Over the past six months, I’ve been on a journey to discover how custom visual assets can enhance SEO performance. I decided to test different design elements across 47 articles on a high-traffic accounting education website.
The experiment involved featured images, infographics, and videos used in both new and existing content. Interestingly, some visuals significantly boosted organic traffic, while others didn’t justify the investment.
Instead of showing that any image can help, my goal was to uncover the ROI of bespoke design elements that could consistently improve organic traffic.
Infographics emerged as the clear winner, with an astounding 110% average increase in organic traffic on the articles that used them.
This taught me a crucial lesson: Custom visuals supercharge already popular pages. They enhance strong content but can’t breathe new life into struggling articles.
I find it intriguing how, despite creating stellar content, it often doesn’t make it to the top of Google’s search results. What holds it back isn’t necessarily quality—there are usually other roadblocks in play. Let me break down how to identify what’s hindering your content’s rankings.
The common advice has always been to create helpful, high-quality content to rank well. However, this piece of advice doesn’t cover the full story of Google’s search algorithm mechanics.
Even if your content is well-researched and aligned with search intent, technical barriers and competition may still impede its visibility. Identifying these barriers is crucial before deciding to rewrite any piece of content.
Before blaming your content’s positioning, it’s essential to assess its quality. I often observe pages that don’t stand out, sometimes being autogenerated with minimal editorial input. Google’s guidelines on helpful content underscore the significance of experience and trust.
Ask yourself: Does your content deliver unique insights, adhere to Google’s preferred format, and offer value beyond the current top results? A ‘yes’ suggests positioning issues; otherwise, focus on enhancing your content’s quality first.
In the competitive 2026 search landscape, various factors such as AI summaries and an increased ad presence are reshaping search results pages, making it harder for organic content to achieve visibility.
Understanding what your content is truly competing against is key. If these external factors push your content down the page, adjustments are necessary to remain competitive.
When questioning why good content isn’t ranking, I employ a diagnostic framework that prioritizes technical issues. Ensuring that your page is indexed and free from technological hurdles is the first and simplest step to address.
Matching search intent with your content’s format is also critical. If your content is misaligned, improving it won’t suffice unless you address the fundamental disconnect.
If a large trust signal gap exists between your domain and your competitors’, repositioning is often necessary to focus on less competitive keywords where you can compete effectively.
The type of website you manage affects which barriers are most significant. For example, SaaS platforms typically face challenges concerning authority more than technical issues, while ecommerce sites contend with technical constraints.
Understanding and applying this diagnostic sequence helps identify and address potential bottlenecks, ultimately allowing your content to rank better by focusing on what truly matters.
In 2026, as the ease of generating good content continues to grow due to AI, positioning becomes crucial. Differentiated, experience-driven content is what stands out and captures attention.
Your strategic question isn’t just about creating good content. It’s about understanding the landscape: What else is required for your content to achieve outstanding results in the search arena?
I’ve often found myself pondering how information, especially outdated or negative, can linger on Wikipedia for years. And then, just as it’s beginning to fade from memory, it resurfaces prominently when AI systems pull it into their algorithms for generated answers.
Wikipedia used to be seen as unreliable, but today it stands as a significant source due to its citations and collaborative nature. It’s a key player for AI search systems, shaping the findings on platforms like ChatGPT and Google.
However, Wikipedia isn’t immune to errors. Sometimes, incorrect or unfairly negative content sticks around, feeding back into AI systems and perpetuating itself through new avenues.
This can create a cycle where misinformation gains longevity and influence, especially on AI-driven search platforms.
Faced with this dilemma, I often wonder how to address negative content once it infiltrates Wikipedia.
How Content Finds its Way to Wikipedia
Achieving a presence on Wikipedia requires verifiability. Esteemed media outlets and verified Wikipedia contributors are the primary sources for content.
These sources act as gatekeepers; hence, Wikipedia sometimes emphasizes verifiability over accuracy, especially when even reputable media can misreport.
Decentralized contributors are fundamental to Wikipedia, and decisions are based on a consensus rather than a single authority figure.
This decentralized nature means quick resolutions for contentious content aren’t always possible.
