Who Owns Web Performance? Building A Framework For Digital Accountability via @sejournal, @billhunt

In my previous article, “Closing the Digital Performance Gap,” I made the case that web effectiveness is a business issue, not a marketing metric. The website is no longer just a reflection of your brand – it is your brand. If it’s not delivering measurable business results, that’s a leadership problem, not a team problem.

But there’s a deeper issue underneath that: Who actually owns web performance?

The truth is, many companies don’t have a good answer. Or they think they do until something breaks. The SEO team doesn’t own the infrastructure. The dev team isn’t briefed on platform changes. The content team isn’t looped in until after a redesign. Visibility drops, conversions dip, and someone asks, “Why isn’t our SEO team performing?”

Because they don’t own the full system, no one does.

If we want to close the digital performance gap, we must address this root problem: lack of accountability.

The Fallacy Of Distributed Ownership

The idea that “everyone owns the website” likely stems from early digital transformation initiatives, where cross-functional collaboration was encouraged to break down departmental silos. The intent was to foster shared responsibility across departments – but the unintended consequence was diffused accountability.

It sounds collaborative, but in practice, it often means no one is fully accountable for performance.

Here’s how it typically breaks down:

• IT owns infrastructure and hosting.
• Marketing owns content and campaigns.
• SEO owns visibility – but not implementation.
• UX owns experience – but not findability.
• Legal owns compliance – but limits usability.
• Product owns the content management system (CMS) – but doesn’t track SEO.

Each group is doing its job, often with excellence. But the result? Disconnected execution. Strategy gets lost in translation, and performance stalls.

Case in point: For a global alcohol brand, a site refresh had legal requirements mandating an age verification gate before users could access the site. That was the extent of their specification. IT built the gate exactly to spec: a page with the statement to enter your birthdate and three pull-down options for Month, Day, and Year, and a check of that date to the U.S. legal drinking age. UX and creative delayed launch for weeks while debating the optimal wording, positioning, and color scheme.

Once launched, the website traffic, both direct and organic search, dropped to zero. This was due to several key reasons:

1. Analytics were not set up to track visits before and after the age gate.
2. Search engines can’t input a birthdate, so they were blocked.
3. The age requirement was set to the U.S. standard, rejecting younger, yet legal visitors from other countries.

Because everything was done in silos, no one had considered these critical details.

When we finally got all stakeholders in a room, agreed on the issues, and sorted through them, we redesigned the system:

• Search engines were recognized and bypassed the age requirement.
• The age requirement and date format are adapted to the user’s location.
• UX developed multiple variations and tested abandonment.
• Analytics captured pre- and post-gate performance.
• UX used the data to validate new landing page formats.

The result? A compliant, user-friendly, and search-accessible module that could be reused globally. Visibility, conversions, and compliance all increased exponentially. But we lost months and millions in potential traffic simply because no one owned the whole picture.

Without centralized accountability, the site was optimized in parts but underperforming as a whole.

The AI Era Raises The Stakes

This kind of siloed ownership might have been manageable in the old “10 blue links” era. But in an AI-first world – where Google and other platforms synthesize content into answers, summarize brands, and bypass traditional click paths – every decision across your digital operation impacts your visibility, trust, and conversion.

Search visibility today depends on structured data, crawlable infrastructure, content relevance, and citation-worthiness. If even one of these is out of alignment, you lose shelf space in the AI-driven SERP. And chances are, the team responsible for the weak link doesn’t even know they’re part of the problem.

Why Most SEO Advice Falls Short

I’ve seen well-meaning advice to “improve your SEO strategy” fall flat – because it assumes the SEO team has control over all the necessary elements. They don’t.

• You can’t fix crawl issues if you can’t talk to the dev team.
• You can’t win AI citations if your content team doesn’t structure or enrich their pages.
• You can’t build authority if your legal or PR teams strip bios and outbound references.

What’s needed isn’t better tactics. It’s organizational clarity.

The Case For Centralized Digital Ownership

To create sustained performance, companies need to designate real ownership over web effectiveness. That doesn’t mean centralizing every task – but it does mean centralizing accountability.

Here are three practical approaches:

1. Establish A Digital Center Of Excellence (CoE)

A CoE provides governance, guidance, and support across business units and regions. It ensures that:

• Standards are defined and enforced.
• Platforms are chosen and maintained with shared goals.
• Learnings are captured and distributed.
• Key performance indicators (KPIs) are consistent and comparable.

2. Appoint A Digital Effectiveness Officer (DEO)

Think of this like a Commissioning Authority in construction – a role that ensures every component works together to meet the original performance spec. A DEO:

• Connects the dots between dev, SEO, UX, and content.
• Tracks impact beyond traffic (revenue, leads, brand trust).
• Advocates for platform investment and cross-team prioritization.

