Tag: Reputation Management

How I Build a Brand AI Search Can Trust and Recommend

For most of the past decade, I treated organic marketing as a visibility game. I wanted brands on Page 1, inside featured snippets, and in front of the people already searching.

That north star has moved.

When I spoke at SMX Advanced on June 5, the question I put to the room was not simply, “How do I get a brand found?” The harder question was, “How do I get that brand chosen?”

In 2026, those answers are no longer the same. The distance between being discovered and being selected is where I see many brands losing ground.

In AI search, my reputation shows up first

The old user journey was messy and multi-step. People explored, compared, checked reviews, read Reddit threads, visited comparison sites, and moved toward a decision over time. Now, a single AI prompt can compress much of that process into one synthesized answer.

AI search does not reward the brand that shouts the loudest in paid media or stuffs the most keywords into metadata. I see it rewarding the brand with the strongest reputation in the places that matter. Reddit discussions, review sites, comparison pages, expert commentary, forums, and editorial coverage are all being absorbed by large language models and blended into recommendations.

In other words, my brand is no longer defined only by what I say about it. It is shaped by how AI understands it, and AI is reading what everyone else has said, too.

Owned content on websites and social channels will always carry a promotional bias. AI systems look for outside validation to support, challenge, or clarify those claims.

That changes the work of organic marketing. I can no longer stop at visibility. I have to build a brand that is found, correctly understood, and ultimately chosen. Those are three separate challenges, and I need a strategy for each one.

Found: I need to appear where my audience actually looks

The first challenge is still discoverability, but the canvas is much wider than Google. People now discover brands through ChatGPT, Reddit, YouTube, TikTok, Google, Quora, LinkedIn, and word of mouth. I have to understand which of those entry points matter most to the specific audience I want to reach.

That starts with mapping the sources my audience genuinely trusts: the publications, platforms, communities, creators, analysts, newsletters, and peer groups that influence their decisions. The intersection of semantic relevance, domain authority, and audience affinity tells me which third-party properties are worth pursuing.

For one B2B audience, that might mean Wired, Tom’s Guide, or an active LinkedIn group where buyers discuss vendors in a specific vertical. For another, it might be r/smallbusiness or a Substack newsletter with 40,000 engaged subscribers.

Once I know where the audience spends time, I can create useful content, earn credible mentions, and participate in the conversations already shaping decisions. This is audience-first, performance-driven PR and organic strategy, not generic brand awareness.

AI search leans heavily on outside validation: this chart shows third-party communities, reviews, and earned media driving 93% of citations versus 7% from owned channels.

The data makes the case even stronger. Across the top commercial sectors analyzed, 93% of AI search citations came from third-party sources. If I only invest in content on my own domain, I risk being invisible to the systems now doing much of the brand discovery work.

Understood: I need consistent signals everywhere

Getting found matters, but it is not enough on its own. If machines are surfacing my brand, they also need to understand it accurately.

LLMs do more than crawl my website. They build a consensus picture from everything available online: reviews, Reddit discussions, press coverage, YouTube commentary, Trustpilot ratings, forum threads, and more. If those signals conflict with the story I am telling about myself, I have a real problem.

If I claim premium positioning while thousands of articles question whether the brand is truly luxury, heavy discounting is part of the public record, and review scores are poor, AI is unlikely to recommend that brand as a premium option. The model has read the broader story, not just the homepage copy.

That is why brand messaging consistency has become an SEO issue. Owned, earned, and paid content all need to reinforce the same core associations. Conflicting signals do not just confuse customers; they can weaken AI visibility.

Digital PR plays a critical role here because it helps shape the external narrative. Through strategic media placements, expert commentary, and search-informed coverage, I can influence what journalists write, what audiences remember, and what models learn.

I also have to think beyond one obvious keyword. The query fan-out, or the range of prompts a potential customer might use, requires positive and consistent answers across every touchpoint an LLM might evaluate.

Chosen: I need trust signals that influence the decision

The third challenge is the hardest and probably the most important. Trust has always been an SEO currency, but as clicks decline and zero-click search becomes more common, trust matters even more.

