Jun 10 2026
AI Search In 2026: Five Findings From 300 Enterprise Marketing Execs

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

Is AI search actually replacing SEO, or do I need to budget for both?

How do I attribute conversions to ChatGPT vs. AI Overviews?

AI is progressing so quickly that it’s hard to keep track of the changes, let alone know how to take action.

That’s why we surveyed 300 marketing executives from large enterprises to understand how they’re responding to AI search and where their organizations stand.

The findings point to a rapidly growing technology, a majority of executives who are bullish on change, and an infrastructure that’s woefully unprepared to support the cacophony of technological changes we’re experiencing.

DOWNLOAD THE FULL AI REPORT

Finding 1: SEO Isn’t Dead & AI Search Is Additive (Not A Replacement)

AI search is showing massive growth. From virtually zero at the beginning of 2023, it now accounts for a mean of 35% of all website traffic.

In two years, AI search has been able to leapfrog decades of growth won by other channels. Naturally, the death of traditional SEO became a popular prediction. If consumers could get contextually rich answers from a chatbot, why would they bother searching at all?

Like history, the results are more complex and subtle. The data shows that traditional SEO’s share of web traffic is growing too. Respondents predicted it will gain a full 8 points of traffic share, from 45% in 2025 to 53% in 2026.

What does this mean?

Think about your own interactions with a chatbot. You bounce ideas around, get pointed to recommended sites,  then often run your own follow-up searches. Just last night I asked ChatGPT for help packing for a trip to Iceland. After getting a firm lecture on the inadequacy of my rain jacket, I headed to Google to actually find and buy one. ChatGPT was responsible for two or three website hits, Google two or three more.

AI search is adding a new mechanism to consumer discovery. Consumers can refine ideas or recommendations in chatbots and switch to search with a more refined query. It’s no surprise that after the emergence of the chatbot, Google is reporting more complex, multimodal traditional searches.

Embrace The Fact That Consumer Behavior Is (Purposefully) Occluded Between Channels

Incidentally, Google is central to the difficulty of parsing traditional SEO from AI search. It deliberately blurs the distinction between search, AI Overviews, and AI Mode, and to protect its position as the leader in search, it has every reason to. Search for a coffee maker in AI Mode, and you’ll be served a sponsored post. Click on it, and you’ll see a paid search campaign UTM tracking link. Advertisers are starting to show up in AI search results, and they don’t even know it’s happening.

ChatGPT (as of today) is only throwing a single UTM source referral with its traffic, leaving marketers knowing the traffic was sourced from ChatGPT, but nothing more. Marketers see much higher intent traffic, but have no context for the referral. To get even a glimpse up-funnel, marketers are resorting to combing through search logs to understand ChatGPT bot behavior on their websites.

You can’t fight these trends. It’s better to lean into your existing strategies while figuring out how to shift for new technologies. Google Gemini Ads are easy; if you run Search Ads, Google has likely already opted you into running them. Watch your campaign outcomes and don’t be surprised when some outliers change behavior. Google will repurpose your Search Ads to find what works in Gemini, you just need to supply the platform with the assets to iterate on the new medium.

ChatGPT is harder, but not impossible. Treat ChatGPT referral traffic as high-intent users who are likely past the initial discovery phase and well into the funnel. Don’t risk churn by forcing them along superfluous funnels.

The technology behind SEO and AI are vastly different. Search ranks content by relevance; AI aggregates multiple signals to distill an answer. Often the same fundamentals serve both technologies: machine-readable text, standards-based schemas, clarity, and social scores all signal quality to algorithms.

But sometimes they pull in opposite directions. In search, you can create two pages to target the exact opposite intent. One page markets an automobile as “luxurious”, while another touts the same car as “affordable.” Search will target each page with a separate intent. An LLM will aggregate all pages related to that product and get confused by the conflicting signals. Are you luxurious or affordable?

To prepare for AI search, beware of situations where SEO strategies actually serve as a detriment to the new technology.

Finding 2: Marketers Are Betting Massive Dollars On AI Search, But Struggle To Measure The Results

As AI search grows in share, it’s no surprise that marketers are setting aside budget. What is surprising is just how much. Sixty-five percent of enterprise executives are allocating at least 25% of their entire marketing budget to AI, and 28% are allocating over half. That’s a significant commitment for a channel where advertising models are still being built out.

Marketers express confidence in measuring the outcomes of these budgets, but a closer look shows cracks. Two-thirds say they are very confident, and 80% say that AI attribution is clearer than traditional SEO.

But in a more detailed follow-up question, 66% also report challenges with the basics of measurement. Fewer than 1 in 5 say they face no measurement challenges at all.

Mohammed Faizan of M&C Saatchi Performance suggests the reason is that current measurement just isn’t up to task: “Teams are confident in what they can see, and what they can see is a small, clean edge of the funnel: clear referrals from AI platforms, last-click conversions. That’s not measurement. That’s noticing the obvious. AI isn’t showing up in your attribution model; it’s hiding inside your branded search growth, your direct traffic lift, your ‘unexplained’ conversion spikes.”

This problem is about to get worse. Measuring referral traffic from ChatGPT is one thing; paying for it is another. As AI search scales into a paid channel, marketers will need attribution frameworks that don’t exist yet.

If a consumer spends a week in chatbot conversations, performing searches, and running into retargeting ads, how do you attribute that sale? The measurement gap that exists today will only widen as spend increases.

The good news is there are steps you can take now.

Embrace All Channels; Measure Whatever You Can

Advertising has become a black box. Algorithms run by the large ad platforms consume an enormous amount of data to predict and serve the most relevant ads. As digital channels multiply, the number of potential touchpoints grow and measurement gets murkier. Marketers will increasingly rely on algorithms to model and attribute spend across their channels.

To feed these models, you need data. The more, the better. Measure organic traffic, paid search, LLM referrals, and every other source you can instrument. The modeled attribution of the future will need that foundation.

Focus On End Impact, Not Platform Reporting

The more abstracted your measurement model becomes from real outcomes, the more you risk misattribution. Advertising has progressed from CPM to CPC to CPA, each shift allowing marketers to find better-performing media sources. But now multiple channels claim the same action.

The best way to avoid duplicated attribution claims isn’t to model share based on what each platform reports, it’s to model the actual sales outcome from the platform investment. OpenAI may not deserve 10% of your budget just because it claims 10% of your sales. An incrementality test could reveal it actually drives 50% of sales. True performance reporting takes the sting out of advertising on emerging technology.

Findings 3-5 Are In The Full Report

Marketers are willing to act quickly with AI: The vast majority think they’ll be executing closed-loop transactions in chatbots by the end of this year.

And so far, despite the negative press, AI is serving as a net-positive for marketers: Only 3% of respondents are seeing negative marketing performance from AI. Yet, when asked about the outlook in the future, concern outweighs their optimism.

Download the full report to see how your competitors are actually spending, measuring, and planning for AI search this year.

EXPLORE THE AI DISCOVERY REPORT

Image Credits

Featured Image: Image by Branch Used with permission.

In-Post Images: Images by Branch. Used with permission.

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Apr 23 2026
The Real Reason Your SEO Team Hasn’t Made The AI Transition Yet via @sejournal, @DuaneForrester

This series has spent five articles mapping what the AI search transition requires of your team, your content, your technical infrastructure, and your strategic framing. This piece addresses the question those five articles don’t answer: How do you actually make the organizational shift happen?

Most teams won’t fail here because they lack vision. The failure mode is execution, specifically the gap between knowing change is necessary and building the structure that makes it real.

The Transition Problem Is A People Problem, Not A Technology Problem

Only about 30% of enterprise SEO teams have restructured roles and responsibilities as a result of AI implementation. That means roughly 70% of teams who understand the shift intellectually haven’t made a structural move yet. The tools exist. The research is available. The urgency is visible in the data. And most teams are still running the same org chart they had three years ago.

This isn’t a strategic failure. It’s a change management failure, and it has a predictable shape. Three stall patterns show up consistently.

Analysis paralysis is the team that has attended every conference session, read every report, and built a compelling internal case, but can’t commit to a starting point because the landscape keeps shifting. The logic feels defensible: Why restructure when the platform behavior might change next quarter? The answer is that waiting for stability in an unstable environment isn’t patience. It’s avoidance dressed up as diligence.

Pilot purgatory is more widespread than most leaders want to admit. A survey of 200 U.S. marketing leaders found that 82% of teams using AI for campaigns are still operating in pilot or experimental mode, with 61% using AI only at the individual level rather than building it into collaborative team workflows. The pilot never fails cleanly; it just never graduates to production.

Reorg fatigue is the subtlest of the three. Teams that have been through digital transformation cycles carry scar tissue. They’ve watched priority initiatives get announced, resourced, and quietly abandoned when the next priority arrived. When a VP announces a pivot to AI visibility, the team’s first internal question often isn’t how to do it; it’s how long until this one goes away, too. Credibility for this transition requires demonstrating that it’s structurally different from the previous three, which means visible commitment in budget, headcount, and KPI design, not just slide decks.

The Resistance Map

Not all resistance is the same, and treating it as a uniform problem produces uniform failure. Four distinct patterns appear in SEO and marketing teams, each requiring a different response.

Seniority-based resistance sounds like: I’ve been doing this for 15 years, and I know what works. This is often the hardest pattern to address because it’s partly legitimate. Senior practitioners have real pattern recognition that junior team members lack, and they’ve watched enough vendor-driven hype cycles to be appropriately skeptical of any new essential framework. The correct response isn’t to dismiss the experience; it’s to reframe the transition as an addition to what they know, not a replacement of it. As established in the context moat piece earlier in this series, the fundamentals of relevance and trust don’t disappear in an AI search environment. They compound. Senior practitioners who make that conceptual bridge become accelerants, not obstacles.

Skills-based anxiety is a different problem entirely. This person isn’t resisting because they distrust the framework; they’re resisting because they don’t know how to operate inside it. The language of vector indexes, structured data expansion, and retrieval architecture is genuinely foreign to someone who built their career on keyword clustering and link building. A useful diagnostic lens here comes from the ADKAR model, a change management framework developed by Prosci that identifies five sequential conditions an individual needs to reach for change to stick: Awareness, Desire, Knowledge, Ability, and Reinforcement. Skills-based anxiety is almost always a Knowledge or Ability gap, not a motivation problem. Treating it as motivation resistance wastes time and confirms the team member’s fear that leadership doesn’t understand what they’re actually being asked to do.

Political resistance is structural, not personal. If AI visibility expands SEO scope to include retrieval architecture, machine-facing content design, and cross-functional data coordination, someone’s budget conversation changes. Marketing ops, IT, and content teams all have a plausible claim on parts of that expanded scope. This resistance rarely surfaces as direct opposition; it shows up as slow approvals, ambiguous priorities, and repeated requests to align with stakeholders before anything moves. The response requires making budget and ownership decisions explicitly, not hoping that clarity emerges from collaboration.

Legitimate skepticism deserves its own category because it’s the resistance pattern most leaders mishandle. When someone asks to see the revenue connection, that isn’t obstruction; it’s the right question. The answer needs to be honest, which means acknowledging that the measurement infrastructure for AI visibility is still developing. Trying to manufacture certainty in response to legitimate skepticism destroys credibility faster than admitting the gap. Acknowledging where the data is incomplete while demonstrating directional progress is more durable.

