SEO Has Been Tactical For 20 Years. GenAI Forces The Strategy Question via @sejournal, @DuaneForrester

For as long as I’ve been in this industry, there’s been debate about whether SEO is strategic or tactical. Most SEOs would like to believe their work is strategic. Many executives see it as tactical. The truth is somewhere in between, and the arrival of generative AI is forcing a new level of clarity.

This matters because “strategy” and “tactics” are not synonyms. In business, strategy is the plan. Tactics are the moves. Confusing the two doesn’t just muddy language. It leads to wasted resources, stalled initiatives, and misplaced expectations for what SEO can and cannot deliver.

Defining Strategy Vs. Tactics

Image credit: Duane Forrester

Business literature has been clear on this for decades, and voices like Porter, Mintzberg, and Drucker shaped how leaders everywhere talk about strategy. Their framing applies directly when we examine SEO’s role today.

Michael Porter is widely recognized as the father of modern competitive strategy. A professor at Harvard Business School, he framed strategy as “choosing to run a different race, because it’s the one you’ve set yourself up to win.” His book “Competitive Strategy” remains one of the foundational texts in business thinking (his book on Amazon – not an affiliate link).

Henry Mintzberg is one of the most cited academics in management and organizational theory. He is famous for noting, “Strategy is not the consequence of planning, but the opposite: its starting point.” He also developed the 5 Ps framework — Plan, Ploy, Pattern, Position, and Perspective — which captures strategy as both deliberate and emergent (Mintzberg’s “The Strategy Concept I: Five Ps for Strategy”).

Peter Drucker is often called the father of modern management. His work shaped how companies think about leadership and decision-making. He emphasized that “the task of leadership is to create an alignment of strengths so strong that it makes the system’s weaknesses irrelevant.” His book “The Practice of Management” is considered a landmark in defining management’s role in aligning strategy with organizational outcomes (Drucker biography at the Drucker Institute).

Tactics, by contrast, are the practical steps. They’re what frontline teams execute, usually with short-term horizons. A strategy might be to compete on customer trust instead of low prices. The tactics are testimonial campaigns, return policies, and training staff to deliver exceptional service.

Other business functions get this distinction. In sales, strategy is deciding to prioritize enterprise accounts. Tactics are outreach sequences and demo scripts. In PR, strategy is positioning the brand as an industry leader. Tactics are pitching journalists and writing press releases. SEO is no different.

The confusion comes because SEO often has to “do it all.” Practitioners are expected to identify opportunities, set priorities, and then execute the work. That’s where the labels blur.

Strategy And Tactics In Traditional SEO

Looking back at the history of SEO makes the divide easier to see.

• Early 2000s, PageRank era: Strategy was simple. Invest in being discoverable on Google. Tactics included link building, directory submissions, and keyword-stuffed pages. Companies that treated SEO purely tactically often succeeded short-term but collapsed when penalties arrived. The strategy was clear. Pursue visibility on Google, while the tactics were the link farms, keyword stuffing, and directory submissions that executed it.
• 2010–2015, Panda and Penguin: Google cracked down on low-quality content and manipulative links. Strategy shifted to “quality and sustainability.” Tactics became pruning thin content, disavowing bad links, and investing in editorial teams. Content farms like Demand Media scaled on tactics, but lacked sustainable strategy, and they were decimated by Panda. Here again, leadership set the strategic shift toward quality, and SEO carried it out through content pruning and link cleanup.
• 2015–2020, Mobile and Core Web Vitals: Strategy was “meet users where they are.” They were on mobile and wanted fast experiences. Tactics were responsive design, structured data, and site speed audits. Companies that made this strategic shift early (e.g., news outlets investing in AMP) gained advantage. The strategic goal was serving users where they were, while SEO implemented the tactical fixes that delivered on it.
• 2020s, BERT and passage indexing: Strategy tilted toward semantic relevance, competing not just on keywords but on meaning and intent. Tactics were writing for topics, structuring content for passage-level retrieval, and emphasizing context. Strategy tilted toward meaning; tactics followed in the form of topic clusters and passage-level optimization.

At every stage, leadership set the strategy (“we need growth from search”), and SEO executed the tactics. Advanced SEOs sometimes influenced strategy by warning about risks or opportunities, but the bulk of work remained tactical.

Strategy And Tactics In GenAI Optimization

Generative AI reshapes the landscape. Instead of 10 blue links, users now get synthesized answers. That changes both the strategic questions and the tactical execution.

Strategic choices now include:

• Deciding whether to compete for visibility across multiple AI engines (ChatGPT, Perplexity, Gemini, Claude, etc.).
• Prioritizing authority signals so your brand is cited in machine answers.
• Choosing where to allocate budget: competing for evergreen visibility in broad topics, or dominating narrow niches where AI coverage is weaker.
• Determining how much to invest in retrievability testing and monitoring as an organizational function.

Tactical execution now includes:

• Structuring content into retrievable chunks sized for vector search.
• Running retrieval tests across platforms to measure exposure.
• Optimizing semantic density so each chunk is information-rich and self-contained.
• Adding schema and structured data to clarify entities and facts.
• Tracking machine-validated authority by measuring whether your content is surfaced or cited in AI responses.
• Query fan-out work to determine opportunities and identify semantic overlap.

