This week’s newsletter is sponsored by Stacker and Airops.

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Venture investors and senior leadership keep asking me the same question on calls: How do you measure this new AI search channel? This question is misguided. AI search is not a new channel; it is an extension of SEO, much as mobile was an extension of desktop SEO. The UI has changed, but the user’s jobs to be done have not.

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Marketing budgets aren’t keeping pace with marketing targets, and now there’s data on exactly how badly.

AirOps surveyed 300+ marketing leaders on what’s actually happening inside their teams: budgets, headcount, AI adoption, channel performance. Not the polished version, the real one.

75.4% got higher pipeline or revenue targets this year. Only 43.0% got a bigger budget to hit them. Just 23% feel confident they can measure what’s working. And 86.6% are prioritizing AI search anyway.

The finding that stood out to me: budget size isn’t what separates the teams growing from the ones stalling. It’s the teams changing their KPIs, shifting channel mix, and building AI capacity that are pulling ahead.

This issue’s worth flagging if you’re heading into planning season with a bigger number and the same budget.

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The simple answer to measurement is: revenue. Every time I am asked this, it surprises me that using revenue to measure AEO was not the first instinct; instead, they explored ways to shoehorn in the old, ineffective ways of measuring SEO.

Every team that tries to do this ends up with a vanity number because, for too long, SEO itself has been built on vanity rankings. Everything in SEO was driven by that one vanity goal rather than revenue, leading to too much busywork instead of revenue-driving initiatives.'

Traffic is not the goal

Traffic fell? Do a technical audit. Rankings fell? Acquire links. Neither of these is a bad exercise in its own right, but they aren’t necessarily tied to the outcomes that triggered the effort. Even worse, the trigger might not have even been that bad. Rankings and traffic drops on their own might not be a big deal if there is no business impact. I once had a client who lost traffic with every Google algorithm update but also saw their revenue increase.

They lost traffic because they had spammy parts of their site that needed to be demoted, but the revenue-generating parts of their site benefited as their and other spammy pages disappeared from the results.

For years, SEO was sidetracked on a complicated journey of rankings disconnected from revenue, and rankings as a success metric alone were declining amid new scrutiny of this channel. This is not the time to make the same mistake with AEO. As a basic premise, AEO should only be deemed successful if it adds business value in terms of revenue or a direct proxy.

AI answers are here to stay and will eventually become dominant when AI mode becomes the default experience; we, marketers, and especially search marketers, need to find a way to measure this channel before it is defined for us by others.

Having a clear revenue goal will keep everyone happy.

AEO doesn’t drive revenue… directly

Transparently, revenue from AEO will be impossible to attribute, despite what all the new AI visibility tools say in their marketing pitches. Historically, attributing SEO to revenue has been challenging because it requires the last click before a conversion to come from an organic search result, unless a company is really good at multi-touch attribution, which is rare.

AEO is even worse because the very nature of the AI result might mean that a user doesn’t need to click anything, so there’s no trackable event. Therefore, building a measurement system that relies on a click will inevitably result in only slight accuracy.

What we need instead is to fully cement the shift in mindset from attribution to influence that was already happening with SEO pre-AEO. In that respect, AEO is closer to brand marketing than to SEO from a conversion standpoint, too. The byproduct of AEO is the squishy, trust me type that will never show up in an analytics dashboard. To measure AEO in revenue, we need to find some proxies.

Brand traffic

An ideal proxy that actually works in analytics is branded search and direct traffic. When a model cites a company by name, this will likely lead to a follow-up click on a search result after a brand search or a direct visit to the website. Obviously, all direct traffic isn't due to the SEO team's efforts, but if there’s a bump above the baseline, it would be fair to give some credit. This is true, especially when there’s no seasonal pattern or viral news item driving the jump.

You can find this in Search Console by looking at queries for the brand terms, and Google Trends should show something similar.

Track this as its own line, separate from organic and paid, and start correlating it with your citation tracking cadence. This isn’t a hard perfect number, but it can be a reasonable signal in an untrackable world.

Paid users can download this template

Ask the users

My favorite proxy, and the one most marketing teams skip, is to ask. You can do this by adding a field to a demand gen form or by simply conditioning sales teams to ask directly. Not everyone will answer, of course, but if your AEO efforts are effective, you can expect this source to appear in your reports.