Running Both Operations At Once

Most teams can’t switch from traditional SEO to AI visibility operations in a single reorg cycle, and the honest answer is that most won’t need to. The practical reality is a period of parallel operation, where traditional work continues while AI visibility capabilities are built alongside it, and for the majority of organizations, that parallel period won’t resolve into a clean new structure. It will simply become how the team operates. The most common near-term pattern is already visible: The existing SEO gets handed AEO responsibilities alongside their current work, budgets don’t expand to match the expanded scope, and the team figures it out. That state will persist for years in most organizations, and in many it will persist indefinitely. New dedicated roles will emerge at larger organizations and in more competitive verticals, but that’s the exception rather than the rule.

Ultimately, the right allocation isn’t a fixed ratio dropped in from outside your organization; it’s a function of where your current traffic and business value are coming from, and how fast that’s shifting. What research on enterprise AI adoption does confirm is a consistent structural principle: Organizations that successfully scale AI spend the majority of their transition effort on people and process, not on the technology layer itself. That inversion, most attention on tools and least on people, is the primary driver of the pilot purgatory pattern described above. Your capacity allocation decisions need to reflect that. Building a new AI visibility capability on inadequate team development produces a capability that exists on paper and stalls in practice.

Two operational principles matter during the parallel period. First, not all traditional SEO activities need equal intensity to maintain. Technical hygiene, crawl accessibility, and core structured data work protect your existing position and directly support AI retrieval; they aren’t legacy activities to deprioritize. High-volume tactical content production, by contrast, is where capacity can be reallocated toward AI-era work without meaningful risk to current performance. Second, the AI visibility workstream needs dedicated ownership, not shared bandwidth. Work that lives in everyone’s job description at the margin of their other responsibilities doesn’t graduate from pilot mode. Someone needs to own the new work as a primary accountability.

Sequencing The Role Transitions

Not all roles change at the same time, and trying to restructure everything simultaneously is how reorg fatigue gets manufactured. A phased sequence reduces disruption while building the internal momentum that carries later phases.

Phase one starts with content strategists, because the conceptual bridge is shortest. The move from “what does my audience search for” to “what context does a retrieval model need to surface my content accurately” is an extension of existing thinking, not a departure from it. As covered in the roles series, this is the capability layer with the most upskilling potential and the least new-hire dependency. Start here, build early wins, and let the internal success story carry credibility into subsequent phases.

Phase two moves to technical SEOs, who face a more demanding knowledge transition. Vector index hygiene, structured data expansion beyond standard schema implementations, and crawl accessibility for AI bots require genuine new technical literacy, and not every existing practitioner will choose to develop it. This is where the upskill-versus-hire question starts to get real, and more on that in the next section. The technical SEO role isn’t disappearing, but its scope is expanding in directions that require deliberate investment.

Phase three introduces roles that may not yet exist on your team: an AI visibility analyst responsible for monitoring retrieval inclusion and brand representation, and someone focused on machine-facing content architecture. These may start as partial responsibilities before they justify dedicated headcount, but they need to exist as named functions with owners before the measurement conversation in phase four can work.

Phase four restructures reporting lines and performance metrics to reflect the new operating model. Teams held accountable to AI visibility outcomes, while their performance reviews are built entirely around traditional organic traffic metrics, produce the behavior you’d expect: compliance theater. This phase shouldn’t wait until phase three is complete; it should be designed in phase one and communicated clearly so the team understands what the finish line looks like from the start.

The Training Investment Decision

Whether to upskill existing team members or hire new ones is often framed as a budget decision. It’s actually a knowledge gap assessment.

If the gap is conceptual, covering how retrieval works, how AI models use structured data, how community signals feed into model training as discussed in the community signals piece, invest in training. These are learnable frameworks, and experienced practitioners who understand the underlying logic of traditional SEO have strong transfer potential. Analysis of more than 10,000 SEO job postings shows a 21% year-over-year increase in AI-related skill requirements, which reflects real employer demand but also signals that the market expects existing practitioners to develop these capabilities, not that companies are replacing their teams wholesale.

If the gap is technical execution, building APIs, working directly with embedding architectures, constructing systems that require software engineering background, the calculus shifts toward hiring or contracting. This is specialized enough that the training timeline to bring an existing practitioner to production competency may exceed the cost and speed of hiring someone who already has it.

A practical diagnostic for each capability gap: ask whether a competent practitioner with your team’s existing background could reach working proficiency in 90 days with focused investment. If yes, train. If the honest answer is longer, or if the gap requires a completely different mental model of how software systems work, consider hiring. The important discipline here is answering honestly rather than answering in the direction of what’s cheaper.

Measuring The Transition Itself

The transition needs its own measurement framework, separate from the visibility metrics the transition is designed to improve. Without it, leadership has no way to distinguish between a team that is genuinely progressing and a team that is performing progress.

Leading indicators tell you whether the structural shift is actually happening: team fluency with retrieval concepts verified through practical exercises rather than self-reporting, the number of AI visibility experiments in active testing rather than sitting in a backlog, and cross-functional collaboration frequency between SEO, content, and technical teams on AI-era work.

Lagging indicators connect to the outcomes the transition is meant to produce: Brand citation share in AI-generated responses, retrieval inclusion rates across major platforms, and the accuracy of brand representation when your content is surfaced. The framework for approaching these metrics was laid out in the GenAI KPIs piece, and the methodology there applies directly to the lagging indicators here.

The honest acknowledgment is that standardized measurement infrastructure for AI visibility is still developing. The industry hasn’t produced the equivalent of what organic search has in terms of agreed-upon tracking methodology. That isn’t a reason to defer the transition; it’s a reason to document your own methodology consistently from the start, so you’re building a proprietary baseline as standards eventually emerge. Companies that begin measuring now, even imperfectly, will have comparative data that teams starting eighteen months from now won’t be able to reconstruct.

A 90-day scorecard for the transition itself should include: at least one role with formal AI visibility responsibilities assigned, a named owner for the dual operating model, at least two active retrieval experiments generating learning data, and a completed skills gap assessment for every team member against the phase three role definitions. None of those are visibility metrics. They’re execution metrics, and execution is where most transitions fail.

Who Wins?

The organizations that navigate this transition successfully won’t be the ones with the clearest vision of what AI search requires. They’ll be the ones that converted that vision into structure: named owners, phased timelines, honest skills assessments, and measurement that tracks the work before it tracks the outcomes. Vision is table stakes, and every team reading this already has it. The ones that pull ahead will be the ones that open Mondays with a plan.

More Resources:

This post was originally published on Duane Forrester Decodes.

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

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Apr 1 2026
Who Owns SEO In The Enterprise? The Accountability Gap That Kills Performance via @sejournal, @billhunt

Enterprise SEO doesn’t fail because teams don’t care, lack expertise, or miss tactics. It fails because ownership is fractured.

In most large organizations, everyone controls a piece of SEO, yet no single group owns the outcome. Visibility, traffic, and discoverability depend on dozens of upstream decisions made across engineering, content, product, UX, legal, and local markets. SEO is measured on the result, but it does not control the system that produces it.

In smaller organizations, this problem is manageable. SEO teams can directly influence content, technical decisions, and site structure. In the enterprise, that control dissolves. Incentives diverge. Workflows fragment. Coordination becomes optional.

SEO success requires alignment, but enterprise structures reward isolation. That mismatch creates what I call the accountability gap – the silent failure mode behind most large-scale SEO underperformance.

SEO Is Measured By The Team That Doesn’t Control It

SEO is the only business function I am aware of that, judged by performance, cannot be delivered independently. This is especially true in the enterprise, where SEO performance is evaluated using familiar metrics: visibility, traffic, engagement, and increasingly AI-driven exposure. The irony is that the SEO function rarely controls the systems that generate those outcomes.

Function Controls SEO Dependency
Development Templates, rendering, performance Crawlability, indexability, structured data
Content Teams Messaging, depth, updates Relevance, coverage, AI eligibility
Product Teams Taxonomy, categorization, naming Entity clarity, internal structure
UX & Design Navigation, layout, hierarchy Discoverability, user engagement
Legal & Compliance Claims, restrictions Content completeness & trust signals
Local Markets Localization & regional content Cross-market consistency & intent alignment

SEO depends on all of these departments to do their job in an SEO-friendly manner for it to have a remote chance of success. This makes SEO unusual among business functions. It is judged by performance, yet it cannot deliver that performance independently. And because SEO typically sits downstream in the organization, it must request changes rather than direct them.

That structural imbalance is not a process issue. It is an ownership problem.

The Accountability Gap Explained

The accountability gap appears whenever a business-critical outcome depends on multiple teams, but no single team is accountable for the result.

SEO is a textbook example as fundamental search success requires development to implement correctly, content to align with demand, product teams to structure information coherently, markets to maintain consistency, and legal to permit eligibility-supporting claims. Failure occurs when even one link breaks.

Inside the enterprise, each of those teams is measured on its own key performance indicators. Development is rewarded for shipping. Content is rewarded for brand alignment. Product is rewarded for features. Legal is rewarded for risk avoidance. Markets are rewarded for local revenue. SEO lives in the cracks between them.

No one is incentivized to fix a problem that primarily benefits another department’s metrics. So issues persist, not because they are invisible, but because resolving them offers no local reward.

KPI Structures Encourage Metric Shielding

This is where enterprise SEO collides head-on with organizational design.

In practice, resistance to SEO rarely looks like resistance. No one says, “We don’t care about search.” Instead, objections arrive wrapped in perfectly reasonable justifications, each grounded in a different team’s success metrics.

Engineering teams explain that template changes would disrupt sprint commitments. Localization teams point to budgets that were never allocated for rewriting content. Product teams note that naming decisions are locked for brand consistency. Legal teams flag risk exposure in expanded explanations. And once something has launched, the implicit assumption is that SEO can address any fallout afterward.

Each of these responses makes sense on its own. None are malicious. But together, they form a pattern where protecting local KPIs takes precedence over shared outcomes.

This is what I refer to as metric shielding: the quiet use of internal performance measures to avoid cross-functional work. It’s not a refusal to help; it’s a rational response to how teams are evaluated. Fixing an SEO issue rarely improves the metric a given department is rewarded for, even if it materially improves enterprise visibility.

Over time, this behavior compounds. Problems persist not because they are unsolvable, but because solving them benefits someone else’s scorecard. SEO becomes the connective tissue between teams, yet no one is incentivized to strengthen it.

This dynamic is part of a broader organizational failure mode I call the KPI trap, where teams optimize for local success while undermining shared results. In enterprise SEO, the consequences surface quickly and visibly. In other parts of the organization, the damage often stays hidden until performance breaks somewhere far downstream.

The Myth: “SEO Is Marketing’s Job”

To simplify ownership, enterprises often default to a convenient fiction: SEO belongs to marketing.

On the surface, that assumption feels logical. SEO is commonly associated with organic traffic, and organic traffic is typically tracked as a marketing KPI. When visibility is measured in visits, conversions, or demand generation, it’s easy to conclude that SEO is simply another marketing lever.

In practice, that logic collapses almost immediately. Marketing may influence messaging and campaigns, but it does not control the systems that determine discoverability. It does not own templates, rendering logic, taxonomy, structured data pipelines, localization standards, release timing, or engineering priorities. Those decisions live elsewhere, often far upstream from where SEO performance is measured.

As a result, marketing ends up owning SEO on the organizational chart, while other teams own SEO in reality. This creates a familiar enterprise paradox. One group is held accountable for outcomes, while other groups control the inputs that shape those outcomes. Accountability without authority is not ownership. It is a guaranteed failure pattern.