These tactics look new, but they build directly on the foundation of traditional SEO. Schema is simply structured markup, refined. Semantic density is the next evolution of topical relevance. Retrieval tests are the modern equivalent of checking indexation. GenAI optimization doesn’t replace SEO; it evolves from it.

GenAI Optimization: Is It A Strategy Or Tactic?

The sudden surge of interest in “GenAI optimization” is a perfect case study in this strategy-versus-tactics debate.

Everyone is talking about chunking, embeddings, and retrievability as if they are strategy. They aren’t. They’re tactics. And treating tactics as strategy is a classic oversimplification, something the industry has been guilty of for decades.

• At the strategic level: Businesses decide that GenAI visibility is essential. They commit budget to becoming retrievable and authoritative across AI systems. They set goals to be cited in machine answers for their core vertical.
• At the tactical level: Teams restructure content into chunks, add schema, run retrieval probes in ChatGPT or Perplexity, and measure citation frequency.

Both layers are needed. The risk comes when companies mistake tactical execution for strategy.

The Cost Of Misalignment

When strategy and tactics are misaligned, businesses lose, and the losses are measurable.

• Missed opportunities: If leadership hasn’t set a strategy for GenAI visibility, tactical work is scattershot. Teams optimize content but don’t know which queries, topics, or surfaces matter. Competitors with clearer strategies win the ground.
• Lost revenue: Without strategy, companies may secure citations in AI answers that don’t align with customer value. The result is visibility without conversion.
• Wasted budgets: Chasing every GenAI trend without a clear North Star leads to investment in tools and audits that deliver no meaningful ROI.
• Eroded trust: When executives believe they’ve funded a strategy but only see tactical outputs, confidence in SEO teams drops. Leadership expected market impact and the team only delivered structural updates.

The lesson is blunt: Businesses don’t usually fail because tactics are poorly executed. They usually fail because tactics aren’t anchored in strategy.

Why SEO Has Been Seen As Tactical

For two decades, SEO has been defined by tactical output. Executives set the strategy (“We need organic growth”), and SEO was tasked with delivering through audits, fixes, optimizations, and publishing.

This framing wasn’t wrong as it reflected the organizational structure. Strategy was set higher up; SEOs carried it out. That’s why SEO often struggled to win budget or a seat in strategic planning meetings. It was seen as execution.

The AI-Driven Shift

Generative AI changes that equation. Machines are absorbing tactical SEO tasks. Today, AI tools can generate meta descriptions, suggest keywords, build internal linking recommendations, even create structured schema markup. Some platforms simulate retrieval patterns directly. What once required specialized SEO execution is increasingly automated.

That doesn’t eliminate SEO. It elevates it. If tactical execution is becoming commoditized, the value shifts to strategy.

This mirrors Microsoft’s research on AI’s occupational impacts, which distinguishes between user goals (strategic intent) and AI actions (tactical execution). Humans set the “why.” AI delivers the “how.”

For SEO, the same shift is underway. The tactical layer is being automated. The strategic opportunity is to lead on visibility, authority, and trust in AI-driven ecosystems.

Drawing The Line With Examples

• Traditional SEO in financial services: The strategy is to dominate “retirement planning for millennials.” The tactics include create calculators, publish evergreen guides, optimize metadata, and build relevant backlinks.
• GenAI optimization in sustainable investing: The strategy is to ensure the brand is a trusted citation in AI answers on ESG funds. The tactics include run retrieval checks in Perplexity, embed structured citations, optimize chunks for semantic clarity, and measure citation frequency in ChatGPT and Gemini.

One sets the direction. The other executes the playbook.

Why This Feels Touchy

Many SEOs call their work strategic because they connect content, technical architecture, and authority signals into a broader picture. In many organizations, they’re the only ones framing visibility at all, but that doesn’t make every action strategic.

That deserves credit. But precision matters. Running an audit is not strategy. Updating a robots.txt file is not strategy. These are tactical actions. If we blur the line, we diminish our influence at precisely the moment AI is eroding the value of tactics.

Where The Balance Lies

So, is SEO strategic or tactical? The honest answer is both, but not equally.

• Historically, SEO has been tactical.
• Today, SEO carries strategic implications, especially as AI reshapes discovery.
• The opportunity is to make the leap: from executing optimizations to shaping how organizations appear in machine-driven answers.

The balance is this: Tactics still matter. You can’t ignore schema, chunking, or retrieval testing. But the differentiator is strategy: deciding which battles to fight, which surfaces to win, and how to align SEO with the company’s long-term positioning.

Why This Matters For SEOs

This isn’t a semantic debate. It’s about influence and survival.

• If SEO is seen as tactical, it’s underfunded, siloed, and brought in too late.
• If SEO is seen as strategic, it gets budget, resources, and a seat in the boardroom.

The GenAI shift creates a once-in-a-generation opening for SEOs to redefine their value. As AI absorbs more tactical execution, the real opportunity is for SEOs to align with company-level strategy and expand their scope into visibility, trust, and authority. Those who recognize the difference between strategy and tactics will step into leadership. Those who stay focused only on tactical execution risk being automated out.