This sounds unsophisticated compared to a dashboard, and it is, but it beats having no data at all.

Account-based matching

Take your list of target accounts, run your citation tracking against the queries those specific buyers would plausibly ask, and check whether the accounts that later entered the pipeline or closed were ones you were being cited for in the months before they showed up. The correlation won’t hold up as proof on a board or in a deck, but it is still a pattern worth watching.

Surveys

My final proxy and the most expensive is share-of-voice surveys. This isn’t the manufactured share of voice that the AEO tools are selling, because those tools invent a denominator that doesn't exist and then report your slice of it with false confidence. Instead, you need to run surveys using a bias-free set of questions that measure brand recognition across a broader set of competitors. This can be run using a basic homegrown panel or outsourced to a market research agency.

Even though these proxies lack accuracy and are difficult to chart, they are far better than building success metrics based on vanity alone.

AEO can be a substantial investment, and no company should be investing in it if it can’t even get to the back-of-the-envelope ROI calculation. Like SEO, if you can’t substantiate why a $10,000 monthly investment would ever break even, take a pause before writing the check.

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Earned Media Is How Brands Get Cited in AI Search

• See why 84% of AI citations come from earned media coverage.

• Learn how brands earn placement across 3,000+ vetted news publishers.

For twenty years, SEO budgets centered on content visibility and backlinks. AI search reshaped both within months.

Large language models gravitate toward brands that are already established and widely cited. That recognition comes from earned media. Muck Rack’s analysis of 25 million+ AI citations found earned media drove 84% of results, with paid content responsible for a mere 0.3%.

Stacker turns brand-funded stories into editorially reviewed content, placed natively across 3,000+ vetted news publishers, with publisher-verified reporting your team can audit. One well-built story on a trusted, high-authority publisher builds more model-visible brand authority than most backlink campaigns combined.

Sign a 12-month plan and your first story is on us

Before you start

You’ll need a Google account with access to the Search Console property you want to track, and Editor access to Google Drive to upload a file. Nothing here requires a paid Google Workspace plan. The whole thing runs on a free Google account.

Step 1: Convert the file to Google Sheets

Upload the Brand_Search_Trends_Tracker.xlsx (linked below) to Google Drive. Double-click it, then in the preview screen, choose Open with > Google Sheets. This creates an editable Sheets copy. Apps Script, which powers the automated Search Console pull, only runs inside native Google Sheets, not inside an Excel file sitting in Drive unconverted.

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• Paid subscribers can download this audit template to help you figure out your jobs to be done.

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When someone opens up a search engine or LLM, they aren’t looking for results for a keyword or prompt; that’s not the goal. Their objective is an answer to a burning need. It might be a plumber who can fix a leak right now. A place to eat dinner tomorrow. Or an answer to something they were curious about.

Jobs to Be Done

Professor and consultant Clayton Christensen figured this out decades ago with a milkshake chain that couldn’t understand why sales wouldn’t budge no matter how many flavors it added. They didn’t unlock growth until someone finally watched who was buying the milkshakes and came to the realization that it wasn’t what - the milkshake - but the why - they were buying milkshakes to make a boring drive less boring and to fill them up until lunch.

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Explore changing behaviors in how people find, click, and buy

Our latest State of Search Q2 2026 report just landed. Built with Datos’ clickstream data, the largest of its kind, it tracks how search behavior continues to shift. AI adoption, changing click patterns, zero-click growth, and where Amazon, Reddit, TikTok, and YouTube fit in. Utilizing real market-wide data, from across the US, UK, and EU.

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Christensen’s approach to asking the “why” is called jobs-to-be-done. Instead of focusing on the product, jobs-to-be-done segments users based on what the product is trying to accomplish. Every product you purchase today has a job-to-be-done, whether it’s something you eat, wear, or use. The same product could serve different jobs-to-be-done for different people. To illustrate this, for some, a car is just a means to go from point to point, while for others it’s a status symbol. Within the utilitarian group, there are also a myriad of uses for the car, which help drive the decision about what car to actually buy. Knowing the customer's job-to-be-done is how you sell the car.