The Core Reality

At its core, enterprise SEO failures are rarely tactical. They are structural, driven by accountability without authority across systems SEO does not control.

Search performance is created upstream through platform decisions, information architecture, content governance, and release processes. Yet SEO is almost always measured downstream, after those decisions are already locked. That separation creates the accountability gap.

SEO becomes responsible for outcomes shaped by systems it doesn’t control, priorities it can’t override, and tradeoffs it isn’t empowered to resolve. When success requires multiple departments to change, and no one owns the outcome, performance stalls by design.

Why This Breaks Faster In AI Search

In traditional SEO, the accountability gap usually expressed itself as volatility. Rankings moved. Traffic dipped. Teams debated causes, made adjustments, and over time, many issues could be corrected. Search engines recalculated signals, pages were reindexed, and recovery, while frustrating, was often possible. AI-driven search behaves differently because the evaluation model has changed.

AI systems are not simply ranking pages against each other. They are deciding which sources are eligible to be retrieved, synthesized, and represented at all. That decision depends on whether the system can form a coherent, trustworthy understanding of a brand across structure, entities, relationships, and coverage. Those signals must align across platforms, templates, content, and governance.

This is where the accountability gap becomes fatal. When even one department blocks or weakens those elements – by fragmenting entities, constraining content, breaking templates, or enforcing inconsistent standards – the system doesn’t partially reward the brand. It fails to form a stable representation. And when representation fails, exclusion follows. Visibility doesn’t gradually decline. It disappears.

AI systems default to sources that are structurally coherent and consistently reinforced. Competitors with cleaner governance and clearer ownership become the reference point, even if their content is not objectively better. Once those narratives are established, they persist. AI systems are far less forgiving than traditional rankings, and far slower to revise once an interpretation hardens.

This is why the accountability gap now manifests as a visibility gap. What used to be recoverable through iteration is now lost through omission. And the longer ownership remains fragmented, the harder that loss is to reverse.

A Note On GEO, AIO, And The Labeling Distraction

Much of the current conversation reframes these challenges under new labels GEO, AIO, AI SEO, generative optimization. The terminology isn’t wrong. It’s just incomplete.

These labels describe where visibility appears, not why it succeeds or fails. Whether the surface is a ranking, an AI Overview, or a synthesized answer, the underlying requirements remain unchanged: structural clarity, entity consistency, governed content, trustworthy signals, and cross-functional execution.

Renaming the outcome does not change the operating model required to achieve it.

Organizations don’t fail in AI search because they picked the wrong acronym. They fail because the same accountability gap persists, with faster and less forgiving consequences.

The Enterprise SEO Ownership Paradox

At its core, enterprise SEO operates under a paradox that most organizations never explicitly confront.

SEO is inherently cross-functional. Its performance depends on systems, processes, platforms, and decisions that span development, content, product, legal, localization, and governance. It behaves like infrastructure, not a channel. And yet, it is still managed as if it were a marketing function, a reporting line, or a service desk that reacts to requests.

That mismatch explains why even well-funded SEO teams struggle. They are held responsible for outcomes created by systems they do not control, processes they cannot enforce, and decisions they are rarely empowered to shape.

This paradox stays abstract until it’s reduced to a single, uncomfortable question:

Who is accountable when SEO success requires coordinated changes across three departments?

In most enterprises, the honest answer is simple. No one.

And when no one owns cross-functional success, initiatives stall by design. SEO becomes everyone’s dependency and no one’s priority. Work continues, meetings multiply, and reports are produced – but the underlying system never changes.

That is not a failure of execution. It is a failure of ownership.

What Real Ownership Looks Like

Organizations that win redefine SEO ownership as an operational capability, not a departmental role.

They establish executive sponsorship for search visibility, shared accountability across development, content, and product, and mandatory requirements embedded into platforms and workflows. Governance replaces persuasion. Standards are enforced before launch, not debated afterward.

SEO shifts from requesting fixes to defining requirements teams must follow. Ownership becomes structural, not symbolic.

The Final Reality

This perspective isn’t theoretical. It’s grounded in my nearly 30 years of direct experience designing, repairing, and operating enterprise website search programs across large organizations, regulated industries, complex platforms, and multi-market deployments.

I’ve sat in escalation meetings where launches were declared successful internally, only for visibility to quietly erode once systems and signals reached the outside world. I’ve watched SEO teams inherit outcomes created months earlier by decisions they were never part of. And more recently, I’ve worked with leadership teams who didn’t realize they had a search problem until AI-driven systems stopped citing them altogether. These are not edge cases. They are repeatable organizational failure modes.

What ultimately separated failure from recovery was never better tactics, better tools, or better acronyms. It was ownership. Specifically, whether the organization recognized search as a shared system-level responsibility and structured itself accordingly.

Enterprise SEO doesn’t break because teams aren’t trying hard enough. It breaks when accountability is assigned without authority, and when no one owns the outcomes that require coordination across the organization.

That is the problem modern search exposes. And ownership is the only durable fix.

Coming Next

The Modern SEO Center Of Excellence: Governance, Not Guidelines

We’ll close the loop by showing how enterprises institutionalize ownership through a Center of Excellence that governs standards, enforcement, entity governance, and cross-market consistency, the missing layer that prevents the accountability gap from recurring.

More Resources:

Featured Image: ImageFlow/Shutterstock

Ecommerce MGMT 0 Comments
Jan 27 2026
How Do You Compete In Agentic Commerce? via @sejournal, @Kevin_Indig

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Agentic commerce transforms organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification. Marketing arbitrage dies; product truth wins.

Image Credit: Kevin Indig

This week, we’re covering:

  • Why agentic commerce filters out marketing-first brands and rewards granular product data.
  • How ChatGPT, Copilot, and Google’s protocols reshape merchant economics and customer relationships.
  • Which feeds to optimize, which protocols to prioritize, and the implementation sequence that matters.
Image Credit: Kevin Indig

Agentic commerce acts as a “great filter,” so to speak, for marketing arbitrage, transforming organic search from a source of cheap traffic into the mandatory gatekeeper of AI verification.

The signal is already visible in the noise. During the 2025 holiday season, AI agents powered 20% of retail sales. Even allowing for loose definitions, the era of agentic commerce has arrived.

All major LLMs now offer direct checkout and new commerce protocols:

  1. ChatGPT has Instant Checkout with Shopify and Etsy, and ACP (Agentic Commerce Protocol).
  2. Microsoft Copilot uses ACP and offers Copilot Checkout with PayPal, Shopify, and Stripe.
  3. Google has embedded checkout in AI Mode and Gemini via its Universal Commerce Protocol (UCP).

The infrastructure question is settled, but the strategic question remains: How do you compete when users don’t need to click through to websites to buy?

1. Agentic Commerce Has A Hole In The Middle

The phrasing “agentic commerce” sets the wrong expectation. Autonomous purchasing, where you give an agent a credit card and monthly allowance to buy on your behalf, is not becoming a reality in the near future.

  • High-priced purchases like plane tickets or cars are too risky to delegate. You have idiosyncratic preferences (airline seat rules, car features) that no agent can reliably model.
  • Low-priced purchases like toilet paper or laundry detergent already have automation via subscription services (Instacart recurring orders, Subscribe & Save). An agent adds no incremental value.
  • The middle ground is smaller than the hype suggests. If high-priced resists delegation and low-priced is already “automated,” where does autonomous purchasing actually generate value?

“Conversational commerce” is a better frame. Instead of 100% automating the act of buying, LLMs compress the funnel by offering far superior research to classic search engines and showing products in the user interface.

  • Models read expert reviews, product specs, ingredient lists, and actual user feedback rather than ranking by keyword bids and conversion history.
  • The value lies in collapsing 14 clicks (Amazon’s disclosed average before purchase) into one or two.

2. Protocols Make Ecommerce “Headless”

The new commerce protocols allow AI agents to directly plug into the backend of your business, instead of crawling your site to show them in a list of search results. Protocols make commerce “headless” and decouple the front from the back-end:

  • Websites become less important as destinations and more important as databases.
  • The game shifts from optimizing landing page design for human eyes to optimizing data feeds for machine ingestion.
  • If your shipping speed, inventory status, or return policy isn’t accessible via API, you are invisible to the agent.

The shift from crawling to protocols collapses the legacy 14-click funnel (search, browse, click, checkout) into just two interactions: (1) the model parses intent by matching expert reviews against real-time inventory, and (2) the user executes a single click to buy using stored credentials.

Image Credit: Kevin Indig

While both protocols, ACP and UCP, enable the same user experience, they offer vastly different terms for the merchant.

OpenAI’s ACP (Agentic Commerce Protocol)

  • The Vision: The “Walled Garden.” OpenAI aims to handle the entire transaction within the chat interface, treating merchants effectively as suppliers.
  • The Trade-off: Efficiency vs. LTV. You gain access to 700 million weekly users, but you lose the direct customer relationship. Because OpenAI currently restricts passing customer emails for marketing, you lose the ability to remarket – effectively killing the 15-20% of Lifetime Value (LTV) that typically comes from post-purchase email flows.

Google’s UCP (Universal Commerce Protocol)

  • The Vision: The “Distributed Layer.” Google extends its Shopping Graph into a transactional layer that sits on top of Search, Lens, and Gemini.
  • The Trade-off: Ownership vs. Competition. Unlike ACP, Google allows merchants to retain the full customer lifecycle, including email rights and loyalty data. The cost is significantly higher competition intensity: Instead of fighting for 10 blue links, you are fighting for one of three “slots” in an AI Overview, making the margin for error in your product data effectively zero.

3. Conversational Commerce Disrupts The Whole Ecosystem

The shift from search to conversation creates a distinct set of winners, losers, and strategic dilemmas.

Buyers get a dramatically better user experience.

  • Discovery: High-consideration purchases (e.g., specific running shoes) shift from clicking through six potentially irrelevant product listing ads to receiving top-tier recommendations based on expert reviews.
  • Cognitive Load: The model handles the research, collapsing the average 14-click journey into one to two interactions.

Merchants face a tradeoff between distribution and control.

  • On ChatGPT: You gain access to early adopters, but lose the direct customer relationship and email marketing rights. You have no leverage over commission rates or recommendation logic.
  • On Google/Copilot: You retain merchant-of-record status, but as the funnel compresses, on-site ad inventory loses value. While conversion rates may rise, total ad revenue falls.

Affiliates die when LLMs disintermediate the click.

  • The Trap: If ChatGPT synthesizes reviews without sending traffic, affiliates stop writing. This creates an “ouroboros” where models train on their own AI-generated output.
  • The Pivot: Publishers must paywall premium content or charge merchants directly for reviews.

Amazon dominates on price and speed, but faces a business model conflict.

  • The Conflict: Retail margins are thin (~1%); profitability comes from the $60 billion advertising business.
  • The Risk: Amazon’s ad machine relies on a 14-click funnel. If conversational commerce compresses this to one click, sponsored product inventory evaporates.
  • The Choice: They must either block crawlers to protect ad revenue (current strategy) or participate and cannibalize it. Walmart joining ChatGPT forces their hand.

Google is best positioned to weather the shift.

  • Parity: They are already monetizing AI Overviews at parity with legacy search.
  • Economics: Higher relevance leads to exploding conversion rates. Advertisers will pay more per click to offset the lower click volume, balancing the ecosystem.

