The Scale of the Problem

Wikipedia processes millions of edits every month. Most are legitimate improvements made by good-faith volunteer editors. But some aren’t, and the categories of problematic edits range from straightforward vandalism to sophisticated targeted campaigns by competitors, disgruntled former employees, or activist investors.

For publicly traded companies, Wikipedia activity spikes around earnings announcements, executive changes, and product launches. For consumer brands, Wikipedia pages attract edits during controversies and product issues. For financial firms, edits during regulatory proceedings are common. The patterns are consistent, and companies without monitoring systems consistently discover problems too late.

The mechanics of the problem are simple: anyone can edit a Wikipedia page in minutes, changes go live immediately, and there is no notification system for article subjects. The only way to know your Wikipedia page has been changed is to check it yourself, or to have a monitoring system do it for you.

The Google and AI Connection

The stakes of Wikipedia changes have expanded significantly with Google’s integration of Wikipedia content and the rise of AI language models. When your Wikipedia page is changed, that change propagates to:

Google’s Knowledge Panel. The information boxes that appear in Google searches for branded queries are largely populated from Wikipedia. Changes to Wikipedia flow into Knowledge Panels quickly, sometimes within hours.

AI-generated responses. Systems including ChatGPT, Google’s AI Overview, Microsoft Copilot, and others use Wikipedia as a primary reference source. When someone asks an AI assistant about your company, the answer is often shaped by what’s currently on your Wikipedia page.

Other aggregators. Dozens of services, from financial data platforms to news aggregators to business directories, pull data from Wikipedia. A change to your Wikipedia page ripples through all of these downstream systems.

This means that the consequences of an undetected Wikipedia edit extend far beyond the people who visit wikipedia.org. The reach of Wikipedia content has expanded to include everyone who interacts with AI systems and Google search.

What Effective Monitoring Looks Like

Professional Wikipedia monitoring involves several components that work together to provide comprehensive coverage:

Real-time edit tracking. Every edit to a monitored article is identified and reviewed as it happens. This allows rapid response when problematic edits appear before they propagate to Google and AI systems.

Edit quality analysis. Not every edit requires a response. Professional monitoring distinguishes between routine good-faith edits, edits that require review, and edits that require immediate action. This filtering is essential to avoid unnecessary interventions that could create friction with Wikipedia’s volunteer community.

Response protocols. When a problematic edit is identified, there are established protocols for response: reverting vandalism, requesting corrections through the Talk page, escalating issues that require more significant intervention. Having these protocols in place allows for fast, effective response.

Trend analysis. Periodic review of edit patterns can identify developing situations, like a coordinated editing campaign, before they become acute problems. It can also identify gaps in an article that should be addressed proactively.

Find Out Where You Stand

The first step is understanding your current situation. When was your Wikipedia page last edited? Who made the changes? What did it say before, and what does it say now? Are there any ongoing issues in the article’s Talk page that indicate potential future problems?

Five Blocks offers a free Wikipedia risk assessment that answers these questions and gives you a clear picture of your Wikipedia exposure. There’s no obligation, and the assessment itself has value regardless of whether you decide to engage further.

Request your free Wikipedia risk assessment →

Frequently Asked Questions

Can I set up my own Wikipedia monitoring?

You can. Wikipedia has built-in watchlist functionality that notifies registered users of changes to articles they’re watching. However, this approach has limitations: it requires a Wikipedia account, it only tells you that a change was made (not whether it’s a problem), and it doesn’t provide the context or response capabilities that professional monitoring offers.

How quickly do Wikipedia changes affect Google?

Google indexes Wikipedia changes quickly, often within hours. The specific timeline varies, but companies should assume that significant changes to their Wikipedia page will be reflected in Google searches within 24-48 hours.

What’s the most common type of problematic Wikipedia edit?

For corporate articles, the most common issues are: vandalism (often temporary but sometimes persistent), the addition of unsourced negative claims, promotional editing that triggers Wikipedia cleanup tags, and removal of important context. The most strategically dangerous edits are usually the subtle ones that don’t look like vandalism.

Does Five Blocks monitor Wikipedia articles we didn’t create?

Yes. Many of our clients come to us with existing Wikipedia articles that they didn’t create and haven’t managed. Monitoring is article-specific, it doesn’t matter who created the article.

This post is the final blog of our series Your Brand on Wikipedia, a practical guide to understanding, editing, and protecting your brand’s presence on Wikipedia.

Previous: What Does a Wikipedia Editing Service Actually Do? A Look Inside Professional Wikipedia Management

The post Wikipedia Page Monitoring: What Changes When No One Is Watching? appeared first on Five Blocks.

The Wikipedia Services Landscape: A Buyer’s Guide

The Wikipedia editing services market exists because Wikipedia matters more than ever, and navigating it professionally requires expertise that most organizations don’t have internally. But the market is unregulated, and the range of quality and ethical standards is enormous.

At one end are services that use sockpuppet accounts, undisclosed paid editing, and other methods that directly violate Wikipedia’s terms of service. These services often claim to “guarantee” article creation or content changes. They typically work until Wikipedia catches them, and Wikipedia has become increasingly sophisticated at identifying and blocking these operations. When they get caught, the damage to the client can be significant: articles deleted, accounts blocked, and the whole episode permanently recorded in Wikipedia’s public editing logs.

At the other end are legitimate professional services that work within Wikipedia’s framework using disclosed COI editing methods. These services can’t guarantee specific outcomes because Wikipedia is ultimately governed by its volunteer editor community, but they produce durable results that survive Wikipedia’s scrutiny.

What a Typical Engagement Looks Like

A legitimate Wikipedia engagement with a company like Five Blocks typically follows this structure:

Initial Assessment. A thorough review of the existing Wikipedia article (or assessment of notability if no article exists). This includes evaluating current content against Wikipedia’s policies, identifying gaps, errors, outdated information, and sourcing issues, and reviewing the existing edit history for previous issues.

Source Research. Wikipedia content must be sourced from independent, reliable sources. A professional service researches what coverage exists for the company, identifies which sources meet Wikipedia’s reliability standards, and maps available sources to potential content improvements.

Content Development. Based on available sourcing, content is drafted that meets Wikipedia’s neutral point of view standards. This is a disciplined writing process, not writing to sound good, but writing to meet a specific encyclopedic standard.

Disclosed Submission. The content is submitted through appropriate channels: either through the Talk page edit request process for articles where COI editing is a concern, or through direct editing for straightforward factual corrections with strong sourcing.

Community Engagement. Professional Wikipedia work often involves ongoing engagement with Wikipedia’s editor community, responding to questions, providing additional sources, working through disputed content in accordance with Wikipedia’s dispute resolution processes.

The Monitoring Layer

For companies with established Wikipedia pages, ongoing monitoring is often the most valuable service. Wikipedia is edited continuously, sometimes helpfully, sometimes not. Without monitoring, a company may not discover for months that significant changes have been made to their page.

