Let me guess: you just spent three months building a perfectly optimized product taxonomy, complete with schema markup, internal linking, and killer metadata. 

Then, the product team decided to launch a site redesign without telling you. Now half your URLs are broken, the new templates strip out your structured data, and your boss is asking why organic traffic dropped 40%.

Sound familiar?

Here’s the thing: this isn’t an SEO failure, but a governance failure. It’s costing you nights and weekends trying to fix problems that should never have happened in the first place.

This article covers why weak governance keeps breaking SEO, how AI has raised the stakes, and how a visibility governance maturity model helps SEO teams move from firefighting to prevention.

Governance isn’t bureaucracy – it’s your insurance policy

I know what you’re thinking. “Great, another framework that means more meetings and approval forms.” But hear me out.

The Visibility Governance Maturity Model (VGMM) isn’t about creating red tape. It’s about establishing clear ownership, documented processes, and decision rights that prevent your work from being accidentally destroyed by teams who don’t understand SEO.

Think of it this way: VGMM is the difference between being the person who gets blamed when organic traffic tanks versus being the person who can point to documentation showing exactly where the process broke down – and who approved skipping the SEO review.

This maturity model:

• Protects your work from being undone by releases you weren’t consulted on.
• Documents your standards so you’re not explaining canonical tags for the 47th time.
• Establishes clear ownership so you’re not expected to fix everything across six different teams.
• Gets you a seat at the table when decisions affecting SEO are being made.
• Makes your expertise visible to leadership in ways they understand.

The real problem: AI just made everything harder

Remember when SEO was mostly about your website and Google? Those were simpler times.

Now you’re trying to optimize for:

• AI Overviews that rewrite your content.
• ChatGPT citations that may or may not link back.
• Perplexity summaries that pull from competitors.
• Voice assistants that only cite one source.
• Knowledge panels that conflict with your site.

And you’re still dealing with:

• Content teams who write AI-generated fluff.
• Developers who don’t understand crawl budget.
• Product managers who launch features that break structured data.
• Marketing directors who want “just one small change” that tanks rankings.

Without governance, you’re the only person who understands how all these pieces fit together. 

When something breaks, everyone expects you to fix it – usually yesterday. When traffic is up, it’s because marketing ran a great campaign. When it’s down, it’s your fault.

You become the hero the organization depends on, which sounds great until you realize you can never take a real vacation, and you’re working 60-hour weeks.

Dig deeper: Why most SEO failures are organizational, not technical

What VGMM actually measures – in terms you care about

VGMM doesn’t care about your keyword rankings or whether you have perfect schema markup. It evaluates whether your organization is set up to sustain SEO performance without burning you out. Below are the five maturity levels that translate to your daily reality:

Level 1: Unmanaged (your current nightmare)

• Nobody knows who’s responsible for SEO decisions.
• Changes happen without SEO review.
• You discover problems after they’ve tanked traffic.
• You’re constantly firefighting.
• Documentation doesn’t exist or is ignored.

Level 2: Aware (slightly better)

• Leadership admits SEO matters.
• Some standards exist but aren’t enforced.
• You have allies but no authority.
• Improvements happen but get reversed next quarter.
• You’re still the only one who really gets it.

Level 3: Defined (getting somewhere)

• SEO ownership is documented.
• Standards exist, and some teams follow them.
• You’re consulted before major changes.
• QA checkpoints include SEO review.
• You’re working normal hours most weeks.

Level 4: Integrated (the dream)

• SEO is built into release workflows.
• Automated checks catch problems before they ship.
• Cross-functional teams share accountability.
• You can actually take a vacation without a disaster.
• Your expertise is respected and resourced.

Level 5: Sustained (unicorn territory)

• SEO survives leadership changes.
• Governance adapts to new AI surfaces automatically.
• Problems are caught before they impact traffic.
• You’re doing strategic work, not firefighting.
• The organization values prevention over reaction.

Most organizations sit at Level 1 or 2. That’s not your fault – it’s a structural problem that VGMM helps diagnose and fix.

Dig deeper: SEO’s future isn’t content. It’s governance

How VGMM works: The less boring explanation

VGMM coordinates multiple domain-specific maturity models. Think of it as a health checkup that looks at all your vital signs, not just one metric.

It evaluates maturity across domains like:

• SEO governance: Your core competency.
• Content governance: Are writers following standards?
• Performance governance: Is the site actually fast?
• Accessibility governance: Is the site inclusive?
• Workflow governance: Do processes exist and work?

