How Visibility Compounds In Brand-Led SEO via @sejournal, @TaylorDanRW

If building a brand is the new SEO cliche, then how visibility compounds is the part that rarely gets explained.

We can all agree, at least on principle, that repeated brand exposure matters. Brands become familiar because they appear consistently over time and across contexts. What is less well understood is how search visibility builds on itself, how it becomes easier to grow once you reach a certain threshold, and why this is often the difference between content that merely exists and content that genuinely drives preference.

This matters because the pressure around AI and LLM visibility has changed the tone of marketing conversations. Leaders want speed. They want the benefits of brand strength without the lead time it typically requires.

That gap between expectation and reality is where many teams end up panicking, producing more content, chasing more mentions, and hoping the sheer volume will create momentum and increase mental availability. That approach rarely works, because compounding is not the same as doing more. Compounding is what happens when each new piece of visibility makes the next one easier to earn.

What “Visibility Compounding” Actually Means

Visibility compounding is the effect where early wins create structural advantages that improve your ability to win again later. This is not an abstract concept, because in SEO, once you start to earn consistent impressions and real engagement across a topic area, certain things tend to follow in a fairly predictable way.

Your pages often get crawled more frequently because the site is being discovered, used, and referenced across the wider web, while your content becomes easier to rank because it sits inside a network of related pages rather than existing as isolated assets. Your internal linking becomes more meaningful because you are connecting real clusters of intent rather than trying to force relevance where it does not exist, and your brand becomes more familiar to users, which quietly improves your ability to earn clicks, repeat visits, and deeper browsing.

None of these things are brand building in the traditional sense, but they are the mechanics that can make brand building cheaper, faster, and more resilient over time. A simple way to describe it is that visibility compounds when your presence creates signals that make your future presence more likely.

Compounding Starts Before Loyalty

One of the reasons SEOs struggle with the brand conversation is that loyalty feels like the finish line, and when nobody is loyal yet, it can feel like the brand work is failing. I feel that this is because, as marketers, we’re trained to look at the conversion funnel, with loyalty/advocacy being the “end-goal.”

In reality, compounding begins much earlier than loyalty, typically with recognition.

If a prospect sees your brand name in search results, then sees it again in a different query a few days later, and then sees it again while they are comparing options, something changes: You are no longer unknown and are now familiar enough to be considered. This is not emotional loyalty; it is mental availability, and it is the earliest stage of preference, which is where SEO can contribute more than many marketers realize.

This is also where AI complicates the picture; users may click less often, but they are still being exposed to sources, brands, and repeated content. Even when attribution becomes harder, the effect of familiarity still exists, and the question is whether your visibility is strong enough for familiarity to form at all.

One Strong Piece Of Content Is Rarely A Strategy

Many teams still treat content like a set of isolated tactical bets, such as one flagship thought leadership piece, one big report, one digital PR campaign, or one new pillar page. These can be valuable, but on their own, they do not tend to compound, because compounding needs continuity and coverage, and it needs a user to see you again and again in ways that feel natural rather than forced.

The truth is that a single great piece of content usually becomes a moment rather than a system, and while a moment might win attention for a week, a system keeps you present for months. Single pieces of content can be fantastic catalysts, but they require support, ladder-up tactics, and more than just distribution to turn them into brand assets that compound visibility.

How Compounding Usually Unfolds

When visibility truly compounds, it often follows a simple loop, even if it takes time to build. It usually starts with coverage, where you publish content that answers real queries, it gets indexed, it earns impressions, and the early performance may be modest, but it establishes presence.

Then you start to earn credibility, because some pages begin to attract links, mentions, engagement signals, and repeat discovery, and you become a source that is referenced rather than a page that exists. Over time, repetition kicks in, users see you again, they click more readily, they browse deeper, they return later, and your brand starts to feel like part of the landscape for that topic.

This is where the system begins to create momentum, because new content can rank faster as it is not fighting for relevance alone, and it is supported by an ecosystem that already signals topical authority and user demand.

Distribution Is Often The Real Differentiator

A lot of SEO conversations get stuck on quality, as though quality is a clear and objective threshold that guarantees results, and quality does matter. The problem is that quality is rarely the differentiator once you are operating in a competitive market, because the differentiator is often distribution.

If your content is not being seen, it cannot compound, and if your digital PR work is not creating repeated brand touchpoints, it cannot compound, while leadership content that does not earn readership cannot compound either. You do not need a perfect piece of content, but you do need content that gets consumed, referenced, and remembered.

This can be uncomfortable for organizations because it makes the work feel less controllable, since writing and publishing can be done internally, but distribution forces you to compete for attention in a public arena. If you want compounding effects, you have to treat distribution as a core capability rather than a nice-to-have.

Visibility Compounding Makes Brand Outcomes Realistic

This is the missing link in much of the current industry advice. Brand building is real, but it is slow, and visibility building is measurable, but it is not always meaningful, and compounding is what connects the two.