3. Build Shared KPIs Across Departments

Most teams optimize for what they’re measured on. If the SEO team is judged on rankings but not revenue, and the content team is judged on output but not visibility, you get misaligned efforts. Create chained KPIs that reflect end-to-end performance.

Characteristics Of A Performance-Driven Model

Companies that close the accountability gap tend to share these traits:

• Unified Taxonomy and Tagging – so content is findable and trackable.
• Structured Governance – clear roles and escalation paths across teams.
• Shared Dashboards – everyone sees the same numbers, not vanity metrics.
• Tech Stack Discipline – fewer, better tools with cross-functional usage.
• Scenario Planning – AI, zero-click SERPs, and platform volatility are modeled, not ignored.

Final Thought: Performance Requires Ownership

If you’re serious about web effectiveness, you need more than skilled people and good tools. You need a system where someone is truly accountable for how the site performs – across traffic, visibility, UX, conversion, and AI resilience.

This doesn’t mean a top-down mandate. It means orchestrated ownership with clear roles, measurable outcomes, and a strategic anchor.

It’s time to stop asking the SEO team to fix what they don’t control.

It’s time to build a framework where the web is everyone’s responsibility – and someone’s job.

Let’s make web performance a leadership priority, not a guessing game.

More Resources:

Featured Image: SFIO CRACHO/Shutterstock

Structured Data’s Role In AI And AI Search Visibility via @sejournal, @marthavanberkel

The way people find and consume information has shifted. We, as marketers, must think about visibility across AI platforms and Google.

The challenge is that we don’t have the same ability to control and measure success as we do with Google and Microsoft, so it feels like we’re flying blind.

Earlier this year, Google, Microsoft, and ChatGPT each commented about how structured data can help LLMs to better understand your digital content.

Structured data can give AI tools the context they need to determine their understanding of content through entities and relationships. In this new era of search, you could say that context, not content, is king.

Schema Markup Helps To Build A Data Layer

By translating your content into Schema.org and defining the relationships between pages and entities, you are building a data layer for AI. This schema markup data layer, or what I like to call your “content knowledge graph,” tells machines what your brand is, what it offers, and how it should be understood.

This data layer is how your content becomes accessible and understood across a growing range of AI capabilities, including:

• AI Overviews
• Chatbots and voice assistants
• Internal AI systems

Through grounding, structured data can contribute to visibility and discovery across Google, ChatGPT, Bing, and other AI platforms. It also prepares your web data to be of value to accelerate your internal AI initiatives as well.

The same week that Google and Microsoft announced they were using structured data for their generative AI experiences, Google and OpenAI announced their support of the Model Context Protocol.

What Is Model Context Protocol?

In November 2024, Anthropic introduced Model Context Protocol (MCP), “an open protocol that standardizes how applications provide context to LLMs” and was subsequently adopted by OpenAI and Google DeepMind.

You can think of MCP as the USB-C connector for AI applications and agents or an API for AI. “MCP provides a standardized way to connect AI models to different data sources and tools.”

Since we are now thinking of structured data as a strategic data layer, the problem Google and OpenAI need to solve is how they scale their AI capabilities efficiently and cost-effectively. The combination of structured data you put on your website, with MCP, would allow accuracy in inferencing and the ability to scale.

Structured Data Defines Entities And Relationships

LLMs generate answers based on the content they are trained on or connected to. While they primarily learn from unstructured text, their outputs can be strengthened when grounded in clearly defined entities and relationships, for example, via structured data or knowledge graphs.

Structured data can be used as an enhancer that allows enterprises to define key entities and their relationships.

When implemented using Schema.org vocabulary, structured data:

• Defines the entities on a page: people, products, services, locations, and more.
• Establishes relationships between those entities.
• Can reduce hallucinations when LLMs are grounded in structured data through retrieval systems or knowledge graphs.

When schema markup is deployed at scale, it builds a content knowledge graph, a structured data layer that connects your brand’s entities across your site and beyond. 

A recent study by BrightEdge demonstrated that schema markup improved brand presence and perception in Google’s AI Overviews, noting higher citation rates on pages with robust schema markup.

Structured Data As An Enterprise AI Strategy

Enterprises can shift their view of structured data beyond the basic requirements for rich result eligibility to managing a content knowledge graph.

According to Gartner’s 2024 AI Mandates for the Enterprise Survey, participants cite data availability and quality as the top barrier to successful AI implementation.

By implementing structured data and developing a robust content knowledge graph you can contribute to both external search performance and internal AI enablement.

