Tag: Governance

How AI Search Is Redefining Global SEO Ownership Now

Earlier this year, I made the case that the core fundamentals of international SEO still matter. I still believe that. Hreflang, localization, technical excellence, and market-specific content remain essential because search engines and LLMs still need to discover, understand, and connect content with the right audiences.

What has changed is the environment those fundamentals now operate in.

For decades, I watched multinational organizations treat markets as mostly separate digital ecosystems. Content created in one market usually stayed there, and governance focused on managing websites, content, and technical implementation across different regions.

Today, those boundaries are much harder to see.

AI systems can translate content, synthesize information from multiple sources, and increasingly sit between organizations and their customers. Information that once lived inside one market can now shape visibility, recommendations, and customer experiences across many regions.

As those boundaries blur, I see the governance challenge expanding. International SEO is no longer only about managing websites across countries. It now requires organizations to manage the knowledge, expertise, and information that search engines and AI systems use to represent them globally.

Why I believe the governance model must change

Historically, many website and localization decisions were built around operational efficiency. Headquarters created content, technology platforms, and standards for global distribution, while local markets adapted those assets for their own audiences.

That model worked because scale often outweighed the limitations of localization. Consistency improved, costs came down, and organizations could deploy content and technology across dozens of markets far more efficiently than local teams could manage independently.

The challenge now is that AI systems are changing what gets rewarded.

Scale and standardization still matter, but search engines and AI systems increasingly look for signals of expertise, relevance, and geographic specificity. Content that reflects local regulations, market conditions, customer expectations, and industry practices often provides context that translation alone cannot reproduce.

At the same time, AI systems can magnify inconsistency. Contradictory product information, conflicting entity definitions, inaccurate regulatory guidance, and fragmented technical implementations can create confusion across search engines, answer engines, and AI-powered experiences.

That is why I do not think organizations can optimize only for efficiency or only for localization anymore. They need governance models that protect global consistency while giving local markets room to contribute the expertise and context that increasingly drive visibility and trust.

Hreflang solved routing, not understanding

In my previous hreflang article, I argued that hreflang still belongs in an international search strategy, even in the age of AI. I stand by that view.

But hreflang does not decide which market perspective should be prioritized when AI systems synthesize information from multiple sources. It also does not determine which content demonstrates the strongest expertise when AI-generated answers are produced.

As search moves from retrieval toward synthesis, I believe organizations need to think beyond routing users to the right page. They also need to govern the knowledge that powers those answers.

What I would centralize

My simplest rule is this: if an activity creates enterprise risk when it is handled inconsistently, it should usually be governed centrally.

Technical SEO standards are a clear example. Search engines and AI systems do not evaluate websites one market at a time. They evaluate the broader ecosystem of signals an organization provides. CMS governance, structured data standards, entity definitions, AI crawler policies, measurement frameworks, and technical infrastructure all benefit from consistency.

Many international organizations have already faced a version of this problem.

Years ago, before hreflang existed, many global companies used IP detection to route users to the market website they believed was most appropriate. The problem was that Google primarily crawled from U.S.-based IP addresses. When Google tried to access French or Japanese content, it was often redirected to the U.S. site instead.

Individual markets could not solve that on their own because the routing rules affected every market at once. The solution required global governance with local input.

I see AI crawler management creating a very similar challenge today.

Organizations now have to decide which AI systems can access their content and whether those systems can reach the market-specific information they are meant to understand. For companies still relying on geographic routing, market gateways, or IP detection, the governance issue should feel familiar even if the technology is new.

The platforms have changed, but the lesson has not. Some decisions are too interconnected to manage market by market.

What I would localize

If technical infrastructure benefits from consistency, content benefits from expertise.

For years, multinational organizations followed a simple model: create content in the primary market, then translate, adapt, and distribute it globally. That approach delivered real efficiencies. It helped organizations scale content production, maintain brand consistency, and support dozens of markets with shared resources and common technology platforms.