When you build visibility in a way that compounds, you create the conditions for brand outcomes to emerge, because familiarity becomes preference over time, preference becomes repeat engagement, repeat engagement becomes trust, and trust becomes the ability to win even when the channel changes.

That last part is what matters most going into 2026, because AI search and LLM interfaces will keep evolving, attribution will remain messy, the surfaces will shift, and traffic patterns will wobble. Brands that rely on isolated wins will keep feeling exposed, while brands that rely on compounding visibility will feel anchored, because their presence is not tied to one page, one keyword set, or one campaign.

What To Focus On If You Want Compounding Effects

If you want visibility to compound, you need to stop thinking only in terms of content output and start thinking in terms of coverage and reinforcement. You build around themes rather than one-off ideas, you publish sequences rather than isolated pieces, and you connect content so it behaves like an ecosystem rather than a library.

You also measure success in a way that reflects compounding, meaning you look beyond whether a page performed in isolation and ask whether it improved your ability to perform again. If content does not make the next piece easier to win, it may still be useful, but it is not compounding.

The Question SEO Leaders Should Be Asking

If AI has forced one useful change in SEO, it is that it has exposed how brittle many visibility strategies really were. Ranking for a handful of high-volume queries was never the same as owning a topic, being present was never the same as being preferred, and building a brand was never something you could do by simply saying the words.

The real question is not whether you need a brand to win in AI search, but whether your visibility strategy is designed to compound, or whether you are producing outputs and hoping time does the rest. Time compounds what is connected and reinforced, and it does not compound what is isolated.

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The User Journey Isn’t Linear Anymore: It’s Always On via @sejournal, @TaylorDanRW

For many years, organizations have relied on a familiar view of the customer journey. The idea that a user moves from awareness to consideration to decision in a neat and predictable line has shaped how brands communicate, measure, and invest.

The rise of AI has shown that this model no longer reflects how people actually behave, and the real journey is fluid, continuous, and influenced by many sources at once.

The shift is not a small adjustment; it is a fundamental change in how demand is created and how decisions are made.

Why The Linear Model No Longer Fits

The linear path breaks because AI now compresses what used to be separate stages into a single moment.

A person can ask for suggestions, comparisons, recommendations, suitability checks, and next steps within one interaction, and the technology responds by folding several layers of intent into one answer.

Users no longer need to progress step by step, as discovery, evaluation, and shortlisting can now happen together. The impact on how brands attract and retain attention is significant, as every touchpoint becomes a potential point of influence.

People enter the journey from many different places and at many other times.

Large language models (LLMs), marketplaces, social platforms, email, traditional search, and emerging assistants all serve as both starting points and accelerators, making the first touchpoint no longer predictable.

The real pattern resembles a series of loops rather than a line, as users move back and forth while refining their wants, and AI tools guide them through this exploration by shaping and clarifying their thinking.

A simple example shows how quickly the linear model breaks.

A person thinking about buying new running shoes might previously have searched for brands, compared prices, read reviews, visited a store, and only then made a decision.

Today, the same person can ask an AI assistant for recommendations based on their running style, previous injuries, preferred terrain, and budget, and receive tailored options in seconds.

The assistant can provide comparisons, highlight differences, explain fit considerations, and even suggest alternatives that the user had not considered.

The process jumps across multiple stages at once, without the user moving through a sequence of pages or channels. The journey becomes a loop of questions and refinements rather than a straight line.

The process is not a funnel; it is a dynamic system driven by intent that evolves with every interaction.

The Rise Of The Always-On Journey

The idea of an always-on journey captures this reality. Decisions are shaped gradually, through signals, prompts, and small moments of influence spread across multiple environments. There is no fixed beginning or end, only windows where your brand becomes relevant based on the user’s needs, context, and constraints.

AI widens these windows by introducing products and services during tasks that might seem unrelated, so discovery is not something brands can schedule or stage. It happens whenever the technology sees an opportunity to help the user progress.

This shift has also been accelerated by the way major technology companies are positioning AI as a core feature of their products. Smartphones, laptops, and operating systems now include assistants that are marketed as everyday companions capable of producing content, planning tasks, answering questions, and guiding decisions.

The advertising behind these features plays a significant role in shaping user behavior, as it encourages people to rely on AI in more situations and across a broader range of personal and professional tasks.

The journey towards adoption follows a simple path: from seeing to understanding, trusting, trying, using, and eventually scaling.

People see the feature highlighted in a product launch or advert, understand the benefit through demonstrations, begin to trust the technology when they see it used in credible scenarios, try it themselves, start using it regularly, and eventually scale it across more tasks. Each step is reinforced by the device ecosystem, which keeps the technology present and available throughout the day.