According to an Ahrefs study, brand appearance in AI Overviews is most strongly correlated with branded web mentions. In practical terms, that means the number of times a brand is positively named across authoritative third-party sources is becoming one of the most powerful signals organic marketers can influence.

That is also the core output of strong digital PR. Based on the last 4,000 pieces of U.S.- and U.K.-based coverage driven for clients, 91% of AI search citations included expert insight rather than branded content or product pages.

That tells me expert-backed, editorially independent coverage is critical. Internal experts are now one of the most valuable assets a brand has. Brands that invest in real thought leadership, original research, and data-backed studies are giving both people and AI systems stronger reasons to trust them.

The three content formats I see consistently supporting LLM inclusion are product roundups and listicles that place a brand inside trusted “best of” editorials, reliable data-backed research that journalists and LLMs can cite, and expert thought leadership that positions real people as credible voices in their category.

A glowing Google search bar cuts through streams of digital data, capturing the fast-moving world of search, shopping visibility, and SEO innovation.

What does not work is chasing inauthentic mentions through artificial link schemes, fake expert personas, or manufactured coverage. Google has already flagged these kinds of tactics in its GEO guidance, and models are getting better at distinguishing genuine authority from manipulated signals.

The reputational risk is also high. If I try to manufacture authority and get caught, I do not just lose visibility. I damage the trust I was trying to build.

This cannot be a one-time effort. Multiple studies, including research from Waseda University, have identified a correlation between AI brand visibility and content recency.

Brands that maintain a steady flow of credible, expert-backed third-party coverage do not just appear more often in AI responses. They appear with more confidence.

Frequency and freshness both matter. A one-off PR campaign is not enough. I need to treat credible external validation as an always-on strategic investment.

The framework I use in practice

When I think about brand discovery in 2026, I come back to three words: found, understood, and chosen.

Found: I map the audience’s real sources of influence and make sure the brand is credibly present across the fragmented ecosystem where discovery now happens.

Understood: I work to make sure everything said about the brand tells a consistent story, matches the desired positioning, and reinforces the associations that drive preference.

Chosen: I continuously build genuine trust signals through earned coverage, expert commentary, and third-party validation, so that when a person or machine compares the brand with a competitor, credible external evidence tips the decision in my favor.

The brands winning in organic search right now have not unlocked some secret technical trick. They have built reputations worth recommending, and they have made sure machines can understand those reputations clearly.

That is where I believe organic marketing has to go next. Instead of chasing the algorithm, I need to build something worth finding, worth understanding, and worth choosing.

Inspired by this post on Search Engine Land.

July 7, 2026
Google Review Glitch: Missing Reviews Under Investigation

I’m tracking a growing Google Business Profile issue after several days of complaints from businesses that say reviews have disappeared from their local listings. Google has now confirmed that it is investigating the reports, and in some cases, review submissions on affected profiles appear to be paused.

What Google said. Google told us that when its systems detect suspicious review activity, it may take several actions, including removing reviews and temporarily pausing reviews on a profile to prevent further abuse. Google also said it is investigating the issue and will restore any reviews that were incorrectly removed.

What I’m seeing. As I documented on the Search Engine Roundtable, there are dozens of complaints in the Google Business Profile Forums from business owners and local SEOs who say their reviews have mysteriously vanished. In some cases, businesses are also unable to receive new reviews on their local listings.

From what I can tell, Google’s review spam detection systems may be identifying certain patterns and aggressively removing or blocking reviews on suspected Google Business Profiles. What remains unclear is whether this is tied to spammers abusing some profiles, a recent algorithmic adjustment, or Google’s systems becoming overly sensitive.

More details. Amy Toman, a volunteer Google Product Expert for Google Business Profiles, shared on LinkedIn that businesses or clients affected by this issue can post in the forum if they want to, but Google is already aware of the problem and working on it. She also noted that no timeline for a resolution has been provided yet.

She said she is seeing a new pattern where, after fake or spam reviews are reported, some Google listings receive a review block and all reviews are hidden. In at least one case, she said the rating was reduced to 0.