Closing Thought

For 20 years, SEO has been tactical excellence in service of growth. With GenAI, the tactical layer is shifting to machines. That makes strategy the defining frontier.

Not because SEOs suddenly became strategists, but because the environment demands it. The question now is: will SEOs step into that role, or will someone else claim it?

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Vitalii Vodolazskyi/Shutterstock

What To Do When the Click Disappears: Surviving SEO In The AI-Driven SERP via @sejournal, @AdamHeitzman

You may have noticed your organic traffic looking different lately. Rankings fluctuate wildly, your content appears in AI summaries one week and vanishes the next, and users are increasingly getting their answers without ever visiting your website.

When 58.5% of searches end without a click, that carefully optimized content you spent weeks perfecting might be feeding AI answers instead of driving traffic to your site.

We’re witnessing the biggest shift in search since Google’s early days. Traditional SEO tactics aren’t enough anymore.

You need a strategy that works when AI systems become the middleman between your content and your audience.

The New Search Reality: AI Is Eating Your Clicks

Let’s be honest about what’s happening.

Google’s AI Overviews now appear for over 11% of all searches according to BrightEdge research, pulling information from multiple sources to create comprehensive answers above your organic results. Users get what they need without clicking through.

But, it’s not just Google. Perplexity processes over 780 million searches monthly, while ChatGPT’s browsing feature handles complex queries that users used to need multiple website visits to answer.

Your Content Is Working, Just Not How You Expected

Here’s what’s particularly frustrating: Your content is often powering these AI responses, but you’re not getting credit or traffic for it.

Search for [email automation] on Google and you’ll see a comprehensive AI Overview that defines the concept, explains how it works in four detailed steps, lists benefits, provides examples, and even mentions specific tools like ActiveCampaign and Mailchimp.

This response synthesizes information from multiple sources into one complete answer that eliminates the need to visit any individual website.

The user gets a definition, step-by-step process, benefits, examples, and tool recommendations all in one place.

Meanwhile, the original content creators who researched and wrote about email automation triggers, personalization strategies, and platform comparisons see their expertise repackaged without receiving the traffic they would have earned from traditional search results.

Screenshot from search for [email automation], Google, July 2025

This is the new normal. Voice search and conversational AI are training users to expect complete answers, not blue links to explore.

Zero-click searches aren’t killing SEO; they’re evolving it. Your content needs to work harder in this new environment.

What Marketers Need To Rethink

Forget everything you know about traditional SEO success metrics. The game has fundamentally changed.

Shift Your Focus: From Rankings To Mentions

That coveted No. 1 ranking? While still valuable, it’s becoming less reliable for driving traffic when AI systems deliver answers directly to users.

Your content increasingly competes to be cited by AI alongside traditional ranking factors.

Rankings still matter, especially for commercial queries where users want to browse options. But, for informational searches where users seek quick answers, your content’s value now extends beyond its position in organic results.

Being featured in an AI Overview from position No. 7 can deliver more brand exposure than ranking No. 3 without AI inclusion.

Think about it this way: When someone asks ChatGPT or Google AI Mode about your industry, does your brand get mentioned? That’s your new battleground.

Your New Success Metrics

Instead of obsessing over click-through rates, you need to start tracking metrics that capture AI influence on your brand:

• Brand mentions in AI responses across platforms tell you whether your content is being cited and referenced.
• Branded search volume spikes often follow AI feature appearances.
• Conversion assists where organic search was part of the user’s journey but not the final touchpoint.
• Customer surveys asking, “How did you hear about us?” reveal AI influence that analytics can’t capture.

I’ve seen clients with flat traffic numbers but 200% increases in brand mentions in AI responses. That’s invisible growth that traditional analytics miss entirely.

Practical Strategies That Work

Here’s how to adapt your SEO approach for AI-powered search. These are strategies I’ve tested with clients across different industries.

Make Your Content AI-Friendly

The most important shift you can make is structuring your content for AI comprehension.

Place your main answer within the first one to two sentences of any piece of content. Think of it like writing a news article where the lead paragraph contains all the crucial information.

If someone asks, “What are the benefits of meditation?” your opening should be, “Meditation reduces stress, improves focus, and enhances emotional well-being through regular practice.” Then expand with details, examples, and supporting evidence.

Look at this great example from NerdWallet:

Screenshot from NerdWallet, July 2025

This approach serves both human readers who want quick answers and AI systems that prioritize clear, immediate responses. When Google’s AI Overview or ChatGPT pulls from your content, that opening statement becomes your brand’s voice in the answer.

I’ve seen this strategy increase AI citation rates by 40% for clients who consistently implement it.

Key formatting strategies that work:

• Structured formats: Transform dense paragraphs into FAQs, numbered lists, and tables that AI can easily parse.
• Schema markup: Use schema.org vocabulary to help large language models (LLMs) understand relationships between information on your site.
• Clear headings: Create content hierarchy with H2 and H3 headings that AI can follow.

A well-structured FAQ section doesn’t just help users. It becomes a goldmine for AI systems looking for clear question-answer pairs.

Consider transforming complex pricing information into tables rather than burying details in lengthy paragraphs.