This pattern means the user is never far from an AI-driven touchpoint, which in turn keeps the journey active at all times. The more familiar users become with these tools, the more naturally they integrate them into decision-making. The result is a journey that does not pause between stages but remains in motion, shaped by continuous access to assistance, advice, and recommendations.

What Organizations Need To Do

Organizations need to adapt by treating every asset as a potential entry point. Product pages, support articles, category pages, guides, tools, videos, and reviews can all be surfaced first, so each one must stand on its own and communicate value without relying on the rest of the site or campaign.

This requires clarity, structure, and consistency, because users (and AI systems) will not follow the path you expect.

Brands also need to think in terms of anticipation rather than reaction. When a user is exploring options, they benefit from seeing comparisons, trade-offs, alternatives, and clear explanations of who a product is for and who it is not for.

These elements help people imagine how the product or service might fit into their situation, which strengthens trust and improves the likelihood of being included in the user’s shortlist, even if the journey restarts several times.

AI tools rely on machine-readable signals to understand a brand, and structured information now carries more weight than ever. Organizations that invest in clear product data, logical information architecture, and consistent descriptions make it easier for AI systems to explain their offer. This is not a technical exercise; it is a strategic requirement for visibility in an environment where users expect fast, accurate guidance.

Conclusion: Adapting To An Always-On Reality

Success will come from supporting the journey rather than trying to control it. People will continue to loop, reassess, and re-enter from new angles, yet they will respond well to brands that stay present, offer clarity, and help them navigate choices with confidence.

The customer journey never truly followed a line, and AI has simply revealed how dynamic it has always been.

Brands that recognize this shift and adapt their approach will build stronger connections and remain relevant in a market that no longer moves from stage to stage but operates continuously, in an always-on.

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Repositioning What SEO Success Looks Like via @sejournal, @TaylorDanRW

In SEO, we are at a turning point, and after more than a decade of chasing rankings and traffic volume, many of us are beginning to recognize the need to have a broader and more meaningful conversation about what “success” really means in SEO.

This article reflects on how these conversations are evolving, why the older definitions are no longer sufficient, and how we can reposition the success metrics we use so that they better align with business value and reflect the reality of changing search behavior.

Narrow Success Window

For many years, success in SEO was defined in fairly narrow terms, where we measured how many keywords ranked in the top 10 or top three, and reported increases in organic sessions, improvements in domain authority, or growth in backlink counts.

These were tangible, easy to track, and often felt convincing in boardroom conversations, but underneath the surface, the limitations of this approach were already apparent.

Rankings, while useful, are ultimately vanity metrics, and if they improve without leading to increased clicks or qualified traffic, or if visitors arrive but never become leads or drive revenue, the SEO team may appear successful, but the business does not necessarily benefit.

We must now begin with the end in mind, asking what the business goal truly is, what value each new lead brings, and how the website supports those aims. The classic metric stack was keyword positioning to impressions, to clicks, to organic traffic, and possibly to conversions, but it no longer reflects the full story, and we need to think more holistically.

Why This Conversation Needs Updating

Several forces are now converging that make the older success yardsticks less reliable, and search behavior is one of the most prominent.

People increasingly expect fast, direct answers, and search engines now deliver results that provide those answers immediately through formats that do not always require a click, such as “zero-click” results.

This significantly changes how we measure success, because if users receive what they need without visiting a site, traditional click-based metrics lose much of their relevance.

The attribution chain is growing more complex, as organic traffic often plays a role early in the decision-making journey or supports brand engagement later in the funnel. The connection between a search visit and a tangible business outcome, such as a sale or a lead, can be indirect, span time, or be difficult to track with confidence.

At the same time, the data itself is becoming noisier and harder to interpret, with increasing levels of bot traffic, variations in device usage, growing privacy constraints, and changes in how users interact with results.

Metrics such as bounce rate, time on site, or even click-through rate are now more vulnerable to misinterpretation.

Expectations of SEO teams have also changed, and we are being asked to deliver clear business value, not just improved rankings. If we are still tracking only vanity metrics, we may be missing the real impact. We need to connect our work directly to outcomes such as revenue, visibility among key audiences, and genuine customer engagement.

It is no longer enough to say that traffic is up by 20%. We need to ask what that increase means for the business and whether those visitors were qualified and led to a meaningful result.

Repositioning Success: What The Conversation Should Focus On

To define SEO success more accurately, we need to reframe the conversation entirely. These are the dimensions I now focus on.

Business Alignment

Real success begins by aligning SEO activity to business outcomes. If the objective is to capture high-value enterprise leads, then reporting traffic to low-intent blog content is no longer meaningful.

Instead, we need to set goals that are measurable, commercially relevant, and clearly linked to strategic priorities, ensuring the SEO team contributes to those priorities in a language leadership understands. When we do that, the conversation shifts away from keyword counts toward the broader question of how much value organic search adds to the business.