Effective monitoring includes tracking all edits to the article in real time, analyzing whether changes are policy-compliant, identifying vandalism or biased editing, and flagging content that requires a response.

Why AI Has Changed the Calculus

The rise of AI language models has significantly increased the stakes of Wikipedia accuracy. Wikipedia is a primary training and reference source for AI systems including ChatGPT, Google’s AI Overview, and dozens of other platforms. This means that errors or outdated content on a Wikipedia page propagate almost immediately into AI-generated responses about the company.

Companies that previously considered Wikipedia monitoring optional now find it essential. The reach of Wikipedia content has expanded beyond anyone who visits wikipedia.org, it now includes everyone who asks an AI assistant about the company.

How to Get Started

The first step for any company is an honest assessment of their current Wikipedia situation. What exists? What’s accurate? What sourcing supports what changes? Five Blocks offers a free Wikipedia evaluation that answers these questions and outlines what a compliant path forward would look like.

Request your free Wikipedia evaluation →

Frequently Asked Questions

How do I know if a Wikipedia editing service is legitimate?

Red flags include: guarantees of specific outcomes, claims of “secret” methods, no mention of COI disclosure, suspiciously low prices, and no verifiable track record. Legitimate services acknowledge that Wikipedia is ultimately controlled by its volunteer community and work transparently within that system.

What does a professional Wikipedia engagement typically cost?

Professional Wikipedia services range widely depending on scope. A one-time article improvement project might run a few thousand dollars. Ongoing monitoring and management is typically a monthly retainer. The cost should reflect the complexity of the work and the expertise required.

How long does a Wikipedia project take?

Timelines depend on the nature of the work and how the Wikipedia editor community responds. Simple factual corrections with strong sourcing can sometimes be addressed within days. Complex article work or article creation for marginally notable subjects can take months. Legitimate services don’t promise quick timelines.

Can Five Blocks create a new Wikipedia page for my company?

We can assess whether your company meets Wikipedia’s notability criteria and, if it does, work toward creating an article through the appropriate channels. We can’t guarantee article creation because Wikipedia’s community ultimately decides what articles to keep, but our notability assessment will give you an honest picture of where you stand.

This post is part of our series Your Brand on Wikipedia, a practical guide to understanding, editing, and protecting your brand’s presence on Wikipedia.

Next: Wikipedia Page Monitoring: What Changes When No One Is Watching?

Previous: Can You Edit Your Own Wikipedia Page? The Conflict of Interest Question Every Executive Asks

The post What Does a Wikipedia Editing Service Actually Do? A Look Inside Professional Wikipedia Management appeared first on Five Blocks.

The COI Trap

Wikipedia’s conflict of interest (COI) policy exists for an obvious reason: people editing articles about themselves, their companies, or their clients have a personal stake in how those articles read. That stake creates bias, even when the editor genuinely believes they’re being objective.

Wikipedia defines a conflict of interest as any editing where you have a close personal or financial relationship with the subject. Editing your own company’s Wikipedia page is a textbook COI situation. This doesn’t make editing impossible, but it does mean there are specific rules you’re required to follow, rules that most executives aren’t aware of when they make their first edit.

What Actually Happens When You Edit Your Own Page

When a company executive edits their own Wikipedia page without following COI guidelines, several things typically happen:

The edit is flagged. Wikipedia’s sophisticated editor community has automated tools and experienced volunteers specifically trained to detect COI editing. New accounts editing corporate articles, IP addresses that trace back to corporate offices, and editing patterns that match promotional content are all triggers.

The edit is reverted. Once identified as a COI edit made without disclosure, the edit will be reversed. The original content, including whatever problem you were trying to fix, is restored.

The article is flagged. The Wikipedia article itself may be tagged with a COI notice, which tells readers that the article’s neutrality is disputed. This is often worse than whatever you were trying to fix.

Your account may be blocked. Repeated undisclosed COI editing can result in your account being blocked from editing Wikipedia entirely.

The permanent record. Every edit you make is stored forever in Wikipedia’s revision history. If you’re later identified as a COI editor, that history will be scrutinized and any undisclosed COI edits may be used to flag your contributions.

The Approach That Works and How We Can Help

Wikipedia does have a process for interested parties to request changes to articles, and it’s specifically designed for situations like this. It’s called the “edit request” process, and it works through the article’s Talk page.

Five Blocks specializes in disclosed COI Wikipedia work. Our team includes experienced Wikipedia editors who understand both the technical rules and the cultural norms of the Wikipedia editor community. We work transparently, disclosing our client relationships and operating within Wikipedia’s guidelines, because it’s both the right approach and the only approach that produces durable results.

We start with a free assessment of your current Wikipedia situation, identify the highest-priority issues, and outline what a compliant engagement would look like for your specific case.

Get your free Wikipedia page evaluation →

The AI Dimension

The stakes for getting your Wikipedia page right have increased significantly with the rise of AI. Wikipedia is a primary training and reference source for AI language models, Google’s Knowledge Panel, and dozens of other information systems. When someone asks an AI assistant about your company, executive, or brand, the response is often shaped by your Wikipedia page.

This means errors, outdated information, or negative content on your Wikipedia page doesn’t just affect people who visit Wikipedia directly. It propagates into AI responses, search results, and knowledge graphs that reach far more people than your Wikipedia page itself ever would.

Frequently Asked Questions

Is it legal to edit your own Wikipedia page?

There’s nothing illegal about it. Wikipedia’s policies are community rules, not laws. But violating those policies has real consequences: reverted edits, flagged articles, and damaged credibility with the Wikipedia editor community.

What if the information on my Wikipedia page is factually wrong?

Even factual errors should be corrected through the Talk page process rather than direct editing, if you have a COI. The proper approach is to post a specific, sourced correction request on the Talk page, this is the compliant way to address even obvious errors.

Can I hire someone to edit my Wikipedia page?

You can hire professional Wikipedia editors, but Wikipedia requires that paid editors disclose their paid status. Any legitimate Wikipedia editing service will operate using disclosed COI methods. Be very cautious about services that claim they can “guarantee” changes or that operate without disclosure, these typically use methods that violate Wikipedia’s terms of service.

How do I know if my Wikipedia page needs work?

Look for outdated information, factual errors, missing context, promotional language that Wikipedia editors have flagged, and content that doesn’t accurately reflect your current business. Five Blocks offers a free assessment that systematically evaluates all of these factors.

This post is part of our series Your Brand on Wikipedia, a practical guide to understanding, editing, and protecting your brand’s presence on Wikipedia.

Next: What Does a Wikipedia Editing Service Actually Do? A Look Inside Professional Wikipedia Management

Previous: How to Edit a Wikipedia Page Without Getting Reverted (or Making Things Worse)

The post Can You Edit Your Own Wikipedia Page? The Conflict of Interest Question Every Executive Asks appeared first on Five Blocks.