Traditional search engines could lean on signals like hreflang and country targeting to understand regional relevance. AI systems increasingly evaluate the content itself. When multiple markets publish nearly identical versions of the same information, language models may treat them as variations of one source rather than distinct expressions of expertise.

To stand on its own, content increasingly needs market-specific signals such as local regulations, terminology, customer expectations, industry practices, and other forms of geographic specificity.

That is why I believe content ownership, audience research, local authority building, regulatory content, and market expertise should usually stay close to the market. The goal is not localization for its own sake. The goal is to make sure expertise comes from the people closest to the customer and that the content reflects the realities of the market it serves.

The strongest multinational organizations will still use global content frameworks, shared resources, and common technology platforms because those efficiencies remain valuable. The hard part is preserving those efficiencies while giving local markets enough space to contribute expertise that is visible, differentiated, and meaningful.

For years, organizations balanced scale against localization. Increasingly, I think they are balancing scale against representation. The markets that remain visible in AI-driven search experiences will often be the ones that contribute enough unique expertise to stand on their own, rather than simply echo the dominant market version.

What I think needs shared ownership

Governance ultimately comes down to accountability. Whether responsibility sits with a Chief Digital Officer, CMO, enterprise search team, or AI governance group matters less than whether ownership is clear. As search becomes more connected to marketing, technology, product, legal, and AI initiatives, organizations need clear decision rights, escalation paths, and accountability.

The companies that succeed will not necessarily be the ones with the largest SEO teams or the most advanced AI tools. I expect the winners to be the organizations that know exactly how knowledge is created, governed, validated, and represented across markets.

My practical rule for determining ownership

For me, the distinction comes down to risk and expertise.

Responsibilities that create enterprise-wide consequences when implemented inconsistently generally belong closer to headquarters. Activities that depend on local customer knowledge, regulations, language, or market conditions are usually best managed in-market.

Many of the most important decisions need both perspectives, which means they are best handled through shared governance.

10 governance decisions I would review with every global SEO team

The exact structure will vary by organization, but I would encourage most multinational companies to evaluate ownership across these areas.

Typically centralized

1. Technical SEO standards

I would centralize these standards to ensure consistency in crawling, indexing, structured data, and technical implementation across markets.

2. CMS and infrastructure governance

I would govern this centrally to prevent fragmentation while maintaining a common technology foundation.

3. Entity definitions and taxonomies

I would keep these consistent so products, services, brands, and organizational relationships are represented clearly across markets.

4. AI crawler and bot governance

I would establish consistent policies for crawler access, monitoring, verification, geographic routing, and exception management. Governance should usually sit with headquarters, while markets should still be able to request business-specific exceptions.

5. Measurement and reporting frameworks

I would centralize reporting definitions so markets are evaluated with comparable success metrics.

Typically localized

6. Market-specific content

I would keep creation and validation close to local teams so content reflects customer needs, regulations, terminology, market conditions, and the geographic signals that help AI systems recognize local relevance. Global content frameworks can still support that work where appropriate.

7. Audience and search behavior research

I would manage this in-market to capture differences in language, intent, customer expectations, and emerging market trends.

8. Local authority building

I would localize this work because market-specific expertise, trust, partnerships, citations, and visibility cannot be fully manufactured from headquarters.

Typically shared

9. Product and knowledge management

I would treat this as shared ownership because it needs global consistency as well as local validation, market expertise, and regulatory accuracy. Headquarters should define the framework, while markets validate that products, services, and policies reflect local realities.

10. AI visibility and representation

I would also make this shared. Headquarters should establish monitoring and escalation processes, while local teams validate market-specific accuracy and identify emerging issues in how products, services, and brands are represented across AI systems.

The new global SEO mandate

I do not think the objective is to centralize everything or localize everything. The real mandate is to place ownership where decisions can be managed most effectively, so the organization can balance consistency with expertise.

Inspired by this post on Search Engine Land.

July 3, 2026
AI Search Trust Is Falling: What Marketers Must Fix

A year ago, I saw 82% of consumers say AI-powered search was more helpful than traditional search. By 2026, that number had fallen to 54%, a 28-point drop in sentiment in just 12 months.