Quality Over Quantity

While traffic volume still has its place, we need to move beyond surface metrics and focus on the quality of visitors, whether they reflect the right intent, whether they engage with content meaningfully, and whether their behavior suggests a pathway toward a business outcome.

Metrics such as engagement depth, lead generation rate, and alignment with target personas tell us far more than raw traffic alone. The question we now ask is whether the right people are finding us and taking action once they do.

Visibility And Market Share In Search

It is not enough to rank well for a few hand-picked terms.

Visibility in search today is about occupying the right positions across a much broader landscape, reaching our audience at various moments of need. This includes winning impressions across multiple query types, appearing in rich results and featured formats, and maintaining a presence that reinforces our authority.

The more we dominate relevant search journeys, the more we influence the market, even when that influence is not reflected in click metrics alone.

Attribution And Value Tracking

We must tie SEO performance directly to measurable business value, whether that is leads, revenue, brand visibility, or contribution to a broader customer lifecycle. That requires stronger analytics frameworks, and the discipline to identify and follow the signals that matter most. Instead of obsessing over rankings, I now focus on the question of how many of our business outcomes can be reliably influenced or supported by organic search, and what that influence is worth.

Adaptability To Search Evolution

Search is no longer static, and with the rise of AI, direct answers, voice, and structured data, our measurement frameworks must evolve just as quickly.

Success might mean gaining impressions in key places, even if those impressions do not always convert directly.

We may see lower click-through rates because our content is being used in answer boxes or overviews. Rather than viewing this as a failure, we should ask whether we are still present, whether our brand remains visible, and whether we are feeding into the new ways people search for and consume information. That adaptability is part of long-term success.

Practical Steps To Have This Conversation

To reposition the conversation, we must first return to the strategic context.

What does the business want to achieve in the next six to 12 months? Growth, market expansion, brand credibility, operational efficiency?

Whatever the goal, we need to ask how organic search supports it, and we must agree early on what success will look like.

This means defining shared metrics that matter. We might look at the percentage of relevant traffic, the number of qualified inbound leads from organic, the revenue pipeline influenced, or the share of voice in a competitive space.

These metrics need to be discussed, agreed upon, and tracked collaboratively. Once we know what matters, we can classify our metrics as leading indicators, lagging outcomes, and diagnostic signals, ensuring we track progress meaningfully from awareness through to value delivery.

When we report results, we must do so in business terms. Rather than quoting percentage increases in traffic, we need to say what that traffic represented, such as how many people matched our target buyer personas, how many converted into something valuable, and what that means in financial or strategic terms.

We also need to acknowledge the complexity of attribution, explaining what can and cannot be measured with precision, and why. When traffic rises but clicks are flat due to zero-click results, or when awareness improves without immediate leads, we need to explain what those patterns mean and what the underlying story really is.

This process should not be static. As search evolves and business priorities shift, we must revisit our KPIs, our assumptions, and our methods. A flexible, open approach builds trust and keeps SEO positioned as a strategic partner rather than just a technical service.

A Case For Reframing Success Now

It is no longer a question of if we should change how we define success in SEO, but when. The risks of holding onto outdated metrics are serious. If we keep measuring keyword rankings and traffic counts, while the business cares about conversion, revenue, and growth, then we risk being seen as disconnected or misaligned.

The result is often loss of confidence, shrinking budgets, and missed opportunities.

But if we reframe how we measure and report success, we gain influence, relevance, and longevity. We align better with leadership goals. We allocate effort where it has the most impact. We stay ahead of search evolution. And most importantly, we build a case for the enduring value of SEO in any business context.

What This Means In Practice

In practical terms, this shift means reporting not only what ranks but what that visibility delivers. When I report on keyword positions, I explain the monthly search potential and the conversion rate of the landing pages they drive. When I talk about traffic growth, I segment it by intent and persona fit, and I show how that growth affects demo requests, contact forms, or sales-qualified leads.

If the click-through rate falls but featured snippets rise, I report the increased visibility and link it to changes in branded search or engagement with our wider content. If backlinks increase, I focus on their relevance and domain quality, and I explain how they influence brand signals and domain authority. Every number I report should tie back to business relevance, not technical vanity.

Final Thoughts

We are long overdue for a new understanding of what SEO success really means. As behavior changes, as platforms evolve, and as expectations increase, we need to be ready to tell a better story – one that shows our work is about value, not vanity.

The results that matter most are the ones that serve the business, influence the market, and build a sustainable presence over time.

If you have been in this industry for a while, now is the moment to lead that shift. Bring your leadership into the conversation.

Ask the right questions. Set the right metrics. Build a measurement framework that makes SEO impossible to ignore.

Because when we position ourselves as strategic contributors and not just technical operators, the work we do will finally get the recognition it deserves.

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Featured Image: Vitalii Vodolazskyi/Shutterstock

Deploying Agentic AI For SEO: A Playbook For Technology Leaders via @sejournal, @TaylorDanRW

Search is moving from queries typed into a box to conversations held with systems that understand intent, context, and outcomes. People no longer look for pages. They look for solutions, guidance, and confidence that they are making the right choice.