Why I care. If I noticed a sudden drop in reviews or stopped receiving new reviews this week, I would consider this issue a likely explanation. For local businesses, reviews can directly affect trust, visibility, and customer decisions, so even a temporary review disruption can be frustrating.

Google is investigating, and I’m watching to see whether missing reviews are restored and whether affected Google Business Profiles can begin receiving new reviews again.

Inspired by this post on Search Engine Land.

July 3, 2026
Paid Brand Mentions in GEO: The Risky Trap I See

As traditional SEO shifts toward GEO, I keep seeing one idea gain momentum: visibility in AI search depends heavily on off-site brand mentions. Because of that, marketers are being pushed to look beyond on-site content and invest more heavily in off-site marketing if they want to show up in AI answers.

I agree that off-site signals matter more in AI search, and there is growing industrywide consensus around that point. The problem is that this shift has also created room for opportunists to repackage shady SEO tactics as legitimate GEO work.

Unfortunately, I believe much of what is being sold under the GEO umbrella is unethical, low quality, and potentially fraudulent.

The deception I see under the GEO umbrella

I have personally audited the work of top-rated GEO vendors that offer brand mention outreach services. What I found was not sophisticated digital PR or thoughtful reputation building. I found providers charging premium prices for questionable work that often looks like paid link building with new packaging.

The first tactic I see is vendors using “research studies” to support their sales narrative. Claims such as “X% of AI visibility is driven by third-party sources” can be stripped of context and used to convince marketers that they need an aggressive, high-volume system for manufacturing brand mentions.

I also see these programs framed as “partnership” building. During the sales process, GEO vendors may describe the work as a way to build relationships with other brands. In practice, many of the so-called opportunities are low-quality paid-placement inventory schemes.

Some vendors are selling PBN brand mentions, placing brands on Private Blog Networks for roughly 10 to 15 times the cost of a typical SEO backlink. Others sell topically irrelevant placements on sites that might publish one page about LMS software and another listicle about crypto wallets.

I have also seen Reddit astroturfing presented as GEO work. Agencies use aged accounts to mass-post brand mentions across irrelevant subreddits, and many of those “mentions” are removed within 30 days because they violate community guidelines.

Why paid brand mentions look like black hat link building to me

When I look at what some GEO outreach vendors are pitching, I see an evolution of black hat link building. It is unethical, and it amounts to an attempt to manipulate AI systems.

I see clients being asked to approve paid mentions

I have seen this happen in Slack. The agency creates a “placement opportunity” for approval, and an internal marketing liaison has to review it. Often, that person is a junior specialist who has not been trained to evaluate whether the referring page is legitimate.

The pitch usually includes a prompt topic, domain authority, citation rate, and publisher placement fee. In one example I reviewed, the fee was $250 in exchange for adding the brand mention.

I also see publisher fees added on top of agency retainers

This is the part I think deserves much more scrutiny. The GEO vendor may pay the publisher fee directly, then invoice the client to recover the cost. That means the client is not only paying the agency retainer, but also funding the paid mention itself.

Why I think volume without relevance creates risk

My view is simple: third-party validation is only valuable when it comes from credible, topically relevant brands. A mention is not automatically useful just because it exists somewhere on the web.

Many GEO vendors argue that AI visibility is a “volume game.” They claim that generating a large number of mentions will meaningfully increase a brand’s “mention rate” in AI answers. I think that framing misses the point.

When vendors treat GEO as a mention-rate, citation-rate, and volume problem, they often ignore the quality and relevance of the source. That is a serious flaw, especially when reputation is central to how brands are understood online.

In one example, I saw a page with several outgoing commercial anchors to LMS software vendors. To me, that is a hallmark signal of paid links. If GEO is a reputation problem, I would not want my brand mentioned on a page loaded with paid links to competitors.

Why inauthentic brand mention spam may only work temporarily

I think some spammy GEO tactics appear to work right now because many LLM citation systems are still immature compared with Google’s advanced spam detection. It is possible that some LLMs currently reward mention volume from low-quality sources that Google would normally ignore.