4. SEO Shifts From Optimizing Clicks To Optimizing Ingestion

We are moving from a world of infinite shelf space (10 blue links, endless pagination) to a world of constrained shelf space (three recommendation slots in an AI response).

In this environment, SEO shifts from optimizing for clicks to optimizing for ingestion. The goal isn’t to get a human to visit your landing page; it’s to get your product data into the agent’s context window with enough authority that it recommends you.

The New “Technical SEO”: Feed quality in the legacy model meant site speed, mobile responsiveness, and Core Web Vitals. In the protocol era, technical SEO is feed integrity. Agents don’t “browse” your site; they query your API. Your website becomes less of a visual destination and more of a structured database. The winners will be merchants who treat their product feed as their primary storefront.

The New “On-Page SEO”: Legacy SEO often rewarded articles that simply summarized what everyone else was already saying to rank for broad keywords. LLMs, however, are trained on that consensus. To be cited now, you must provide Information Gain, the delta between what the model already knows and the unique value you provide on top of the consensus.

  • You cannot “market” your way out of inferior specs. If you claim to be the “best running shoe for flat feet,” the model doesn’t look for adjectives; it validates your arch support measurements against podiatry standards in its training set.
  • Your content must shift from general engagement to structured “Product Truth.” LLMs prioritize detailed comparison tables, proprietary test results (e.g., “we dropped this phone 50 times”), and ingredient breakdowns. If your data isn’t structured for easy ingestion/verification, the model will bypass you for a source that is.

The New “Off-Page SEO”: Backlinks still matter, but their function changes. Instead of passing “link juice” for ranking, they now serve as verification sources for reputation synthesis, together with reviews and web mentions.

  • LLMs scrape third-party sites (e.g., Reddit, specialized forums, expert review sites) to form a consensus. A high volume of verified, specific reviews on trusted third-party platforms is the strongest signal you can send.
  • In a world where an AI suggests three options, brand familiarity becomes a tie-breaker. Brand advertising and organic brand building return as a critical lever to ensure users recognize the recommendation the AI provides.

5. The End Of “Marketing Brands”

The last decade allowed white-label brands to arbitrage their way to growth via ads, but agentic commerce acts as the quality filter for this model. While humans are swayed by slick branding, LLMs are dispassionate readers of data that will not recommend a “premium” product when the specs prove it is identical to a generic alternative.

The shift to protocols creates a paradox: Models understand long-tail intent perfectly but fulfill it with fat head inventory.

  • Safety Bias: Models prefer consensus to avoid hallucinations. A niche brand looks like noise; a Category King looks like truth.
  • The RAG Reality: RAG tools typically only scan the top 10-20 search results. Since search engines already favor authority, RAG often just reinforces the incumbents.

The only force that overrides this bias is granular data. Your merchant feed acts as the Claim, but RAG acts as the Trust Layer to verify it.

The market bifurcates:

  • The Incumbents win general intent via “trust” (consensus).
  • The Specialists win specific intent via “granularity” (specs), but only if they rank in the top search results.

If you expose data points the giants ignore (e.g., exact sourcing, chemical analysis), the model’s reasoning engine must select you to fulfill the constraint, but only if you rank on page 1 to be fetched.

Organic search is no longer about the click; it is the prerequisite for agentic verification.

Featured Image: Paulo Bobita/Search Engine Journal

Ecommerce MGMT 0 Comments
Dec 31 2025
5 Key Enterprise SEO And AI Trends For 2026

Enterprise SEO is at the center of some fascinating and fundamental shifts right now. From mainstream media coverage in the Wall Street Journal and Forbes to the Associated Press, Business Insider, Entrepreneur, and more. The role of search and SEO and its impact on enterprise brands and their visibility in a new AI era made all the headlines.

In this article, I will delve deeper into five key enterprise SEO and AI trends for 2026 with tips to help you keep pace with change and prepare for future success.

Image by author, December 2025

How Enterprise SEO Has Changed

As we enter 2026, enterprise SEO strategies will shift in line with the significant changes in how users search and interact across multiple search and AI engines, from discovery to conversion.

The new reality facing enterprises is that search behavior is no longer linear or universal as user behavior shifts from single-destination search to multi-platform conversations.

While Google remains dominant with 90% market share, the growth and evolution of AI discovery engines such as ChatGPT and Perplexity mean marketers are not just optimizing for traditional search; they are also optimizing for AI and LLM visibility.

The need for “Search Everywhere Optimization” has become critical for large enterprises as generative and answer-based AI engines form their own “opinions” and outputs that influence a brand’s presence (are they discoverable) and whether they are recommended (how they are perceived).

Brands that have invested in core, foundational SEO and adapt to the nuances of being visible and cited as the trusted and authoritative source in their industry across multiple AI platforms already have a huge head start in 2026.

5 Essential Enterprise SEO And AI Trends To Watch In 2026

1. SEO Fundamentals Become The Bedrock For AI Success Everywhere

Technical SEO foundations will prove essential for agentic, GEO, and AEO performance.

SEO foundations are the prerequisite for AI visibility: without clean technicals, strong information architecture, and quality content. Without it, generative (GEO) and answer-based (AEO) efforts simply have nothing reliable for AI systems to ingest, understand, or cite. In practice, generative and answer-based AI optimization is less a replacement for SEO and more an evolution layered on top of it. Both evolve together.

Technical SEO (crawlability, indexation, architecture, Core Web Vitals, structured data) is what makes your content machine-readable for LLM crawlers and AI overview systems. Classic SEO pillars – intent-mapped content, E-E-A-T signals, internal linking, and performance – are the signals AI systems and answer engines lean on to choose which sources to surface and trust.

All AI optimization strategies build directly on these foundations with the additional focus on restructuring sites and content, so generative engines can parse entities and quote or cite them in answers.

Foundational SEO technical elements act as a translation layer between your content and AI systems. With schema markup, you provide AI engines with a roadmap to understand:

  • Customer Q&As and help resources.
  • Detailed product specifications and features.
  • User feedback and testimonials.
  • Content creator expertise and qualifications.

I expect all these new types of AI optimization disciplines to mature further in the coming years as more brands and marketing experts lean into experienced SEOs for advice on how LLMs retrieve, rank, and cite sources.

Optimization For The Agentic Era

AI agents are now browsing on behalf of users—not just indexing for later but fetching information in real time. BrightEdge internal tracking shows these agents now account for roughly 33% of organic search activity, and that share is climbing.

These agents, including GPTBot, ClaudeBot, Perplexity Bot, and Google-Extended, represent a major shift in how content gets discovered and delivered. They do not render JavaScript, require high performance, and need plain-text information to assist users in the moment. Brands that are not visible to AI crawlers risk being invisible to the next generation of consumers. In this new era, brands must optimise for agent conversions—making it easy for AI to retrieve information, present it accurately, and drive action.

Key focus areas:

  • Technical Fundamentals: Prioritise site speed, crawlability, and technical health so AI agents can access your content in real-time conversations.
  • Content Structure: Clear content hierarchy, descriptive product information, and logical page structure help AI agents understand and recommend your offerings.
  • Structured Data: Implement schema markup so agents accurately understand pricing, availability, reviews, and specifications.
  • AI-Ready Protocols: Adopt standards like MCP servers and llms.txt files to guide AI crawlers to important content efficiently.

2. Content Quality Becomes The Differentiator For AI Visibility

E-E-A-T and content diversity will matter more than ever for SEO and AI success.

Top-performing content will prioritize clarity and cognitive ease, delivering high information value while minimizing effort for the reader. AI tools do not cite content that repackages existing information; they can generate that themselves. What they do cite are unique insights, original content, and trusted sources.

Content Tips For Winning AI Visibility

  • Open with concise, insight-led summaries.
  • Structure with tight sections and clear headings.
  • Lead with story, then data – relatable anecdotes improve engagement and make content quotable.
  • Write for ingestion. Use questions, definitions, and concise examples that LLMs can absorb.

Optimizing For Multimodal Search

Text-based search is no longer the sole player. Multimodal search – combining text, voice, image, and video – is becoming standard practice. BrightEdge data shows a 121% increase in ecommerce-related YouTube citations for AI Overviews.

Image from author, December 2025
  • Repurpose content across formats. Do not rely solely on written content.
  • Invest in utility-driven content: calculators, templates, checklists, and tools.
  • Share content on channels AI tools regularly pull from: Reddit, YouTube, and key social networks.
  • Implement detailed technical markup for videos and images.

Building For Query Fan-Out

To succeed, brands must move beyond static rankings and build omnichannel content networks that meet users wherever their queries lead. Brands that demonstrate how their products solve specific problems will win in AI search. Buyers increasingly expect AI to recommend the best solution for their situation.

  • Rebuild strategies around audience personas and user intent.
  • Map the related questions and variations triggered by core topics.
  • Create interconnected content ecosystems distributed across platforms so all LLMs can cite.
  • Design content as training data – extractable, semantically rich, and machine-readable.

Publishing across multiple content formats increases citation stability:

  • For Google AI: Focus on visual assets and shopping feed optimization. Users are in discovery mode and expect product-rich experiences. Ensure structured data enables inclusion in AI Overviews and Shopping Graph integration.
  • For ChatGPT: Build authority through comprehensive, well-structured content. Users arrive pre-qualified and deeper in the funnel. Optimize for being cited as a trusted source when ChatGPT synthesizes answers.
  • For Perplexity: Prioritize authoritative, citation-worthy content. Users actively verify sources and click through at higher rates. Deliver research-grade content that earns consistent citations.

3. Measuring Brand Authority Will Shift From Presence To Perception

New SEO and AI measurement methods evolve from brand mentions to “how” they are mentioned.

As more users turn to AI assistants for early-stage answers, top-of-funnel content will shift from search visibility to model influence. LLMs have become the new awareness engines. The brands appearing in AI answers will dominate through education and earning citations from trusted sources.

Brand Sentiment And Trust

In 2026, brand visibility in AI search will hinge on trust. Earned media—social mentions, reviews, quality backlinks—shapes how AI models and users perceive your brand. LLMs prioritize content from trusted, credible sources.

Five Essential AI Search Metrics:

  • AI Presence Rate: Percentage of target queries where your brand appears in AI responses.
  • Citation Authority: How consistently you are cited as the primary source.
  • Share of AI Conversation: Your semantic real estate in AI answers versus competitors.
  • Prompt Effectiveness: How well your content answers natural language prompts.
  • Response-to-Conversion Velocity: How quickly AI-influenced prospects convert.

Brands with strong pre-existing recognition will receive more positive mentions in AI responses. For marketers, the measurement mindset shift is important. Instead of competing for a spot on a results page, you’re competing to be referenced as a trusted source inside the answer itself.

Marketers must optimize for influence, shaping the informational environment so machines and people understand their brand as intended.

4. Multi-Platform Success Demands New SEO And Marketing Approaches To AI

Organizations will need integrated SEO, media, and PR strategies.

The complexity of modern enterprise marketing demands a new organizational approach. Success requires seamless integration between SEO, content, technical teams, and AI specialists.

BrightEdge data reveals approximately 34% of AI citations come from PR-driven coverage, with another 10% from social channels. Off-site reputation work feeds directly into AI visibility.

SEO Merges With Brand And Omnichannel

SEO is becoming inseparable from brand and omnichannel marketing. Key integration requirements:

  • Align paid and organic messaging. Ads and AI summaries frequently appear side by side.
  • Coordinate PR and content. Third-party coverage directly influences AI citations.
  • Expand brand mentions with influencers and affiliates for product-led searches.