Agentic AI pushes this shift further. Instead of waiting for instructions, agents act on goals. They discover information, compare options, trigger workflows, and adjust based on feedback. For digital leaders, this means visibility is no longer only a ranking problem. It becomes a problem of influence inside AI systems.

SEO now touches product, data, knowledge management, and experience design. This playbook explains how to prepare for that shift, build capability, and lead change.

Search Is Becoming AI-Mediated

AI systems have become the layer between users and the web. They read content on behalf of users, make selections instead of requiring users to browse, and influence decisions in ways that search pages once did.

This shift changes how people interact with information. Users now ask broader, more complex questions, expecting systems to understand nuance and intent. The traditional act of navigating through links is giving way to direct answers and immediate actions.

Content can no longer be designed solely for human readers. It must also be structured in ways that AI systems can interpret accurately and confidently. In this environment, trust and evidence carry more weight than keywords or search optimization tactics.

Winning in search today means becoming part of the models that shape decisions, not just appearing in the results.

What Agentic AI Means For SEO And Digital

Agentic AI is changing how people discover and choose brands. Discovery now depends on how well models learn from your content, the paths users take on your site, and the external signals that establish credibility. These systems decide when your brand is relevant, based on what they understand and trust.

During evaluation, AI compares your product, price, quality, reviews, and suitability for a given user against other options. It looks for proof, tests claims, and weighs real signals over marketing language.

When supporting decisions, AI doesn’t just provide information. It actively guides users toward what it considers the best fit. Your brand might be brought forward or quietly passed over, depending on how well it matches user needs.

In this landscape, SEO is no longer just about publishing content. It’s about shaping how AI systems perceive your brand and when they choose to recommend it.

New Operating Model For SEO

The future of search brings marketing, product, and data teams into a shared effort. Success depends on how well these areas work together to shape how AI systems perceive and present your brand.

The key is building structured knowledge that AI can easily process and apply. Instead of designing for clicks and views, focus on creating journeys that help users complete tasks through the systems guiding them. It’s also critical to train these systems with the right brand messages, supported by clear evidence and consistent proof points.

Ongoing visibility requires monitoring how models reference your brand, how they rank it, and how they reason about its relevance. This means continuously refining the signals you send, improving your content, updating product data, and reinforcing trust in every interaction.

The goal remains clear and hasn’t really changed from our technical goals for SEO. Make it easy for AI agents to understand, trust, and ultimately recommend your brand.

Maturity Model

The goal is not automation alone. It is intelligence and improvement at scale.

Technical And Data Foundations

To prepare for agentic SEO, organizations need more than traditional content systems built for publishing. They need strong foundations that help AI systems understand, evaluate, and act with confidence.

This starts with clarity, which means crafting messaging that is consistent, accurate, and easy for machines to interpret. Structure is also essential, requiring content, data, and signals to be organized in ways that align with how AI systems process and reason through information.

Key components of this are:

• Structured data that turns content into machine‑readable knowledge.
• Knowledge graphs that explain relationships between products, categories, and needs.
• Taxonomy and naming standards to ensure consistency across pages, feeds, and assets.
• APIs and automation for publishing and optimization, so agents can trigger updates.
• Clean product and service data, including specifications, pricing, and availability.
• Evaluation systems to audit AI outputs and detect hallucinations or misalignment.
• Identity and trust signals, including reviews, authority, certifications, and product proof.

This calls for a shift from simply building web pages to creating a well-organized information architecture. The goal is to structure information in a way that AI systems can easily navigate, understand, and apply.

In practice, this means bringing together product data, content metadata, and customer intent into a single, connected system. It involves defining the key entities your business represents, such as products or services, and mapping how they relate to what users are trying to accomplish. Content feeds and structured data should reflect the actual state of the business rather than just marketing language.

Equally important is creating feedback loops that show how AI systems interpret and reference your brand. These insights help you see where your content is being used, how it is being understood, and whether it is guiding users toward your brand. With this information, you can keep refining what you share to improve how systems recognize and recommend you.

Instead of asking, “How do we rank for this query?” leaders will ask, “How do systems understand us, trust us, and act on our information?”

KPI And Measurement Model

Traditional key performance indicators still hold value, but they no longer capture the full picture. Rankings and session metrics continue to provide insight, yet they now exist within a broader framework shaped by how AI systems retrieve, interpret, and act on information. Ranking reports will sit alongside AI retrieval dashboards, and session counts will be evaluated alongside metrics focused on task completion and user outcomes.

In my opinion, you should also be looking to monitor:

• Share of voice in AI assistants.
• Retrieval and inclusion rate in AI answers.
• Brand alignment and brand safety in model outputs.
• Presence in multi‑step reasoning chains.
• Task completion and conversion paths from AI systems.
• Cost per automated workflow and cost per agent‑driven action.
• Model education, data freshness, and trust scores.