A scalable schema markup strategy requires:

• Defined relationships between content and entities: Schema markup properties connect all content and entities across the brand. All page content is connected in context.
• Entity Governance: Shared definitions and taxonomies across marketing, SEO, content, and product teams.
• Content Readiness: Ensuring your content is comprehensive, relevant, representative of the topics you want to be known for, and connected to your content knowledge graph.
• Technical Capability: Cross-functional tools and processes to manage schema markup at scale and ensure accuracy across thousands of pages.

For enterprise teams, structured data is a cross-functional capability that prepares web data to be consumed by internal AI applications.

What To Do Next To Prepare Your Content For AI

Enterprise teams can align their content strategies with AI requirements. Here’s how to get started:

1. Audit your current structured data to identify gaps in coverage and whether schema markup is defining relationships within your website. This context is critical for AI inferencing.

2. Map your brand’s key entities, such as products, services, people, and core topics, and ensure they are clearly defined and consistently marked up with schema markup across your content. This includes identifying the main page that defines an entity, known as the entity home.

3. Build or expand your content knowledge graph by connecting related entities and establishing relationships that AI systems can understand.

4. Integrate structured data into AI budget and planning, alongside other AI investments and that content is intended for AI Overviews, chatbots, or internal AI initiatives.

5. Operationalize schema markup management by developing repeatable workflows for creating, reviewing, and updating schema markup at scale.

By taking these steps, enterprises can ensure that their data is AI-ready, inside and outside the enterprise.

Structured Data Provides A Machine-Readable Layer

Structured data doesn’t assure placement in AI Overviews or directly control what large language models say about your brand. LLMs are still primarily trained on unstructured text, and AI systems weigh many signals when generating answers.

What structured data does provide is a strategic, machine-readable layer. When used to build a knowledge graph, schema markup defines entities and the relationships between them, creating a reliable framework that AI systems can draw from. This reduces ambiguity, strengthens attribution, and makes it easier to ground outputs in fact-based content when structured data is part of a connected retrieval or grounding system.

By investing in semantic, large-scale schema markup and aligning it across teams, organizations position themselves to be as discoverable in AI experiences as possible.

More Resources:

Featured Image: Koto Amatsukami/Shutterstock

Google’s Antitrust Ruling: What The Remedies Really Mean For Search, SEO, And AI Assistants via @sejournal, @gregjarboe

When Judge Amit P. Mehta issued his long-awaited remedies decision in the Google search antitrust case, the industry exhaled a collective sigh of relief. There would be no breakup of Google, no forced divestiture of Chrome or Android, and no user-facing “choice screen” like the one that reshaped Microsoft’s browser market two decades ago. But make no mistake – this ruling rewrites the playbook for search distribution, data access, and competitive strategy over the next six years.

This article dives into what led to the decision, what it actually requires, and – most importantly – what it means for SEO, PPC, publishers, and the emerging generation of AI-driven search assistants.

What Led To The Decision

The Department of Justice and a coalition of states sued Google in 2020, alleging that the company used exclusionary contracts and massive payments to cement its dominance in search. In August 2024, Judge Mehta ruled that Google had indeed violated antitrust law, writing, “Google is a monopolist, and it has acted as one to maintain its monopoly.” The question then became: what remedies would actually restore competition?

The DOJ and states pushed for sweeping measures – including a breakup of Google’s Chrome browser or Android operating system, and mandatory choice screens on devices. Google countered that such steps would harm consumers and innovation. By the time remedies hearings wrapped, generative AI had exploded into the mainstream, shifting the court’s sense of what competition in search could look like.

What The Court Decided

Judge Mehta’s ruling, issued September 2, 2025, imposed a mix of behavioral remedies:

• Exclusive contracts banned. Google can no longer strike deals that make it the sole default search engine on browsers, phones, or carriers. That means Apple, Samsung, Mozilla, and mobile carriers can now entertain offers from rivals like Microsoft Bing or newer AI entrants.
• Payments still allowed. Crucially, the court did not ban Google from paying for placement. Judge Mehta explained that removing payments altogether would “impose substantial harms on distribution partners.” In other words, the checks will keep flowing – but without exclusivity.
• Index and data sharing. Google must share portions of its search index and some user interaction data with “qualified competitors” on commercial terms. Ads data, however, is excluded. This creates a potential on-ramp for challengers, but it doesn’t hand them the secret sauce of Google’s ranking systems.
• No breakup, no choice screen. Calls to divest Chrome or Android were rejected as overreach. Similarly, the court declined to mandate a consumer-facing choice screen. Change will come instead through contracts and UX decisions by distribution partners.
• Six-year oversight. Remedies will be overseen by a technical committee for six years. A revised judgment is due September 10, with remedies taking effect roughly 60 days after final entry.

