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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Explaining Google’s AI Search Experiments To Your C-Suite via @sejournal, @TaylorDanRW

Google is testing a series of experimental AI-powered features that could change how people interact with search and digital discovery.

Tools like Doppl, Food Mood, Talking Tours, and Learn About are not yet mainstream, but they give us a glimpse into where Google may be heading. Each experiment highlights a distinct way AI can influence consumer experiences, ranging from shopping and travel to food and education.

For business leaders, the importance lies in how these features could influence visibility, customer engagement, and competitive positioning if they are developed further.

Having these on your radar now can avoid sharp surprises and knee-jerk tactical pivots later down the line.

Doppl

Doppl is a new experimental app from Google Labs that lets users try on different looks and explore their personal style. It blends fashion discovery with AI-driven recommendations, acting like a personal stylist in app (on both Android and App Store).

This was initially talked about on the Google blog in 2024 and referred to as Virtual Try-On (VTO).

Screenshot from labs.google/doppl, September 2025

Given the adoption statistics Google has claimed around Google Lens and Circle to Search, Doppl could further change how consumers might approach online fashion and homeware buying.

Instead of browsing catalogs or searching by product type, users can explore outfits in a more playful, visual, and interactive way. This creates opportunities for fashion brands that invest in rich product imagery and metadata, while also introducing risks for those that fail to prepare.

It also highlights that outfit imagery doesn’t need to be professional; users can use Doppl to visualize outfits from their friends’ photos, branded Instagram posts, or what you include in your blogs and style guides.

For ecommerce websites, Doppl could reduce the importance of traditional product listings and increase the value of enriched product data. Style-driven discovery may also accelerate purchase decisions, compressing the sales cycle from browsing to checkout.

For example, a fashion retailer that provides detailed imagery, size data, and styling suggestions may see its outfits recommended more often when users test new looks in Doppl. A competitor with limited product data and imagery could be excluded from the experience entirely.

The takeaway for leaders here is that Doppl illustrates how AI may reshape online shopping into an interactive discovery experience. Fashion and lifestyle retailers should prioritize high-quality product data and imagery to remain competitive.

Food Mood

Food Mood is a recipe generator that combines ingredients and cooking styles to provide creative inspiration for meals.

Instead of entering exact recipes, users can describe their mood or inspiration and receive unique fusion-style ideas.

If rolled out and expanded, this could shift recipe discovery from rigid keyword searches to open-ended, experience-based prompts.

Screenshot from artsandculture.google.com/experiment/food-mood/,September 2025

Food Mood is less about finding the perfect “chicken pasta” recipe and more about encouraging users to experiment. For food publishers and recipe sites, the challenge will be ensuring their content is structured and tagged so it can be effectively integrated into these creative outputs.

Recipe publishers that rely on SEO traffic could see reduced visibility if users embrace AI-generated inspiration instead of searching for specific dish names. On the other hand, sites that invest in structured recipe data, nutritional information, and culinary storytelling may benefit by having their recipes pulled into Food Mood’s suggestions.

This being said, Food Mood is still experimental, and as the result below shows from my testing, it still needs some refinement around ingredient quantities and measurements.

The generative response to make a meal for one, combining the cuisines of Curaçao and Norway… The ingredients list might be off… (Screenshot from Food Mood, September 2025)

A food blog known for creative plant-based recipes might be highlighted when a user asks Food Mood for “a fun weekend dinner that feels indulgent but healthy.” If the content is tagged and structured correctly, it could be surfaced in ways traditional keyword targeting never allowed.

Food Mood shows how search may evolve toward inspiration-driven discovery. Recipe sites and food brands should prepare by enriching their content with detailed metadata that connects recipes to moods, occasions, and dietary preferences.

Talking Tours

Talking Tours is an active audio experiment from Google Arts & Culture that allows users to tour cultural landmarks in Street View.

Instead of passively looking at images, users can listen to narrated, AI-generated stories about what they are exploring.

This has the potential to change how people engage with cultural and travel content. Rather than relying solely on guidebooks or blog posts, users may interact with AI-driven narratives directly inside Google’s ecosystem. It offers an immersive layer that could shift attention away from traditional content publishers.

Screenshot from artsandculture.google.com/experiment/talking-tours/, September 2025

For travel businesses, the opportunity lies in being part of the authoritative content that fuels these AI tours. Travel agencies, tour operators, and cultural organizations that create structured, authentic content may find new visibility if their information is integrated. Without that presence, competitors or third-party providers could dominate the AI-driven storytelling.