Build Citation-Worthy Authority

Creating content that AI systems want to reference requires a fundamental shift from aggregating existing information to generating original insights.

Publish studies, proprietary data, and exclusive interviews that can only come from your organization.

LLMs prioritize original sources over aggregated information, making your research significantly more likely to be cited and attributed.

Instead of stating facts directly, frame them as insights from your organization. “According to our research at [Company Name]” or “Based on our analysis of 10,000 customer surveys” signals to AI systems that the information comes from a specific, credible source.

This technique helps ensure that when LLMs pull information from your content, they’re more likely to include your brand name in the response.

Building topical authority through comprehensive content clusters is more important than ever. Create interconnected content that thoroughly covers your expertise area from multiple angles.

If you’re in the gardening space, don’t just write one article about composting. Create a comprehensive resource covering composting basics, troubleshooting common problems, seasonal considerations, and advanced techniques, then link these pieces together strategically.

This clustering approach works because LLMs assess credibility partly based on depth and breadth of coverage.

Sites that demonstrate comprehensive knowledge on topics are more likely to be seen as authoritative sources worth citing.

I’ve watched brands jump from occasional mentions to consistent AI citations by implementing this strategy over six to 12 months.

Diversify Beyond Traditional Search

Don’t put all your eggs in the Google basket. AI systems pull information from diverse sources, and expanding your content distribution increases your chances of being included in LLM training data and responses.

Recent research from Ahrefs analyzing 78.6 million AI responses across Google AI Overviews, ChatGPT, and Perplexity reveals which platforms get cited most frequently.

The data shows clear patterns in what each AI system prefers to reference.

Platforms worth prioritizing based on AI citation data:

• YouTube: Dominates Perplexity citations (16.1% mention share) and ranks high in AI Overviews (9.5%), making video content crucial for AI visibility.
• Reddit: Heavily favored by Google AI Overviews (7.4% mention share) but absent from ChatGPT and Perplexity’s top citations.
• News and industry publications: ChatGPT shows a strong preference for news outlets like Reuters and Apple News, making media coverage valuable.
• Wikipedia: Leads citations across all three platforms, emphasizing the importance of having your brand or expertise documented on authoritative reference sites.

The research reveals that different AI systems have distinct preferences.

Google’s AI Overviews favor user-generated content from Reddit and Quora, while ChatGPT prioritizes news sources and authoritative publications.

Perplexity shows the strongest preference for YouTube content alongside Wikipedia.

Each platform has its own content style and audience, so adapt your messaging accordingly.

A LinkedIn post about industry trends might become a source for business-related AI responses, while a YouTube video explanation could be referenced for educational queries.

The key is maintaining consistent expertise and messaging across all channels.

Testing your content directly in different AI platforms gives you immediate feedback on how it’s being interpreted and used.

Ask ChatGPT questions related to your expertise and see if your content appears in the responses. Query Perplexity about industry topics you’ve covered.

This direct testing helps you understand how different AI systems process and present your information, allowing you to refine your approach based on real results.

Measuring Success In A Post-Click World

Traditional metrics aren’t telling the whole story anymore, and honestly, this is where most marketers struggle with the transition to AI-era SEO.

You’re used to clear, quantifiable metrics like organic traffic and click-through rates. Now you need to track influence that often happens without any direct interaction with your website.

Track AI Visibility Across Platforms

Start by monitoring featured snippets and AI Overview inclusions. These placements often indicate that AI systems are pulling from your content, even if they don’t generate the clicks you’re used to seeing.

Set up alerts for when your content gets featured because these appearances frequently correlate with increases in branded search volume and direct traffic.

Check if your brand appears when users ask AI tools about your industry. Search for your company name in ChatGPT, Perplexity, and Google’s AI Overview to see how you’re being represented.

You might discover that your brand is being mentioned in contexts you didn’t expect, giving you insights into how AI systems perceive your authority.

Social media monitoring becomes more important in this landscape because people often discuss insights they learned from AI summaries.

Set up tracking for mentions where people reference concepts or data points that originally came from your content, even if they don’t directly cite your brand.

These conversations indicate that your content is influencing discussions, even when traditional attribution models miss the connection.

Attribution Modeling For Invisible Influence

The challenge with zero-click searches is that they force you to rethink how you measure content success.

A user might read your advice in an AI summary today, then visit your site directly next week after remembering your brand name. Traditional last-click attribution completely misses this connection, making your SEO efforts appear less valuable than they actually are.

Implement first-touch attribution models that credit SEO for starting customer journeys, even when other channels complete the conversion.

Survey your new customers about how they first discovered your brand, and you’ll often find they mention seeing your content in search results or AI responses weeks before converting. This qualitative data fills in gaps that analytics can’t capture.

Look for patterns where direct traffic increases after your content gets featured in AI responses. Create custom UTM parameters for content that frequently appears in AI summaries.

While you can’t track every citation, you can identify trends in how AI-discovered content influences broader marketing performance.

Watch for increases in newsletter signups, demo requests, or branded searches following AI feature appearances.

Google Analytics 4’s attribution modeling can help you understand these multitouch journeys better than previous versions. Configure it to show conversion assists where organic search was part of the user’s path but not the final touchpoint.