As Judge Mehta put it, “Courts must… craft remedies with a healthy dose of humility,” noting that generative AI has already “changed the course of this case.”

How The Market Reacted

Investors immediately signaled relief. Alphabet shares jumped ~8% after hours, while Apple gained ~4%. The lack of a breakup, and the preservation of lucrative search placement payments, reassured Wall Street that Google’s search empire was not being dismantled overnight.

But beneath the relief lies a new strategic reality: Google’s moat of exclusivity has been replaced with a marketplace for defaults.

Strategic Insights: Beyond The Headlines

Most coverage of the decision has focused on what didn’t happen – the absence of a breakup or a choice screen. But the deeper story is how distribution, data, and AI will interact under the new rules.

1. Defaults Move From Moat To Marketplace

Under the old model, Google’s exclusive deals ensured it was the default on Safari, Android, and beyond. Now, partners can take money from multiple providers. That turns the default position into a marketplace, not a moat.

Apple, in particular, gains leverage. Court records revealed that Google paid Apple $20 billion in 2022 and paid $26.3 billion in 2021  – the figure is not to any one company, but Apple likely represents the largest recipient – to remain Safari’s default search engine. Without exclusivity, Apple can entertain bids from Microsoft, OpenAI, or others – potentially extracting even more money by selling multiple placements or rotating defaults.

We may see new UX experiments: rotating search tiles, auction-based setup flows, or AI assistant shortcuts integrated into operating systems. Distribution partners like Samsung or Mozilla could pilot “multi-home defaults,” where Google, Bing, and an AI engine all coexist in visible slots.

2. Data Access Opens An On-Ramp For Challengers

Index-sharing and limited interaction data access lower barriers for rivals. Crawling the web is expensive; licensing Google’s index could accelerate challengers like Bing, Perplexity, or OpenAI’s rumored search product.

But it’s not full parity. Without ads data and ranking signals, competitors must still differentiate on product experience. Think faster answers, vertical specialization, or superior AI integration. As I like to put it: Index access gives challengers legs, not lungs.

Much depends on how “qualified competitor” is defined. A narrow definition could limit access to a token few; a broad one could empower a new wave of vertical and AI-driven search entrants.

3. AI Is Already Shifting The Game

The court acknowledged that generative AI reshaped its view of competition. Assistants like Copilot, Gemini, or Perplexity are increasingly acting as intent routers – answering directly, citing sources, or routing users to transactions without a traditional SERP.

That means the battle for distribution may shift from browsers and search bars to AI copilots embedded in operating systems, apps, and devices. If users increasingly ask their assistant instead of typing a query, exclusivity deals matter less than who owns the assistant.

For SEO and SEM professionals, this accelerates the shift toward zero-click answers, assistant-ready content, and schema that supports citations.

4. Financial Dynamics: Relief Today, Pressure Tomorrow

Yes, investors cheered. But over time, Google could face rising traffic acquisition costs (TAC) as Apple, Samsung, and carriers auction off default positions. Defending its distribution may get more expensive, eating into margins.

At the same time, without a choice screen, search market share is likely to shift gradually, not collapse. Expect Google’s U.S. query share to remain in the high 80s in the near term, with only single-digit erosion as rivals experiment with new models.

5. Knock-On Effects: The Ad-Tech Case Looms

Don’t overlook the second front: the DOJ’s separate antitrust case against Google’s ad-tech stack, now moving toward remedies hearings in Virginia. If that case results in structural changes – say, forcing Google to separate its publisher ad server from its exchange – it could reshape how search ads are bought, measured, and monetized.

For publishers, both cases matter. If rivals gain traction with AI-driven assistants, referral traffic could diversify – but also become more volatile, depending on how assistants handle citations and click-throughs.

What Happens Next

• September 10, 2025: DOJ and Google file a revised judgment.
• ~60 days later: Remedies begin taking effect.
• Six years: Oversight period, with ongoing compliance monitoring.

Key Questions To Watch:

• How will Apple implement non-exclusive search defaults in Safari?
• Who qualifies as a “competitor” for index/data access, and on what terms?
• Will rivals like Microsoft, Perplexity, or OpenAI buy into distribution slots aggressively?
• How will AI assistants evolve as distribution front doors?

What This Means For SEO And PPC

This ruling isn’t just about contracts in Silicon Valley – it has practical consequences for marketers everywhere.