The Seven Reasons Your Wikipedia Edit Will Get Reverted

1. You Edited Without Disclosing Your Conflict of Interest

Wikipedia’s conflict of interest (COI) policy requires that anyone editing an article about their own company, employer, or clients must disclose that relationship. If you edit your own company’s Wikipedia page without disclosure, experienced editors will identify you, often through IP address lookup or account history, and revert your changes. The edit won’t just be removed; it may be flagged, and future edits from your account or IP may face extra scrutiny.

2. Your Source Doesn’t Meet Wikipedia’s Notability Standards

Wikipedia’s verifiability policy requires that content be supported by reliable, independent, secondary sources. A press release doesn’t count. Your company blog doesn’t count. Even a well-written article on your own website doesn’t count. The sources need to be from publications that are independent from the subject, newspapers, industry journals, academic publications, and established online media with editorial standards. If your source fails this test, the content will be removed regardless of how accurate it is.

3. The Tone Was Promotional

Wikipedia’s neutral point of view (NPOV) policy prohibits promotional language. Phrases like “industry-leading,” “best-in-class,” “award-winning,” or “pioneering” will trigger an immediate revert. So will superlatives, subjective claims, and anything that reads like marketing copy. Wikipedia editors are trained to spot promotional language, and they remove it systematically. Even factually accurate content can be reverted if the framing is promotional.

4. You Added Content That Lacks Independent Verification

This catches companies off guard. You know your own history better than anyone, but Wikipedia doesn’t accept your account of events as authoritative. If a fact about your company isn’t reported in an independent source, it can’t appear on your Wikipedia page, even if it’s completely true. Many companies try to add significant milestones, product launches, or leadership information only to have it removed because the event was never covered in qualifying external media.

5. The Edit Was Made by a New or Suspicious Account

Wikipedia’s editor community has developed sophisticated pattern recognition for detecting COI editing. A new account making significant changes to a corporate article is an immediate red flag. So is an account with no other editing history. So is an account that was clearly created specifically to edit one article. Automated bots and experienced human editors will flag and often revert these edits automatically.

6. You Removed Negative Information

Attempting to remove properly sourced negative information is one of the most common, and most quickly reversed, edits companies make. If there’s a documented controversy, legal issue, or negative coverage that’s been reported in reliable sources, that information belongs on the Wikipedia page. Removing it will be flagged as a clear COI edit, the information will be restored, and your attempted edit will be permanently recorded in the article’s history.

7. You Used Wikipedia’s “Edit” Button Rather Than the Talk Page

For anyone with a conflict of interest, the correct process is to request edits through the article’s Talk page rather than making direct edits. This is called the “edit request” process. When you bypass it and edit directly, you’re not just making a tactical error, you’re violating Wikipedia’s COI editing guidelines. The Talk page approach is slower, but it’s the only compliant method for interested parties.

Why This Matters Beyond Wikipedia

Wikipedia’s influence has expanded dramatically in the AI era. When someone asks an AI assistant about your company, the response is often grounded in your Wikipedia article. When Google displays a Knowledge Panel, the data comes largely from Wikipedia. When journalists research your company, Wikipedia is frequently their starting point.

A failed edit attempt that gets reverted and logged creates a permanent record. More importantly, it doesn’t solve the underlying problem, your Wikipedia page continues to say whatever it says, and that content continues to feed into every downstream platform that relies on Wikipedia as a data source.

The Professional Alternative

The disclosed COI approach is the only Wikipedia-sanctioned method for companies to engage with their own articles. It involves:

• Creating an account with disclosed affiliation
• Using the Talk page to request specific, sourced edits
• Engaging with the Wikipedia editor community transparently
• Building a record of good-faith participation

This approach works, but it requires understanding Wikipedia’s culture, policies, and the specific dynamics of your article. Companies that try to shortcut this process consistently fail. Companies that work within it consistently succeed.

What Should You Do Right Now?

Start with an honest assessment of your Wikipedia page. What does it currently say? What’s accurate, what’s outdated, and what’s missing? What sources exist that could support an update?

Five Blocks offers a free Wikipedia page assessment that evaluates your current page against Wikipedia’s standards, identifies the highest-priority issues, and outlines a compliant path forward. There’s no obligation, and the assessment itself gives you a clearer picture of where you stand.

Request your free Wikipedia assessment →

Frequently Asked Questions

Can I fix my Wikipedia page if it has errors?

Yes, but the process requires following Wikipedia’s COI guidelines. The safest approach is to use the Talk page to request corrections, citing reliable independent sources for each change you’re requesting.

What happens if my edit gets reverted?

The reversion is permanent in the edit history. Repeated reverted edits can lead to your account being flagged or blocked, and can make the article more closely watched by editors, making future compliant edits harder.

Can I create a Wikipedia account and not disclose my affiliation?

You can, but Wikipedia’s terms of use require disclosure for paid editing. Undisclosed paid editing violates Wikipedia’s policies and can result in account bans. More practically, experienced editors often identify COI editors through editing patterns even without disclosure.

How long does the Talk page process take?

It varies. Simple factual corrections with strong sourcing can sometimes be addressed within days. More complex changes may take weeks. Having a professional Wikipedia editor manage the process can significantly accelerate it.

Does Five Blocks make the edits directly?

Our approach is based on disclosed COI editing, we work transparently within Wikipedia’s system, not around it. This means requesting edits through proper channels rather than making direct edits that could be interpreted as undisclosed COI editing.

This post is part of our series Your Brand on Wikipedia, a practical guide to understanding, editing, and protecting your brand’s presence on Wikipedia.

Next: Can You Edit Your Own Wikipedia Page? The Conflict of Interest Question Every Executive Asks

Previous: Can Anyone Edit Wikipedia? Yes, and That’s Exactly the Problem for Your Brand

The post How to Edit a Wikipedia Page Without Getting Reverted (or Making Things Worse) appeared first on Five Blocks.

How Wikipedia Editing Actually Works

Wikipedia doesn’t require an account to make edits. You can navigate to any unprotected article, click “Edit,” change whatever you want, and hit “Publish.” Your changes go live immediately. There’s no approval queue, no editorial review, no fact-check before publication.

What there is, however, is an elaborate after-the-fact system. Every edit is logged permanently. IP addresses are recorded for unregistered editors. Thousands of volunteer editors patrol articles using automated tools that flag suspicious changes. Bots revert obvious vandalism within seconds. And experienced editors watch high-profile pages like hawks.

So while anyone can edit, not every edit survives. The real question isn’t whether you can edit your company’s Wikipedia page, it’s whether changes to that page will be noticed and reversed before they’ve already done damage.