That does not mean people are abandoning AI search. In fact, 70% of consumers say they are using AI tools for search more than they did last year. The tension is clear: adoption is rising, but trust is slipping.

That is the core issue I believe search marketers need to solve in 2026. It is no longer enough to appear in AI answers. I need my brand, and the brands I work with, to be visible, accurate, credible, and trusted when AI systems surface information.

To understand the shift, Fractl partnered with Search Engine Land to expand our 2025 research. We surveyed 1,008 U.S. consumers and 150 marketers to compare how consumer trust, marketer adoption, and brand strategy are changing in the AI search era. Disclosure: I am the co-founder of Fractl.

Here is what I believe the data means for 2026 search strategy.

Consumers are using AI more, but trusting it less

AI search adoption is no longer the main story. Seventy percent of consumers report increased use of AI tools for search over the past year, while only 3% say their use has decreased. The bigger question is whether people trust what those tools return.

One surprising finding is that baby boomers now find AI more helpful than Gen Z, 63% to 47%. That challenges the assumption that younger users automatically embrace AI while older users lag behind. What I see instead is a more complicated market where trust has to be earned across every generation.

In 2025, only 3% of consumers said AI was less helpful than traditional search. By 2026, that skeptic group had grown to 17%, nearly six times larger than the year before. Even among the 54% who still find AI helpful, enthusiasm is softer: 37% say it is only somewhat more helpful, while 17% say it is much more helpful.

I think hallucinations and low-quality AI content are changing how people evaluate the entire channel. Consumers may use AI because it is convenient, but convenience does not automatically create confidence.

AI content volume has become a brand trust risk

In 2025, 20% of consumers said heavy AI use would reduce their trust in a brand. In 2026, that number rose to 39%. For me, that makes AI content scale a reputational issue, not just an operational decision.

If I publish AI-assisted content at scale without disclosure, strong editorial standards, or obvious quality signals, I am asking my audience to trust a process they are increasingly skeptical of. That is a risk more brands need to take seriously.

Gen Z is especially strict. Fifty-four percent of Gen Z consumers say heavy AI use in a brand’s marketing would decrease their trust, compared with 32% of baby boomers and 33% of Gen X. Women are also more likely than men to penalize brands for heavy AI use, 44% vs. 34%.

That matters because Gen Z is often the audience most likely to engage deeply, share content, shape online conversations, and influence long-term organic visibility. If that audience matters to a brand, AI-generated filler is not a harmless shortcut.

Disclosure is now a consumer expectation

Across every major content format, more than 80% of consumers want AI-generated content labeled. Video leads at 91%, followed by images at 90%, audio at 87%, and written content at 84%. More than half of respondents strongly agree with labeling in every category.

I do not read that as a mild preference. I read it as a near-universal expectation. The brands that treat AI disclosure as optional are creating a gap between how they operate and what their audiences want.

Consumers still believe AI will shape the future of search. Sixty-four percent agree that AI will replace traditional search engines within five years, nearly unchanged from 66% in 2025. The channel is not going away. But being present in AI results and being trusted in AI results are now two different challenges.

Google still leads on trust, especially for buying decisions

When consumers are making purchase decisions, 39% turn to Google first. Reddit follows at 15%, AI tools at 14%, and review sites and friends or family each at 11%. The trust people have built with Google has not automatically transferred to AI tools.

Platform preference also changes by query type. Google dominates five of six major search categories. It is the first stop for local businesses, product research, travel planning, and health questions. YouTube overtakes Google for how-to content, while ChatGPT is now the second-most-used destination for health questions and ranks strongly for product research, travel planning, and how-to content.

That tells me there is no single AI search platform to optimize for. I need to map content strategy to actual user behavior: where people search, what they are trying to decide, and which platforms influence confidence at each stage.