• Distribution volatility planning. SEM teams should budget for a world where Safari queries become more contestable. Test Bing Ads, Copilot Ads, and assistant placements.
• Assistant-ready content. Optimize for concise, cite-worthy answers with schema markup. Publish FAQs, data tables, and source-friendly content that large language models (LLMs) like to quote.
• Syndication hedge. If new index-sharing programs emerge, explore partnerships with vertical search startups. Early pilots could deliver traffic streams outside the Google ecosystem.
• Attribution resilience. As assistants mediate more traffic, referral strings will get messy. Double down on UTM governance, server-side tracking, and marketing mix models to parse signal from noise.
• Creative testing. Build two-tier content: a punchy, fact-dense abstract that assistants can lift, and a deeper explainer for human readers.

Market Scenarios

• Base Case (Most Likely): Google retains high-80s market share. TAC costs rise gradually. AI assistants siphon a modest share of informational queries by 2027. Impact: margin pressure more than market share loss.
• Upside for Rivals: If index access is broad and AI assistants nail UX, Bing, Perplexity, and others could win five to 10 points combined in specific verticals. Impact: SEM arbitrage opportunities emerge, and SEO adapts to answer-first surfaces.
• Regulatory Cascade: If the ad-tech remedies impose structural changes, Google’s measurement edge narrows, and OEMs test choice-like UX voluntarily. Impact: more fragmentation, more testing for marketers.

Final Takeaway

Judge Mehta summed up the challenge well: “Courts must craft remedies with a healthy dose of humility.” The ruling doesn’t topple Google, but it does force the search giant to compete on more open terms. Exclusivity is gone; auctions and assistants are in.

For marketers, the message is clear: Don’t wait for regulators to rebalance the playing field. Diversify now – across engines, assistants, and ad formats. Optimize for answerability as much as for rankings. And be ready: The real competition for search traffic is just beginning.

More Resources:

Featured Image: beast01/Shutterstock

The Problem With Always-On SEO: Why You Need Sprints, Not Checklists via @sejournal, @coreydmorris

There’s a lot that goes into SEO. And, now, more broadly into being found online and online visibility overall, whether we’re talking about an organic result in a search engine, an AI Overview, or through a large language model (LLM).

With SEO being a discipline that often takes a long time (compared to ads and some other channels and platforms), with a large amount of complexity, technical aspects, contradictions of how it works, and even disagreements, it has to be organized in a way that can be implemented.

Over the years and decades, this has resulted in the acceptance of specific “best practices,” along with the fact that it is a longer-term commitment. That, ultimately, has led to the use of checklists and specific cadences to accomplish what is typically seen as an “ongoing” and never-ending discipline.

In full disclosure, you’ll find articles written by me that talk about checklists and ways to structure the work that is important to be visible and found online. I’m not saying we have to throw them out, but we can’t simply do the list or activities.

“Always-on SEO” sounds great in theory: ongoing optimization, constant monitoring, and steady progress. But in reality, it often becomes a nebulous set of tasks without priority, strategy, or momentum.

This article challenges the default mindset of treating SEO as a perpetual checklist and proposes a sprint-based approach, where work is grouped into focused time blocks with measurable goals.

By approaching SEO in strategic sprints, teams can prioritize, measure, adapt, and improve – all while staying aligned with larger business goals.

The Problem With Perpetual SEO Checklists

What I often see with SEO checklists is a lack of prioritization. Everything becomes a task, but nothing is deemed critical.

The checklist might have “right” and “good” things in it, but it isn’t weighted or prioritized based on any level of strategic approach or potential level of impact.

And, when there’s a lack of direction, we often can end up with a set of actions, activities, or tactics that have no clear end or evaluation defined. This ends up getting us into a place of just “doing SEO” without being able to objectively say what the result was or how things were improved.

Like any digital marketing channel, activity without the right anchor or foundation, in SEO, can result in wasted effort.

Technical fixes and content updates may not support meaningful business goals and can be a huge investment of time and money that ultimately don’t impact the business. And, activity without results or clear direction can drive SEO teams and professionals to boredom or burnout.

I’ve taken over a number of situations where a business thought SEO didn’t work for them or that the team was not competent enough due to stakeholder confusion.

When activity doesn’t generate results and you find it out a year into an investment, it is hard to recover, especially when no one really knows what “done” or what success looks like in the effort.

I say all of this not to bring up pain, say that checklists aren’t good, or even that the ongoing tactics aren’t right. I’m simply saying we have to have a deeper understanding and meaning behind what we’re doing in SEO.

What Sprint-Based SEO Looks Like

SEO sprints are focused and time-bound (e.g., four weeks) efforts with specific goals tied to strategy. Rather than working on everything at once, you work on the highest-impact priorities in chunks.