The Wikipedia-to-AI Pipeline

Here’s what most executives don’t fully grasp: Wikipedia isn’t just a website people visit. It’s a primary data source for AI language models, Google’s Knowledge Panel, and dozens of other platforms that aggregate information about companies and public figures.

When someone asks an AI assistant about your company, a significant portion of that response is shaped by your Wikipedia page. When Google displays your company in a Knowledge Panel, most of that data comes from Wikipedia. When journalists research your organization, they often start there.

That means a vandalized Wikipedia page doesn’t just affect the people who visit Wikipedia. It affects every AI-generated answer about your company. And unlike a Wikipedia edit that gets reverted in an hour, AI training data persists.

What We See in Practice

At Five Blocks, we’ve managed Wikipedia engagements for Fortune 500 companies, major financial institutions, and global consumer brands, including pages that had been repeatedly vandalized with no effective response in place.

Our Wikipedia team monitors hundreds of corporate Wikipedia pages using our proprietary WikiAlerts™ platform. The patterns are consistent: pages for publicly traded companies see spikes in edits around earnings announcements and executive transitions. Pages for consumer brands get hit during product controversies. Pages for financial firms attract edits from anonymous accounts during regulatory proceedings.

Most companies don’t know their Wikipedia page has been edited until the damage has already propagated, into Google’s Knowledge Panel, into news coverage, into AI responses. The firms that fare best are the ones with monitoring and response systems already in place.

The Disclosed COI Approach

When companies need to update or correct their own Wikipedia pages, there’s a compliant way to do it. Wikipedia’s own guidelines recommend “disclosed conflict of interest” editing, creating an account, declaring your connection, and proposing changes on the article’s talk page rather than editing directly. Independent editors can then review and implement qualifying changes.

This approach is slower than direct editing, but it produces more durable results. Changes made through disclosed COI are less likely to be reverted, and the transparent process protects companies from the reputational risk of being caught editing their own pages without disclosure.

Does Your Company Have a Wikipedia Page? Here’s What to Do Next

If you don’t know the current state of your company’s Wikipedia page, what it says, who’s been editing it, and what changes are being proposed, you’re operating blind in one of the most influential information ecosystems on the internet.

We offer free Wikipedia page evaluations that assess your current content, edit history, sourcing quality, and AI exposure, and give you a clear picture of where things stand. No commitment required.

Wondering about the current state of your Wikipedia page? Contact us for a free page evaluation →

Frequently Asked Questions

Can anyone really edit any Wikipedia page?

Most Wikipedia pages can be edited by anyone, even without an account. However, some high-profile or frequently vandalized pages are “semi-protected” or “fully protected,” restricting who can edit them. Even on open pages, edits are tracked and can be quickly reverted by other editors. If you’re concerned about changes to your company’s page, we offer free page evaluations to assess your exposure.

How does Wikipedia editing affect Google and AI results?

Wikipedia is one of the primary sources for Google’s Knowledge Panels and AI language model training data. Changes to your Wikipedia page, accurate or otherwise, can quickly propagate into search results and AI responses. Monitoring your Wikipedia page is an essential part of managing your digital reputation in the AI era.

What is the right way for a company to update its Wikipedia page?

The recommended approach is disclosed COI editing: create an account, declare your relationship to the subject, and propose changes on the article’s talk page. Independent Wikipedia editors will review and implement qualifying changes. This is more transparent and produces more durable results than attempting to edit directly. Our team specializes in this process — contact us for a free assessment.

What should I do if my company’s Wikipedia page has been vandalized?

Act quickly, but carefully. Reverting vandalism directly can raise conflict of interest concerns if done by someone connected to the company. The best approach is to have an experienced Wikipedia editor review and revert the changes through proper channels. We can help, contact us for an emergency assessment.

This post is the first in our series Your Brand on Wikipedia, a practical guide to understanding, editing, and protecting your brand’s presence on Wikipedia.

Next: How to Edit a Wikipedia Page Without Getting Reverted (or Making Things Worse)

The post Can Anyone Edit Wikipedia? Yes, and That’s Exactly the Problem for Your Brand appeared first on Five Blocks.

Sometime milliseconds matter more than money — how Time to First Byte is quietly reshaping brand visibility in the AI era…

For years, the playbook was simple: optimize for Google, rank on page one, and let the traffic roll in. But as millions of consumers now get their answers from ChatGPT, Perplexity, and Google’s AI Overviews instead of scrolling through search results, a new technical reality is emerging, and it’s catching many of the world’s largest companies off guard.

If your corporate website is too slow, AI systems may never see your content at all.

The culprit? A metric most communications professionals have never heard of: Time to First Byte.

What Is TTFB, and Why Should You Care?

Time to First Byte (TTFB) measures how quickly a server begins responding after receiving a request. When someone, or something, asks your website for information, TTFB captures the milliseconds between the request and the very first byte of data being sent back.

For human visitors, a slow TTFB means frustrating load times. For AI crawlers, it means something far more consequential: your content may simply be skipped.

Here’s the technical reality that’s reshaping digital reputation: AI systems operate under strict latency budgets. When ChatGPT or Perplexity need to fetch real-time information to answer a query, they can’t wait around. If your server takes too long to respond, the crawler moves on. Your carefully crafted content, your company’s narrative, your leadership’s bios, your crisis messaging, never enters the AI’s knowledge base.

Google recommends a TTFB of 200 milliseconds or less. Industry benchmarks suggest anything above 500ms is problematic. Yet our analysis of Fortune 500 corporate websites reveals that many fall well above these thresholds, with some enterprise sites clocking in at 1.5 to 2 seconds before delivering their first byte of data.

The AI Crawl Budget Problem

Think of it like a library with limited reading time. Traditional search engines like Google have decades of infrastructure investment and can afford to be patient, they’ll come back, render JavaScript, and eventually index your content. AI crawlers don’t have that luxury.

When OpenAI’s GPTBot or Anthropic’s ClaudeBot visits your site, they’re operating on what’s essentially a “processing budget.” These systems need to ingest, understand, and vectorize millions of pages. If your site is slow to respond, has massive file sizes, or requires extensive JavaScript rendering, the crawler may timeout or only partially index your content.

Recent research tracking over 500 million GPTBot requests found that sites with response times under 200 milliseconds receive significantly more complete indexing than slower competitors. The data is clear: faster servers help with freshness, retrieval quality, and the likelihood of your content appearing in AI-generated answers.

The JavaScript Blind Spot

Speed isn’t the only factor working against enterprise websites. There’s another technical hurdle that’s even more problematic: most AI crawlers cannot execute JavaScript.

Unlike Google’s crawler, which uses a sophisticated rendering engine that can process JavaScript-heavy pages, AI crawlers from OpenAI, Anthropic, and Perplexity essentially operate like it’s 2010. They fetch raw HTML and move on. They don’t wait for your scripts to load, don’t execute your React components, and don’t see anything that’s dynamically injected after the initial page load.