That creates a temporary window of effectiveness, perhaps one to two years, before AI platforms improve their authority and spam signals. I believe marketers who prioritize high-volume mentions over brand safety risk confusing LLMs about their entity and damaging their reputation.

Lily Ray’s view aligns with this concern. She argues that some GEO and AEO companies lack the experience to anticipate how Google and AI platforms may treat their tactics once stronger countermeasures are built into training data, indexes, and results.

She also points back to the first Penguin update in 2012, when Google began suppressing inorganic links. In that context, paid mentions on low-quality sites look like another evolution of spammy link building, and I think it is naive to assume search and AI platforms will not eventually catch on.

The unnecessary risk I see GEO vendors creating

This type of work can cause real damage. Glenn Gabe has described it as an evolution of paid link schemes, and I think that description fits what many marketers are being sold.

Marketing leaders are not just wasting time and money. They may be buying tactics that disappear, damage brand reputation, confuse LLMs about their entity, and pull resources away from more durable marketing work.

There may also be legal risk. The FTC says paid advertisements must include clear disclosures. Yet after paid or “negotiated” brand mentions are added to content pages, many websites do not update those pages to disclose that the placements were sponsored.

How I evaluate GEO vendor claims about off-site mentions

When I evaluate GEO vendors, I start with one basic concern: many prioritize mention volume over source quality. That does not mean every off-site mention strategy is bad, but it does mean the claims deserve pressure testing.

If a vendor claims that most AI brand discovery comes from third-party sources, I ask whether that actually proves paid or negotiated low-quality mentions cause a brand to appear more often in AI answers. In my view, it does not.

If a vendor says listicles and third-party pages are the main lever, I ask whether that supports paying to appear on thin, irrelevant, AI-generated listicles. Again, I do not think it does.

If a vendor argues that AI search is different and traditional SEO quality judgment no longer applies, I push back. Google says the opposite for its AI search features: SEO best practices still matter, there are no special optimizations required for AI Overviews or AI Mode, and pages still need to follow Search policies.

More broadly, I do not see substantial evidence that adding a paid mention to a cited page will make a brand appear more often, that low-quality long-tail publishers improve AI search visibility, that citation rate beats source quality, or that traditional SEO and brand safety principles are obsolete in AI search.

Paying for “25 brand placements” to chase a “10-15% mention-rate lift” is not how I think marketers should approach AI search. I would rather pursue off-site mentions that reflect genuine category validation from trusted businesses, reputable publishers, and real communities.

Inspired by this post on Search Engine Land.

July 3, 2026
Doug Davis on Building Lasting Trust Through Community Validation

Chatting with Doug Davis, the visionary Founder of Voted Number One, offers a refreshing perspective on how genuine community trust can transform a business’s credibility. In a world where consumers face too many choices and are skeptical of self-promotion, Doug’s insights into local-level trust-building are invaluable. He explains why community backing signifies strong business credibility and how local companies can unwittingly harm trust despite providing high-quality work. Doug also delves into how a business’s reputation increasingly hinges on customer testimonials rather than self-advertisements.

First Page Sage: Many businesses think visibility equals trust. Doug, can you shed light on where companies often get recognition and credibility wrong?

Doug: A common mistake is equating attention with trust. A business might be well-known but still lack authentic trust within its community. Companies often focus excessively on advertising while neglecting the customer experiences that genuinely shape their long-term reputation.

What truly counts is whether people are willing to recommend a business without any personal gain. That’s a very telling indication of trust. True community trust is developed through consistent, reliable interactions over time.

First Page Sage: Voted Number One emphasizes community-driven recognition over internal rankings. Why does this matter now more than ever?

Doug: People rely more on collective community experiences than on polished corporate assertions. Community-driven recognition showcases genuine, repeated positive interactions, not just catchy marketing phrases.

Trust within communities grows cumulatively. When individuals repeatedly hear about the same business from close acquaintances, neighbors, or fellow professionals, natural confidence builds, which is hard to fabricate through artificial means.

First Page Sage:: In competitive local markets, what factors actually guide consumer decisions when comparing providers?