Common sprint types:

• Content optimization sprints.
• Technical SEO fix sprints.
• Internal linking improvement sprints.
• New content creation sprints.
• Authority/link building sprints.

You can also combine types into a custom sprint. Regardless of whether you stay in a category or make one that contains blended themes or tactics, it needs to be anchored to an initial strategy, plan, or audit for your first one.

Each sprint ends with measurable outputs, documented outcomes, and clear learnings. The first one might be rooted in an initial plan, but each subsequent sprint will include a retrospective review from the previous one to help fuel continuous learning, efficiencies, improvements, and ultimate impact.

Benefits Of SEO Sprints

A quick win benefit is gaining focus. Pivoting away from a generic checklist to sprint structure results in solving a defined problem, not tackling a vague backlog.

As noted earlier, sprints are time-based as well. By having the right length (not too short or small of a sample size, yet too long and repeating tactics that aren’t effective), you gain the benefits of agility and an adaptable longer-term approach overall.

Agility in sprints allows you to adjust based on performance and new insights. Checklists are not only generic or often disconnected from strategy, but are getting out of date constantly with shifts in online visibility optimization sources and methods.

Accountability and team clarity come more naturally as well. It’s easier to report on and justify value with clear before/after comparisons and to keep people engaged and in the know on what’s happening now and what’s next.

This matters for overall business alignment of key performance indicators (KPIs) and not getting too deep and lost in the jargon, technical aspects, and “hope” for return on investment (ROI) versus seeing shorter-term, higher-impact efforts.

Sprints can be tied directly to goals (revenue, lead generation, funnel support) and not just rankings or other KPIs that are upstream and further removed from business outcomes, and shorter-term expectations can take pressure off of long-term waiting for something to happen.

How To Implement Sprint-Based SEO

Start with strategy. Identify what matters to the business and where SEO fits. Define sprint themes and objectives, and make them specific enough to be meaningful and measurable.

Example: “Improve organic conversions for top 5 services pages” vs. “Improve rankings.”

Build a backlog or tactics plan, but don’t treat it like a checklist. Use it to feed sprint plans, but not overwhelm day-to-day work.

In short:

• Plan your first sprint: Choose one clear objective, timeline, and outcome.
• Track and review: Report on progress, document what was done, and define what’s next.
• Iterate: Use learnings from each sprint to improve the next.

When (And Where) “Always-On” SEO Still Applies

Certain things do need continuous attention. I’m not saying that it is right for 100% of your sprints to be 100% custom.

There are recurring things that could, or likely should, go into sprints or be monitored and maintained by regular or routine audits or checklists, e.g., crawl errors, broken links, technical issues, etc.

But, this maintenance work shouldn’t be the SEO strategy. It should support it. Use “always-on” as infrastructure or basics, not direction, and remember that the checklist isn’t the strategy, and if you have one, it is a planning tool, not necessarily your tactical plan and roadmap to ultimate SEO ROI.

Why It’s Time To Rethink “Always-On” SEO

I’ve hit on it enough, but I will wrap up by reminding you that endless to-do lists don’t move the needle.

Checklists can be good things and full of the “right” tactics. However, they often lack strategy and don’t serve shorter attention spans or allow for enough agility.

Sprint-based SEO helps teams be more strategic, productive, and aligned with the business overall, with room to implement prioritized tactics, tied to overall goals, and adjust to market and business needs and conditions.

Shifting your team from “always-on” to “intentionally paced” is a move to start seeing results and not just activity.

More Resources:

Featured Image: wenich_mit/Shutterstock

The CMO & SEO: Staying Ahead Of The Multi-AI Search Platform Shift (Part 1)

Some of the critical questions that are top of mind for both SEOs and CMOs as we head into a multi-search world are: Where is search going to develop? Is ChatGPT a threat or an opportunity? Is optimizing for large language models (LLMs) the same as optimizing for search engines?

In this two-part interview series, I try to answer these questions to provide some clear direction and focus to help navigate considerable change.

What you will learn:

• Ecosystem Evolution: While it is still a Google-first world, learn where native AI search platforms are growing and what this means.
• Opportunity vs. Threat: Why AI platforms create unprecedented brand visibility opportunities while demanding new return on investment (ROI) thinking.
• LLM Optimization Strategy: Why SEO has become more vital than ever, regardless of the AI and Search platform, and where specific nuances to optimize for lie.
• CMO Priorities: Why authority and trust signals matter more than ever in AI-driven search.
• Organizational Alignment: Why CMOs need to integrate marketing, PR, and technical teams for cohesive AI-first search strategies.

Where Do You Think The Current Search Ecosystem Might Develop In The Next 6 Months?