SEO needs JTBD (Jobs-to-be-done)

SEO strategists rarely run the same exercise as the milkshake store. Instead, SEO has grown into an entire industry built around ranking for search terms rather than asking what job the searcher was actually trying to get done. With my clients, I try to get them to answer this question by approaching this challenge with Product-Led SEO, but for the most part, the industry is still keyword-obsessed. Now that AI has rewired how people search, SEO as a whole might finally be forced to confront a question it should have always asked.

Keyword searches were never THE job because a search term is just a proxy for what someone is looking for. So much SEO content reads like it was written for a robot instead of a human with a need because ranking on a search term was seen as the goal rather than the medium. This is why AI Overviews are so popular: they get a person to the required level of satisfaction faster, which is Google’s JTBD.

When a person searches for “best laptop,” they don’t have the time to read a two-thousand-word article on the topic; they want to buy a laptop. An AI Overview or AI response that understands the user, plus all the research on the topic, and suggests just three possibilities out of hundreds is exactly what the user needs to make a purchase right away.

JTBD still has a search journey

There will still be organic searches after that AI response, but far fewer than if they started with a list of 50 possibilities. One important callout for all the noise about declining organic search and value is that this person will most certainly visit fewer websites in their buyer’s journey now that they have AI options to help them, but they are still only going to purchase one laptop. The buyer’s journey has been simplified and condensed, but the overall demand has not changed.

Trying to shoehorn more content to retain lost traffic misses the point of what AI is doing. When that person searches, their jobs-to-be-done have not changed; the right approach is still to focus on that job. Rather than focus on the wrapper or content, focus on what’s inside: the need to buy a laptop.

Despite AI’s influence, everything about SEO is still relevant. There is still a need for helpful content, programmatic results, and, of course, well-optimized websites. The only change worth paying attention to is whether the value of the search results can be delivered in an AI result. If you are selling an offline service or product, you are likely in the clear; however, if your value is solely in showcasing content, that business model will continue to shrink.

AI doesn’t make SEO obsolete

A number of years ago, I consulted for Faire, a marketplace for wholesale goods. Their traffic has been affected by AI results, but the jobs-to-be-done have not changed; only the journey has. Users searching for the types of products that Faire offers still need to use Faire to connect with the wholesaler.

Compare that to someone searching "can I mix beer and wine?" or “how much is the Euro worth in dollars today.” That job ends the moment they get a trustworthy answer, with no follow-up visit required and no purchase waiting at the end. When the job finishes inside the answer, that traffic is gone permanently. When the job requires a transaction, a service, or a physical task, the traffic simply relocates to a later point in the journey.

Alternatively, if someone searches “is this skin mole concerning” or “why does my toaster oven spark”, there is absolutely a follow-up that will happen in search results. In the first example, with the mole, the user will seek out a dermatologist who meets their needs. In the second example, the user will likely want to purchase a new toaster oven, and there will be a journey of discovery and narrowing down to make that purchase.

You can’t hack JTBD in AEO

AEO makes the job framing even more unavoidable, and this is the part most companies buying AEO as a service get wrong. An LLM isn’t ranking a page. It’s hiring a source to help it finish a job on behalf of the user in the middle of a conversation, and the bar for getting hired is completely different from the bar for ranking in search results. LLMs don’t count or weight algorithm ranking factors. LLMs synthesize the most trustworthy answers to finish a job for a specific person. If AEO efforts are built to satisfy a checklist rather than actually finish someone’s job, the model will eventually ignore that site and pull from whoever did the work of solving the real problem.

For example, companies are producing FAQ sections engineered to be citation bait, structured for extraction, and stripped of any actual point of view because someone told them LLMs reward that format. LLMs are grading format and don’t really care that it checked an FAQ box. FAQs appear to drive LLM results because the content actually resolves the job the user brought into the conversation; the presence of FAQ content in LLM results is confirmation bias of the tactic, when in reality they work because that’s what the user wanted.

If the FAQs are just formatted to look extractable while leaving the substance thin, this is the AEO equivalent of keyword stuffing, a trick that worked for about six months before the system caught up.

Success is solving the JTBD

To succeed in AEO, the answer is the same as in SEO. It’s not another content audit or technical SEO effort. Audit your users’ jobs-to-be-done and optimize for them in an AI-centric way. Don’t focus on the means to an end, the tactic, but focus on the end itself: The job.