Digital PR Becomes A Core SEO And AI Success Factor

Earned media has become essential for securing mentions and citations in AI-driven search. As LLMs and generative engines decide which sources to reference, brands must focus on building trust, authority, and credibility within their field of specialism. This means going beyond traditional link-building to cultivate genuine recognition from industry publications, respected analysts, and trusted voices in your sector. The brands that consistently appear in high-quality editorial coverage, expert roundups, and authoritative reviews will be the ones AI systems learn to trust and recommend.

How to implement:

  • Treat branded search volume as a vital top-of-funnel metric.
  • Build relationships with publishers, influencers, and review platforms.
  • Activate internal thought leaders for interviews, podcasts, and expert commentary.
  • Monitor your AI visibility and track brand representation across platforms.

5. Automation Becomes Non-Negotiable For SEO And AI Scale

Large enterprises will need to rely on automation to scale SEO and AI performance.

The complexity of managing SEO across traditional search and multiple AI platforms is becoming immense. Ensuring sites are structured for agentic crawl visibility, managing fixes that impact performance at speed, and producing content at scale make manual SEO tasks unsustainable, hampering productivity and performance.

Automation is no longer a competitive advantage; it’s a requirement for AI survival.

  • AI Visibility Monitoring: Track brand presence across AI platforms automatically. Manual checking is impossible at scale.
  • Content Optimization: Use AI tools to find gaps, optimize structure, and ensure content meets AI-readability standards.
  • Technical SEO: Automated site fixes for agentic crawling, schema validation, and performance monitoring across large site portfolios.
  • Reporting and Insights: Generate automated dashboards combining traditional SEO metrics with AI citation data.

Utilizing AI Correctly

Enterprises must establish internal governance and alignment on AI use for SEO and content. This means:

  • Using AI for insights, creation, optimization, and scale automation.
  • Maintaining human oversight for strategy, quality control, and brand voice.
  • Balancing efficiency gains with authenticity. AI-generated content alone will not earn citations.
  • Building workflows that combine AI speed with human expertise and storytelling.

Enterprise SEO Focus For 2026

Google still dominates, so marketers should always have that as their primary focus: traditional search, AI Overviews, and AI Mode. At the same time, monitoring and optimizing for the growth of emerging AI discovery and answer-based engines will be essential in 2026.

Enterprise SEO professionals need to focus on:

  • Managing enterprise SEO with all marketing disciplines: site-to-brand teams.
  • Internal governance and alignment on AI use for SEO and content.
  • Utilizing AI correctly for insights, creation, optimization, and scale automation.
  • CEO and CMO stakeholder management, guiding understanding of search and AI changes.
  • Ensuring your brand is cited and sourced as the authority, regardless of search or AI engine.

To succeed in 2026, SEO must evolve into influence optimization with a renewed laser focus on building authority through thought leadership and credible third-party signals.

More Resources:

Featured Image: Master1305/Shutterstock

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Oct 8 2025
The 5 Hidden Organizational Forces That Undermine Enterprise SEO via @sejournal, @billhunt

If you’ve read “From Line Item to Leverage” or “Who Owns Web Performance?,” you know I’ve argued that enterprise SEO failures are rarely due to incompetence or lack of effort. The playbook is known. The teams are capable. The opportunity is massive. Yet results often stall or underdeliver.

Why?

Because the real problem isn’t only technical, it’s organizational. The website might be modern, the content fresh, and the SEO team skilled. But underneath the surface, hidden forces are quietly undermining performance: political turf wars, outdated workflows, key performance indicator (KPI) misalignment, and siloed ownership.

These aren’t bugs in the system. They’re features of how many organizations operate. Until we confront them, no amount of tactical SEO or any of the current alphabet soup of AI optimization schemes will produce strategic outcomes.

​​Across hundreds of enterprise search performance audits, I have found these five forces are the biggest blockers of SEO progress, not crawl errors or content gaps.

Force 1: Structural Silos And The Fallacy Of Distributed Ownership

Many enterprises have convinced themselves that “distributed ownership” is modern and empowering. But when everyone owns the website, no one is accountable for outcomes. Product owns UX. Brand owns messaging. IT owns the CMS. SEO owns … what exactly?

The result is fragmented decision-making and reactive prioritization. Optimization becomes an endless round of ticket submission and compromise. Big problems fall through the cracks because no single person is tasked with connecting the dots.

In “Who Owns Web Performance?,” I broke down the dangers of this model – and the alternative: centralized digital accountability with clear authority to align stakeholders and drive performance.

Force 2: Incentive Misalignment And The KPI Trap

Most enterprise teams aren’t incentivized to care about organic search performance. Developers are measured on delivery speed. Content teams are judged on brand tone. Paid media is chasing return on ad spend (ROAS).

This is the classic KPI trap: When each team optimizes for its success metrics, no one is accountable for shared business outcomes. The result? Collaboration stalls, priorities diverge, and high-impact opportunities like SEO fall through the cracks, not because teams aren’t trying, but because the system pulls them in different directions.

This creates massive opportunity costs. Even when teams want to collaborate, their KPIs pull them in different directions. Without shared goals and visibility, SEO becomes a bottleneck rather than a multiplier.

Force 3: Political Gatekeeping And Departmental Turf Wars

Let’s say the SEO team identifies a technical issue that’s hurting crawlability. They submit a ticket. Nothing happens. Why?

Because the dev team has a different backlog and a different boss.

SEO often finds itself in the middle, lacking the priority, budget, or political capital to push changes through. Decisions are filtered through layers of management that prioritize their own fiefdoms over collective outcomes.

This isn’t personal. It’s structural. But it kills velocity.

We need executive air cover. Someone who sees digital performance as a cross-functional mandate that directly impacts the bottom line, and not a side hustle for marketing.

Force 4: Change Aversion Masquerading As Process

How often have you heard this: “That’s not how we do things?”

It sounds like a process, but it’s really fear. Fear of change, fear of accountability, fear of being wrong.

Enterprise inertia is real. Established brands often cling to workflows that were optimized for a different era – print, events, old-school PR. SEO’s iterative, fast-moving nature clashes with these cycles. That friction slows everything down.

If your content takes six weeks to publish and two months to update a template, you’re not playing the same game as Google.

Force 5: The Devaluation Of Web As A Strategic Channel

Too many executive teams still view the website as a marketing brochure. Something the CMO owns and the IT team maintains.

But as argued in “Closing the Digital Performance Gap,” the website is now a strategic revenue engine, support channel, and trust platform. It’s the digital front door and the only channel you fully control.

When leadership doesn’t treat it that way, performance suffers. Investments are piecemeal. Priorities are reactive. And talent leaves because they’re stuck defending the basics.

Case In Point: When All 5 Forces Collide

At Hreflang Builder, I worked with a large CPG company that had identified a $25 million monthly cross-market cannibalization problem across more than a dozen brands. The culprit? Poor implementation of hreflang elements. Due to different content management systems and web structures, hreflang XML sitemaps were the only option for them.

They had tried to solve the cannibalization problem, but the organization’s decentralized structure made it nearly impossible. Regional development teams, a patchwork of digital agencies, and siloed market ownership meant no one had end-to-end control.

The internal process was a nightmare: 60+ days to make a simple XML sitemap change, with hreflang page alternates maintained manually in Excel files. One-third of the URLs were invalid. Markets weren’t notified of new pages. Updates require submitting support tickets to an already backlogged IT queue.

Let’s connect the dots:

  • Silos (Force 1): Each region wanted its own solution, even though this was a global requirement. No one entity owned the problem.
  • KPI Misalignment (Force 2): Despite measurable cannibalization, SEO fixes weren’t prioritized because they didn’t map to short-term KPIs.
  • Political Turf Wars (Force 3): IT didn’t want to license an external solution nor take responsibility for building an internal solution. The global SEO team wanted a commercial solution. Local teams demanded local control or their agency to manage it.
  • Change Aversion (Force 4): Those managing the manual spreadsheet process resisted change. “It works well enough,” they argued, despite overwhelming evidence that it didn’t.
  • Web Devaluation (Force 5): Even with $25 million in monthly loss, there was no executive mandate or budget to solve it. Management views this as a Google issue, not a business problem.

Everyone acknowledged the cannibalization. Everyone intuitively knew the external solution was cheaper than the losses. But no one wanted to cede control to a centralized fix. This is what happens when no one owns the whole picture.

Why This Matters: These Forces Compound

Each of these forces is dangerous on its own. But together, they form a silent killer of enterprise SEO:

  • The SEO team lacks authority.
  • Other teams lack incentive.
  • Decisions are slow and political.
  • Execution is trapped in a legacy process.
  • And the web isn’t treated as strategic.

In the era of AI-powered search, these organizational flaws are no longer just speed bumps; they’re structural liabilities. AI Overviews and generative engines reward sites that are fast to update, intensely structured, and unified in message. When SEO is hindered by bureaucratic lag, misaligned priorities, or outdated processes, you not only lose rankings but also become invisible in the results entirely.

Web effectiveness now demands real-time coordination across content, data, tech, and performance. That’s not possible when decisions are stuck in silos and SEO is treated as a reactive service ticket.

And here’s the shift no one’s talking about: SEO’s value isn’t just in rankings, it’s in data structure, discoverability, and serving the buyer’s journey. Generative search surfaces answers. If your content isn’t connected, structured, and licensed, or can’t answer fundamental questions, it will be skipped.

Even internal site search, untouched by AI results, is often neglected. We’ve helped clients unlock millions in value by optimizing internal search data, which is frequently the clearest signal of what users want but can’t find.

In this new world, treating SEO as a patchwork of technical fixes is organizational malpractice. It’s time to treat it like the infrastructure for digital visibility it truly is.

A Better Path Forward

Fixing this doesn’t require heroics. It requires leadership.

Executives must:

  • Designate accountable ownership of web performance.
  • Align KPIs across content, dev, and marketing teams.
  • Fund SEO as infrastructure, not just a channel.
  • Remove structural bottlenecks and reframe SEO as a strategy.
  • Govern with outcomes, not outputs.

This is a mindset shift as well as an organizational shift.  Organizations need to move from just optimizing pages to redesigning the organizational systems that enable performance.

Because the real search problem isn’t the algorithm, it’s the org chart.

And that’s fixable.

More Resources:

Featured Image: Roman Samborskyi/Shutterstock

Ecommerce MGMT 0 Comments
Sep 24 2025
Newfold Digital Sells MarkMonitor As Part Of Strategic Refocus via @sejournal, @martinibuster

London-headquartered corporate domain management company Com Laude announced the acquisition of its competitor, MarkMonitor, previously one of the holdings of Newfold Digital.

Newfold Digital Simplifies Portfolio

Newfold Digital owns many top Internet brands like Yoast, Bluehost, Register.com, and Domain.com, all businesses that focus on small and medium-sized businesses. This divestiture may be a sign that Newfold Digital may be shifting away from the enterprise market and toward focusing its portfolio of web services on the SMB end of the market.

The official Newfold Digital press release states:

“The sale is part of Newfold Digital’s strategy to simplify its portfolio and double down on the areas where it can deliver the greatest value to customers – its core brands, Bluehost and Network Solutions. ”

Stu Homan, Head of MarkMonitor, commented:

“With this acquisition, Markmonitor has found owners who value our dedicated corporate services as much as our customers do. Com Laude is deeply committed to preserving and building upon our ability to continue to deliver industry-leading customer service while growing to new levels with dedication and investment.