Doug: It boils down to clarity and evidence. Since most consumers aren’t industry experts, they look for signs that reduce uncertainty. They want assurance that a business has consistently delivered for others like them.

Specificity makes a business stand out quickly. Clear communication regarding a company’s experience, processes, and results outshines vague promises. Consistent touchpoints build trust faster, while inconsistency can arouse consumer hesitance.

First Page Sage:: With consumer decisions increasingly swayed by community recommendations and automated systems, how crucial is genuine customer advocacy?

Doug: Genuine customer advocacy is now essential. Modern systems focus on patterns of trust rather than singular claims. Businesses that naturally generate customer support are more likely to sustain their visibility and credibility.

Authentic advocacy often stems from operational excellence rather than marketing tricks. Communities back businesses that consistently deliver, solve problems effectively, and communicate transparently.

First Page Sage:: What practical habits should local business owners adopt to build enduring reputations?

Doug: Building a lasting reputation requires treating trust as a key operational target rather than a mere branding effort. This means ensuring consistency, responsiveness, and follow-through, even in busy times.

Furthermore, documenting real customer experiences and outcomes, as well as community involvement, significantly enhances credibility. Avoiding complacency is vital as a strong reputation is never guaranteed; it requires continuous reinforcement through action.

For more on Voted Number One’s recognition platform, visit votednumberone.com.

Inspired by this post on First Page Sage Blog.

June 11, 2026
How Wikipedia Fuels AI’s Spread of Misleading Information

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.

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Wikipedia and AI: The Disruption of Brand Image

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.

Inspired by this post on Search Engine Land.

May 12, 2026
Master Google’s Removal Tools for SEO & Reputation Success

When I get a call from a client about a negative search result, my usual response might be to suppress it or claim there’s nothing I can do. However, these aren’t the only options. Google’s removal tools offer a middle ground worth exploring.
Google actually provides tools to remove or deindex content from search results, but they’re underused and often misunderstood. Let me break down what each tool does, when to utilize it, and what its limitations are—so I can handle client situations accurately and manage expectations effectively.
Before using any tool, I always clarify an important distinction with clients: the difference between removal and deindexing. Though they seem similar, they achieve different outcomes.
Removal at source: This means deleting the content from its original site. Once it’s gone, Google will automatically remove it from its index after re-crawling. This is the ideal situation but relies on the site owner taking action.
Deindexing: Google simply removes the URL from its search results, even if the page still exists. However, anyone with the direct link can still access it. Most of Google’s self-service tools offer this option.
The takeaway here is that deindexing addresses a search issue but not a content issue. If the content itself poses a problem, deindexing can minimize risk without completely solving the issue. This distinction is crucial when advising clients.
Google’s various removal tools serve different purposes. Let me walk you through them.
The URL removal tool: Located in Google Search Console, this tool allows me to temporarily hide a URL or directory from search results for up to six months. I find it useful for outdated pages I don’t want people to see, like old press releases.
The outdated content removal tool: This public tool lets you request Google to deindex pages that have been removed or changed but still show in search results. It’s a time-saver after the source has been changed, triggering a recrawl rather than an actual removal.
The Results About You tool: Launched recently, this tool helps me request the removal of personal information categories from Google Search, greatly expanded to include sensitive data like government-issued IDs and non-consensual explicit imagery.
Legal removal requests: For issues outside self-service categories, I can submit legal requests for removal based on different grounds like defamation or copyright violations.
The personal content removal form: Separate from the Results About You tool, this form manages the removal of non-consensual explicit images and other sensitive information found on third-party sites.
It’s important to understand the limitations of these tools. None of them can force third-party sites to delete content or remove content from other search engines. They don’t permanently fix content issues; that’s where suppression strategies come in handy.
When managing client expectations, it’s crucial for me to explain that Google isn’t a content moderator and its tools cover very specific cases. Suppression is often the best strategy when these tools are inapplicable.
For challenging cases, companies like Erase.com handle direct outreach and legal escalation, offering a bridge between self-help tools and litigation.
By understanding and effectively using these tools, I can better manage online reputations and set realistic expectations with my clients.

Inspired by this post on Search Engine Land.

April 14, 2026