A cultural travel company that produces detailed content about European landmarks might benefit from incorporating Talking Tours’ insights during a virtual tour of Rome. Without participation, their competitors may own the conversation.

This also offers would-be travellers the opportunity to explore landmarks and other key locations ahead of travelling, which could influence the comparison and deliberation phases of the decision-making process.

Talking Tours points to a future where immersive, AI-driven experiences shape travel planning. Travel brands should ensure their content is authoritative, structured, and ready to be used in AI-generated narratives.

Learn About

Learn About is an experiment that helps users learn new topics at their own pace using conversational AI. Acting like a digital tutor, it breaks down complex ideas into simple explanations and guides learners to further resources.

Screenshot from learning.google.com/experiments/learn-about/, September 2025

For education providers, this alters how learners find and engage with content. Instead of searching for “best beginner coding course,” a student might ask Learn About to “explain how websites work” and then follow guided prompts.

Learn About uses various YouTube and web results as sources, and from experimentation, it isn’t afraid to show older content and videos (even those with “for 2023” in the video title) if it believes the content and source are strong enough.

Educational publishers and online learning platforms may experience shifts in traffic if “Learn About” becomes a common entry point. Being cited in AI-driven tutoring sessions could become as valuable as traditional SEO discovery. Institutions that provide well-structured, authoritative, and trustworthy content stand to gain.

A site offering structured beginner-friendly coding lessons might be featured in Learn About when a user begins exploring “how to build a website.” If absent, a competitor may be the one shaping the learner’s first impression of the topic.

Learn About underscores the need for clear, structured, and authoritative educational content. Providers should optimize not only for keywords but also for AI-driven educational journeys.

Preparing For AI Experiments In Search

Google’s experimental features like Doppl, Food Mood, Talking Tours, and Learn About reveal how search may evolve from keyword-driven results to AI-guided discovery experiences beyond what we perceive as traditional search.

These experiments may not all become mainstream, but they indicate where search is heading. Businesses that begin preparing now will be better positioned if and when these ideas are rolled out more widely.

Is your organization ready to compete in a world where AI guides the first step of customer discovery?

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Multimodal Search Is Reshaping The Funnel For SEOs And Marketers via @sejournal, @TaylorDanRW

For years, marketers built their strategies around a clear and visible funnel: awareness, consideration, conversion.

It worked well in a web where behaviors were traceable, people clicked links, visited pages, signed up, bought a product, or bounced.

We were able to track almost all of it, and we had attribution models that helped show return on investment (ROI) to specific channels (with varying degrees of accuracy and certainty).

The journey hasn’t disappeared, but it’s harder to detect, and it has become a lot more convoluted.

People are still moving through a decision-making process; they’re just doing it across fragmented platforms, using tools that don’t always leave clear signals behind.

Whether it’s asking ChatGPT, browsing Reddit, scrolling through TikTok, or speaking to a voice assistant, user behavior is fluid, multimodal, and largely invisible to traditional analytics.

We can no longer assume that a user’s next step will be a trackable one.

They might ask an AI model for a summary. They might compare products across 10 different surfaces before ever visiting your site.

They might never fill out a form, but forward the website to a colleague, and they’ll fill out the form as a single session, tracked as “Direct,” having never been on your site before.

That doesn’t mean the funnel is gone; it’s just become almost untrackable.

What The Funnel Actually Is

The traditional marketing funnel breaks down the customer journey into three core stages:

• Top of Funnel (TOFU): Awareness-level content that introduces your brand or product to a broad audience. Think blog posts, social media content, or explainer videos.
• Middle of Funnel (MOFU): Consideration-level content that helps users evaluate options. This includes comparison guides, product demos, and email nurturing sequences.
• Bottom of Funnel (BOFU): Conversion-level content aimed at driving action, like purchase pages, pricing breakdowns, or testimonials.

Marketers used to map content to each of these stages, creating clear pathways for users to follow from curiosity to conversion.

That model still applies, but how users move between these stages is now anything but linear.

What Multimodal Search Really Means

Multimodal search isn’t just about the difference between typing a query, speaking it out loud, or snapping a photo.

It’s about the way users fluidly engage across different platforms and media types to explore, evaluate, and decide.

A single purchase journey might involve:

• Googling a general topic.
• Watching explainer videos on TikTok or YouTube.
• Reading niche discussions on Reddit.
• Browsing listings on Amazon.
• Comparing reviews on third-party blogs.
• Asking follow-up questions to an AI assistant.