This creates a troubling scenario for modern corporate websites. Many enterprise sites rely on JavaScript frameworks to deliver content, product information, executive bios, news releases, even basic navigation. To a human visitor with a browser, the site looks beautiful and fully functional. To GPTBot, it’s a blank page with a header and footer.

An analysis by Vercel and MERJ found zero evidence of JavaScript execution by GPTBot across half a billion requests. The same limitation applies to ClaudeBot, PerplexityBot, and most other AI crawlers. If your content requires JavaScript to display, AI systems simply cannot see it.

A Real-World Example: The Invisible Product Launch

Consider this scenario: A major consumer brand launches a new product line. They invest heavily in a sleek, modern microsite with interactive features, animated product showcases, and JavaScript-powered content sections. The site looks stunning. Traditional SEO is optimized. Press coverage links back appropriately.

Three months later, when consumers ask ChatGPT “What’s new from [Brand]?” or Perplexity “Tell me about [Brand’s] latest products,” the AI responses reference old information, or worse, a competitor’s offerings. The microsite, despite its beauty and its Google rankings, never made it into the AI’s knowledge base.

The brand’s communications team is baffled. The problem? The microsite’s TTFB averaged 1.2 seconds, and the product descriptions were rendered entirely via JavaScript. From the AI crawler’s perspective, the launch might as well never have happened.

What This Means for Reputation Management

At Five Blocks, we’ve spent years helping brands understand how digital platforms shape their narratives. The rise of AI-powered search represents the most significant shift in information discovery since Google’s emergence, and it brings new technical requirements that go beyond traditional SEO.

The implications for reputation are substantial:

1. Controlled narratives may not reach AI audiences. If your carefully managed corporate website is slow or JavaScript-dependent, the definitive information about your company may never enter AI training data or real-time retrieval systems.
2. Faster competitors get cited first. When AI systems need to answer questions about your industry, they’ll pull from sources that are accessible. If your competitor’s content loads in 150ms with clean HTML while yours struggles at 800ms behind JavaScript rendering, their narrative shapes the AI response.
3. Crisis content timing becomes critical. During a reputational crisis, every hour matters. If your response statement lives on a slow, JavaScript-heavy newsroom page, it may take significantly longer to propagate into AI systems, if it propagates at all.
4. Wikipedia and third-party sources gain outsized influence. When AI crawlers struggle to access primary corporate sources, they lean more heavily on Wikipedia, news coverage, and other third-party content. You lose control of your own story.

The Technical Fixes That Matter

Addressing these challenges requires coordination between communications teams and IT infrastructure. Here’s what actually moves the needle:

Optimize Server Response Times. Target a TTFB under 200ms. This may require CDN implementation, server-side caching, and infrastructure upgrades. Many enterprise WordPress sites, in particular, struggle with response times that can be dramatically improved through proper configuration.

Implement Server-Side Rendering. If your site uses JavaScript frameworks like React, Vue, or Angular, implement server-side rendering (SSR) to ensure that critical content is present in the initial HTML response. This lets AI crawlers see your content without waiting for JavaScript execution.

Audit What Crawlers Actually See. Disable JavaScript in your browser and visit your key pages. What remains is what AI crawlers see. If executive bios, product information, or corporate messaging disappear, you have a problem that needs immediate attention.

Prioritize Critical Content in HTML. Ensure that your most important reputation-relevant content, leadership information, company overview, key messaging, exists in static HTML rather than being loaded dynamically.

Monitor AI Crawler Access. Review server logs for GPTBot, ClaudeBot, and PerplexityBot activity. Are they successfully accessing your key pages? Are requests timing out? This data reveals whether AI systems can actually reach your content.

The Bigger Picture: Infrastructure as Reputation Strategy

For communications professionals accustomed to thinking about narratives, messaging, and media relationships, the idea that server response times affect reputation may feel foreign. But in the AI era, technical infrastructure is communications infrastructure.

The question isn’t just “What story are we telling?” but “Can AI systems even hear us?”

As AI-powered search continues to grow, and all indicators suggest it will only accelerate, brands that invest in technical accessibility will have a structural advantage. Their content will be more consistently indexed, more frequently cited, and more accurately represented in the AI-generated answers that increasingly shape public perception.

Those that don’t will find themselves shouting into a void, their carefully crafted messages trapped behind slow servers and invisible JavaScript, while faster, more accessible sources define their narrative instead.

Curious whether AI systems can access your corporate content? Five Blocks’ AIQ platform tracks how your brand appears across ChatGPT, Perplexity, Google AI, and other AI-powered platforms, including whether your key pages are being successfully indexed. Contact us for an assessment.

The post The Speed Tax: Your Slow Corporate Site Is Hurting You in AI Search appeared first on Five Blocks.

LAST UPDATED – October 2025

Wikipedia Editing for Companies: Best Practices and Considerations

One of the most common questions we get regarding Wikipedia is whether a company can edit its own page.

The short answer is – it’s complicated, and there are important guidelines to follow.

Wikipedia’s Stance on COI – Conflict of Interest

Wikipedia’s purpose is to provide an encyclopedia of impartial knowledge. Content that is promotional, self-serving, or biased will often get flagged or removed by other editors.

Wikipedia’s official policy states:

“You are discouraged from writing articles about yourself or organizations (including their campaigns, clients, products and services) in which you hold a vested interest.”

In essence, editing the Wikipedia page about your own company is discouraged, as Wikipedia aims to ensure its content remains unbiased.

 The Risks of Undisclosed Editing

It’s crucial to understand that:

• Wikipedia editors frown upon and may penalize pages that appear to have been edited by the company without transparency.
• Your IP address is recorded and visible to others. Never attempt to edit anonymously using a company-owned IP address.
• The Wikipedia editor community actively tracks changes. Your edits can trigger alerts for engaged editors, who may swiftly revert or challenge your modifications.

Free Consultation regarding your brand’s Wikipedia challenge: Contact Us

The Gold Standard: Disclosed Conflict of Interest (COI) Editing

Over the past few years, a new approach to Wikipedia editing has emerged, known as “disclosed conflict of interest” editing. This method is now considered the best practice for companies seeking to improve their Wikipedia presence. Here’s how it works:

1. The company or individual creates a Wikipedia user account.
2. They disclose their connection to the subject of the article on their user page or the article’s talk page.
3. Instead of directly editing the article, they suggest changes on the talk page, providing reliable sources to support their proposals.
4. Independent Wikipedia editors review these suggestions and implement them if they meet Wikipedia’s guidelines.

This approach aligns with Wikipedia’s values of transparency and allows companies to advocate for fair representation while respecting the platform’s rules.

Why Choose Disclosed COI Editing?