Each domain gets scored independently, then VGMM looks at how they work together. Because excellent SEO maturity doesn’t matter if the performance team deploys code that breaks the site every Tuesday or if the content team publishes AI-generated nonsense that tanks your E-E-A-T signals.

VGMM produces a 0–100% score based on:

• Domain scores: How mature is each area?
• Weighting: Which domains matter most for your business?
• Dependencies: Are weaknesses in one area breaking strengths in another?
• Coherence: Do decision rights and accountability actually align?

The final score isn’t about effort – it’s about whether governance actually works.

What this means for your daily life

Before VGMM-style governance:

• Product launches a redesign → You find out when traffic drops.
• Content team uses AI → You discover thin content in Search Console.
• Dev changes URL structure → You spend a week fixing redirects.
• Marketing wants “quick changes” → You explain why it’s not quick (again).
• Site goes down → Everyone asks why you didn’t catch it.

After governance maturity improves:

• Product can’t launch without SEO sign-off.
• Content AI usage has review checkpoints.
• URL changes require documented SEO approval.
• Marketing requests go through defined workflows.
• Site monitoring includes automated SEO health checks.

You move from reactive firefighting to proactive prevention. Your weekends become yours again.

The supporting models: What they actually check

VGMM doesn’t score you on technical SEO execution. It checks whether the organization has processes in place to prevent SEO disasters.

SEO Governance Maturity Model (SEOGMM) asks:

• Are there documented SEO standards?
• Who can override them, and how?
• Do templates enforce SEO requirements?
• Are there QA checkpoints before releases?
• Can SEO block launches that will cause problems?

Content Governance Maturity Model (CGMM) asks:

• Are content quality standards documented?
• Is AI-generated content reviewed?
• Are writers trained on SEO basics?
• Is there a process for updating outdated content?

Website Performance Maturity Model (WPMM) asks:

• Are Core Web Vitals monitored?
• Can releases be rolled back if they break performance?
• Is there a performance budget?
• Are third-party scripts governed?

You get the idea. Each domain has its own checklist, and VGMM shows leadership where gaps create risk.

Dig deeper: SEO execution: Understanding goals, strategy, and planning

How to pitch this to your boss

You don’t need to explain VGMM theory. You need to connect it to problems leadership already knows exist.

• Frame it as risk reduction: “We’ve had three major traffic drops this year from changes that SEO didn’t review. VGMM helps us identify where our process breaks down so we can prevent this.”
• Frame it as efficiency: “I’m spending 60% of my time firefighting problems that could have been prevented. VGMM establishes processes so I can focus on growth opportunities instead.”
• Frame it as a competitive advantage: “Our competitors are getting cited in AI Overviews, and we’re not. VGMM evaluates whether we have the governance structure to compete in AI-mediated search.”
• Frame it as scalability: “Right now, our SEO capability depends entirely on me. If I get hit by a bus tomorrow, nobody knows how to maintain what we’ve built. VGMM establishes documentation and processes that make our SEO sustainable.”
• The ask: “I’d like to conduct a VGMM assessment to identify where our processes need strengthening.”

What success actually looks like

Organizations with higher VGMM maturity experience measurably better outcomes:

• Fewer unexplained traffic drops because changes are reviewed.
• More stable AI citations because content quality is governed.
• Less rework after launches because SEO is built into workflows.
• Clearer accountability because ownership is documented.
• Better resource allocation because gaps are visible to leadership.

But the real win for you personally: 

• You stop being the hero who saves the day and become the strategist who prevents disasters. 
• Your expertise is recognized and properly resourced. 
• You can take actual vacations. 
• You work normal hours most of the time.

Your job becomes about building and improving, not constantly fixing.

Getting started: Practical next steps

Step 1: Self-assessment

Look at the five maturity levels. Where is your organization honestly sitting? If you’re at Level 1 or 2, you have evidence for why governance matters.

Step 2: Document current-state pain

Make a list of the last six months of SEO incidents:

• Changes that weren’t reviewed.
• Traffic drops from preventable problems.
• Time spent fixing avoidable issues.
• Requests that had to be explained multiple times.

This becomes your business case.

Step 3: Start with one domain

You don’t need to implement full VGMM immediately. Start with SEOGMM:

• Document your standards.
• Create a review checklist.
• Establish who can approve exceptions.
• Get stakeholder sign-off on the process.

Step 4: Show results 

Track prevented problems. When you catch an issue before it ships, document it. When a process prevents a regression, quantify the impact. Build your case for expanding governance.