Why Outdated Negativity Sticks
Wikipedia acknowledges its contentious nature and even features a page of its controversies collected over the years. Negative or outdated information can endure for many reasons. Often, they stem from initial high-profile issues, resurrected long after factual changes end the original narratives.
Citations
Citations on Wikipedia come with a sense of permanence. Once information is supported by ‘reputable’ sources, detaching it from credibility proves difficult, remaining even when discredited long ago.
The Echo Chamber Effect
The digital world is incredibly impactful. Wikipedia’s dual role as both influencer and influenced means it can both absorb and project out dated narratives. AI platforms make this echo louder.
Risk Aversion
Wiki editors avoid the appearance of bias, often retaining content from verified sources despite needing updates or corrections.
Differing News Coverage
Negative narratives receive more media attention than positive stories. Corrections also get less notice than initial reports, skewing the sources Wikipedia uses.
Wikipedia’s Role in AI Search
Wikipedia serves as a primary source for AI, enhancing its perceived credibility, and ChatGPT and Google’s narratives often distill Wikipedia’s information alongside Reddit and news media.
This situation is intensified by shifting user habits. Increasingly, people depend on AI-generated summaries, often skipping the essential step of verifying the source material themselves.
Consequently, when AI highlights negative Wikipedia content, it influences public perception swiftly.
In my experience with online reputation management, I once helped a marketing company – let’s call them Organization Z – recover from outdated allegations. These plagiarism claims, dismissed long ago, still haunted their Wikipedia page.
The focus on this ‘controversy’ clouded the fact that Organization Z had been exonerated. As AI search engines sourced their information from Wikipedia, users wrongly encountered terms like “controversy” and “plagiarism” when searching for the brand.
This incorrect narrative continued to echo online despite the claims being cleared.
Navigating Negative Wikipedia Content
Before attempting solutions, it’s crucial to know what doesn’t work. Editing your own Wikipedia page can be problematic and draws scrutiny. Removing content without strong justification contravenes Wikipedia’s policies.
Here’s a step-by-step approach recommended by ORM experts to handle negative or outdated Wikipedia content:
1. Perform an Audit
Identify circulating claims and their sources. Highlight outdated or flawed citations.
Check if the current Wikipedia information stands balanced and relevant.
2. Compare to Current Coverage
Assess how Wikipedia content aligns with current online portrayals of the brand or issue. This is similar to performing an AI narrative audit.
Identify missing context or emphasized inaccuracies, bridging gaps between Wikipedia’s version and reality.
3. Address the Citations
With mismatches identified, aim to amend or enhance the citations Wikipedia references. Work to reflect current facts through reputable third-party publications.
4. Strengthen Positive Coverage
Focus on building your brand’s positive reputation online. Highlight accomplishments and reliable contributions to your field so that Wikipedia naturally reflects this in time.
AI Search: Raising the Stakes
Wikipedia remains a powerhouse in information, but its dependence on citations can coat outdated or negative narratives with longevity.
AI engines can exacerbate these issues by amplifying such stories in their generated responses.
While direct control over Wikipedia content isn’t possible, shaping the cited sources can influence updates. Regular auditing for balanced coverage and maintaining updated information is key to steering public perception.
As I delve deeper into enhancing my workflow, I realize that effective agents thrive on comprehensive context. Thanks to Profound’s Knowledge Bases, I empower my agents with my unique brand voice, product intricacies, and messaging guidelines.
Now, I’m excited to share that integrating these knowledge bases with Notion and Google Drive is easier than ever. This integration allows me to streamline my processes and maintain consistency.
I’ve discovered a fascinating truth about search in the age of AI: brand authority often outshines topical authority. The landscape of search has shifted, and it’s time for us to adapt.
While topical authority remains a beloved concept among SEO consultants pitching content, brand authority holds the reins in today’s AI-driven search landscape. Marketers have long discussed brand authority, though it was often dismissed or left to brand teams post-sitemap adjustments.
AI’s emergence has upended the traditional approach, revealing underlying issues. Search is crucial for the global economy, and the industry’s marketing approach needs re-examination. More content doesn’t automatically confer authority. In fact, AI search champions brands gaining notable visibility, mentions, and real demand.