Win in AEO by going back to first principles and asking, for every tactic, what job is actually being hired here, and whether I'd trust this to finish it for someone I cared about. Put yourself in your users’ shoes before you trust something you read online or recommended by a tool or software.

Build for the user, and you are aligned with the future, whatever that might be.

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Earned Media Is How Brands Get Cited in AI Search

• See why 84% of AI citations come from earned media coverage.

• Learn how brands earn placement across 3,000+ vetted news publishers.

For twenty years, SEO budgets centered on content visibility and backlinks. AI search reshaped both within months.

Large language models gravitate toward brands that are already established and widely cited. That recognition comes from earned media. Muck Rack’s analysis of 25 million+ AI citations found earned media drove 84% of results, with paid content responsible for a mere 0.3%.

Stacker turns brand-funded stories into editorially reviewed content, placed natively across 3,000+ vetted news publishers, with publisher-verified reporting your team can audit. One well-built story on a trusted, high-authority publisher builds more model-visible brand authority than most backlink campaigns combined.

Sign a 12-month plan and your first story is on us

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This week’s newsletter is sponsored by Foundation Marketing and Ahrefs.

• Paid subscribers can download this Python crawler, which calculates the click depth of your pages, and check out this post for my roadmap template.

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Much of SEO has changed with the introduction of AI, and link building has not survived the transition unscathed either. I believe most of what passed for link building over the last decade was already obsolete before AI-assisted search showed up. A lot of link building was built around manufacturing signals rather than earning them, and over the last decade, Google has significantly improved its ability to detect and ignore these sorts of links.

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Another great new feature launch from Ahrefs.

"Ask Ahrefs" is now live and can be accessed on any Ahrefs page.

It reads the page you're on and your whole workspace. So you can ask it to: → summarize what's on screen → compare your site to a competitor → explain why your mentions spiked last week.

It's an agent, not a chatbot. It runs its own API queries, navigates the tools, and applies filters to get the data instead of asking you to.

Free for Ahrefs users while in beta.

Regardless, the discipline of link building done properly is still very much alive, and I will write a future post on what that actually looks like today, but that is a different argument for a different week. What I want to go deep on right now is the category of links that are the easiest to acquire and carry an outsized impact on everything else you are trying to do in SEO: internal linking.

What are internal links?

Internal links are internal references to pages within your site. Writers insert them to get people to read more content, but search engines understand them with the same algorithms they use to understand external links to your site. Instead of building authority from other sites, they build topical authority within a site. They are significantly easier to manipulate and obtain than external links.

Nobody has to negotiate a rate with an outreach agency working off a spreadsheet of domains and hope the receiver on the other end actually responds. You already own every one of these links, and most companies are sitting on far more unused authority than they realize.

Technical SEO has diminishing value in improving a site’s traffic because even a site with terrible structure can do well as the underlying AI in the algorithms will unwind bad best practices. Internal linking is the exception. I have never worked with a large website that couldn’t benefit from better internal linking.

Two reasons internal linking matters

1. The PageRank logic: every page is weighted by every page linking into it, and those votes are then logarithmically counted to pass authority to a new page. Therefore, a link from your homepage carries more weight than a link buried eight pages deep.

2. Internal links are how content gets discovered in the first place, by crawlers and now by AI systems trying to understand what your site knows. A page with zero internal links pointing to it is an orphan, and orphaned pages are functionally dead even when they're indexed, because nothing on your own site vouches for them. If nothing on your own site vouches for a page, don't expect an AI answer engine to go find it and vouch for it either. A page can be indexed and functionally invisible at the same time.
   
   Click depth is critical because a page that takes five clicks from the homepage to will be crawled far less frequently than one that is closer to the homepage. My opinion is that any important pages should live three clicks or fewer from the homepage.

How to fix internal linking

Generally, new content constantly links back to old content because you reference the foundational piece, you cite the earlier post, you build on what already exists. The reverse almost never happens. Old content doesn’t get updated to link forward into new content, because that requires someone to remember the old page exists and go do the work. Therefore, authority flows backward in time, while everything published this year sits there starving for the links that would actually help it rank or get cited.