Our entire team is excited to bring Com Laude’s advanced tools and services to our customers, and to be part of the most exciting development in corporate domain services since Markmonitor invented the white glove service model twenty-six years ago.”

Previous to the acquisition, Com Laude was a competitor of MarkMonitor, offering services that were similar to MarkMonitor but with key differences and technologies like an AI-powered domain management dashboard.

Com Laude is headquartered in London, United Kingdom, and MarkMonitor is in Boise, Idaho, which is not commonly regarded as the center of Internet commerce or technology but is actually a growing regional technology hub.

Benjamin Crawford, CEO of Com Laude, remarked:

“Markmonitor is the best-known name in domain services for corporate customers, having virtually invented the category twenty-six years ago, and since then grown a long list of blue-chip customers with its “white glove” customer service. Com Laude offers market leading advanced tools and bespoke services in domains and online brand protection, developed for the world’s largest companies and most valuable brands. Together we will be uniquely positioned to protect and grow the digital presence of any company that needs assistance with its domain names, internet infrastructure and security, online brand protection, internet policy and compliance, and online strategy.”

Read Com Laude’s announcement:

Com Laude to Acquire Markmonitor in a Landmark Transaction

Featured Image by Shutterstock/thodonal88

Ecommerce MGMT 0 Comments
Jul 30 2025
Why Your SEO Isn’t Working, And It’s Not The Team’s Fault via @sejournal, @billhunt

Over the years, I have been asked to audit numerous enterprise search programs, transforming them into world-class solutions.

Time and again, I found that the SEO teams were smart, capable, and executing the playbook, but the results weren’t materializing.

Rankings were volatile. Organic traffic plateaued. The executive team grew frustrated. Eventually, someone asked the inevitable: “Is our SEO team underperforming?”

Most of the time, the answer was no. The team wasn’t failing; the system around them was.

This article explores the structural, organizational, and leadership-level reasons why SEO fails inside even the most sophisticated enterprises.

Spoiler: It has little to do with keyword research or broken links, and everything to do with the invisible walls that constrain real performance.

It builds on themes from my article, “The New Role Of SEO In The Age Of AI,” where I explore how SEO is evolving into a broader organizational discipline, one rooted in systems thinking, structured content, and strategic alignment.

Misdiagnosing The Problem: SEO As A Siloed Function

In most companies, SEO is still viewed as a tactical function buried within marketing. It’s rarely integrated into upstream product planning, development processes, or digital governance.

So, when organic traffic and performance lag, leadership looks at the SEO team’s workflows, agency partners, or performance dashboard, but not at the system that surrounds them.

That’s like blaming the pit crew when the car hasn’t been upgraded in years.

5 Structural Reasons SEO Doesn’t Deliver

And now, in the AI era, there’s a new layer of complexity: the platform itself may be working against you.

Generative engines and search assistants are not just routing traffic; they’re rewriting how discovery happens.

If your content isn’t structured to be consumed and credited by AI, then even the best efforts by your SEO team won’t yield results.

Visibility isn’t just earned through optimization; it’s granted by systems trained to synthesize, summarize, and, sometimes, sidestep attribution entirely.

Here are the most common issues I see inside underperforming organizations:

1. No Executive Ownership Of Visibility

Every SEO team has the all-too-common story of being uninformed about a technical or content update until after it has already occurred, and then being expected to recover the lost performance magically.

That wasn’t an isolated oversight; it was an artifact of a siloed organization that didn’t truly value SEO.

When significant changes to the site’s architecture, platforms, or content workflows occur without input from search specialists, visibility suffers, regardless of the team’s skill level.

SEO success often hinges on decisions made far outside the SEO team’s control: site architecture, content management system (CMS) capabilities, translation workflows, and legal restrictions.

If no one at the leadership level owns findability as an outcome, SEO efforts get buried under technical debt and decision inertia.

2. Misaligned Incentives

SEO is a long-game discipline, but quarterly performance, traffic deltas, and campaign outcomes are the metrics most teams focus on.

When teams are rewarded for volume, not visibility, they focus on what’s easy to publish, not what’s hard to get discovered.

3. Content Without Strategy

In today’s search landscape, content must not only be helpful, but it must also be interpretable by machines. AI systems increasingly determine what gets surfaced, cited, or synthesized into answers.

If your content lacks structure, clarity, or semantic relevance, it may never reach the end user. This isn’t a failure of effort; it’s a failure to adapt to how visibility is brokered in an AI-first environment.

Companies often produce massive volumes of content with little to no strategy for discoverability, relevance, or user need.

One of the biggest mindset shifts needed is moving from “just accurate” to “genuinely helpful” content information that not only ranks but also resolves a user’s need, aligns with their search intent, and builds trust across formats and platforms.

If content isn’t structured for AI interpretation, indexed efficiently, or mapped to actual search behavior, it’s noise, not value.

4. Tech Bottlenecks And CMS Handcuffs

The SEO team may know what needs to be fixed, but can’t implement changes due to rigid CMS limitations, lack of dev resources, or cross-team politics.

SEO becomes a report generator, not a performance enabler.

5. Lack Of A Visibility Operating Model

Few organizations have a system for aligning product, content, UX, dev, and analytics around shared visibility goals.

Without a repeatable model and clearly identified roles, data handoffs, and escalation paths, SEO success is ad hoc and unsustainable.

It’s Not A Talent Problem. It’s A Systems Problem

Most SEO teams are aware of what needs to happen. But, unless they’re empowered structurally — with access, authority, and allies — they’re set up to fail.

It’s like asking a builder to construct a skyscraper with no blueprints, a shared plan, or the ability to move materials.

When executives recognize this as a systems issue, not a personnel one, transformation becomes possible.

What The C-Suite Should Be Asking Instead

Rather than “Why isn’t our SEO working?” leadership should be asking:

  • Who owns visibility at the organizational level?
  • Do our teams have a shared model for findability?
  • Are we rewarding the behaviors that lead to durable visibility, or just short-term volume?
  • Can our content and site architecture be understood by AI engines, as well as by humans?
  • Are our internal key performance indicators (KPIs) aligned with these new external discovery realities?

Reframing SEO As Infrastructure, Not Just A Channel

Modern SEO now sits at the intersection of content strategy, data modeling, and AI accessibility.

If you’re not designing your digital presence to be ingested by large language models or cited by answer engines, you’re ceding control to the platforms.

You’re optimizing for a web that no longer exists, and leaving performance on the table for competitors who’ve embraced AI-mode discoverability.

The most successful organizations treat SEO like digital infrastructure, a foundational capability embedded into everything from product design to knowledge management.

They invest in:

  • Schema and structured data governance.
  • Visibility Service Level Agreements (SLAs) across departments.
  • Shared taxonomies and content architectures.
  • Measurement frameworks that include AI surfacing and non-click impact.
  • Collaboration and knowledge sharing.

Final Thought: Clear The Path, Then Judge Performance

If your SEO isn’t delivering, don’t start by blaming the team. Start by auditing the system around them. Fix the structural blockers. Build the operating model. Assign executive ownership.

Then, and only then, can you ask whether the team is performing because even the best F1 driver can’t win a race if the vehicle they’ve been given is unreliable, outdated, or built without alignment between the systems.

Editor’s note: This article is the first in a series from Bill Hunt set to be published monthly. Each article will build on the others.

The series offers a clear, differentiated voice to speak the language of senior leadership while honoring the technical integrity of search.

More Resources:

Featured Image: Zamrznuti tonovi/Shutterstock

Ecommerce MGMT 0 Comments
Jun 30 2025
How Enterprise Search And AI Intelligence Reveal Market Pulse

The last few years have fundamentally transformed how businesses and consumers discover, evaluate, and engage with brands.

What began as a digital acceleration in 2020-2021 has evolved into an AI-driven revolution that’s reshaping the entire search landscape in 2025 across every industry vertical.

Where organizations once relied on monthly snapshots and historical data, today’s market reality demands real-time AI intelligence with a 360-degree view across all platforms.

The traditional customer journey – whether B2B, B2C, or D2C – which used to span multiple sessions, site visits, and vendor comparisons, can now unfold in a single AI interaction.

When a decision-maker asks Google AI Overviews, ChatGPT, or Perplexity for the best HR software, skincare routine, or investment strategy, they’re no longer sifting through dozens of links.

AI immediately assembles a shortlist with commentary, pros and cons, and implicit recommendations.

AI Search Revolution: From Information Retrieval To Active Evaluation

Recent BrightEdge data reveals the magnitude of this shift: Impressions on all content have skyrocketed by over 49% since the launch of AI Overviews, while Google still maintains over 90% of market share.

However, the game has undergone a fundamental change. AI isn’t just retrieving information; it’s actively evaluating, framing, and recommending brands before prospects even click a link.

Consider the stark reality facing all marketers: Only 31% of AI-generated brand mentions are positive, and of those, just 20% include direct recommendations.

Source: BrightEdge, June 2025

This means that whether you’re marketing enterprise software, consumer products, or direct-to-consumer services, how your brand appears in AI results across Google AI Overviews, ChatGPT, and Perplexity varies dramatically depending on the AI model, its training data, and interpretive logic.

The growth trajectory tells the story:

  • ChatGPT: 21% growth in the last month.
  • Perplexity and Gemini: Remaining about one-tenth of ChatGPT’s size.
  • Claude, Meta, and Grok: Another one-tenth smaller than Perplexity and Gemini.

This isn’t just channel diversification; it’s a complete redefinition of discoverability where AI serves as both gatekeeper and advisor.

Being Aware Of What Is Going On In Your Broader Markets

Understanding The New Market Dynamics

Many marketers have traditionally taken an immediate and microscopic approach to SEO. Without thinking, the focus goes straight to the keyword and the link.

However, working across all market segments requires a shift in mindset towards understanding not just the business but the broader market and economic implications that may affect how you tailor your strategy.

Overall, market factors influence short-, mid-, and long-term strategies. Utilize models such as PEST analysis to understand what is going on in the market from a political, economic, social, and technological perspective:

  • Political: AI regulation, data privacy laws, elections, and new compliance requirements.
  • Economic: AI’s compression of decision cycles, changing sales velocities, and market volatility.
  • Social: Consumer behavior is shifting toward AI-assisted purchasing, altering the dynamics of brand trust.
  • Technological: AI model capabilities, real-time indexing, cross-platform optimization requirements.

The MAP Framework For AI Search Success

Modern marketing intelligence requires mastering three critical dimensions: Mention, Authority, and Performance – what I call the MAP Framework for AI search success.

This framework applies whether you’re marketing SaaS solutions, consumer electronics, fashion brands, financial services, or any product or service in today’s AI-influenced marketplace.

Mentions: Beyond Traditional Rankings

While Google still commands the foundation with over 90% market share, the ecosystem has diversified rapidly.

AI Overviews (AIOs) now appear in over 11% of Google queries – a 22% increase since debuting last year.

More significantly, longer, more complex queries have increased by 49% in AI Overviews since May 2024, specifically designed to support complex B2B decisions.

In contrast, ranking-style content and comparison queries have decreased by 60% and 14%, respectively.

BrightEdge data shows the industries with the strongest AI Overview presence are healthcare, education, B2B tech, and insurance. Travel and entertainment are on the rise, while ecommerce has not seen rapid growth in the past year.