This reveals the true value of your SEO efforts in an environment where direct attribution becomes increasingly difficult.

Tools And Techniques For Modern Measurement

SparkToro helps you understand where your audience discovers content and which sources they trust.

Use it to identify if your brand is being mentioned in the same contexts as industry leaders, indicating you’re gaining mindshare even without direct clicks.

This competitive intelligence reveals whether your AI strategy is working compared to others in your space.

Beyond traditional tools, create a systematic monitoring approach using multiple AI platforms.

Set up monthly checks to see if your citation frequency is increasing and which topics generate the most AI references.

Document examples of how your content gets referenced and summarized to understand what formats work best.

Remember that influence in AI responses often correlates with long-term brand growth, even if immediate traffic metrics look flat.

While comprehensive research on AI citation impact is still emerging, the pattern mirrors what we’ve seen with other “zero-click” features like featured snippets, brand exposure through authoritative citations can drive awareness and consideration that results in direct searches and conversions over time.

The key is connecting these invisible influences to eventual business outcomes.

Building Long-Term Resilience In An AI-First World

The brands that thrive in this new landscape will not just adapt to current changes.

They will anticipate what comes next and build systems that can weather the unprecedented volatility that AI-powered search brings.

Prepare For AI Volatility

Traditional core Google algorithm updates happen a few times per year and usually follow predictable patterns.

With each model update, LLMs can change their behavior, creating unprecedented volatility in search visibility that most SEO professionals haven’t experienced before.

Your content might appear in ChatGPT responses one week and disappear the next. This isn’t a bug or a penalty. It’s how LLMs work.

They constantly learn and adjust their understanding of what constitutes authoritative information based on new training data and updated models.

Instead of panicking over daily fluctuations, track broader patterns in brand mentions, branded search volume, and conversion trends.

These metrics provide more stable indicators of your content’s impact than individual AI citations, which can vary significantly based on model updates and algorithmic adjustments.

Your brand needs to be what I call “retypeable,” the kind of name people remember and search for when they’re ready to take action.

When users encounter your brand in an AI summary, they should immediately associate it with your core value proposition and remember it later when they’re ready to engage.

Build Flexible Systems

Set up processes to review and refresh your most important pages quarterly.

LLMs prioritize current information more heavily than traditional search engines, so maintaining content freshness becomes critical for sustained AI visibility.

Develop relationships with other authoritative sources in your industry through collaborations, partnerships, and cross-references.

The more your brand appears in connection with recognized authorities, the stronger your credibility signals become for AI systems.

These relationships create natural mentions across different content formats and platforms that extend beyond what you can control directly.

The Future Of SEO Is About Influence, Not Clicks

The shift to AI-powered search is changing not just how people find information but also how brands build authority and trust.

Companies that recognize this early and adapt their strategies accordingly will own the conversation in their industries, while others struggle to understand why their traditional SEO efforts aren’t delivering the same results.

Your content is still working. It’s influencing decisions, building brand awareness, and driving conversions.

You just need new ways to measure and optimize for its impact in an environment where visibility doesn’t always equal clicks, but influence still equals business growth.

More Resources:

Featured Image: LariBat/Shutterstock

Closing The Digital Performance Gap: Why The C-Suite Must Take Web Effectiveness Seriously via @sejournal, @billhunt

Over the years, I’ve worked with numerous companies that engaged me to create world-class Search organizations and win the global search game, only to block the majority of the initiatives required to achieve that goal. This disconnect often stems from how the C-suite perceives its website.

In too many boardrooms, the site is still seen as a digital brochure and an expense managed by marketing, with limited scrutiny or strategic oversight. Yet, that same site touches nearly every phase of the customer journey, investor perception, partner evaluation, and talent acquisition.

In my previous article, “Why Your SEO Isn’t Working – And It’s Not The Team’s Fault,” I detailed how structural issues, not underperforming teams, were usually the root cause of poor SEO outcomes. In “The New Role Of SEO In The Age Of AI,” I introduced the shift from traditional optimization toward visibility in AI-driven systems.

This article brings those ideas together under a single call to action: It’s time for executive leadership to own web performance as a measurable, managed business function.

What Is The Digital Performance Gap?

The Digital Performance Gap is the measurable distance between your online potential and actual business outcomes. Most companies are leaking performance through misaligned teams, disconnected key performance indicators (KPIs), outdated platforms, or siloed operations.

Symptoms include:

• Underwhelming organic traffic and conversions.
• Disconnected websites across departments or geographies.
• Content that ranks but doesn’t convert (or worse, can’t even be found).
• Slow responsiveness to AI shifts and platform changes.
• Tools and vendors operating without return on investment (ROI) oversight.

In short: You’re paying for a Ferrari and driving it like a lawnmower.

From Pit Crew To Performance System: A Better Analogy

Imagine you’re the owner of an F1 racing team. You’ve got the budget, the ambition, and a roster of great people – from engineers to mechanics to a world-class driver.

However, the engine design was handled by a team that never consulted with the race strategist. Your telemetry data doesn’t reach the pit wall. The car is fast in theory, but coordination is poor, and outcomes are inconsistent.