Treat every page as a candidate for cross-linking. This must be built into the publishing workflow to update old pages, or ideally build dynamic logic that automatically inserts links into older pages on a schedule so there’s less manual work to do.

Context doesn’t matter as much as you think

Too many teams aim for context in internal links, which isn’t necessarily bad, but it misses the whole magical point of these links: driving more discovery. Focusing only on contextual relevance creates a biased feedback loop because your most popular topic already has the most content.

Popular links to popular. Niche stays niche forever, because the internal linking logic never gives it a shot.

To fix it, break it on purpose by ignoring context. Inject randomness into the linking logic, and I mean structurally engineered randomness. You can have dynamic logic on a page that inserts new content in a link box, but your logic must link to random pages.

This is obviously not the best editorial link strategy when a page about pricing suddenly links to a product launch blog post from eighteen months ago, but it will work. This needs to be truly random, so every page has a chance to be exposed to internal links.

A massive (millions of pages) site I worked on that did this saw crawl, indexation, and most importantly, traffic improve dramatically within weeks. Pages that had sat flat for over a year with zero internal links pointing to them got crawled again within days of turning on random link injection, and a meaningful chunk of them doubled organic traffic shortly after, without a single word of the content changing.

Server-side only

Ideally, any logic you use to build these links happens on the server because otherwise your links could be invisible to search engines. If your site runs on any JavaScript framework that renders links client-side after the page loads, it’s highly possible that these links will be invisible to crawlers and AI systems. Minimize this risk by making the links render server-side.

How you know it’s working

Once your new link algorithms are in place, pull crawl stats from any log analysis tool and run a crawl to compare your orphan page count before and after. If traffic to old pages starts moving without anyone touching the content itself, you can chalk this up to your linking efforts. Internal linking is the one thing in SEO that you can never invest too much in, so if you are looking for some low-hanging fruit, go pick this one.

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94% of B2B buyers now use AI when researching software. So who’s AI citing when they look for tools like yours?


Well, it’s usually not you. We analyzed 57 million citations across 50 brands: only 2.2% of citations on early-stage buyer queries pointed to brand-owned domains.

Your SEO, PR, and social don’t fix this on their own. And we’ve learnt over the last 12 months that getting cited by AI is its own discipline. The Hidden Selection Phase report shows where AI pulls its answers from, and what separates the brands it cites from the ones it skips.

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Getting itchy and instead want to see where your brand stands? Book a call for a free content audit with the leading AI visibility agency for B2B.

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Email me with questions or problems accessing the script.

Getting this running takes about five minutes on any machine with Python installed, and there’s no real “deployment” required since it’s a script you run once and let finish, not a service that sits on a server.

First, confirm Python 3 is installed by running python3 --version in a terminal. Anything 3.7 or newer works fine.

Next, install the two libraries the script depends on:

pip install requests beautifulsoup4

If that fails with a permissions error, add --user to the end of the command, or set up a virtual environment first with python3 -m venv venv followed by source venv/bin/activate on Mac or Linux, or venv\Scripts\activate on Windows, then run the pip install again inside that environment.

Save the script file somewhere accessible, then run it against the site in question:

python3 click_depth_crawler.py https://yoursite.com

That’s the whole deployment. It runs locally on whatever machine you launch it from, whether that’s a laptop or a server you SSH into, and it reaches out to the target site over normal HTTP requests the same way a browser would, so there’s nothing to install on the site’s side and no code to add to the site itself.

A few flags matter depending on the size of the site. For anything with more than a couple thousand pages, raise the ceiling before running: python3 click_depth_crawler.py https://yoursite.com --max-pages 10000. If the crawl feels too slow, the delay between requests can be lowered with --delay 0.1, though pushing it too aggressively risks tripping rate limiting or bot protection on the target site, so raise it in small increments rather than dropping straight to zero. Output lands in a file called click_depth.csv in whatever folder the script was run from, unless a different name is specified with --output filename.csv.

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• dofollow.com helps leading SaaS brands win visibility across Google and AI search. Every day you wait is another day competitors build authority. Book a call

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This week’s newsletter is sponsored by North Star Inbound and Semrush

• (Sponsored) AI Search Has Ads Now. Is Your Brand In Them, Or Missing? Trendos Ad Radar - see who’s buying placement inside ChatGPT, get their ad copy, plus AI visibility data. Free, 100 prompts.