To answer the first question, I think we are witnessing something really fascinating right now. The search landscape is undergoing a fundamental transformation that will accelerate significantly over the next six months.

While Google still dominates with about 90% market share, AI-powered search platforms are experiencing explosive growth that is impossible to ignore.

Let me put this in perspective. ChatGPT is showing 21% month-over-month growth and is on track to hit 700 million weekly active users.

Claude and Perplexity are posting similar numbers at 21% and 19% growth, respectively. But here is what has caught my attention: Grok has seen over 1,000% month-over-month growth. Source BrightEdge Generative Parser and DataCube analysis, July 2025.

Sure, it is starting from a tiny base, but that trajectory makes it the dark horse to watch. Meanwhile, DeepSeek continues its gradual decline following its January surge, which highlights the volatility in this emerging market. I will share more on that later.

In A Google First World, User Behavior Is Also Evolving On Multiple AI Platforms

What is particularly interesting is how user behavior is evolving. People are not just switching from Google to AI search — they are starting to mix and match platforms based on their specific needs. I am seeing users turn to:

• ChatGPT for deep research.
• Perplexity for quick facts.
• Claude, when they need reliable information.
• Google when they want comprehensive breadth.

Image from BrightEdge, August 2025

The CMO AI And SEO Mindset Shift

From a marketing perspective, this creates a massive change in thinking. SEO is not just about Google anymore – though that is still where most of the focus needs to be.

Marketers will need to consider optimizing for multiple AI engines, each with its own distinct data ingestion pipelines. For ChatGPT and Claude, you need clear, structured, cited content that AI models can safely reuse. For Perplexity, timeliness, credibility, and brevity matter more than traditional keyword density.

It is no longer about optimizing just for clicks; it is about optimizing for influence and citations and making sure you appear in the proper context at the right moment within all these distinct types of AI experiences.

The Search Bot To AI User Agent Revolution

ChatGPT and its ChatGPT-User agent are leading the charge.

In July, BrightEdge’s analysis revealed that ChatGPT’s User Agent real-time page requests nearly doubled its activity. In other words, it shows that users relying on real-time web searches to answer questions almost doubled within just one month.

For example, suppose you are looking to compare “Apple Watch vs. Fitbit” from current reviews. In that case, the ChatGPT user agent is acting as your browsing assistant and operating on your behalf, which is fundamentally different from traditional search engines and crawlers.

Image from BrightEdge, August 2025

In summary, I believe the next six months will establish what I term a “multi-AI search world.” Users will become increasingly comfortable switching between platforms fluidly based on what they need in that moment. The opportunity here is massive for early adopters who figure out cross-platform optimization.

Is The Rise Of AI Platforms Like ChatGPT An Opportunity Or A Threat That CMOs Need To Be Aware Of?

It is all opportunity.

Each AI platform is carving out its own distinct identity. Google is doubling down on AI Overviews and AI Mode. ChatGPT is making this fascinating transition from conversational Q&A into full web search integration.

Perplexity is cementing itself as the premier “answer engine” with its citation-first, mobile-focused approach, and they are planning deeper integrations with news providers and real-time data.

Claude is expanding beyond conversation into contextual search with superior fact-checking capabilities, while Microsoft’s Bing Copilot is positioning itself as this search-plus-productivity hybrid that seamlessly blends document generation with web search.

The rise of AI platforms represents both a transformative opportunity and a strategic challenge that CMOs must navigate with sophistication and strategic foresight.

Learn More: How Enterprise Search And AI Intelligence Reveal Market Pulse

CMOs And The Shift From Ranking To Referencing And Citations

And that brings me to a huge mindset shift: We are moving from “ranking” to “referencing.” AI summaries do not just display the top 10 links; they reference and attribute sites within the answer itself.

Being cited within an AI summary can be more impactful than just ranking high in traditional blue links. So, CMOs need to start tracking not just where they rank, but where and how their content gets referenced and cited by AI everywhere.

Technical Infrastructure Requirements And CMOs Leaning Into SEO Teams

On the technical side, structured data and clear information architecture are no longer nice-to-haves – they are foundational. AI relies on this structure to surface accurate information, so schema.org markup, clean technical SEO, and machine-readable content formats are essential.

Image from BrightEdge, August 2025

Brands, The CMO, And The Authority And Trust Premium

Here is something that is becoming critical: Authority and brand trust matter more than ever. AI tends to pull from sites it considers authoritative, trustworthy, and frequently cited. This puts a premium on long-term brand-building, thought leadership, and reputation management across all digital channels.

You need to focus on those E-E-A-T signals (Experience, Expertise, Authoritativeness, Trustworthiness) for both humans and AI algorithms.