Sound familiar?

That’s how many enterprise websites operate. Everyone is working hard in their silos. But without integrated planning, shared goals, or clear leadership, the system can’t perform at its full potential.

Web effectiveness isn’t just about the “driver” (e.g., SEO or content teams)—it’s about the entire vehicle and how the organization supports it. And the C-suite? They’re the race directors. When the director doesn’t orchestrate the team, the whole system suffers.

In elite racing, the pit crew doesn’t just change tires. They analyze data, forecast risks, and adapt in real time. Their split-second coordination with the driver wins races. That’s what a web performance system should look like–fully integrated, real-time, and strategically directed.

But instead of this synergy, most digital organizations resemble a collection of vendors and internal teams using different playbooks, judged by different KPIs, and waiting for executive direction that never comes.

You can’t win the race if the engine team is optimizing for safety, the strategist is optimizing for top speed, and the pit crew is trying to meet tire budget KPIs. That’s not cross-functional excellence, it’s cross-functional chaos.

Web Effectiveness Is A Business Metric

Web Effectiveness is the degree to which your digital presence delivers against real business goals.

It spans:

• Findability (SEO, search, AI discoverability).
• Usability (conversion, performance, accessibility).
• Relevance (structured content that solves user needs).
• Integration (connected to customer relationship management or CRM, data layers, product feeds).

This isn’t marketing fluff. It’s operational excellence.

When no one owns it, everyone loses.

• IT may control infrastructure.
• Marketing manages messaging.
• Sales owns conversion.
• Legal redlines half the useful copy.

But no one owns the outcome. That’s a leadership failure.

The High Cost Of No Ownership

When the C-suite doesn’t take web performance seriously, the costs compound:

• Visibility declines. You’re outranked by competitors who understand AI’s new rules.
• Opportunity evaporates. Valuable search terms go unanswered – or worse, answered by the platforms themselves.
• Budgets get wasted. You pay for tools, agencies, and tech that aren’t integrated or even used.
• Your story gets told by others. Generative engines summarize what they find. If your content isn’t structured or visible, you’re not even in the conversation.

Even companies that only exist online often fail to fully leverage the very platform that drives their value.

What Executive Ownership Looks Like

Executive ownership doesn’t mean micromanaging metadata – it means ensuring that:

• Web outcomes are tied to business KPIs.
• Budgeting reflects strategic priority, not departmental silos.
• SEO, UX, content, and dev teams are operating under a unified model.
• Vendor evaluations include contribution to visibility and performance.
• Someone is accountable for closing the performance gap.

Consider creating a Web Effectiveness Center of Excellence or appointing a Digital Effectiveness Officer to champion this mandate.

A Framework For Closing The Gap

To transition from fragmented efforts to strategic impact, organizations require a shared operating model. Here’s a high-level Web Effectiveness Framework:

1. Governance: Who owns what? Are responsibilities clear?
2. Visibility: Can search engines and AI systems discover, interpret, and cite your content?
3. Experience: Are you delivering what users need – on every device, in every format?
4. Optimization: Are you using the platforms, features, and data you already pay for?
5. Measurement: Are you tracking impact, not just traffic?

This framework can be scaled across divisions, regions, and lines of business. The key is treating your site not as a brochure, but as your most valuable digital asset.

Final Thought: Time To Step In

Closing the Digital Performance Gap starts with a mindset shift: from cost center to growth platform. From tactical ownership to strategic leadership.

Today’s website is no longer just a reflection of your brand—it is your brand. It’s where customers decide to trust you, where partners evaluate your credibility, and where investors form first impressions. Yet far too often, this central asset is owned by no one, governed by outdated workflows, and limited by KPIs that belong to another era.

Let’s be clear: digital excellence doesn’t happen by accident. It’s the result of intentional alignment between leadership, teams, and technology. And that alignment starts with the C-suite.

CMOs must champion performance and not just promotion. CTOs must prioritize enablement and not just uptime. CEOs must encourage cross-functional alignment, efficiency, speed, agility, and clarity to ensure optimal performance.

Web effectiveness should no longer be framed as a project, initiative, or marketing tactic. It’s a performance system. A business function. A shared responsibility. And if you don’t have someone responsible for web performance at the leadership level, it’s time to create that role. A Digital Effectiveness Officer, a Center of Excellence, or, at a minimum, a cross-functional ownership council that brings visibility, accountability, and forward momentum.

Because here’s the truth: If you don’t own your website’s performance, someone else will define your digital reputation—and capture your audience. Bring web effectiveness into the boardroom. Align your teams. Close the gap.

More Resources:

Featured Image: SvetaZi/Shutterstock

More Fan-Out SEO Tactics

Large language models such as Google’s AI Mode, ChatGPT, and Perplexity anticipate likely follow-ups to an initial query and provide the combined info in a single answer. Google called it “fan out” results when announcing the practice in a March 2025 blog post. Hence we now use the term for all generative AI platforms.

In “SEO for Google’s AI Fan-Out Results,” I addressed the basics. The platforms typically explain their fan-out reasoning, or we can deduce it from the response. For example, answers often include safety precautions, selection criteria, and additional sources for informed decisions.