Too many SEOs overlook the reasons people choose, trust, and remember brands. In this new world of AI search, such ignorance stands out even more. That’s why brand authority prevails—but not in the way our typical SEO tools might suggest.
Previously, the meaning of topical authority was intended to highlight genuine expertise through useful work, citations from others, and a growing associated reputation. This builds your brand’s association with a topic, which in turn, creates authority and fosters brand development.
However, the industry often marketed topical authority commercially, emphasizing volume over value. Technical SEO became a niche, links were outsourced or repackaged, but content was the consistent agency engine.
Pre-AI, this made sense. Creating good content involved rigorous processes and offered substantial value, earning rankings and supporting commercial interests. In contrast, topical authority introduced the misguided idea that mere keyword coverage equated to expertise, diluting the concept’s original intent.
Another intriguing aspect of authority is understanding what others say about you, rather than solely focusing on self-published content. Google’s Jun Wu highlighted the importance of ‘mention information’—how search engines discern topics, identify sources, and map relationships.
Our modern term for this is brand co-occurrence. Being consistently mentioned by authoritative sites and communities solidifies your brand’s association with a topic, elevating market perception and authority.
Many might pitch the concept of topical authority as building a comprehensive keyword strategy, but actual authority requires originating valuable data and sharing insights that engage audiences and capture media attention.
The changing economic landscape of AI means that traditional advertising methods through content must evolve. With AI offering direct answers, the value of certain traditional SEO practices is diminishing. Users, like my AI-liking father, prefer quick, synthesized information over cumbersome web browsing.
The rise of AI citations in search metrics has become a focus, but they differ from authentic human endorsements. Real influence is reflected through human testimonies, where your brand is discussed, cited, and recommended.
If measuring brand authority, brand searches present a clearer indicator of growth. If more people search specifically for your brand, it signals rising demand and market presence—a more accurate reflection of impact than solely relying on AI citations.
Traditional SEO still plays a role, ensuring you’re found where it matters—be it in search rankings or marketplaces. Yet, brand authority distinctly drives recommendations, and AI search is starting to favor consolidated options, often mentioning specific brands and solutions.
The future echoes the demand for meaningful engagement and widespread brand visibility. Though SEO isn’t dead, a simplistic keyword-centric approach is fading. A holistic approach integrating positioning, PR, reviews, and content as interconnected elements is pivotal.
In an era where fitness and visibility are equal determinants of success, brands must excel in products and services while ensuring their market presence is robust and omnipresent. After all, brand authority is what truly wins, confirming that mediocrity no longer warrants attention.
I’ve been diving into the world of ChatGPT lately, and it turns out there’s a fascinating bias toward commercial intent in its fan-out analysis. Let me break down what this means for our content strategy.
Over the course of my tests with 90 ChatGPT prompts, I discovered that commercial prompts lead to web searches a whopping 78.3% of the time, while informational prompts only did so 3.1% of the time.
This discrepancy got me thinking about how to craft our content to increase the chances of being featured in ChatGPT responses.
ChatGPT doesn’t source every response from the same place. Some responses are derived from its training data, while others are based on live web searches. This process, known as query fan-out, involves expanding a prompt into several background searches, and then curating a synthesized response from multiple topics. If our pages don’t fit into these subtopics, we miss out on getting pulled in.
So, our challenge now is more than just ranking well; it’s about ensuring our pages open the door to this fan-out process from the get-go.
In our samples, informational pages fell short. I encourage you to continue reading to uncover the paths the system actually favored.
I conducted tests across three industries: beauty, legaltech/regtech, and IT. The analysis explored prompt intents, the resulting query expansions, and the intents portrayed by those expansions.
The main takeaway is that most queries are aligned with commercial, rather than purely informational, intent.
Why This Question Matters Now and the Role of Query Fan-Outs
Understanding query fan-outs is crucial because it shifts the content creation approach. The system expands a prompt into several background searches, retrieving and synthesizing information from these subtopics.
This behavior triggers parallel web searches connected to the initial prompt, providing opportunities for mentions and citations.
Multi-query expansion is a fundamental design element in today’s search systems. As Google describes AI Mode, it breaks a question into subtopics, searches them simultaneously across sources, and combines the results into one coherent response.