Even Amazon itself is leaning into AI-led search with Rufus, its generative shopping assistant. This is multimodal search.

Image from author, August 2025

Google is layering AI Overviews and AI Mode into its core search experience, offering summarized insights and altering the sequence of discovery.

Users no longer click 10 blue links. They skim summaries, compare sources at a glance, and dive deeper only if curiosity is triggered and a user acts on it.

Multi-modal means multi-platform, multi-surface, and multi-behavior.

It requires us to plan for nonlinear journeys, where influence happens in places we don’t control, and impact happens without attribution.

This shift demands a change in how we create and distribute content:

• We must think beyond a single persona or journey and instead design for overlapping intent signals.
• We must publish in formats that match user behavior across channels: text, video, audio, structured data, and conversational prompts.
• We must recognize that old attribution models, based on last click or visible touchpoints, no longer reflect reality.

If we design content around one channel, one format, or one assumed path, we’re missing the majority of how people actually search, explore, and decide.

The challenge now is to understand user intent without seeing every step. To stay present in invisible paths. To meet people in the middle of journeys we can’t fully track.

The funnel still matters. But, reaching people inside it requires a different mindset, one that’s built for anticipation, not just observation and end goal metrics.

Multimodal As The Gateway For The Next Generation

For the next generation of internet users, multimodal isn’t just a feature; it’s the foundation.

Gen Z is growing up with tools that let them search the world visually, conversationally, and socially.

They don’t see these modes as alternatives to traditional search; they see them as default behaviors.

Google’s data reflects this shift. Gen Z (18-24 year olds) is currently the fastest-growing demographic using Google Search.

And among that cohort, 1 in 10 searches now begin with a visual interaction, and using tools like Google Lens or Circle to Search.

Image from author, August 2025

Instead of typing a query, users highlight parts of an image, scan real-world objects, or interact directly with on-screen content.

This visual-first, intent-rich behavior is a window into how the next generation navigates information. It blends curiosity with immediacy – and it bypasses traditional keyword-driven journeys entirely.

Marketers need to understand this shift not as a niche use case, but as a sign of things to come.

If we’re not building content and experiences that match these native behaviors, we risk being invisible in the very spaces where influence now begins.

What This Means For SEOs And Marketers

Speak To The Whole Persona

Personas and audience segmentation still matter, maybe more than ever, but we can’t speak to people at just one stage or in one format.

Mental availability now has to be a core part of any digital marketing strategy.

It’s not about being everywhere for everyone, but about being present across enough moments and modes that your brand is part of the conversation when decisions are being made.

The old way of choosing a format, identifying a single funnel stage, and publishing content to fit is no longer enough.

We need to create for complexity. That means producing content that reaches both the 1% and the 99% of your target persona, ranging from niche, problem-aware research queries to broad, ambient brand mentions in trending content.

Think Beyond The Visible Funnel

Every digital touchpoint is a chance to build familiarity and relevance.

And in a landscape where visibility is often obscured, casting a wider, more thoughtful net across intent types, platforms, and formats is how you maximize your odds of being chosen, even if you never see the full journey play out.

Rethink Distribution And Domain Dependence

Content distribution now plays a critical role in both SEO and broader brand strategy.

We want our messaging to be present wherever users are searching, reading, watching, or asking questions. That means treating our website as one, but not the only, SEO and AI optimization asset.

In my opinion, content and SEO strategies that focus only on the owned domain are limiting their effectiveness.

Search engines and AI models are increasingly drawing context, citations, and understanding from a wide range of sources across the open web.

If your brand only shows up on your own site, you reduce your discoverability, authority, and influence.

To compete in the AI-shaped web, marketers need to distribute content intentionally across partner sites, third-party platforms, social channels, structured formats, and multimedia content ecosystems.

Visibility is earned across surfaces, not confined to a single domain.

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Should I Still Invest In SEO? (Yes, But Not In The Old Way) via @sejournal, @TaylorDanRW

How users are starting to interact with the internet has changed and soon will be unrecognizable from the internet we’ve grown comfortable with.

With Google integrating AI-powered features into Search, and the rise of third-party large language models (LLMs), it’s a different search experience.

Over the past few months, many CMOs I’ve spoken with, as well as business founders, have been asking the same questions around continuing investment in different marketing channels, including continuing investment in SEO.

I was fortunate to attend and speak at Google’s Search Central Live in Bangkok last week, and during the opening keynote, there was one snippet that has stood out for me that goes a long way to answering this question:

Traffic patterns may fluctuate: Long-held traffic patterns are likely to fluctuate, creating new opportunities for all sites. Past success on Search may not guarantee future success.