Before making a purchase decision, the average consumer checks 2.4 platforms. Gen Z checks 2.5, millennials 2.4, Gen X 2.3, and baby boomers 2.2. This behavior is consistent enough that I now think of search optimization as a multi-platform visibility strategy, not a rankings-only discipline.

A brand that appears in Google results but nowhere else can lose to a brand that appears in Google, shows up in Reddit discussions, gets cited by ChatGPT, and has strong third-party review content. Visibility now has to travel with the buyer.

AI is changing marketing operations quickly

AI now touches 53% of marketing work on average, up from 38% in 2025. In practical terms, the equivalent of one full workday per week has shifted to AI-assisted workflows in just 12 months. Fifty-nine percent of marketers say AI is involved in at least half their work, while 27% say it is involved in three-quarters or more.

For SEO and content teams, this means competitors are moving faster. But speed alone is becoming commoditized. Accuracy, original insight, expert judgment, and brand credibility are much harder to copy.

Marketers are also feeling pressure to adopt AI. Fifty-five percent of marketing roles report a 7-out-of-10 level of pressure to use it. SEO and analytics teams feel that pressure most, while PR is not far behind. As AI makes generic content easier to produce, the advantage shifts toward what AI cannot automate well: judgment, relationships, trust, and reputation.

The quality tradeoff is real. Only 26% of marketers say AI made their work both faster and better. Nearly half say it made their work faster but more generic, and 7% report an outright quality decline.

That is where I see a major competitive opening. If other teams are scaling generic AI content while I invest in original data, expert quotes, third-party validation, and earned brand mentions, I am building assets that are more visible, credible, and retrievable across search engines, social platforms, and LLMs.

AI governance is still too weak

About three in four organizations conduct human editorial review before publishing AI-generated content. Sixty-two percent check for brand voice, 54% check facts, and 42% conduct legal or compliance review. Only 27% evaluate content for bias.

That means nearly half of AI-generated content may enter the market without fact-checking, legal review, or plagiarism checks. Too many teams are still relying on surface-level review: Does it sound right? Is the tone appropriate? Are there typos?

In a year when consumers are already prepared to distrust generic AI content, I see governance as one of the cheapest gaps to close and one of the most expensive to ignore.

The disclosure gap is just as serious. Heavy, generic AI use is now a brand-trust liability, yet only 20% of organizations always disclose AI use to their audiences. Compare that with the 84% average consumer demand for labeling written content, and the disconnect is obvious.

The takeaway is not to abandon AI. It is to stop treating governance as optional. Every AI workflow needs accuracy checks, transparency standards, bias review, and human accountability before content reaches an audience.

AI hallucinations are already a brand problem

A year ago, about 22% of marketers tracked LLM visibility. In 2026, that figure barely moved to 24%. At the same time, 27% of brands have already been misrepresented in AI-generated responses, and 14% say an AI inaccuracy has affected a customer relationship, sale, or PR situation.

More brands have been misrepresented by AI than have a formal monitoring process. That should concern every search and communications team.

If AI is summarizing my category, comparing my product, or explaining my brand incorrectly, that is not only an SEO issue. It is a reputation risk, a revenue risk, and a PR issue waiting to escalate.

When AI misrepresents a brand, I believe fixing the source matters more than arguing with the output. That can mean reaching out to publishers for updates, correcting owned profiles, improving brand pages, and publishing clear correction content tied to the entity.

Organic traffic is under pressure, not in freefall

Half of the marketers surveyed reported organic traffic declines since the launch of AI Overviews, and 61% blame AI. That is meaningful, but it is not the whole story.

The larger shift is not simply from Google to ChatGPT. It is from search as a destination to search as a behavior. People are asking, comparing, validating, and deciding across platforms, communities, assistants, and review environments.

The same marketers reporting organic losses are often finding visibility elsewhere. Fifty-seven percent report growth from social platforms such as TikTok, Reddit, and YouTube. Forty percent see growth from AI assistants such as ChatGPT, Gemini, and Perplexity. Thirty-one percent see growth in direct or branded traffic, while only 10% report no visibility growth anywhere.