Authority: When AI Forms Opinions About Your Brand

The most critical insight for all marketers is understanding how AI systems interpret and present brand information across every category.

Our research shows significant variation in how brands are portrayed across different AI platforms and industries:

  • Finance brands: Positive mentions align around regulatory compliance and security content.
  • Healthcare brands: Accuracy and credibility drive positive AI sentiment.
  • Technology brands: Innovation and reliability serve as primary AI evaluation criteria.
  • Consumer brands: Customer reviews, product quality, and brand reputation influence AI recommendations.
  • Retail/Ecommerce: Price competitiveness, product availability, and user experience drive AI mentions.
  • Professional services: Case studies, client success stories, and industry expertise shape AI perception.

Whether AI is evaluating enterprise software, consumer products, or professional services, it effectively writes the evaluation criteria and creates shortlists without brands having direct input, making perception management mission-critical across all verticals.

Performance: New Metrics That Matter

Traditional key performance indicators (KPIs), such as rankings, impressions, and traffic, aren’t disappearing, but they’re insufficient for AI-driven discovery.

While impressions on all content have skyrocketed by over 49% since the launch of AI Overviews, click-throughs have steadily declined, with a nearly 30% reduction since May 2024.

Yet, conversion rates remain strong, suggesting that AI successfully qualifies leads before they reach websites.

Essential AI Search Metrics:

  1. AI Mention Rate: Percentage of target queries where your brand appears in AI responses.
  2. Citation Authority: How consistently you’re cited as the primary source.
  3. Share of AI Conversation: Your semantic real estate in AI answers versus competitors.
  4. Prompt Effectiveness: How well your content answers natural language prompts.
  5. Response-to-Conversion Velocity: Speed at which AI-influenced prospects convert.

Monthly reporting cycles have become obsolete. AI-generated results can shift within hours based on content updates, prompt trends, or model training, demanding real-time monitoring capabilities.

Read more: How AI Is Changing The Way We Measure Success In Digital Advertising

Combining Business & Search Intelligence To Understand The Pulse Of The Customer

AI Intelligence With Comprehensive Market Insights

Modern marketing intelligence extends far beyond traditional keyword monitoring, requiring a 360-degree view across all consumer touchpoints and all key AI search engines.

Today’s successful organizations – whether B2B, B2C, or D2C – leverage AI to understand market pulse through multiple lenses:

Real-Time Consumer Intelligence

AI agents now research on behalf of consumers across all categories, from enterprise software to skincare products.

These agents analyze your brand through your digital presence, social proof, customer reviews, and competitive positioning.

They’re becoming sophisticated evaluation consultants that assess everything from product specifications to brand values.

Cross-Industry Predictive Modeling

Advanced business intelligence now incorporates AI behavior patterns to forecast demand shifts across all sectors.

When AI systems consistently recommend specific product categories, highlight particular brand attributes, or emphasize certain consumer benefits, these signals predict broader market movements – whether in B2B procurement, consumer purchasing, or direct-to-consumer trends.

Omni-Engine And LLM Sentiment Analysis

Different AI platforms treat content differently across all industries.

For consumer brands, ChatGPT might emphasize user reviews and social proof, while Perplexity focuses on expert analysis and technical specifications.

For B2B brands, LinkedIn-integrated AI may prioritize professional endorsements, whereas general AI platforms tend to emphasize case studies and return on investment (ROI) data.

Understanding these platform-specific nuances enables strategic content distribution across every marketing vertical.

From Search To AI As The Voice Of Every Customer

In many ways, AI is the voice of the customer across all industries.

Search queries contain intent signals, SERP analysis reveals how customers prefer to consume content, and keyword reports enable us to produce content that resonates – whether for enterprise buyers, individual consumers, or any audience in between.

However, especially in an agentic world, AI is not just forming opinions. It is taking actions for users. In shopping, it can actually make transactions for people.

Keeping a daily pulse on new insights impacting your market and on what is changing in AI responses daily should be of mandatory importance for those who want to benefit from fresh, new opportunities.

For example, a single result and opinion generated by AI in a search can significantly impact revenue in just one day.

During important seasons (especially in retail), subtle category-related demand shifts will require granular action.

New product launches require daily monitoring so stakeholders can see the daily impact and adjust accordingly, leveraging AI to automatically optimize the offering.

Utilizing Business Intelligence To Understand And Visualize The Pulse Of The Market

Real-Time AI Platform Monitoring

More than ever, organizations are seeking business intelligence (BI) to transform data into actionable insights that can be quickly leveraged across traditional search and every AI engine where customers discover solutions.

BI enables marketers to easily analyze insights for larger-than-usual data sets to uncover new opportunities and highlight campaign strategy inefficiencies.

Here is an example from my company:

Source: BrightEdge, June 2025

This type of intelligence can inform you about what is happening now and what has happened in the past across all discovery channels.

Many types of business intelligence can help deliver digestible snapshots of the current state of your market, not just for SEO but also for digital, sales, product, and customer service functions.

Entity-Based SEO For AI Discovery Across All Verticals

Move beyond keywords to comprehensive topic authority, regardless of your industry.

AI prioritizes content from known, trusted entities, making authoritative content three times more likely to be cited in AI responses across B2B software, consumer electronics, fashion, healthcare, financial services, and every other vertical.

Implement robust schema markup, ensure consistent entity references across all digital properties, and build connections with recognized authorities in your space – whether that’s industry analysts, consumer advocates, or subject matter experts.

360-Degree AI Platform Strategy

Success requires presence and optimization across traditional search and every AI engine where your customers might discover solutions. This means:

  • Google Search & AI Overviews: Still the foundation with 90%+ market share.
  • ChatGPT: 21% growth rate, emphasis on conversational discovery.
  • Perplexity: Research-heavy platform with strong citation emphasis.
  • Vertical-specific AI: Industry tools, shopping assistants, and specialized platforms.
  • Social AI Integration: AI features within LinkedIn, TikTok, Instagram, and other social platforms.
  • Voice & Mobile AI: Alexa, Siri, Google Assistant across devices.

Consumer Intelligence Integration

Traditional search data must be combined with the following:

  • Social listening across AI-integrated platforms.
  • Review and rating sentiment from AI-crawled sources.
  • Purchase behavior data as it relates to AI recommendations.
  • Cross-platform brand mention analysis.
  • Consumer journey mapping across AI touchpoints.
  • Competitive intelligence from AI responses.

This approach reveals not just what consumers search for but how AI interprets and presents your brand across every possible discovery moment.

Mobile Vs. Desktop AI Optimization

Mobile and desktop AI Overviews aren’t just different sizes. They’re fundamentally different products targeting distinct user behaviors.

According to BrightEdge Generative Parser data from May and June 2025, these platforms serve different user intents and require tailored optimization strategies.

Key Mobile Vs. Desktop Differences:

Mobile Opportunities:

  • Ecommerce AIOs appear three times more often (13.5% vs 4.5% on desktop).
  • Mobile shows more size variability, suggesting Google is actively experimenting with format and content.
  • Users are in discovery/shopping mode, making mobile ideal for product research and comparison.

Desktop Patterns:

  • Takes 80% more screen space than mobile (1110 px vs. 617 px).
  • AIOs appear 39% more frequently than mobile.
  • More consistent, predictable sizing patterns.
  • Users want detailed, comprehensive information delivery.

As Jim Yu, CEO of BrightEdge, notes: “If marketers are not paying attention to how AI operates on different devices, they may be missing some key opportunities, especially in ecommerce!”

Read more: Newly Released Data Shows Desktop AI Search Referrals Dominate

Strategic Implications:

  • Mobile users require discovery-focused, shopping-oriented content optimization.
  • Desktop users need comprehensive, detailed information architectures.
  • Ecommerce brands must prioritize mobile-first AIO strategies.
  • Content strategy should consider device context alongside traditional keyword targeting.
  • Search marketers must ensure teams optimize for both user experiences simultaneously.

Vertical-Specific AI Optimization

Industry-specialized AI models are emerging for cybersecurity, manufacturing, fintech, and healthcare.

Content strategies must account for domain-specific AI companions that understand industry nuance and evaluate solutions using sector-appropriate criteria.

This can be visualized via daily dashboards, visualizations, and custom-based reports, and can be used to:

  1. Analyze industry trends in real-time across all AI platforms.
  2. Visualize category demand and inventory in real time.
  3. Compare historical data with current trends across traditional and AI search.
  4. Create and forecast based on predictive modeling that includes AI behavior patterns.
  5. Aggregate different sources of data from search engines and AI platforms.
  6. Identify new buyer trends across all customer segments.
  7. Monitor brand presence, perception, and performance.
  8. Find inefficiencies in product or pricing strategy based on AI recommendations.
  9. Identify key correlations between search activity and mentions of AI platforms.
  10. Plan across all discovery channels and map content to key AI touchpoints.
  11. Evaluate marketing campaign effectiveness across traditional and AI-driven channels.

Conclusion

Success in 2025’s marketing landscape requires understanding that AI isn’t just a channel. It’s becoming the primary interface between your brand and potential customers across every industry and buying scenario.

The organizations that master the MAP Framework (Mention, Authority, and Performance) while maintaining a 360-degree view across traditional search, AI engines, and consumer intelligence will be the ones AI recommends when it matters most.

The shift from traditional search to AI-powered discovery isn’t coming – it’s here.

Marketers across B2B, B2C, and D2C who embrace comprehensive AI intelligence tools, implement real-time monitoring across all platforms, and optimize for AI evaluation criteria will capture market opportunities.

In this new reality, staying attuned to the market means understanding not only what customers search for but also how AI interprets, evaluates, and presents your brand across every possible touchpoint.

The future belongs to brands that learn to collaborate with AI, guide its understanding across all platforms, and position themselves to stand out in an era where artificial intelligence often makes the first – and sometimes, final – impression, whether someone is buying enterprise software, choosing a restaurant, or selecting a healthcare provider.

Unless otherwise indicated, any data mentioned above was taken from this BrightEdge study. 

More Resources:

Featured Image: innni/Shutterstock

Ecommerce MGMT 0 Comments
Jun 4 2025
How To Host Or Migrate A Website In 2025: Factors That May Break Rankings [+ Checklist] via @sejournal, @inmotionhosting

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

Is your website struggling to maintain visibility in search results despite your SEO efforts?

Are your Core Web Vitals scores inconsistent, no matter how many optimizations you implement?

Have you noticed competitors outranking you even when your content seems superior?

In 2025, hosting isn’t just a backend choice. It’s a ranking signal.

In this guide, you’ll learn how hosting decisions impact your ability to rank, and how to choose (or migrate to) hosting that helps your visibility.

Learn to work with your rankings, not against them, with insights from InMotion Hosting’s enterprise SEO specialists.

Jump Straight To Your Needs

Best For Hosting Type How Easy is Migration?
Growing SMBs VPS Easy: Launch Assist (free)
Enterprise / SaaS Dedicated Very Easy: White-Glove + Managed Service

Don’t know which one you need? Read on.

Hosting Directly Impacts SEO Performance

Your hosting environment is the foundation of your SEO efforts. Poor hosting can undermine even the best content and keyword strategies.

Key Areas That Hosting Impacts

Core Web Vitals

Server response time directly affects Largest Contentful Paint (LCP) and First Input Delay (FID), two critical ranking factors.

Solution: Hosting with NVMe storage and sufficient RAM improves these metrics.

Crawl Budget

Your website’s visibility to search engines can be affected by limited server resources, wrong settings, and firewalls that restrict access.