This raises a key strategic question: should we focus more on top-of-funnel educational content or on lower-funnel comparison, shortlist, and recommendation content?
I designed this experiment to address that problem.
We aimed to see where fan-out occurs by intent category across selected industries: informational, commercial, transactional, or branded.
The hypothesis was clear: while informational prompts wouldn’t trigger fan-out, commercial ones would, and would either remain at the same level or move further down the funnel.
ChatGPT fan-outs were observed to align predominantly with commercial intent.
Disclaimer: This analysis reflects observed prompt expansion behavior in ChatGPT. Although Google AI Mode is cited for context to illustrate multi-query expansion as a pattern, it is not evidence of ChatGPT’s architecture.
The Setup: What We Tested
The experiment sampled 90 prompts, focusing heavily on informational intent.
Prompt intent
Prompts
Share of sample
Prompts with fan-out
Fan-out rate
Informational
65
72.2%
2
3.1%
Commercial
23
25.6%
18
78.3%
Branded
1
1.1%
0
0.0%
Transactional
1
1.1%
0
0.0%
Our sample primarily featured informational prompts, with some commercial and very few branded and transactional ones.
The test was structured around three industries: beauty/personal care, legaltech/regtech, and IT/tech.
The Result: Commercial Prompts Dominated
The findings were clear and conclusive.
Of the 90 prompts, 20 triggered a fan-out. Remarkably, 18 were of commercial intent and only 2 were informational.
Informational prompts accounted for a mere 10% of fan-out triggers (2 out of 20). When they triggered expansion, they were reframed into more evaluative, solution-seeking subqueries.
This indicates that, in this sample, commercial intent overwhelmingly influenced fan-out.
These 20 prompts resulted in 42 fan-out queries, averaging 2.1 per triggered prompt.
Here’s a breakdown of those fan-out queries:
39 were commercial.
2 were branded.
1 was informational.
Even if a prompt led to expansion, it typically transformed into a focus on comparison, feature filtering, shortlist creation, or brand-specific exploration, not broad educational discovery.
Methodology: Our Analytical Approach
Our experiment involved 90 prompts across three industries, mainly informational with fewer commercial prompts, and minimal branded and transactional queries.
The analysis involved:
Choosing a representative set of prompts.
Identifying fan-outs.
Classifying each fan-out by intent.
Analyzing distribution by prompt metadata.
Our approach followed three key steps:
Classifying prompts by intent labels.
Counting prompts that triggered any fan-out.
Reviewing expansion queries and their intent labels.
This process revealed two distinct perspectives:
A prompt-level view to determine which prompts instigated fan-out.
A fan-out-query view to assess the intent of downstream expansions.
This distinction is important: the first identifies prompts that initiate the fan-out path, while the second examines where the system proceeds once engaged.
Interpreting the Results: Fan-Outs Trend Down-Funnel
The clearest takeaway is that, in this instance, fan-outs behave more like decision support rather than topic exploration.
Commercial prompts frequently opened new discovery paths.
Once open, these paths typically remained commercially focused.
The system often expanded into comparisons, feature-based analyses, product listings, and pricing inquiries.
Here are some illustrative examples:
“Suggest the best accounting software for small business and explain why” expanded to a commercial comparison query on features.
“What are the top AI document management systems for lawyers?” led to multiple product-centered legaltech queries.
“What are the best products for skin care?” grew into a shortlist-style inquiry around product categories and reviews.
The rare informational examples expose more about the system’s tendencies than the rules themselves.
“I need an open-source document management system. What can you suggest?” initially coded informational, shifted to solution recommendations.
“AI tools for legal research and document automation” also redirected into clearly commercial/evaluative queries.
Ultimately, even broad prompts frequently translate into more focused, commercially driven retrieval paths.
Implications for Our Content Strategy
Let’s not abandon informational content; however, we should recognize that informational content alone doesn’t consistently align with fan-out expansions, at least in this dataset.
If our goal is to shine in AI responses tied to product selection or vendor discovery, we need to strengthen our coverage with content that lines up with these downstream commercial intents.
Consider the following:
Creating “best-of” and shortlist pages
Developing thorough comparison pages
Writing “which tool should I choose” guides
Feature-led category explainers
Alternative option pages
Evaluation-focused FAQs
Incorporating recommendation passages in broader educational pieces
In practical terms, our content model should integrate both top- and bottom-of-funnel strategies, with strong commercial bridges.