Should I Still Invest In SEO?

SEO is one of the few marketing channels that compound over time and investment.

Paid campaigns stop the moment you pause spending, but a strong organic program can keep driving traffic, leads, and sales long after it’s been implemented.

Often, it performs better over time, depending on how your competitors react.

That compounding effect is what separates SEO from most other digital investments. Every decent piece of content, every technical fix, every solid backlink adds to a base that grows stronger the more you invest.

SEO isn’t dead. It’s evolving.

That means fast, mobile-first websites, content demonstrating expertise and experience, clean internal links, and a solid structure; content that plays well with AI summaries and result variations, and more than anything, seeing SEO as part of your brand presence, not just a traffic lever.

Why Some Brands Are Pulling Back

There’s a rising anxiety in the air, caused by a number of unknowns and changes in our data, such as the great decoupling we’re witnessing.

Some CMOs are questioning whether SEO and content are still worth the effort.

There are a few reasons for this, namely that the SERP has changed dramatically. AI Overviews and expanded result features push the traditional organic links further down the page.

Some brands see less return from the same level of effort, and the result is frustration and, in some cases, panic.

At the same time, reporting is harder and attribution is messier.

It’s not always easy to show exactly where SEO contributes, especially when its influence spans across discovery, consideration, and conversion, which can make it a target when budgets begin to tighten.

Some teams are also misreading the signals, but in reality (in my opinion), we’re using the wrong measurement techniques, and measuring the new Search ecosystem by the standards of the old.

They assume that if fewer people click, fewer people are engaging, but visibility itself is valuable. Just because someone doesn’t click today doesn’t mean they won’t take action tomorrow.

In my opinion, pulling back now is the wrong move. Organic search remains the biggest visibility lever on the web, and when you stop investing in content, you’re choosing to disappear.

In an AI-first search world, visibility starts before the click.

The brands that stay active will be the ones users see and remember. This is no longer just about blue links and last click, it’s about brand recognition and building visibility across the multiple faces of the modern Search ecosystem.

Content’s Evolving Role In SEO

Top-of-funnel traffic might not be what it once was, but it’s still powerful.

Being visible in an AI Overview or response to a generic query still influences perception. It can lead to brand searches, direct visits, or conversions later down the line.

I don’t think the metric is how many people see your result. It’s how many go on to take meaningful action. SEO now runs across the funnel, and across formats. It’s not just 10 links on a page anymore.

Content has to work harder. A single piece might need to satisfy different intents, answer multiple questions, or show up in several places, from featured snippets, videos, product results, or AI-generated outputs.

SEO And AI

AI-powered search is splitting discovery across more surfaces. It’s not just Google anymore. It’s ChatGPT, Perplexity, Gemini, and others.

To stay visible in that world, you still need content. In fact, content is the price of admission. If you’re not producing it, you’re not part of the conversation.

SEO now includes shaping how AI systems understand your brand. If you’re not contributing to the information ecosystem, someone else is deciding your narrative.

Strategic SEO Investment

Smart SEO means:

• Durable content that keeps working.
• Authority-building through links, mentions, and structure.
• A balance between fast wins and long-term gains.
• Understanding and answering layered queries that do more than just inform – they convert.

For bigger businesses with multiple brands or sites, there’s an extra edge. Google and AI models understand entity relationships.

Coordinated content can strengthen authority across brands, especially in a world where AI pulls from consensus.

So, Should You Still Invest In SEO?

If you’re asking whether SEO still works, the answer is yes, but not in the old way.

It’s not just a traffic source; it’s becoming your visibility layer for both traditional Search, Google’s AI features, and many LLMs.

It’s fast becoming a lever for reputation and brand visibility, and a strategic asset as well as a marketing channel.

The real question is whether you can afford not to invest.

Paid traffic dries up the second you stop paying. Organic builds on itself. It’s one of the few channels that gives you more tomorrow for what you do today.

As AI changes how search looks and works, SEO stays relevant because it supports every layer of digital presence. It creates a base you own, not rent.

The brands that win next are the ones that stay active. The ones that keep showing up, even when the rules shift.

Content isn’t just about clicks. It’s about influence. It’s about being there when people are asking the big questions, wherever they’re asking them.

In a shifting landscape, SEO gives you something stable. A long-term play that doesn’t vanish when your budget runs out. For businesses planning beyond the quarter, it’s still one of the smartest bets you can make.

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