• It’s compliant with Wikipedia guidelines, avoiding the risks associated with undisclosed editing.
• It allows for more aggressive advocacy on behalf of your brand, as you’ve already informed the community of your connection.
• It typically leads to better long-term outcomes and a more stable Wikipedia presence.

How We Can Help
At Five Blocks, we specialize in navigating Wikipedia’s complex landscape for our clients. Our services include:

1. Notability Analysis: Determining whether an entity has the required coverage for Wikipedia editors to accept and publish a new article.
2. Current Page Analysis: Reviewing challenges and opportunities with existing content and sources.
3. Content Planning and Drafting: Preparing content and sourcing for potential Wikipedia inclusion.
4. Community Engagement: Working with the Wikipedia editor community to review and submit proposed changes via the appropriate talk pages.
5. Vandalism Monitoring: Utilizing tools to monitor Wiki pages so that swift action can be taken when needed.
6. Foreign Language Pages: Assisting in creating Wikipedia pages in other languages, following the specific guidelines for each.

Free Consultation regarding your brand’s Wikipedia challenge: Contact Us

Important Considerations

• Wikipedia’s editing guidelines are far more intricate than they may appear. The sources, writing style, and editor interaction require significant experience to manage effectively.
• Timing is crucial. Introducing a Wikipedia page in advance of a potential crisis may be beneficial, while doing so during a crisis could backfire.
• Building a positive Wikipedia presence takes time and patience. It’s a long-term strategy that requires consistent effort and adherence to Wikipedia’s principles.

Ready to Improve Your Wikipedia Presence?

If you’re looking to create or improve your company’s Wikipedia page, we offer free consultations to discuss your options and determine the best approach for your specific situation. Our team of experts can guide you through the process of disclosed COI editing, ensuring compliance with Wikipedia’s guidelines while working towards your goals.

Free Consultation regarding your brand’s Wikipedia challenge: Contact Us

Still have questions? See our FAQ

How do I edit an error on my company’s Wikipedia page?

While anyone can edit Wikipedia, it’s best to use the disclosed COI approach.
Best is to work with an experienced firm to suggest changes on the article’s talk page, providing reliable sources to support your corrections.

How do you create a Wikipedia page?

Wikipedia has strict standards for notability, citations, and conflict of interest. We recommend consulting with professionals to navigate these requirements and increase the chances of your page being accepted and maintained.

Can you edit your own Wikipedia page?

Direct editing is discouraged. Instead, work with an experienced team to ensure you are utilizing the disclosed COI method to suggest changes on the talk page and let independent editors implement them.

Who can edit a Wikipedia page?

Anyone can edit most Wikipedia pages, but it’s important to follow the platform’s guidelines, especially regarding conflict of interest.

Can I see who edited a Wikipedia page?

Yes, go to the ‘View History’ tab at the top right of the Wikipedia page. The username of the editor appears next to each change, along with the date.

Remember, working with Wikipedia requires patience, transparency, and adherence to the platform’s principles. By following these guidelines and best practices, you can work towards achieving a fair and accurate representation of your company on one of the world’s most visited websites.

Free Consultation regarding your brand’s Wikipedia challenge: Contact Us

The post Can you edit your or your company’s Wikipedia page? appeared first on Five Blocks.

TLDR: The changing Search landscape – from simply compiling results to attempting to give searchers the essence of a brand – requires those managing reputations to keep up with emerging tech to best serve their clients. AI is the latest destination in this journey.

In recent years, the field of Public Relations has undergone a significant transformation, evolving so that it now emphasizes building reputation as an overarching goal. PR stresses reputation because it goes beyond what a brand sells or how they came to be, it’s who the brand is. The same is true for individuals. While people often want to be known for their accomplishments and thought leadership – the ultimate goal is the desired reputation – who they are.

The Changing Nature of Reputation

Before mass media, reputation was primarily shaped by word-of-mouth and direct experiences. However, as the world became more interconnected and complex, we found ourselves interested in companies, brands, and people that we couldn’t personally interact with or judge for ourselves. This change led to the development of new, more sophisticated ways to assess reputation.

For decades, traditional media played a crucial role in shaping public opinion about brands and individuals. This meant that companies could influence their reputation by smartly influencing or buying media coverage – a practice that continues to this day, albeit in different forms.

The Internet Revolution

The advent of the internet brought about a seismic shift in how reputations are formed and managed. It created a vast array of potential sources for people to draw upon when seeking to understand what a brand or individual is really about. This digital landscape meant that many different actors could impact reputation. It also offered brands and individuals many opportunities to influence what people read and think about them.

The Role of Search Engines

Search engines immediately played a pivotal role in this new digital reputation ecosystem. Early search engines like Yahoo were primarily focused on directing users to specific websites. Over time, especially as Google came on the scene, they evolved to help users find specific pieces of information or answers to queries.

For the past 20 years, search engines have generally been keyword-based rather than intent-based, meaning they don’t  understand complex queries. They provide a set of potentially interesting results based on the keywords entered. For instance, if you type “McDonald’s,” the search engine can’t reliably know whether you want company information, financial data, or directions to the nearest restaurant.

In recent years, search engines have become increasingly sophisticated. They’ve gotten better at understanding user intent and providing more relevant results. They can see the device you are using, your location, your search history, etc. They can now also better satisfy more searchers by offering a variety of potentially relevant pages, sites, and other content, and then allow you to choose your own path. For a brand search, you’ll typically see the corporate website at the top, followed by sources like Wikipedia, financial information, social media profiles, news articles, and job listings. For individuals, you might see more pictures, information about family, and often details about their financial situation.(This is much easier to see using our proprietary IMPACT tracking and analysis platform.)

Key developments in search evolution:

1. Traditional Search Engines (1990s): Keyword-based, limited understanding of context.
2. Google Dominance (2000s): Advanced algorithms, PageRank, more relevant results.
3. Semantic Search (2010s): Better understanding of user intent and context.
4. AI-powered Search (2020s): Conversational, context-aware, synthesizing information from multiple sources.

Search engines are constantly self-optimizing to satisfy the maximum number of users. If people stop clicking on Bloomberg profiles, Google won’t prioritize them as prominently for companies and executives. As new platforms like TikTok gain popularity, search algorithms quickly find ways to integrate their  content into search results.

Google: The Accidental Reputation Snapshot Machine

In this effort to provide a variety of sources to satisfy searchers, Google has inadvertently created a powerful tool for assessing digital reputation. Understanding an entity’s reputation requires bringing together various perspectives – what the entity says about itself, what the media reports, what experts think, what peers say, what consumers experience, and what the general public believes. These are largely the same inputs that Google uses to populate its search results.

As a result, Google search results have become perhaps the most efficient way to get a representative snapshot of an entity’s reputation. They provide facts about what an entity has done and is doing now, as well as what various stakeholders think and say about it.