Search optimizers increasingly target fan-out queries for on-page optimization tactics, such as publishing an FAQ section for likely fan-out answers to increase the site’s chances of being cited.

It’s a valid strategy, but there is much more to fan-out optimization.

Position Products

Fan-out responses are unpredictable. Gemini can fan out differently for the same prompt. Yet all fan-out results identify areas to target.

For a prompt of “best organic skincare brands to try,” Gemini would likely fan out to include prominent brands, “brands for sensitive Skin,” most affordable brands, and “unique specialties,” such as “plant-powered formulas,” cruelty-free brands, and brands that use natural ingredients for makeup colors.

Prompting Gemini again with the same query could produce fan-out results for official certifications for organic products. Another might include user ratings and reviews.

Collectively, all fan-out angles can help position products.

Target Sources

Generative AI platforms vary in topic expertise. For example, prompting “best running shoes” in ChatGPT typically includes fan-out results from Runner’s World, owing to its thorough comparisons and lists for runner-related products.

Knowing these oft-cited publications can impact merchants’ product positioning. Prompt multiple platforms, such as ChatGPT, Perplexity, and Gemini, and note the citations.

Third-party tools can help by running the prompts and generating citation reports. For example, Otterly.ai creates a consolidated report of “most cited domains” based on your tracked prompts. The report shows results from ChatGPT, Perplexity, Microsoft Copilot, and Google’s AI Overviews, revealing the overlapping citations.

From there, merchants can solicit those editors and writers on social media and elsewhere to explore visibility tactics such as contributing a column or becoming an editorial source.

Otterly.ai’s report lists the most cited URLs on leading large language models for a given prompt. Click image to enlarge.

Reveal Journeys

Shoppers start buying journeys differently. Some search niche terms, while others seek solutions to specific needs. All require unique landing pages and content to engage with a brand.

Knowing potential fan-out questions can refine customer acquisition. Start with a Gemini prompt, for example:

What fan-out queries does Gemini or AI Mode use for “best organic skincare brands to try”?

In my testing, Gemini responded with options:

• What are the benefits of organic skincare? You can start with different well-known pain points of non-organic skincare to attract people researching those to your brand, for example: What are parabens? What skincare ingredients can cause breakouts? What ingredients should I avoid for sensitive skin? Do common skincare preservatives cause allergies?

• What is the difference between “natural” and “organic” skincare? (You can target audiences researching “natural” skincare by explaining how organic skincare may be what they are looking for)

• What common ingredients in non-organic skincare should be avoided?

Addressing these questions on-site can drive citations in fan-out responses and thus attract new prospects, such as those unaware of an organic option.

ChatGPT Vs. Google At Every Stage Of The User Journey via @sejournal, @Kevin_Indig

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More data shows ChatGPT isn’t taking market share away from Google.

Instead, it’s expanding the range of use cases and blurring the line between searching for information and performing tasks.

I looked at Similarweb data to understand how this affects four different stages of the user journey across Google and ChatGPT:

1. Usage.
2. Behavior.
3. Outbound clicks.
4. Converting.

What I found is that ChatGPT adoption is, essentially, a 400,000-pound locomotive barreling down the tracks with no intention of stopping anytime soon.

User conversations within ChatGPT are rich in context, which leads to higher conversion rates when intent shifts from information seeking or generating to buying.

Lastly, and also most notably for SEOs and growth marketers, ChatGPT is sending a lot more users out to the web.

Of course, all of these stats are still small in comparison to Google.

However, no effort from Google has been able to slow the momentum of ChatGPT’s runaway train. About the data I used in this analysis:

• Data source: Similarweb (shoutout to Sam Sheridan).
• Time period examined: July 2024 – June 2025 (last 12 months) vs. July 2023 – June 2024 (previous 12 months).
• I also examined U.S. vs. UK user behavior.

Image Credit Kevin Indig

People are rushing to ChatGPT.

Over the last 12 months in the U.S., ChatGPT visits grew from 3.5 to 6.8 billion visits (+94%).

In the UK, it was even faster: 131% YoY, from 868 million to 2 billion.

Over the same time span, Google growth stagnated. Here’s what the data showed:

• U.S. stagnation: -0.85% (196 vs. 194 billion).
• UK stagnation: -0.22% (35.56 vs. 35.49 billion).

Image Credit: Kevin Indig

To put it into perspective: Google had almost 200 billion visits in the U.S. over the last 12 months, compared to ChatGPT’s 6.8 billion.

So, ChatGPT has a mere 3.4% of Google’s total visits.

However, if growth rates hold steady, in theory, ChatGPT could hit Google’s volume in the next five years.

My hypothesis: It’s almost guaranteed that ChatGPT won’t hit Google’s visit volume because there are too many moving parts (energy/chip limitations, training data, quality improvements, regulation, etc.).

But consider that Google has declined by -0.85% (~2 billion visits) year-over-year, and you can see where this is going.

Visits can only tell you so much.

Recent data from Semrush and Profound suggests that one-third to two-thirds of user intent when interacting with AI chatbots is generative, meaning users use ChatGPT to do and less to search ^[1, 2].

Leaked chats from ChatGPT and other AI chatbots confirm the aggregate data.