A comprehensive piece can still be beneficial, provided it contains elements that the system can readily transform into decision-support inquiries.
An educational piece that lacks direct references to products, tradeoffs, features, use cases, or selection criteria is less likely to match the system’s fan-out paths.
In short, consider not only answering the obvious inquiries but also forecasting the subsequent evaluative step the system might generate behind the scenes.
Understanding Our Limitations
These results offer direction rather than universal truths.
90 prompts highlight a pattern, but don’t establish AI retrieval behavior as a law.
The prompt mix skews heavily towards informational content, with few branded or transactional samples. The findings don’t signify absence.
While diverse, the dataset isn’t normalized for brand, style, or use case. Some sectors lean easily into product-discovery language.
This analysis observed recorded fan-outs rather than controlling for platform-level testing. It reflects what occurred within this set rather than guarantees of ChatGPT’s constant behavior.
Google’s fan-out description provides context; however, this isn’t a Google AI Mode test. It’s ChatGPT-centric, with strategic—not architectural—takeaways.
Next Steps for Testing
Future versions of this test should further isolate the question while widening the dataset.
A follow-up should map fan-outs to specific content formats.
The aim isn’t solely to affirm that commercial intent triumphs, but to pinpoint which page templates and structures proficiently capture AI-preferred fan-out paths.
I recently realized that search engines, including those powered by AI, are not changing the ultimate goal—they’re raising the bar. Creating content that provides clear, in-depth answers with expertise is more important than ever.
The March 2026 core update from Google focused on surfacing relevant and satisfying content for users across all sites. This underscores a simple truth: people turn to Google for answers.
In our fast-paced, on-the-go lives, searchers want content that solves their problems, imparts new knowledge, or assists decision-making. If my content delivers, it thrives. Otherwise, no SEO trick will push it to page one or get it featured in AI Overviews.
How modern search systems surface helpful content
AI Overviews have grown from covering 6.49% of queries in January 2025 to 15.69% by November 2025, according to a Semrush study. Currently, they appear for 25-50% of searches, highlighting how search engines and LLMs are efficiently collaborating. It’s an exciting period for SEO professionals like me, eager to create content that aligns with user intent.
Techniques like retrieval-augmented generation (RAG) and query fan-out come to my aid, helping my useful content feature prominently in AI Overviews.
RAG empowers AI to source relevant information from multiple places before responding to a query, while query fan-out decomposes a search into related queries for a comprehensive response. These concepts underscore a shift in SEO, now focusing beyond keywords to genuinely satisfy user questions and intent.
Why this raises the bar for SEO in 2026 and beyond
Emerging systems are increasingly adept at filtering out thin, redundant content. Instead, Google’s focus on TurboQuant illustrates a push toward recognizing substantial, unique content that shares authentic experiences and original research. As SEOs, we must pivot toward creating content with true depth, clarity, and expertise.
Depth: No longer about word count, depth means addressing main and follow-up questions comprehensively.
Clarity: My audience is busy, seeking quick, understandable answers. The ability to scan and grasp information easily is key.
Expertise: I need to demonstrate real-world know-how and credibility that my audience can trust.
It’s refreshing to see that it’s no longer just about ticking SEO boxes. The emphasis on providing genuine value elevates what’s considered good SEO beyond core basics.
Why visibility matters more than clicks for local SEO
Small and service-based businesses depending on SEO-driven leads can apply these strategies, as success now hinges on visibility over clicks. AI platforms frequently recommend businesses without direct website links, shifting the narrative to maximize brand visibility online.
While tools exist to measure AI metrics, they can be costly. As Elizabeth Rule notes, measuring visibility is like gauging a wave with a ruler—hence the importance of open dialogue between stakeholders and SEO teams when defining success.
What ‘helpful content’ looks like in practice
Here are five strategies I utilize for creating genuinely helpful content:
1. Answer follow-up questions
I explore overarching queries and anticipate subsequent questions my audience might have. The People Also Ask section on SERP is a valuable resource, offering new angles and questions to address in my content.