When search engines encounter these issues, they index fewer pages and visit your site less often.

Solution: Upgrade to a hosting provider that’s built for SEO performance and consistent uptime.

Indexation Success

Proper .htaccess rules for redirects, error handling, and DNS configurations are essential for search engines to index your content effectively.

Many hosting providers limit your ability to change this important file, restricting you from:

– Editing your .htaccess file.

– Installing certain SEO or security plugins.

– Adjusting server settings.

These restrictions can hurt your site’s ability to be indexed and affect your overall SEO performance.

Solution: VPS and dedicated hosting solutions give you full access to these settings.

SERP Stability During Traffic Spikes

If your content goes viral or experiences a temporary surge in traffic, poor hosting can cause your site to crash or slow down significantly. This can lead to drops in your rankings if not addressed right away.

Solution: Using advanced caching mechanisms can help prevent these problems.

Server Security

Google warns users about sites with security issues in Search Console. Warnings like “Social Engineering Detected” can erode user trust and hurt your rankings.

Solution: Web Application Firewalls offer important protection against security threats.

Server Location

The location of your server affects how fast your site loads for different users, which can influence your rankings.

Solution: Find a web host that operates data centers in multiple server locations, such as two in the United States, one in Amsterdam, and, soon, one in Singapore. This helps reduce loading times for users worldwide.

Load Times

Faster-loading pages lead to lower bounce rates, which can improve your SEO. [Server-side optimizations], such as caching and compression, are vital for achieving fast load times.

These factors have always been important, but they are even more critical now that AI plays a role in search engine results.

40 Times Faster Page Speeds with Top Scoring Core Web Vitals with InMotion Hosting UltraStack One. (Source: InMotion Hosting UltraStack One for WordPress )Image created by InMotion Hosting, 2025.

2025 Update: Search Engines Are Prioritizing Hosting & Technical Performance More Than Ever

In 2025, search engines have fully embraced AI-driven results, and with this shift has come an increased emphasis on technical performance signals that only proper hosting can deliver.

How 2025 AI Overview SERPs Affect Your Website’s Technical SEO

Google is doubling down on performance signals. Its systems now place even greater weight on:

  • Uptime: Sites with frequent server errors due to outages experience more ranking fluctuations than in previous years. 99.99% uptime guarantees are now essential.
  • Server-Side Rendering: As JavaScript frameworks become more prevalent, servers that efficiently handle rendering deliver a better user experience and improved Core Web Vitals scores. Server-optimized JS rendering can make a difference.
  • Trust Scores: Servers free of malware with healthy dedicated IP addresses isolated to just your site (rather than shared with potentially malicious sites) receive better crawling and indexing treatment. InMotion Hosting’s security-first approach helps maintain these crucial trust signals.
  • Content Freshness: Server E-Tags and caching policies affect how quickly Google recognizes and indexes new or updated content.
  • TTFB (Time To First Byte): Server location, network stability, and input/output speeds all impact TTFB. Servers equipped with NVMe storage technology excel at I/O speeds, delivering faster data retrieval and improved SERP performance.
Infographic Illustrating How Browser Caching Works (Source: Ultimate Guide to Optimize WordPress Performance )Created by InMotion Hosting. May, 2025

Modern search engines utilize AI models that prioritize sites that deliver consistent, reliable, and fast data. This shift means hosting that can render pages quickly is no longer optional for competitive rankings.

What You Can Do About It (Even If You’re Not Into Technical SEO)

You don’t need to be a server administrator to improve your website’s performance. Here’s what you can do.

1. Choose Faster Hosting

Upgrade from shared hosting to VPS or dedicated hosting with NVMe storage. InMotion Hosting’s plans are specifically designed to boost SEO performance.

2. Use Monitoring Tools

Free tools like UptimeRobot.com, WordPress plugins, or cPanel’s resource monitoring can alert you to performance issues before they affect your rankings.

3. Implement Server-Side Caching

Set up caching with Redis or Memcached using WordPress plugins like W3 Total Cache, or through cPanel.

4. Add a CDN

Content Delivery Networks (CDNs) can enhance global performance without needing server changes. InMotion Hosting makes CDN integration easy.

5. Utilize WordPress Plugins

Use LLMS.txt files to help AI tools crawl your site more effectively.

6. Work with Hosting Providers Who Understand SEO

InMotion Hosting offers managed service packages for thorough server optimization, tailored for optimal SEO performance.

Small Business: VPS Hosting Is Ideal for Reliable Performance on a Budget

VPS hosting is every growing business’s secret SEO weapon.

Imagine two competing local service businesses, both with similar content and backlink profiles, but one uses shared hosting while the other uses a VPS.

When customers search for services, the VPS-hosted site consistently appears higher in results because it loads faster and delivers a smoother user experience.

What Counts as an SMB

Small to medium-sized businesses typically have fewer than 500 employees, annual revenue under $100 million, and websites that receive up to 50,000 monthly visitors.

If your business falls into this category, VPS hosting offers the ideal balance of performance and cost.

What You Get With VPS Hosting

1. Fast Speeds with Less Competition

VPS hosting gives your website dedicated resources, unlike shared hosting where many sites compete for the same resources. InMotion Hosting’s VPS solutions ensure your site runs smoothly with optimal resource allocation.

2. More Control Over SEO

With VPS hosting, you can easily set up caching, SSL, and security features that affect SEO. Full root access enables you to have complete control over your server environment.

3. Affordable for Small Businesses Focused on SEO

VPS hosting provides high-quality performance at a lower cost than dedicated servers, making it a great option for growing businesses.

4. Reliable Uptime

InMotion Hosting’s VPS platform guarantees 99.99% uptime through triple replication across multiple nodes. If one node fails, two copies of your site will keep it running.

5. Better Performance for Core Web Vitals

Dedicated CPU cores and RAM lead to faster loading times and improved Core Web Vitals scores. You can monitor server resources to keep track of performance.

6. Faster Connections

Direct links to major internet networks improve TTFB (Time To First Byte), an important SEO measure.

7. Strong Security Tools

InMotion Hosting provides security measures to protect your site against potential threats that could harm it and negatively impact your search rankings. Their malware prevention systems keep your site safe.

How To Set Up VPS Hosting For Your SEO-Friendly Website

  1. Assess your website’s current performance using tools like Google PageSpeed Insights and Search Console
  2. Choose a VPS plan that matches your traffic volume and resource needs
  3. Work with your provider’s migration team to transfer your site (InMotion Hosting offers Launch Assist for seamless transitions)
  4. Implement server-level caching for optimal performance
  5. Configure your SSL certificate to ensure secure connections
  6. Set up performance monitoring to track improvements
  7. Update DNS settings to point to your new server

Large & Enterprise Businesses: Dedicated Hosting Is Perfect For Scaling SEO

What Counts As An Enterprise Business?

Enterprise businesses typically have complex websites with over 1,000 pages, receive more than 100,000 monthly visitors, operate multiple domains or subdomains, or run resource-intensive applications that serve many concurrent users.

Benefits of Dedicated Hosting

Control Over Server Settings

Dedicated hosting provides you with full control over how your server is configured. This is important for enterprise SEO, which often needs specific settings to work well.

Better Crawlability for Large Websites

More server resources allow search engines to crawl more pages quickly. This helps ensure your content gets indexed on time. Advanced server logs provide insights to help you improve crawl patterns.

Reliable Uptime for Global Users

Enterprise websites need to stay online. Dedicated hosting offers reliable service that meets the expectations of users around the world.

Strong Processing Power for Crawlers

Dedicated CPU resources provide the power needed to handle spikes from search engine crawlers when they index your site. InMotion Hosting uses the latest Intel Xeon processors for better performance.

Multiple Dedicated IP Addresses

Having multiple dedicated IP addresses is important for businesses and SaaS platforms that offer API microservices. IP management tools make it easier to manage these addresses.

Custom Security Controls

You can create specific firewall rules and access lists to manage traffic and protect against bots. DDoS protection systems enhance your security.

Real-Time Server Logs

You can watch for crawl surges and performance issues as they happen with detailed server logs. Log analysis tools help you find opportunities to improve.

Load Balancing for Traffic Management

Load balancing helps spread traffic evenly across resources. This way, you can handle increases in traffic without slowing down performance. InMotion Hosting provides strong load balancing solutions.

Future Scalability

You can use multiple servers and networks to manage traffic and resources as your business grows. Scalable infrastructure planning keeps your performance ready for the future.

Fixed Pricing Plans

You can manage costs effectively as you grow with predictable pricing plans.

How To Migrate To Dedicated Hosting

  1. Conduct a thorough site audit to identify all content and technical requirements.
  2. Document your current configuration, including plugins, settings, and custom code.
  3. Work with InMotion Hosting’s migration specialists to plan the transition
  4. Set up a staging environment to test the new configuration before going live
  5. Configure server settings for optimal SEO performance
  6. Implement monitoring tools to track key metrics during and after migration
  7. Create a detailed redirect map for any URL changes
  8. Roll out the migration during low-traffic periods to minimize impact
  9. Verify indexing status in Google Search Console post-migration

[DOWNLOAD] Website Migration Checklist

Free Website Migration Checklist download from InMotion Hosting – step-by-step guide to smoothly transfer your websiteImage created by InMotion Hosting, May 2025

    Why Shared Hosting Can Kill Your SERP Rankings & Core Web Vitals

    If you’re serious about SEO in 2025, shared hosting is a risk that doesn’t come with rewards.

    Shared Hosting Issues & Risks

    Capped Resource Environments

    Shared hosting plans typically impose strict limits on CPU usage, memory, and connections. These limitations directly impact Core Web Vitals scores and can lead to temporary site suspensions during traffic spikes.

    Resource Competition

    Every website on a shared server competes for the same limited resources.

    This becomes even more problematic with AI bots accessing hundreds of sites simultaneously on a single server.

    Neighbor Problems

    A resource-intensive website on your shared server can degrade performance for all sites, including yours. Isolated hosting environments eliminate this risk.

    Collateral Damage During Outages

    When a shared server becomes overwhelmed, not only does your website go down, but so do connected services like domains and email accounts. InMotion Hosting’s VPS and dedicated solutions provide isolation from these cascading failures.

    Limited Access to Server Logs

    Without detailed server logs, diagnosing and resolving technical SEO issues becomes nearly impossible. Advanced log analysis is essential for optimization.

    Restricted Configuration Access

    Shared hosting typically prevents modifications to server-level configurations that are essential for optimizing technical SEO.

    Inability to Adapt Quickly

    Shared environments limit your ability to implement emerging SEO techniques, particularly those designed to effectively handle AI crawlers. Server-level customization is increasingly important for SEO success.

    In 2025, Reliable Hosting Is a Competitive Advantage

    As search engines place greater emphasis on technical performance, your hosting choice is no longer just an IT decision; it’s a strategic marketing investment.

    InMotion Hosting’s VPS and Dedicated Server solutions are engineered specifically to address the technical SEO challenges of 2025 and beyond. With NVMe-powered storage, optimized server configurations, and 24/7 expert human support, we provide the foundation your site needs to achieve and maintain top rankings.

    Ready to turn your hosting into an SEO advantage? Learn more about our SEO-first hosting solutions designed for performance and scale.

    LEARN MORE

    Image Credits

    Featured Image: Image by Shutterstock. Used with permission.

    In-Post Image: Images by InMotion Hosting. Used with permission.

    Ecommerce MGMT 0 Comments