Step 5: Expand systematically

Once SEOGMM is working, expand to related domains (content, performance, accessibility). Show how integrated governance catches problems that individual domain checks miss.

Why governance determines whether SEO survives

Governance isn’t about making your job harder. It’s about making your organization work better so your job becomes sustainable.

VGMM gives you a framework for diagnosing why SEO keeps getting undermined by other teams and a roadmap for fixing it. It translates your expertise into language that leadership understands. It protects your work from accidental destruction.

Most importantly, it moves you from being the person who’s always fixing emergencies to being the person who builds systems that prevent them.

You didn’t become an SEO professional to spend your career firefighting. VGMM helps you get back to doing the work that actually matters – the strategic, creative, growth-focused work that attracted you to SEO in the first place.

If you’re tired of watching your best work get undone by teams who don’t understand SEO, if you’re exhausted from being the only person who knows how everything works, if you want your expertise to be recognized and protected – start the VGMM conversation with your leadership.

The framework exists. What’s missing is someone in your organization saying, “We need to govern visibility like we govern everything else that matters.”

That someone is you.

Dig deeper: Why 2026 is the year the SEO silo breaks and cross-channel execution starts

Read more at Read More

We know AI responses are probabilistic – if you ask an AI the same question 10 times, you’ll get 10 different responses.

But how different are the responses?

That’s the question Rand Fishkin explored in some interesting research.

And it has big implications for how we should think about tracking AI visibility for brands.

In his research, he tested prompts asking for recommendations in all sorts of products and services, including everything from chef’s knives to cancer care hospitals and Volvo dealerships in Los Angeles.

Basically, he found that:

• AIs rarely recommend the same list of brands in the same order twice.
• For a given topic (e.g., running shoes), AIs recommend a certain handful of brands far more frequently than others.

For my research, as always, I’m focusing exclusively on B2B use cases. Plus, I’m building on Fishkin’s work by addressing these additional questions:

• Does prompt complexity affect the consistency of AI recommendations?
• Does the competitiveness of the category affect the consistency of recommendations?

Methodology

To explore those questions, I first designed 12 prompts:

• Competitive vs. niche: Six of the prompts are about highly competitive B2B software categories (e.g., accounting software), and the other six are about less crowded categories (e.g., user entity behavior analytics (UEBA) software). I identified the categories using Contender’s database, which tracks how many brands ChatGPT associates with 1,775 different software categories.
• Simple vs. nuanced prompts: Within both sets of “competitive” and “niche” prompts, half of the prompts are simple (“What’s the best accounting software?”) and the other half are nuanced prompts including a persona and use case (”For a Head of Finance focused on ensuring financial reporting accuracy and compliance, what’s the best accounting software?”)

I ran the 12 prompts 100 times, each, through the logged-out, free version of ChatGPT at chatgpt.com (i.e., not the API). I used a different IP address for each of the 1,200 interactions to simulate 1,200 different users starting new conversations.

Limitations: This research only covers responses from ChatGPT. But given the patterns in Fishkin’s results and the similar probabilistic nature of LLMs, you can probably generalize the directional (not absolute value) findings below to most/all AIs.

Findings

So what happens when 100 different people submit the same prompt to ChatGPT, asking for product recommendations?

How many ‘open slots’ in ChatGPT responses are available to brands?

On average, ChatGPT will mention 44 brands across 100 different responses. But one of the response sets included as many as 95 brands – it really depends on the category.

Competitive vs. niche categories

On that note, for prompts covering competitive categories, ChatGPT mentions about twice as many brands per 100 responses compared to the responses to prompts covering “niche” categories. (This lines up with the criteria I used to select the categories I studied.)

Simple vs. nuanced prompts

On average, ChatGPT mentioned slightly fewer brands in response to nuanced prompts. But this wasn’t a consistent pattern – for any given software category, sometimes nuanced questions ended up with more brands mentioned, and sometimes simple questions did.

This was a bit surprising, since I expected more specific requests (e.g., “For a SOC analyst needing to triage security alerts from endpoints efficiently, what’s the best EDR software?”) to consistently yield a narrower set of potential solutions from ChatGPT.

I think ChatGPT might not be better at tailoring a list of solutions to a specific use case because it doesn’t have a deep understanding of most brands. (More on this data in an upcoming note.)

Return of the ’10 blue links’

In each individual response, ChatGPT will, on average, mention only 10 brands.

There’s quite a range, though – a minimum of 6 brands per response and a maximum of 15 when averaging across response sets.

But a single response typically names about 10 brands regardless of category or prompt type.