So, even when we compare visits to ChatGPT vs. Google, they’re not leading to the same outcome.

But, against that argument, I will say that Google morphs more into a mirror of ChatGPT with AI Mode – and every generative intent has a high chance of including information along the conversation journey that is sourced to other sites or creators.

The conversational nature of AI chatbots means intent is fluid and can change from one prompt to the next.

Along the way, it’s likely users come across information in their conversations that would’ve been a classic Google Search for products or solutions.

At the end of the day, ChatGPT is continuing its adoption as the fastest-growing product on earth to date.

What does that mean for you?

• Stay the course.
• Keep tracking referral traffic, conversions, and topic visibility on Google + ChatGPT.
• Optimize for visibility with a strong focus on classic SEO.
• Keep an ear to the ground and learn as much as you can. Things are evolving fast, and clarity will come with time.

Quick reminder here: I recently transitioned my WhatsApp group over to Slack. I share ongoing news and learnings throughout the week openly and freely, so it’s a great place to stay updated without all the extra (and sometimes overwhelming) noise. No need to be a premium subscriber to get access to the main discussion channel. Join here!

Old habits are hard to break.

People are used to searching on Google a certain way, while ChatGPT is a green field.

For the overwhelming majority of us, our first experience with ChatGPT was a conversation, so we all adopted it as the default way to engage.

Image Credit: Kevin Indig

As a result, the average query prompt length on ChatGPT vs. Google is:

• 80 words vs 3.4 in the U.S.
• 91 words vs. 3.2 in the UK.

Even informational prompts are 10 times longer (~38 words) on ChatGPT than on Google. People ask more detailed questions, which reveal much more about themselves and their intent.

Together with a growing context window, ChatGPT returns much more personalized and (usually) better informational answers – I’m still waiting on consistently better commercial/purchase intent outcomes.^[3] AI chatbots compress the user journey from many queries over several days to one conversation with lengthy prompts.

For you, this means it’s even more critical to monitor the right prompts.

(I shared a trick with premium subscribers for finding prompts in Google Search Console from AI Mode in Is AI cutting into your SEO conversions?)

As referral traffic from Google reached historic lows, ChatGPT’s referral traffic reached new highs.

Image Credit: Kevin Indig

Over the last 12 months, ChatGPT’s U.S. referral traffic to websites jumped by +3,496% (UK: +5,950%), from 14 to 516 million (after cleaning up referrals to Openai.com, which are mostly authentications).

In comparison, Google’s outgoing referral visits grew only +23% in the U.S. and 19% in the UK.

When you consider Google referrals include navigational searches (people navigating to the homepage of a brand) and ad clicks (ChatGPT doesn’t yet have ads), 23% is not much at all.

ChatGPT’s referral traffic to external websites makes up ~27% of Google’s (1.9 billion, in the last 12 months), based on the data. That feels high, in my field observation.

Also consider that ChatGPT’s goal is not necessarily to send out traffic but to keep the conversation going until users have the optimal response.

That being said, referral traffic has grown and continues to do so. Until recently.

According to Profound, ChatGPT’s referral traffic was down -52% between July 21 and August 20. ^[4] And that’s significant.

Time will tell whether this is just an experiment or a final decision.

For you, this means you should see more ChatGPT referral traffic over the last 12 months if you optimize well.

You might not see an increase of +3,500%, but if you’re not seeing at least some growth, it’s likely your competitors are.

Conversions from ChatGPT are small in comparison with Google (in volume), but they’re growing rapidly.

The whole narrative of investing in AI visibility optimization (AEO/GEO/LLMO) banks on the fact that it will continue at the same pace and become meaningful.

So far, it seems like that bet will work out.

Image Credit: Kevin Indig

When ChatGPT sends traffic to sites, the conversion rate is usually higher than Google’s. As of June 2025:

• ChatGPT’s conversion rate of transactional traffic was 6.9% in the U.S. compared to 5.4% for Google.
• In the UK, ChatGPT reached 5.5%, which is on par with Google.

ChatGPT sends higher-quality traffic to websites, at least in the U.S.

I define quality in this context as “higher intent,” meaning visitors are more likely to convert into customers.

The reason ChatGPT traffic is of higher quality is that users get answers to their questions in one conversation. When they click out, they’re “primed” to buy.

To me, the bigger question is how purchase decisions are influenced before a click happens (or even when no click-out happens).

For you, this means:

1. Look at which pages get referral traffic. Take the average referral traffic and optimize pages that get some but below-average clicks.
2. Optimizing for citations matters because citations are what get clicked. Look at the citation gap between your competitors and your site.
3. Look for conversion optimization opportunities (in-line CTAs, lead gen assets, quizzes, etc) on pages that get ChatGPT referral traffic. Using a standard heatmap tool will point you to areas of the page that are ideal for a little CRO.

ChatGPT has all the ingredients to become the next big user platform on which other companies can build – just like Google 25 years ago:

1. Usage is growing.
2. Behavior is rich in context.
3. Referral traffic is shooting up.
4. Conversions happen at a healthy rate.

Now, traffic and conversations just need more volume.

They’re still tiny in comparison.

Featured Image: Paulo Bobita/Search Engine Journal