2. Show expertise and experience
By sharing my specialized knowledge and firsthand insights, I build trust and connect with my audience. This approach aligns with the principles laid out in the helpful content update of 2022.
3. Structure content clearly
Recognizing that readers often skim, I employ clear structures that leverage headings and bullet points to facilitate quick and easy information retrieval, crucial for both mobile and desktop users.
4. Be authentic
Authenticity resonates best with my audience. Avoiding fluff and filler, I aim to deliver concise, relevant content right to the point of the user’s query.
5. Ask ‘who, what, and how?’ about your content
I reflect on semantic triples rooted in relevance engineering to provide structure and substance. Who am I reaching, what needs do they have, and how can I satisfy those requirements?
As the only narrator of my story, I’m in a unique position to explain my processes and convey why my business or brand is impactful and worthwhile.
Helpfulness is the competitive edge
The cornerstone of an effective SEO strategy persists through each core update: Create truly helpful content. Focus on resolving audience issues, answering queries completely, and leveraging personal expertise to foster engagement.
In a landscape driven by AI and sophisticated retrieval systems, thin, generic content falls by the wayside. If I align my content with the genuine needs of searchers, we soar to the forefront, no trickery required.
I’ve realized that just adding more content won’t automatically boost my SEO. In fact, it can dilute my website’s authority, split rankings, and waste crawl budget. So what’s really driving visibility now? Let’s explore!
Many believe the best way to grow organic visibility was to publish more and more content, thinking that covering every angle of a topic would ensure traffic growth. I used to think that too.
Like many SEO teams, I used to follow content calendars based on search volume targets, believing content quantity equaled growth. But lately, I’ve noticed the effort doesn’t always match the outcomes.
I’ve learned that simply adding more pages doesn’t guarantee increased visibility. Instead, it can dilute the overall performance. I find maintaining a large content library challenging, as it can lead to internal competition and fewer pages appearing in search results.
The real challenge now is understanding why a lot of my content fails to enhance visibility, not just producing more of it.
For a long time, simply increasing content volume worked well. Search engines relied on keyword matching and topical coverage, which meant expanding into different keyword variations often captured more demand.
I found that competition was significantly lower, and the limited high-quality search results made it easier to gain visibility quickly. Publishing frequently seemed to enhance domain authority, signaling freshness and relevance.
But now, the conditions have changed. The search ecosystem evolved, making the relationship between content volume and visibility less predictable.
Entering this new landscape, I’ve encountered content saturation. Most relevant topics have established pages with links and data years in the making. A new page tends to be at a disadvantage.
When creating content around adjacent keyword variations, I noticed a trend of similar queries being directed to the same URL, making it hard for multiple pages to perform well.
The development of AI overviews impacted a significant share of informational queries, reshaping the landscape of informational content and consequently the efforts I’ve put into volume strategies.
I’ve come to understand Google’s indexing limits and that low-value URLs drain valuable crawl activity. Thin or redundant content becomes deprioritized, never contributing meaningfully to search competition despite constant additions.
The reality I’ve faced is that the content library behaves as a system at scale, which can lead to problems compounding over time.
Publishing each page creates an obligation—a debt, so to speak—to keep it updated and relevant. At scale, this quickly becomes overwhelming; a library isn’t merely a collection of assets, but a series of commitments.
I’ve realized that focusing editorial resources on keeping a library from becoming a liability prevents us from strengthening existing high-performing pages.
Google allocates a finite crawl budget. If my site’s content volume expands without quality or authority gains, it can reduce the crawl frequency and reliability for high-value pages.
Search engines prefer signals being consolidated rather than rewarding each competing page individually. Without clear authority, overlapping queries often perform worse.
Broadly expanding my content range without depth erodes topical authority rather than building it. Maintaining consistent subject matter expertise is crucial for SEO success.
Sites publishing high volumes without strong engagement harm domain-level quality assessments, thereby affecting better-performing pages. I learned the hard way that more mediocre content introduces risks to overall engagement.
Turning to a new model means shifting focus from sheer volume to impactful content. Publishing is about creating pieces that truly add value and earn visibility.
Auditing reveals that a few pages generate most traffic while many offer little to none, diverting precious resources and attention.