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This week’s newsletter is sponsored by North Star Inbound and Surfer

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Over the course of my career, both in-house and as a consultant, I have worked with some genuinely exceptional SEO managers and in-house teams, people who understood the craft deeply, who built programs that compounded over the years with tangible business outcomes. All of those people share something that does not show up in most job descriptions, and the gap between what they do and what most companies hire for has only widened as the job has become more complex.

[Sponsored by Semrush]

Discover how people are really searching, discovering, and engaging online

AI continues to reshape how users discover, engage, and click across search environments. But most teams are still making decisions without a clear picture of what’s actually shifting.

The Datos State of Search Q1 2026 report, created in collaboration with Rand Fishkin, maps real search behavior across millions of users in the US, EU, and UK. AI adoption, regional contrasts, e-commerce discovery, and intent signals are all explored in detail using real clickstream data.

Get your free report

SEO is not a checkbox

The version of the role that most organizations seek and what many of their SEO employees fill is a ticket-creating function. This SEO manager sits between the many different teams and functions as an SEO toll booth, blocking other teams' progress to lift product launches into Google’s stated best practices.

If a feature request horrifyingly violates one of these guidelines, the SEO team will prevent a launch for the sake of SEO hygiene. One of my favorite types of calls I do on Intro is when I get to be the one to give the green light to “just do it,” because every SEO “expert” a company has hired has blocked true business progress. The toll booth does not build anything.

The SEO toll booth has the glorious job of translating requests into Jira issues and then waiting to see what ships. They don’t initiate strategy beyond checking SEO boxes, and their reporting consists of sharing rankings and now prompting visibility as though summarizing data were the same as driving results. When asked to tie those dashboards back to business metrics, they just can’t. The job has drifted so far from what actually drives business performance; it’s no surprise to me that companies are shedding SEO teams as they shift to AI-first thinking.

The org holds it back

This is not a personal failing of the people in those roles, and I have met many who would like to shift their responsibilities, but they can’t. The structure itself is broken. SEO got attached to marketing org charts as a channel rather than a capability, so the best they could do in this role is “conduct” the work of other teams. Engineering owns the infrastructure, while product builds the roadmap. SEO is left with just telling those other teams what to do. This is just an SEO suggestion box with a title.

Some in the marketing world are trying to address this problem by applying different titles to the same role. Calling an SEO manager an engineer without changing any expectations or reporting infrastructure doesn’t change the outcomes. Likewise, giving SEO teams new external tools doesn’t change much if they still lack control over the internal levers that drive progress. A new title attached to the same broken structure produces the same broken results.

This model was already showing cracks before AI started reshaping how answers get surfaced online, which is why my approach has been to put SEO into the product so the SEO team has access to internal levers.

User empathy is required

The next era of search will not reward people who are solely focused on managing workflows or keeping stakeholders informed about prompt visibility. Growth will come, as it always should, from people who understand what search users are actually trying to accomplish when they type a query or ask an AI assistant a question, and how a product's architecture can either support or destroy that intent at scale.

That is not a traditional marketing skill, nor is it purely a technical skill in the way most SEO practitioners use that word. As before, this is a product skill, specifically the kind of deep, user-obsessed, systems-aware product thinking that gets applied to the question of how search engines and AI models decide which answers deserve to be seen.

This person looks more like a product manager than anything else, and not a marketing engineer or a content strategist who has picked up a few technical certifications along the way. What I would actually look for in that person is a specific combination of capabilities that I have rarely seen packaged together in a single job description, but that I have consistently seen in the SEO managers who produced outsized results.

SEO managers are product managers

Let’s call this an SEO product manager, as I have argued many times in this weekly newsletter. This PM has to understand what a user means when they search, not just the words they type, and that requires genuine curiosity about people and problems rather than proficiency in a research methodology. The query is the user raising their hand to ask for something they need solved, and the job is to understand the problem well enough to build something that a search engine or AI model trusts enough to cite. The managers I have worked with who had this quality never needed to be told to think about the user first.

