behavior that would normally help us evolve our strategy.

What We’re Gaining

But for all we’re losing in this necessary shift, we’re also gaining a few things.

Coverage of queries no keyword list ever catches. Zero-click queries, brand-new phrases, generational vernacular, localized slang. These are exactly the places where intent-based matching outperforms manual keyword selection. Not because humans are inattentive, but because the space is too big and too dynamic to enumerate.

A lower maintenance tax. Negative keyword lists that stretch into the thousands, endless query audits, SKAG construction, quarterly match type experiments. A lot of that work was overhead imposed by the gap between advertiser language and user language. Closing that gap algorithmically frees up hours for strategy, creative, and measurement.

Access to signals no advertiser can match manually. Google’s LLM-driven query understanding sees more of the user’s journey than any keyword list ever will. If you’re unwilling to let those signals into your targeting, you’re choosing to compete in an auction where your opponents have information you don’t.

The Data Already Shows The Shift

We just ran Optmyzr’s 2026 Match Type Study across nearly 130,000 non-branded campaigns in more than 14,000 accounts, totaling roughly $99 million in spend. It’s the clearest quantitative sign I’ve seen that practitioners are already adapting to this new reality, whether they’ve articulated it or not.

A few highlights that matter here:

  • Exact Match’s share of non-branded spend has collapsed from 37.1% in 2022 to 27.6% today. Most of that drop happened in the last 24 months. Advertisers haven’t stopped using Exact, but they are shifting towards less control and letting the AI handle more of the targeting.
  • On branded terms, the story flips. Exact Match delivers 6.61x ROAS at a $0.90 CPC, nearly double the ROAS of either alternative. Brand intent is known intent, and Exact still owns it.
  • Phrase Match is now the workhorse. It drives 40% of non-branded conversions and posts a 15.7% conversion rate, well above Exact’s 10.5% and Broad’s 8.5%. Phrase has become what Exact used to be: the default tool for scalable, intent-respectful discovery.
  • Broad Match keeps climbing. It now represents 38.8% of non-branded spend, the single largest bucket. Its ROAS still trails the other two, but its volume contribution makes it no longer optional for most advertisers.

The industry has already been migrating toward looser, more intent-driven matching on non-branded queries while preserving tight control where intent is certain. AI Max just turns the dial further in the same direction.

Menachem Ani, who runs the agency JXT Group and joined me on a recent episode of PPC Town Hall, described the same playbook from the trenches. Start new lead gen campaigns on manual CPC and Phrase Match. Collect good-quality traffic for a few weeks. Promote the winners to Exact. Only then layer Broad and Smart Bidding on top.

Exact, he said, is “too specific for a new campaign.” Broad is “overly aggressive right at the beginning.” Phrase is “the sweet middle spot” – flexible enough to find intent the advertiser didn’t think of, tight enough to keep the data usable.

It’s a big shift when the agency practitioners you’d expect to defend keyword-level control now independently arrive at “Phrase first, Exact later, Broad on top.”

5 Shifts Worth Making In The Next 6 Months

1. Separate Branded And Non-Branded Campaigns

This was always best practice. In a world where intent-based matching blurs campaign boundaries, a sloppy brand separation is the difference between 6.61x ROAS and 3.0x. Build a dedicated branded campaign. Lock it to Exact. Stop letting non-branded creep in.

2. Invest In The Signals Google Actually Reads – Including Offline Conversions

Your landing pages, feed quality, asset library, and business data aren’t just conversion-rate inputs anymore. They’re the targeting inputs AI Max uses to decide when you appear. If you spent 10 years refining keyword lists and one year refining URL and asset hygiene, flip the ratio.

For lead gen specifically, the highest-leverage move is piping qualified-lead data back to Google. Menachem’s rule of thumb was practical: Salesforce, HubSpot, and Zoho have native integrations. For anything else, Make.com or Zapier will send the events back for you. You need roughly 100 leads a month to generate the 30+ qualified conversions Smart Bidding and AI Max want to see before they’ll optimize toward them.

The difference between optimizing to “cost per lead” and “cost per qualified lead” is usually the difference between a campaign that looks good on a dashboard and one that actually grows the business.

3. Treat Negative Keywords As Your Last Line Of Control

In the AI Max era, negatives still work. They’re the most powerful remaining tool for saying “not that, not ever” to the machine. Maintain them aggressively. Automate the additions. This is where brand safety, budget discipline, and irrelevance prevention now live.

4. Test AI Max Where You’d Already Run PMax – And Test It With A Hold-Out

Menachem put this more cleanly than I could: “Use AI Max when you would use PMax.” The underpinnings are the same. The algorithm is pulling search and shopping signals together and deciding where your ad fits. The prerequisites are the same too: enough conversion volume, clean tracking, and a business the algorithm has been taught to recognize.

The accounts getting the most out of AI Max aren’t the ones that flipped the switch and walked away. They’re the ones that ran it against a proper hold-out, measured incrementality, and kept their keyword-based campaigns running on the traffic where those still had an efficiency edge – usually branded terms and proven high-intent exact queries.

5. Upgrade Your Own Skill Stack

The future PPC manager isn’t a keyword picker. They’re an intent engineer; someone who can translate business goals into the signals Google’s system will learn from, debug a semi-black box with query reports and experiments, and explain to a client what’s actually happening inside their account, even when the dashboard only shows aggregate results.

That’s a harder job than picking keywords. It’s also a more defensible one.

The Bigger Picture

I helped build a system designed for control; what’s being built now is a system designed for leverage. Conflating the two is why so many practitioners feel frustrated by tools that are actually performing. Control was about dictating terms to Google. Leverage is about feeding the engine the right signals and letting the auction execute at a scale no human team can match.

Our 2026 data shows the industry is already halfway through this transition. For PPC teams, the question isn’t whether to adapt, but how fast. The keyword as an advertiser artifact is dead. We are moving toward an auction powered by intent alone. Your job is no longer to defend the old interface, but to master the inputs the new one requires.

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