That is why I think 2026 brand visibility depends on brand mentions and entity authority across the web, not just individual page rankings in Google.

Marketers are prioritizing the easiest tactics

Many teams are moving in the right general direction: community building, earned authority, owned audiences, expert content, and traffic diversification. The most prioritized strategies include building brand presence on social platforms at 59%, GEO and AEO optimization at 54%, and creating authoritative expert content at 44%.

Half of surveyed marketers say organic traffic has fallen since AI Overviews arrived, but the data points to pressure rather than collapse, with 30% reporting no change.

But the least prioritized strategy is original research and data, at only 15%. I see that as a strategic inversion.

Original, proprietary research is one of the hardest content assets for AI to replicate or commoditize. It earns citations, attracts links, builds topical authority, and gives journalists, communities, search engines, and AI systems something distinctive to reference.

In GEO, the same pattern appears. Many marketers are using content-led tactics that AI can easily replicate. Long-tail FAQs can help with AI Overviews, and schema can support structure, but neither one builds credibility by itself.

As organic search pressure grows, marketers are finding brand visibility gains across social platforms, AI assistants, direct traffic and Google AI features, according to Fractl and Search Engine Land.

The stronger moat is entity authority: proprietary data, expert perspectives, topical depth, and third-party validation. These are the assets that make a brand worth citing.

GEO measurement is lagging behind execution

Only a little more than half of marketers are confident in their GEO strategy, and only 12% have measurable results. That is understandable for a newer channel, but GEO is becoming too important to manage casually.

Marketers are leaning into practical GEO tactics, with FAQ optimization leading the pack, while entity authority, original research and citations trail behind.

I believe visibility tracking, citation monitoring, branded search lift, and AI-assisted conversion analysis all need more attention. Teams that can prove GEO ROI will be able to defend and grow investment while others are still guessing.

The main barrier to deeper AI integration is not leadership buy-in. Only 2% cite that as the obstacle. The top barrier is team training and skill gaps at 26%, followed by tool fragmentation at 20%, budget constraints at 19%, unclear ROI at 12%, and legal or compliance concerns at 12%.

For search teams, that means AI literacy, prompt strategy, content quality control, and GEO measurement skills may be more valuable right now than adding another tool to the stack.

Most marketers see early signs their GEO strategy is working, but only 12% report measurable results, highlighting a major gap in AI search measurement.

What I would do for a 2026 search strategy

First, I would audit the brand’s AI footprint. I would query the brand name across ChatGPT, Gemini, Perplexity, and Google AI Overviews, then document what is accurate, what is missing, and what is wrong. Waiting until an AI error becomes a PR issue is too late.

Second, I would invest in entity authority and original research. AI cannot invent legitimate proprietary survey data, named expert perspectives, verified brand facts, or original market analysis. Those assets become more valuable as AI systems get better at rewarding genuine authority.

Third, I would distribute visibility across multiple platforms. Google organic remains necessary, but it is no longer sufficient. A brand needs a consistent presence in Reddit discussions, YouTube content, AI assistant responses, review platforms, and earned media.

Fourth, I would build AI content governance, not just AI content workflows. Consumer demand for AI disclosure ranges from 84% to 91% across formats, while only 20% of brands always disclose. That gap is a reputational liability and may become a legal and regulatory one.

Fifth, I would close the GEO measurement gap. If I can connect AI search mentions to traffic, lead quality, and revenue, I can prove ROI at a time when most teams cannot. That creates a budget and strategy advantage that compounds.

Finally, I would double down on what AI cannot easily replicate: proprietary data, named experts, human-verified claims, transparent sourcing, and a consistent high-quality brand voice. In 2026, the brands that treat quality as a strategic differentiator are the ones most likely to be surfaced, cited, and trusted.

Methodology

Fractl and Search Engine Land surveyed 1,008 U.S. consumers and 150 marketers in Q2 2026. The consumer sample was nationally representative across age, gender, and region. The marketer sample included companies ranging from fewer than 10 employees to more than 5,000 and covered roles in SEO, content, social, analytics, paid media, PR, and marketing leadership.