Naturally, we can’t just have a PM building something without also understanding SEO. This PM needs an intuitive understanding of how search engines and AI models reason about relevance, which goes beyond conventional technical SEO. An SEO product manager has a mental model of how Google and the LLMs that increasingly surface answers assess whether a source is authoritative.

The PM understands the reasoning behind the algorithm, not just the outputs of the tools that measure it. They intuitively know how topical depth signals expertise and how a product's architecture conveys meaningful information to a crawler about the organization behind it. This goes so much beyond just spouting off Domain Authority calculations.

Data understanding is a requirement

Many SEO managers I meet have no idea how to pull their own data, and frankly, with AI, there’s no excuse for that today. An SEO manager might not have access to all data, but they should be able to use and manipulate the internal data they do have. Sharing a report from an external SEO tool is not excusable. The manager needs to be able to sift through sometimes messy data to form a hypothesis. This is a different cognitive task than sending out a weekly update.

They should be able to look at a GSC export and see a strategic story in it, connect crawl data to business metrics in a way that makes an engineering team care about fixing something they would otherwise deprioritize.

Technical skills

Technical skills have always been a key component of SEO teams, but with AI, they are becoming much less important. Whereas in the past an SEO manager might have needed to read source code and even recommend code fixes, AI can handle this. This means that soft skills become the most important skill in today’s SEO work.

The SEO product manager has to be able to operate credibly in all those conversations, build trust with people with different incentives, and move work forward through relationships rather than org-chart positions. Soft skills are where many otherwise talented SEO practitioners fail, because the skill required is fundamentally political in the best sense of that word: the ability to understand what other people care about and to connect your priorities to their goals.

Check out this fantastic interviewing guide from Nick Leroy.

AI utilization is required

It should go without saying, but any SEO PM can use AI as a genuine force multiplier without surrendering the judgment that makes the work worth doing. AI can generate content variations, run site audits, surface patterns in crawl data, monitor how LLMs are citing competitors, and accelerate almost every repeatable task in the SEO workflow. What it cannot do is decide what matters.

Those calls require accumulated experience and the kind of business judgment that only develops through doing the work with real stakes attached. The SEO product manager uses AI to move faster and cover more surface area, while remaining the person who decides where to point all that capability. Handing the judgment over to the tool is the fastest way to produce a lot of confident, well-formatted work that leads nowhere.

Many SEO and AEO JDs look like past JDs but use AI-driven language. Today’s JDs need a solid update (paid subscribers can use the attached template). The job needs an upgrade that captures these skill sets and doesn’t just swap keyword-ranking reports for prompt rankings. You need someone who can walk into a product review and understand what is being built and why. This is a person who can make their own roadmap of what should be built and defend it.

The best SEO managers I have worked with already operate this way, even if their titles never reflected it and their org charts gave them no formal authority to do so. Some of them are on the job market because their companies mistakenly thought AI transformations meant they didn’t need SEO at all. These people are available, and if you are ready to take the leap to real growth from AEO, I am happy to make introductions. Just reply to this email.

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Every CEO I know has done the same thing in the last two years. They opened ChatGPT, typed in their company name, didn’t like what came back, and then emailed the SEO team or agency to fix it. For the leaders I have had this conversation with, I have been able to redirect their efforts. This post is for everyone else.

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That instant reaction makes sense on the surface. SEO is about visibility in search, LLMs are the new search, voila, the SEO team owns the problem. The logic checks out. It is also completely wrong, and this thinking risks making things worse.

Marketing has always owned SEO

This isn’t just an AI issue; it begins with putting SEO in a marketing org. One of the many reasons a company leaves its SEO potential unfulfilled is that it inadvertently silos the person responsible for SEO, leaving the SEO manager as a marketing individual contributor who must go through their marketing lead to get anything done.

The marketing lead then needs to negotiate for bandwidth on the product and engineering teams to get anything done. This organizational placement about where SEO even sits, especially at a time when the industry is being rewritten, pollutes every resourcing decision and budget conversation.

The organizational structure of SEO as marketing leaves the team without the authority to get things done. An SEO team that can write code has always been rare. Therefore, the best an SEO team can do is make requests of other teams, and those requests are not requirements.

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• If you will be at Google I/O this year, let me know if you are there and would want to grab coffee

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