As measurement evolves, the focus moves from tracking visitor numbers to understanding how AI systems shape decisions. To navigate this shift, leaders should design metrics that reflect influence within these systems. Visibility will measure whether the brand is appearing in AI-generated responses and assistant-led interactions.

Accuracy will assess whether the brand is being represented correctly and safely across touchpoints. Trust will reflect whether AI systems choose your content and signals over others when making recommendations. Action will capture whether AI-driven experiences result in tangible outcomes like leads, bookings, or purchases. Efficiency will show whether AI agents are reducing manual effort, improving speed, and delivering better user experiences.

Success will no longer be defined by visibility alone but by a brand’s ability to perform across discovery, decision support, and operational impact.

Talent And Capability Model

Agentic SEO is not a standalone skill set, it draws from a mix of disciplines that span marketing, data, and product. Success in this space requires a collaborative approach, where expertise is integrated rather than siloed.

Future-facing teams bring together SEO and content strategy, data and automation engineering, product and user experience thinking, as well as governance and prompt development. Legal and compliance awareness also play a critical role, ensuring that outputs remain responsible and aligned with brand and regulatory standards.

These teams operate in cross-functional pods, organized around delivering customer outcomes rather than managing individual channels. This structure allows them to move faster, adapt to change, and create more cohesive experiences across AI-driven platforms.

Modern SEO teams include several key roles. The SEO strategist focuses on how AI systems search, retrieve, and rank content. The data engineer manages the integrity of structured content, metadata, and live data feeds. The automation specialist builds the workflows and agents that connect information to user actions. The AI evaluator audits model outputs to ensure accuracy, brand alignment, and safety. The product partner bridges SEO efforts with real user journeys, making sure that discovery leads to meaningful interaction and conversion.

As this approach matures, teams will spend less time producing content manually and more time designing the systems, signals, and experiences that guide AI behavior and improve how users discover and engage with the brand.

The First 90 days

Days 1 To 30: Foundation And Alignment

• Audit content, data, and search performance.
• Map where AI already touches customer journeys.
• Identify gaps in structure, trust signals, and data quality.
• Set goals for AI visibility and agent‑driven workflows.

Days 31 To 60: Build And Test Pilots

• Launch structured data and knowledge base improvements.
• Test AI‑assisted content and QA pipelines.
• Introduce early agent monitoring for SEO signals.
• Create evaluation benchmarks for AI accuracy and brand safety.

Days 61 To 90: Scale And Govern

• Deploy automation in high‑impact workflows.
• Formalize model governance and feedback loops.
• Train cross‑functional teams on AI‑ready processes.
• Build dashboards for AI visibility, trust, and conversion.

Future Outlook

Search will not disappear. It will merge into tasks, journeys, and decisions across devices and interfaces. Brands that train AI systems, structure knowledge, and build agent‑ready operations will lead.

The winners will not be those who automate content. They will be those who help users and systems make better decisions at speed and scale.

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Featured Image: Collagery/Shutterstock

Preparing C-Level For The Agentic Web via @sejournal, @TaylorDanRW

Artificial intelligence is changing how the web works. Search engines, voice assistants, and generative platforms are altering how people find information and make decisions.

The internet is no longer built only for human visitors. Brands now operate in an environment where both people and intelligent systems interact with their content, reshaping how websites are designed, found, and measured.

Dual Audiences

The modern web now serves two audiences.

Websites are designed not only for people to read and navigate, but also for AI systems that interpret and act on information on behalf of users. This change is as significant as the move to mobile-first design.

Traditional search practices that focused on keyword visibility, human readability, and click-through rates are becoming less effective. AI-generated summaries in search results, along with tools like ChatGPT, Perplexity, and Gemini, surface information directly to users without them visiting a site. Website traffic and engagement data are becoming less reliable measures of success.

Brands need content that performs two functions. It must provide value and clarity for human visitors while also being structured in a way that can be understood and used by AI systems. This calls for new thinking around design, content structure, and data transparency.

Redefining Visibility

Visibility is no longer only about ranking highly on a search results page. It now depends on how often a brand’s information is cited or used by AI systems.

Brands with well-organized data, clear product details, and content that machines can interpret are more likely to appear in AI-driven environments. Websites should utilize modular, structured frameworks that separate content from design, allowing AI agents to easily process the information.

Modern SEO now extends beyond technical optimization and backlinks. It includes preparing data for language models and voice assistants, product feeds, and FAQ content to help make brand information accessible both to people and to machines.

Content strategies also need to evolve. Pages should be written to answer user questions directly, not just target keywords. AI systems prioritize clarity, authority, and logical structure. Brands that provide straightforward, useful information are more likely to appear in AI summaries and responses.