The Wikipedia Factor

Alongside search engines, Wikipedia has emerged as a significant source of reputation information. Wikipedia aims to include topics of consequence in their live, crowd-sourced encyclopedia, with facts determined and agreed upon by a community of editors following a set of guidelines. Earned media from reliable sources tends to be the most accepted sources for populating information on Wikipedia.

The influence of Wikipedia extends beyond its own platform. Search engines like Google and Bing rely heavily on Wikipedia to determine notability of an entity. In essence, having a Wikipedia page has become a marker of notability. Wikipedia often features prominently in Google and Bing search results, including in knowledge panels for branded searches.

Tracking Wikipedia edits (and suspected vandalism) became essential to managing a brand, which was why we developed WikiAlerts.  

The Imperative of Digital Reputation Management

If a desirable reputation is an important goal, it is crucial for brands to monitor their various online platforms, work to optimize different types of sites and content, and proactively build a more ideal online presence. Companies invest in their digital reputation to accomplish specific goals – increase sales, attract desirable staff, impress partners, influence the media – and sometimes to satisfy ego needs.

Improving a non-ideal reputation can involve crowding out unfavorable content or focusing on ideal content that directly or indirectly answers the types of questions people want to know. Monitoring Google and other key platforms like Wikipedia is essential, as it allows entities to see what stakeholders are seeing and helps map out areas for improvement.

Strategies for Reputation Improvement

Clients most often become concerned with their search presence when there is content that they don’t want to see. Their focus is therefore primarily on what they want to remove. Improvement to digital reputation can come in several ways:

• Getting a page or site removed
• Getting a piece of content changed or updated
• Demoting unwanted content
• Generating and promoting new desirable content

Determining the best approach requires expertise and experience. Many service providers develop and use a single set of tactics applied across all clients, often relying on specific tricks or optimizations they’ve developed. These companies are often focused primarily on demoting unfavorable content rather than building an ideal online presence.

The Five Blocks Approach

Our company, Five Blocks, uses a different approach to both meet the client’s short-term need to address negatives or solve specific issues, while also working to enhance the entity’s digital reputation for the long term. Our focus is on building the ideal online presence that covers who a brand is or aspires to be, based on a variety of perspectives. This strategy follows the logic that Search has become not simply a collection of random results, but a way to see your company or executives holistically. Using proprietary technology, the company performs peer analyses to surface opportunities – types of content or pages that appear prominently for peers and may therefore also suggest opportunities for the client.

Our data-centric approach, facilitated by our technology, means we track and analyze how entities are represented in various languages and locations, as well as the differences that we see depending on the keyword searched.

The AI Revolution

The emergence of AI models like ChatGPT, Copilot, Gemini, and others has ushered in a new era in reputation management. These AI models can do things that Google alone cannot:

• Understand more complex or nuanced queries
• Read the content of web pages and extract answers directly
• Synthesize information gleaned from various sources
• Formulate responses that directly address the query
• Provide results in specific requested formats

In an AI-powered world, we expect people to ask more complex questions to get directly to their desired outcomes. Of course, AIs will need to evolve to provide trusted (verified) answers, and we are already seeing this begin to happen. While Google-type searching is likely to persist alongside smarter AI-powered searches, brands and executives will soon find that their reputation lives both in Google and in the “brains” of a small number of very influential AIs.

The Power and Peril of AI Perceptions

What each of these AIs thinks about your brand can be critical. For example, when queried with a specific question about the two candidates for U.S. president, while many AIs declined to answer, Gemini consistently reported that one candidate was not a good man, while the other was. As AIs gain more influence, their perceptions about a brand or executive may become the most influential voice impacting decisions on topics ranging from how good a singer someone is, to who should be the president.

This is just one small example of how the pervasive use and de facto acceptance of AI will come with potentially far-reaching consequences. Changing the system will be very difficult – early attempts to slow down the development of smarter generative AI models do not seem to have been effective.

Shaping AI Perceptions

Currently the best option is to help shape what the AI models are saying about you, your brand, or the topics that are important to you. To do this, you first need to know what they are currently saying. This includes understanding the facts the AI models present, the topics they mention related to your entity, and the sources they return in support of their answers.  You’ll also want to track unfavorable content that is being returned. 

It will be important to see how each of these is changing over time – are they the same as before, getting better, or getting worse? It will be instructive to compare with peers – do they get the same treatment? Perhaps the AIs are not aware of some of your content or facts, or maybe they are outdated or have confused the entity with another similarly named one.

Years ago Five Blocks developed our IMPACT tracking and analysis platform, focused on Google searches. Since then we have been adapting our own capabilities to better serve clients, and will soon be launching a new platform, AIQ to address the new challenges AI Search presents.

AIQ provides a useful snapshot of how your brand is being seen by AIs. It also shows you trends – the topics and sources gaining prominent, versus those that are waning. 

PR firms and their clients can use this information to chart a course that will help shape AI’s understanding of the entity, giving clients more control over how they are perceived.

Where we go from here

As the world of search continues to evolve with the rise of AI, it is advisable for brands and individuals to stay ahead of the curve. By understanding how they’re perceived not just in traditional search engines like Google, but also by AI models, clients can take proactive steps to shape their digital reputation and maintain control over their narrative in this new frontier of information.

The post The Evolution of Search, PR, and Reputation Management  appeared first on Five Blocks.

By analyzing the search results for your brand and your competitors, you can uncover patterns of thought, and identify the questions and concerns that your stakeholders have. This is because Google’s algorithm depends on satisfying as many of the searchers as possible. This means that Google is already working to understand what people think and what they want to know. Tapping into this information can be invaluable in making decisions that shape your online presence and addressing potential challenges head-on.

One of the most prominent examples of AI in action on Google’s search results page is the People Also Ask feature. This section typically includes four questions and answers that Google deems most relevant to the search query. By examining these questions across multiple brands in your industry, you can gain a general understanding of what people are thinking and what they want to know about your brand and your competitors.

To demonstrate the power of this approach, we used Five Blocks IMPACT™, our tracking and analytics platform, to analyze the search results for several outdoor sportswear brands like North Face, Columbia, and Patagonia on March 31st in North America. We then used an AI  to identify patterns in the People Also Ask sections and summarize how each brand is perceived, as well as any obvious challenges and potential lessons from their peers.

The People Also Ask Questions and Answers collected automatically by Five Blocks IMPACT and then used for this analysis.

The analysis revealed some surprising insights. For example, searchers seemed particularly concerned with where each company is based. But the AI went much further, characterizing how each brand is seen by stakeholders, based on the questions asked and providing recommendations for what they could learn from their competitors. While AI models can make mistakes, we found this to be a fascinating instantaneous analysis.

A visualization of search results for various sportswear brands as seen in Five Blocks IMPACT and used for this analysis.

Here are the conclusions that the AI model provided:

Canada Goose

• Seen as an expensive but high-quality and durable brand, known for its premium materials.
• Can learn from Patagonia’s focus on sustainability and ethical sourcing.