The CMOs’ SEO And AI Competitive Advantage

The CMOs who are proactively adapting to these shifts – rethinking measurement, technical SEO, brand trust, and cross-team integration – are the ones positioning their enterprises for continued visibility and influence.

The move to AI-driven search is rapid, but savvy enterprise marketers are seeing this as an opportunity to deepen brand engagement and become a trusted source for both human users and AI engines.

It is challenging, but the potential upside for brands that get this right is enormous.

It is a whole new way of thinking about ROI.

Learn More: How AI Search Should Be Shaping Your CEO’s & CMO’s Strategy

Do You Think Optimizing For LLMs Is The Same As Search Engines, As Google Suggests?

Following Google Search Central Live in Thailand, and Gary’s advice that SEOs don’t need to optimize for GEO, I think that Gary’s absolutely right, and putting any acronym debates behind us, foundational SEO remains the same, particularly with Google search.

SEO has never been more vital, and AI is accelerating the need for specialists in this area. Your website still needs to be fast, mobile-friendly, and technically sound. Search engines and AI systems alike need to crawl and index your content efficiently. Technical optimizations like proper URL structures, XML sitemaps, clean code, and fast loading times are still paying dividends.

The CMO, SEO, And LLM Optimization Fundamentals

Now, when we talk about optimizing for all LLMs, there is a similarity in the reality that success still lies in core SEO – primarily technical SEO – and content fundamentals.

Strong internal linking helps AI crawlers understand how your pages connect. Make sure all pages are easily crawlable. Answer related questions throughout your content using clear headings, schema markup, and FAQ sections, and figure out what people are trying to accomplish to give them the answer and be the cited source in AI results.

LLM Platform-Specific Differentiation

However, as more brands are being discovered and interpreted across multiple AI platforms, it is also vital to understand that each has its own interface, logic, and way of shaping brand perceptions.

Each platform has developed distinct strengths: ChatGPT Search provides a comprehensive narrative context. Perplexity shines with visual integration and related content. Google AI Overview excels at structured, hierarchical information.

Here is a nuanced example. When users ask comparison questions like “what’s the best?,” ChatGPT and Google’s approaches are similar. But when users ask action-oriented questions like “how do I?,” they part ways dramatically. ChatGPT acts like a trusted coach for decision-making, while Google AI remains the research assistant.

Image from BrightEdge, August 2025

Trust Signal Variations

Different platforms also show distinct trust signal patterns. Google AI Overviews tends to cite review sites and community sources like Reddit, asking “what does the community think?”.

ChatGPT appears to favor retail sources more frequently, asking, “where can you buy it?”. This suggests these platforms are developing different approaches to trust and authority validation.

Three-Phase AI Optimization Framework For The CMO And Marketing Teams

Here is a framework for organizations to follow.

• Start by tracking your AI and brand presence across multiple AI engines. Monitor how your visibility evolves over time through citations and mentions across AI Overviews, ChatGPT, and beyond.
• Next, focus on understanding variations in brand mentions across key prompts. Quickly identify which prompts from ChatGPT, AI Overviews, and other AI search engines generate brand mentions so you can optimize your content efficiently.
• Finally, dive deeper into specific prompts to understand why AI systems recommend brands. Utilizing sentiment analysis provides precise insights into which brand attributes each AI engine favors.

Learn More: The Triple-P Framework: AI & Search Brand Presence, Perception & Performance

The CMO: AI, Search, And Cross-Team Integration Thinking

One thing I am seeing work well is tighter integration across marketing and communications teams. Paid and organic strategies must align more than ever because ads and organic AI overviews often get presented together – your messaging, branding, and targeted intent need to be entirely consistent.
Plus, your PR and content teams need better coordination because off-site mentions in media, reviews, and authoritative sites directly influence who gets cited in AI summaries.

Conclusion: Embracing The Multi-AI Search Transformation

The CMOs who are proactively adapting to the shifts are positioning their organizations for sustained competitive advantage in this evolving landscape.

Big Picture, to put this all in perspective.

The 3 Big Questions From CMOs On AI And Search

1. AI would kill Google: No, it has turbocharged it.
2. SEO is dead: No, it’s actually more important than ever. AI is reshaping search, which means we need to understand what this transformation entails. Generative Engine Optimization (GEO) builds upon core SEO foundations and requires more integrated, higher-quality technical approaches.
3. Everything changes? The more things change, the more they stay the same.

In Part 2 of this series, topics covered will include the future of traditional SERP search and how agentic SEO might change the search funnel. Learn how these changes impact the role of SEO and all teams that fall under the CMO remit.

More Resources:

Featured Image: jd8/Shutterstock