Personalization At Scale

AI is expanding how brands personalize content and recommendations. Machine learning and first-party data allow for tailored experiences at a scale that was not previously possible.

The challenge is maintaining a consistent brand identity while using automated personalization. Without strong frameworks, brand messaging can become inconsistent or lose tone.

To avoid this, organizations should build clear structures, tone-of-voice guidance, and defined data governance. Modular content systems make it possible to create personalized messages without losing consistency. Each variation should feel part of the same brand experience.

A strong data strategy is essential. Customer Data Platforms and analytics tools help brands understand context and behavior, enabling more relevant and timely communication. Human oversight remains important to ensure brand values and tone are respected across automated outputs.

Measuring Success In The AI Era

As AI reduces clicks and sessions, traditional marketing metrics are less meaningful. C-level leaders are focusing more on results than activity. The key question has become how effectively a brand’s content or product is being chosen or recommended by intelligent systems.

Brands can measure performance in three areas:

1. Agent Visibility And Selection

This reflects how often AI systems reference or prioritize a brand’s content. Tracking brand mentions and inclusion across AI platforms is becoming an important new visibility metric.

2. AI-Driven Traffic Referrals

Although click-throughs are fewer, visitors who arrive via AI recommendations often convert more quickly. Measuring how these users behave can reveal intent and content quality.

3. Brand Sentiment And Experience Quality

In personalized environments, success is not only about visibility but also how users feel. Measuring satisfaction, accuracy, and tone across AI interactions is key.

To do this effectively, brands need updated analytics. Tools that assess visibility in generative systems and track AI-driven referrals are beginning to emerge. Integrating these into broader measurement frameworks will be essential.

Preparing For The Open Agentic Web

The next phase of web development is the open agentic web, where AI systems can browse, interpret, and act across sites on behalf of users. These agents can make bookings, complete purchases, and retrieve information without direct user input.

New web standards are supporting this transition. Protocols such as NLWeb are helping make content easier for AI systems to access. This aims to create smoother interaction between users, brands, and intelligent systems.

Businesses should start adapting their digital infrastructure now. Content management systems, APIs, and data models should serve both human users and AI agents. Making information accessible in a structured, secure way will determine how effectively brands participate in this environment.

This shift also brings new decisions. Some brands may allow AI systems to use their content to improve visibility, while others may prefer to limit access. Each approach affects how visible and discoverable the brand becomes.

Leaders should see this as a major transition. Those who act early to build structured, machine-readable foundations will have an advantage. Those who delay risk losing visibility as AI systems become key gateways to information.

What C-Level Needs To Know

Executives should focus on three main areas as the open agentic web develops:

1. Build A Flexible Digital Infrastructure

Invest in structured, modular systems that can evolve with AI standards. APIs, data models, and schemas should be consistent and accessible.

2. Update Performance Metrics

Shift away from traffic and CTRs. Focus on agent selection, task completion, and performance outcomes that reflect both human and machine interactions.

3. Align Teams Around Data And Content

AI integration spans marketing, technology, and product functions. Shared frameworks are needed to ensure tone, data, and strategy stay consistent.

What Brand Teams Need To Do

Marketing teams should turn these strategies into practical action.

They need to create content that answers questions clearly, maintain clean data structures, and design experiences that both humans and machines can interpret. Testing structured formats such as conversational FAQs, knowledge hubs, and metadata-rich content will help future-proof visibility.

Measurement practices must also evolve. Teams should begin testing tools that monitor how often AI platforms reference their content and how structured data contributes to discoverability.

A New Web For Humans And Machines

The web is moving towards closer interaction between people and intelligent systems. Success will depend on how well brands design experiences that are both understandable and trustworthy for both parties.

For business leaders, the goal is to build digital systems that operate clearly and efficiently. For brands, it means creating content and structures that work with AI rather than against it.

The open agentic web will reward brands that connect visibility, personalization, and measurement into a single strategy. Those that act early will help shape how this new phase of the internet develops.

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Featured Image: Anton Vierietin/Shutterstock

How People Use ChatGPT & What It Means For The C-Suite via @sejournal, @TaylorDanRW

ChatGPT adoption is accelerating at a scale rarely seen in technology.

By mid-2025, around 700 million people worldwide were using it every week, sending 18 billion messages, which is roughly 10% of the global adult population. For a new technology, this speed of adoption has no precedent.

Yet if you look at your analytics dashboards, you will not see a corresponding surge in referral traffic from ChatGPT. That is because adoption does not always translate into clicks or visits. In today’s AI-driven environment, adoption itself is value. It changes how people learn, shop, and make decisions, often long before they interact with your brand through search, social, or direct channels.

A new study from OpenAI and Harvard sheds light on how people are actually using ChatGPT. The findings identify shifts in consumer behavior, productivity patterns, and global reach. All of these carry implications for CMOs, CEOs, and CFOs.