Where noted, findings are compared year over year against the same questions asked in Fractl’s 2025 consumer study conducted with Search Engine Land.

Inspired by this post on Search Engine Land.

July 3, 2026
Elevate SEO Success with Strong Governance Models
Let me guess: I just spent three months meticulously crafting an optimized product taxonomy, complete with schema markup, internal linking, and standout metadata.

Then, out of nowhere, the product team decided to launch a site redesign without looping me in. Now half of my URLs are broken, the new templates have stripped away my structured data, and my boss is wondering why our organic traffic plummeted by 40%.

Sound familiar?

Here’s the thing: this isn’t an SEO failure, but a governance failure. It’s been costing us countless nights and weekends trying to fix problems that never should have occurred.

This article sheds light on why weak governance keeps breaking SEO, how AI advancements have raised the stakes, and how a visibility governance maturity model can help SEO teams transition 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 I’m 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 I’m 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, I’m the only person who understands how all these pieces fit together.

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

I become the hero the organization depends on, which sounds great until I realize I can never take a real vacation, and I’m 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. Imagine it as a health checkup that evaluates 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.

Most importantly, VGMM translates your expertise into language that leadership understands. It protects your work from accidental destruction, so you can focus on strategic, creative, growth-focused work that truly matters.

Inspired by this post on Search Engine Land.
February 10, 2026
How Organizational Issues Cause SEO Challenges, Not Technical Ones

Through over 20 years of experience in varying SEO roles, I’ve witnessed a recurring theme: the root of SEO performance issues often stems from organizational factors, not technical glitches.

Many times, problems manifest through decision rights, lack of ownership, and insufficient processes. These often precede noticeable traffic dips, obfuscating the real issues beneath the surface.

The technical fixes may expose symptoms but rarely uncover why progress has stalled.

No governance

The real limitations become apparent much earlier, rooted in reporting structures and decision-making authority. When SEO stumbles, governance—or lack thereof—is often to blame.

I discovered that when ownership of CMS templates was unclear or when cross-departmental priorities conflicted, SEO suffered. It wasn’t until I understood governance that the underlying issues became clear.

Only two companies in my career had the right conditions, with clear ownership and structured release pathways. Leaders recognized the importance of deliberately managing visibility, rather than reacting post-traffic drops.

Elsewhere, metadata and schema often didn’t limit performance. Organizational behavior did.

Beware of drift

Quarterly sales pressures often lead to sites making numerous small, seemingly innocuous changes that accumulate over time. These can range from navigation alterations by a new UX hire to content wording tweaks.

Individually, these shifts may not seem detrimental; however, collectively, they contribute to a decline in performance. This is something industry commentary often glosses over—while tangible technical fixes are more teachable, they aren’t where SEO outcomes are typically determined.

SEO loses power when it lives in the wrong place

I’ve observed how such drift can negatively impact rankings, with SEO unjustly taking the fall. Often, the actual cause was a lack of governance, which became apparent when outside agencies confirmed conclusions I had already reached.

The placement of SEO within an organization’s structure profoundly influences whether potential issues are identified early or only discovered post-launch. It affects whether changes are implemented promptly or languish for months.

SEO embedded under marketing, product, or IT each faces a unique set of challenges, restricting its effectiveness when placed too low on the organizational hierarchy.

Changes by engineering, product, or marketing often ship without SEO input, leading to misalignments that can reduce the efficacy of SEO strategies.

Positioning the SEO function

When SEO lacks proper placement within the organizational framework, it devolves into a reactive, cleanup role. The best results come when SEO is sufficiently integrated to influence early decision-making processes.

Organizations where SEO achieved significant success had the SEO function near leadership, ensuring visibility into upcoming changes and the ability to coordinate across departments.

The most favorable outcomes arose in environments where SEO acted as an integrated part of the infrastructure, reinforcing its importance as a contributor to long-term visibility and consistency.

Inspired by this post on Search Engine Land.

February 3, 2026