My strategy now involves merging overlapping intent pages and removing thin content. Producing new pages with authority and signal potential is key.
To impact SEO, content must address truly unaddressed issues, providing unique perspectives and targeting specific intents.
As I move forward, my focus will be on creating fewer, but quality-driven sources of information relevant to users and credible to search engines.
Depth ensures authority and relevance, while targeted distribution and being citation-worthy enhance the chance to stand out and drive SEO success.
Every day, I notice how our attention becomes more fragmented as new information platforms emerge.
With entrants like AI search and proprietary spaces on social networks, we’re bombarded by noise from every direction.
In this deluge of information, trust is slipping, even in previously reliable sources like search engines and social media.
In times of uncertainty, we revert to the most timeless source of trust: other people. To be visible, I must appear across multiple platforms, especially those led by people.
Search is a trust experience
Rachel Botsman, a trust expert, defines trust as “a confident relationship with the unknown.” It’s the element we rely on when facing uncertainty.
As humans, I search for information when uncertain, relying on three layers of trust: self-trust, platform trust, and source trust.
The entire search process hinges on trust, and the most effective support comes from other humans.
An example of my own search journey to find a trusted answer
Recently, I decided to buy new shoes. My search began with AI tools, where I conducted low-trust research using ChatGPT and Claude.
Seeking high trust in pricing and delivery, I turned to Amazon for reviews and pricing, then to Google for deeper insights from Reddit and YouTube.
Bombarded by low-trust social media ads, I finalised my decision with high-trust recommendations from friends and a local running shop.
Search journeys now span dozens of platforms and sources
Research by Yext found that 75% of consumers use more platforms now than a year ago, but only 10% trust the first result. Reflecting on my 65-source journey, most were people-led, matching a trend in professional decision-making.
The 2026 Edelman Trust Barometer reinforces that amidst rising uncertainty, people seek advice from those they trust most.
So how do you turn trust into visibility?
To influence someone’s search journey, I aim to appear on all information-searching platforms and in as many people-led sources as possible.
Start by earning mentions in people-led spaces and build genuine trust. This naturally leads to visibility on major platforms.
For instance, Adidas Terrex was visible at every touchpoint in my journey, reflecting its active engagement and trust-building with consumers.
Through events and community initiatives, Adidas fosters engagement, enhancing visibility through hashtags and social platform mentions.
Where to go to earn people’s trust
Building relationships lays the foundation for trust. I start by engaging in communities, events, social media, and forums where genuine conversations occur.
Select places with active, two-way communication where you can authentically connect and build a trustworthy presence.
How to engage in trust-building spaces
The priority is helping, not selling. I listen first to understand what people need, then engage meaningfully to build trust.
Start by listening, not talking
Before jumping in, I learn what ‘helpful’ means in the specific space and identify how I can support the community’s needs.
Engage to build trust
Building trust takes time and involves personalized interactions and consistent presence as a genuine individual, not as a brand representative.
Turn conversations into scalable trust
Using insights from personal interactions, I create scalable assets that support people’s aspirations, reinforcing trust on a larger scale.
For example, a guest-posting program for professionals looking to amplify their personal brand can be a powerful tool for fostering trust.
What does this actually look like in action?
In my journey from marketing to community building, I experienced firsthand how focusing on helping rather than selling leads to building trust and achieving visibility.
By listening and engaging with genuine support, an SEO SaaS partner grew visibility in our community, resulting in substantial business impact.
Building trust is a long-term visibility bet
Trust persists as a critical factor in information seeking. By embedding trust-building into my business strategies, I ensure lasting visibility across current and future platforms.
Remember, prioritizing trust preserves visibility beyond algorithms, creating enduring presence in an ever-evolving digital landscape.
I often wonder how to adapt my content marketing strategies in today’s AI-driven world. With AI acting as the discovery layer, it’s crucial for me to rethink how my content is found and consumed.
I’ve learned that developing a robust content marketing strategy in the AI era requires integrating original insights citations in AI-generated answers. This approach is vital to enhancing the visibility and credibility of my content.
The reasoning-based discovery layer offered by AI provides an unprecedented opportunity for me to reach audiences more effectively. By leveraging these AI capabilities, I can ensure that my content not only reaches but resonates with my target audience.