The big difference is in how much the pool of brands rotates across responses – competitive categories draw from a much deeper bench, even though each individual response names a similar count.

Everything old (in SEO) truly is new again (in GEO/AEO). It reminds me of trying to get a placement in one of Google’s “10 blue links”.

Dig deeper: How to measure your AI search brand visibility and prove business impact

How consistent are ChatGPT’s brand recommendations?

When you ask ChatGPT for a B2B software recommendation 100 different times, there are only ~5 brands, on average, that it’ll mention 80%+ of the time.

To put it in context, that’s just 11% of all the 44 brands it’ll mention at all across those 100 responses.

So it’s quite competitive to become one of the brands ChatGPT consistently mentions whenever someone asks for recommendations in your category.

As you’d expect, these “dominant” brands tend to be big, established brands with strong recognition. For example, the dominant brands in the accounting software category are QuickBooks, Xero, Wave, FreshBooks, Zoho, and Sage.

If you’re not a big brand, you’re better off being in a niche category:

When you operate in a niche category, not only are you literally competing with fewer companies, but there are also more “open slots” available to you to become a dominant brand in ChatGPT’s responses.

In niche categories, 21% of all the brands ChatGPT mentions are dominant brands, getting mentioned 80%+ of the time.

Compare this to just 7% of all brands being dominant in competitive categories, where the majority of brands (72%) are languishing in the long tail, getting mentioned less than 20% of the time.

A nuanced prompt doesn’t dramatically change the long tail of little-seen brands (with <20% visibility), but it does change the “winner’s circle.” Adding persona context to a prompt makes it a bit more difficult to reach the dominant tier – you can see the steeper “cliff” a brand has to climb in the “nuanced prompts” graph above.

This makes intuitive sense: when someone asks “best accounting software for a Head of Finance,” ChatGPT has a more specific answer in mind and commits a bit more strongly to fewer top picks.

Still, it’s worth noting that the overall pool doesn’t shrink much – ChatGPT mentions ~42 brands in 100 responses to nuanced prompts, just a handful fewer than the ~46 mentioned in response to simple prompts. If nuanced prompts make the winner’s circle a bit more exclusive, why don’t they also narrow the total field?

Partly, it could be that the “nuanced” questions we fed it weren’t meaningfully more narrow and specific than what was implied in the simple questions we asked.

But, based on other data I’m seeing, I think this is partly about ChatGPT not knowing enough about most brands to be more selective. I’ll share more on this in an upcoming note.

Dig deeper: 7 hard truths about measuring AI visibility and GEO performance

What does this mean for B2B marketers?

If you’re not a dominant brand, pick your battles – niche down

It’s never been more important to differentiate. 21% of mentioned brands reach dominant status in niche categories vs. 7% in competitive ones.

Without time and a lot of money for brand marketing, an upstart tech company isn’t going to become a dominant brand in a broad, established category like accounting software.

But the field is less competitive when you lean into your unique, differentiating strengths. ChatGPT is more likely to treat you like a dominant brand if you work to make your product known as “the best accounting software for commercial real estate companies in North America.”

Most AI visibility tracking tools are grossly misleading

Given the inconsistency of ChatGPT’s recommendations, a single spot-check for any given prompt is nearly meaningless. Unfortunately, checking each prompt just once per time period is exactly what most AI visibility tracking tools do.

If you want anything approaching a statistically-significant visibility score for any given prompt, you need to run the prompt at least dozens of times, even 100+ times, depending on how precise you need the data to be.

But that’s obviously not practical for most people, so my suggestion is: For the key, bottom-of-funnel prompts you’re tracking, run them each ~5 times whenever you pull data.

That’ll at least give you a reasonable sense of whether your brand tends to show up most of the time, some of the time, or never.

Your goal should be to have a confident sense of whether your brand is in the little-seen long tail, the visible middle, or the dominant top-tier for any given prompt. Whether you use my tiers of ‘under 20%’, ‘20–80%’, and ‘80%+’, or your own thresholds, this is the approach that follows the data and common sense.

What’s next?

In future newsletters and LinkedIn posts, I’m going to build on these findings with new research:

• How does ChatGPT talk about the brands it consistently recommends? Is it indicative of how much ChatGPT “knows” about brands?
• Do different prompts with the same search intent tend to produce the same set of recommendations?
• How consistent is “rank” in the responses? Do dominant brands tend to get mentioned first?

This article was originally published on Visible on beehiiv (as Most AI visibility tracking is misleading (here’s my new data)) and is republished with permission.

Read more at Read More