Arc’teryx

• Perceived as a popular and functional outdoor brand, with some questions around its Chinese ownership.
• Can learn from Canada Goose’s reputation for premium materials and durability, and from Patagonia’s environmental initiatives.

Burberry

• Firmly established as a luxury fashion brand, with questions about its history and store locations.
• Can learn from Arc’teryx’s technical expertise in outdoor apparel and from Patagonia’s commitment to sustainability.

Columbia Sportswear

• Seen as a good mid-range outdoor brand, often compared to The North Face.
• Can learn from Arc’teryx’s focus on technical performance and innovation, and from Patagonia’s sustainability efforts and brand purpose.

Moncler

• Perceived as a high-end luxury brand, known for its expensive but high-quality winter wear.
• Can learn from Patagonia’s transparency and ethical practices, and from Arc’teryx’s technical expertise in outdoor gear.

Patagonia

• Widely recognized for its sustainability efforts and commitment to environmental causes.
• Can learn from Canada Goose’s reputation for premium materials and durability, and from Arc’teryx’s technical innovation in outdoor apparel.

REI

• Seen as a reputable outdoor co-op.
• Can learn from Patagonia’s strong brand purpose and environmental initiatives, and from Arc’teryx’s technical expertise in outdoor gear.

The North Face

• Perceived as an expensive but high-quality outdoor brand, with some questions about its popularity and branding.
• Can learn from Patagonia’s sustainability efforts and brand purpose, and from Arc’teryx’s technical innovation in outdoor apparel.

Using only Google Results of peers, we can then paint a picture of how Google, and perhaps their searchers, see each brand. 

A visualization of what we learned using this Data and AI analysis of the Google Results.

Using AI-powered search data analysis, brand and communications directors can identify areas where their brand’s image might not align with their goals and adjust their digital reputation management strategy accordingly. This data serves as a kind of  insightful preliminary focus group, providing valuable insights that can be tracked over time.

In today’s fast-paced digital landscape, staying on top of how your brand is perceived is more important than ever. Leveraging the power of AI and search data analysis, you can uncover hidden insights, address potential challenges, and ensure that your brand is resonating with your target audiences.

Five Blocks specializes in digital reputation management, combining cutting-edge technology and personalized service to help our clients overcome digital reputation challenges. Our advanced data analysis and AI-powered insights allow us to identify the root causes of various issues and uncover overlooked opportunities for improvement. We work closely with your communications team to develop and implement sustainable solutions that deliver long-lasting results. For more information or to see what we can do with your brand’s data, contact us.

The post Unlocking Stakeholder Perception of your Brand Using Google Search Data and AI appeared first on Five Blocks.

As these models become increasingly integrated into our daily lives, the importance of Wikipedia in shaping brand reputation cannot be overstated, since it is a major source for training AIs.

Importance of Wikipedia in AI Training
According to The New York Times, “Wikipedia is probably the most important single source in the training of AI models.” The platform’s vast trove of crowdsourced knowledge, covering a wide range of topics, provides invaluable data for AI models to learn from. Without access to this information, the development of current generative AI capabilities might not have even been possible. (Here’s some additional information on how AIs/LLMs/Chatbots are trained.) 

Impact on Brand Reputation
With AI models like ChatGPT, Claude AI, and Gemini having been trained on Wikipedia, inaccurate or biased information on the site can lead to negative or incorrect information about a brand, potentially harming its reputation. With so much riding on the underlying information in Wikipedia, ensuring the positivity and accuracy of a brand’s Wikipedia presence has become more important than ever.

Recommendations
Given Wikipedia’s elevated status, our recommendations for companies, brands, and individuals are to work within the Wikipedia guidelines to do the following:

1. Maintain: Create and/or maintain a well-structured, robust Wikipedia page for your brand or personal profile.
2. Update Accurately: Make sure the page remains updated and accurate with current facts, figures, and noteworthy achievements.
3. Include more sources: Since LLMs utilize all of the content, include as many relevant, verifiable sources, as appropriate – these should only help the AI training.
4. Go Multilingual: Consider developing a presence across multiple language editions of Wikipedia. LLMs often learn from content in various languages, and the more you play an active role, the better. Also, consider that English is often the hardest language version of Wikipedia to impact, and other language versions can be very easy to edit.
5. Other Wiki pages: LLMs can learn about your brand and industry from any Wikipedia article, so consider getting relevant information added to relevant industry articles, not just the ones about your brand.
6. Talk Pages: Leverage Wikipedia’s “Talk” pages to include additional relevant information, as LLMs may also use these for training.
7. Images: Consider submitting relevant images via Wikimedia Commons to enhance your Wikipedia page and improve AI model understanding.
8. Categorize: Utilize Wikipedia’s category system to ensure your page is properly categorized and connected to the ideal topics.
9. Monitor: Monitor your Wikipedia presence for edits that may introduce inaccuracies, outdated information, or bias; address issues appropriately and promptly. Do the same for other relevant pages related to your company or brand. Our free WikiAlerts service provides tracking of Wikipedia and Talk pages.
10. Wikidata: Beyond Wikipedia, leverage Wikidata, Wikipedia’s sister project, a powerful database of community-contributed structured data that LLMs will increasingly use to verify facts.

Do We Even Need Wikipedia in a World of AI?
An interesting question that has been raised recently is whether there is even a need for Wikipedia. Since the content is taken from various third-party sources, and the LLMs presumably have access to the sources and probably many more, why can’t an AI produce Wikipedia content that would be as good or better than content created by Wikipedia editors?

To answer this question there have been various attempts to utilize AI to write sections of Wikipedia pages, but so far, despite the great capabilities of AI, they have not been proven to produce content that is up to par. It is possible that this will change at some time in the future, but for now there still seems to be tremendous benefit derived from the human (crowdsourced) process that helps create a Wikipedia page. Perhaps AIs that are trained on this process will eventually produce content that is recognized to be of high enough quality. 

Conclusion
Ongoing tracking of how AI models represent your brand, and what role Wikipedia may be playing, can help you identify areas for improvement within Wikipedia and beyond. 

As the use of Wikipedia in AI training continues to grow, we believe that the future of brand reputation management will be even more closely tied to Wikipedia. By actively managing their Wikipedia presence, companies can ensure that AI models have access to an important trusted source of accurate and up-to-date information, ultimately leading to a more positive online reputation.

Five Blocks specializes in digital reputation management for platforms including Google and Wikipedia, combining cutting-edge technology and personalized service to help our clients overcome digital reputation challenges. Our advanced data analysis and AI-powered insights allow us to identify the root causes of digital reputation issues and uncover overlooked opportunities for improvement. We work closely with your communications team to develop and implement sustainable solutions that deliver long-lasting results.

For more information or to see what we can do with your brand’s data contact us.

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