Work Vs. Non-Work Usage

By mid-2024, ChatGPT was being used almost equally for work and non-work purposes. A year later, non-work usage had surged to nearly three-quarters of all activity, with work-related conversations accounting for around a quarter. This was not only the result of new users joining for personal use, but also due to the increasing popularity of the platform. The data shows that existing users themselves were evolving their habits, leaning more heavily on ChatGPT in their personal lives.

For a CMO, this signals that consumers are weaving AI into their daily routines in ways that reshape how they discover products and services. For a CEO, it underscores that ChatGPT is not confined to the office and is becoming a mass-market behavior that seamlessly integrates into everyday life. For a CFO, the message is that non-work adoption has significant economic value, with researchers estimating consumer welfare gains of $97 billion annually in the United States alone.

Core Use Cases: Guidance, Information, And Writing

The vast majority of ChatGPT usage falls into three categories:

• Practical guidance.
• Information seeking.
• Writing.

Practical guidance includes tutoring, teaching, how-to advice, and creative ideation. Information seeking often looks like a direct substitute for web search, as people ask ChatGPT about current events, products, or factual queries. Writing encompasses the production and improvement of emails, documents, summaries, and translations.

At work, writing dominates. Four in 10 work-related messages concern writing tasks, and most of these are not new generation but rather editing or improving text that users bring to the model. Education is also a notable use case, with roughly 1 in 10 messages asking for tutoring or teaching support.

This matters to the CMO because it indicates that brand discovery is increasingly occurring through AI chat, rather than traditional search result pages.

It matters to the CEO because it demonstrates that AI is becoming a decision-support and creativity tool, not just a way to automate repetitive tasks. And it matters to the CFO because writing and editing at scale represent measurable efficiency gains, translating into more output per worker.

Lesser Use Cases: Coding And Companionship

Some use cases that have attracted outsized attention turn out to be smaller in reality. Only 4.2% of ChatGPT conversations are about programming, a far lower share than rival tools like Claude, which report one-third of their work-related conversations tied to coding. Companionship and emotional support are even less common, accounting for under 2% of ChatGPT usage.

For a CMO, this highlights that ChatGPT is primarily a tool for mass consumer behavior, rather than a niche coder’s tool or a therapy companion.

For a CEO, it confirms that ChatGPT’s role in the market is broad and mainstream.

For a CFO, it suggests that monetization does not hinge on high-value enterprise niches but is instead driven by widespread consumer engagement.

Who Uses ChatGPT: Demographic Shifts

The study also tracks striking demographic changes. In its early months, ChatGPT’s user base skewed heavily male, with around 80% of active users having traditionally masculine names.

By mid-2025, that imbalance had disappeared, with usage now at parity and even slightly higher among women. Age is another clear factor: Nearly half of all adult messages come from users under 26, though older users tend to use ChatGPT more for work-related purposes. Growth is fastest in low- and middle-income countries, indicating that adoption is spreading well beyond the wealthy, early-adopter markets. Among professions, highly educated workers lean on ChatGPT more at work, often using it as an advisor or research assistant.

These findings should capture the CMO’s attention because they indicate a widening and diversifying audience, with younger generations incorporating ChatGPT into their habits in ways that could last a lifetime.

The CEO will see opportunities in emerging markets and among new consumer segments as global adoption accelerates. The CFO can take confidence in the fact that adoption is broad-based across demographics, reinforcing the case for long-term subscription models and monetization strategies.

Interaction Styles: Asking Vs. Doing

When people interact with ChatGPT, about half the time, they are seeking advice, guidance, or information. Around 4 in 10 conversations involve asking ChatGPT to complete a specific task that can be slotted into a workflow. The remainder are less clearly defined.

Asking has grown faster than doing, suggesting that users increasingly see ChatGPT as a partner in thought rather than simply a tool for execution.

For the CMO, this means consumers are engaging in dialogue with AI at the very moment of intent, making it vital to anticipate how brand messages surface in those exchanges. For the CEO, it highlights a shift in how knowledge work is done, with AI shaping decision-making as much as task performance. For the CFO, the implication is that the value of ChatGPT lies not just in time saved but in the quality of decisions it helps users make, which is a less tangible but no less significant form of productivity.

Why This All Matters For The C-Suite

The rise of ChatGPT is not just about referral traffic or attribution models. It represents a new layer of consumer and worker behavior that is already reshaping how decisions are made, how information is accessed, and how productivity is achieved.

For marketing leaders, this means rethinking brand visibility in AI-mediated discovery.

For CEOs, it means recognizing ChatGPT adoption as a mainstream societal shift, not a side experiment.

For CFOs, this means expanding the measurement of value beyond clicks and conversions to include consumer surplus, efficiency, and global market potential.

In short, we now operate in an AI-first world where adoption itself is the signal, not the click.

Editor’s Note: Any data mentioned above was taken from the OpenAI study unless otherwise indicated.

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Featured Image: Collagery/Shutterstock