AI Hallucinations About Your Brand: What to Do When Models Get the Facts Wrong?

A procurement officer asks ChatGPT to recommend vendors in your category. The model confidently states your company discontinued its flagship service two years ago. The service is live and growing. A prospective client asks Gemini to compare you with a competitor. Gemini advises against your company, citing a “lack of customer support,” which you have had operational for a decade. A journalist uses Perplexity to fact-check your latest announcement. Perplexity attributes your product launch to a different company entirely.

These are not hypothetical scenarios. They are documented outcomes of AI hallucinations: instances where language models generate plausible-sounding but factually incorrect information about real companies, products, and people. The models do not fabricate with intent. They predict the most statistically likely word sequences based on their training data. When that data is incomplete, contradictory, or outdated, the model fills gaps with confident-sounding guesses that can be entirely wrong.

The scale is documented. A 2025 study by Columbia University’s Tow Center for Digital Journalism tested 1,600 queries across eight AI search engines and found that over 60% of responses contained factual errors. Error rates varied sharply by platform: Perplexity showed 37% inaccurate responses, ChatGPT Search 67%, and Grok-3 approximately 94% (Tow Center / CJR, March 2025). Hallucinations are not a minor glitch. They are a structural feature of how large language models work, and mathematical research has demonstrated they cannot be fully eliminated.

For PR and communications teams, this creates a new category of reputation risk that requires a new response playbook.

Three Types of AI Errors That Damage Brand Reputation

Not all hallucinations are equally harmful. From Insightland’s brand visibility audits across ChatGPT, Gemini, Perplexity, and Copilot, three categories emerge consistently.

Fabricated facts. The model invents information with no basis in any source. Examples: attributing a product to the wrong company, stating an incorrect founding date, naming a CEO who has never worked at the company. This happens most often when the model lacks sufficient data about a brand and fills the gap with plausible-sounding but false details extrapolated from patterns in similar companies.

Outdated information. The model relies on content published months or years ago. If a company rebranded, updated pricing, launched a new service line, or discontinued a product, but the most prominent online sources predate these changes, the model describes the company as it was, not as it is. Muck Rack’s analysis shows that AI models strongly favor content from the past 12 months (Muck Rack, May 2026), but if no recent content about the brand exists, older sources fill the void.

Distorted positioning. The model cannot determine what makes the brand distinctive. When a company’s unique selling proposition is communicated inconsistently across its own website, press releases, and third-party mentions, the model either omits the brand from competitive comparisons or describes it in generic terms that fail to differentiate it. In one Insightland client case, the brand was consistently excluded from “best of” comparisons because competitors had clearly articulated differentiators (certifications, production origin, quality metrics) and the client did not.

A concrete example from practice: Gemini actively recommended against an Insightland client specifically because a single third-party source incorrectly stated the company lacked a customer service hotline. The hotline was fully operational. The correction required contacting the publisher, updating the source, and publishing new PR materials confirming the hotline’s availability. The full process from identification to resolution took approximately one quarter.

Why Non-English and Smaller-Market Brands Are More Vulnerable

The hallucination problem is structurally worse for brands operating outside the English-language ecosystem. Research published in NCBI/PMC (2025) found that language models hallucinate significantly more frequently with non-English inputs: the hallucination rate was 30.2% for Korean versus 13.4% for English, and top-1 accuracy dropped from 74.5% to 59% (NCBI/PMC, 2025).

While no equivalent study exists specifically for Polish, French, or other European languages, the mechanism is the same: models have less training data in these languages, fewer sources for cross-verification, and consequently “guess” more often instead of citing verified facts.

This is compounded by a structural brand bias documented in academic research. A study published in the Findings of EMNLP 2024 demonstrated that language models systematically favor globally recognized brands, with recommendation rates between 88% and 100% for luxury brands from high-income countries (Kamruzzaman, Nguyen, Kim, EMNLP 2024). For a mid-market European brand with limited global media presence, this means:

• The model has less data to work with, increasing the probability of hallucination.
• When it does mention the brand, it does so with lower confidence, making errors more likely.
• When errors occur, fewer alternative sources exist to correct them automatically.

For PR teams working with brands in Central and Eastern Europe, this creates both a risk and an opportunity. The risk is higher default vulnerability. The opportunity is that the corrective actions (consistent entity signals, targeted earned media, structured data) are well within PR’s existing skill set and represent a clear competitive advantage for teams that implement them early.

Five Steps to Correct AI Hallucinations About Your Brand

Correcting hallucinations is not a one-time project. It is a repeatable operational process.

1. Build a hallucination register. Create a spreadsheet or tracking system with columns for: prompt (question asked), AI model, date tested, response content, error type (fabrication / outdated / distortion), severity (critical: affects pricing, legal claims, service availability; medium: positioning, context; low: minor inaccuracies), suspected source of error, correction status.
2. Fix your own sources first. Start with what you control directly. Ensure your company website contains current, unambiguous information: full company name, precise service/product descriptions, pricing (if public), contact details, key personnel names and titles. Verify that the same information is consistent across your LinkedIn page, Google Business Profile, Wikidata entry, and any industry directories where the company is listed. Inconsistency across your own properties is one of the most common root causes of AI distortion.
3. Fix external error sources. If the hallucination traces to a specific article, directory listing, or review platform, contact the publisher or author and request a correction. Simultaneously, publish new PR materials containing accurate information on open-access, high-authority platforms. The goal is to create multiple credible sources that contradict the erroneous one, giving the model stronger signals of what is correct.
4. Strengthen entity signals. AI models recognize brands as “entities” based on consistent, repeated information across multiple sources. The more sources that provide the same correct facts about your company, the less likely the model is to fabricate alternatives. Key actions: update your Wikidata entry, ensure structured data on your website (schema.org: Organization, Product, Person) is accurate and complete, maintain consistent brand naming across all channels.
5. Test and repeat. After implementing corrections, verify whether AI models have updated their responses. Perplexity (which searches the web in real time) may reflect changes within days. ChatGPT and Claude, which use periodically updated knowledge bases, may take weeks to months. Schedule re-testing on a consistent prompt set every two to four weeks. Do not expect instant results.

What Cannot Be Fixed (and What to Do About It)

Not every hallucination can be traced to a specific source that can be corrected. Sometimes a model “guesses” based on general patterns in training data rather than any identifiable source. In these cases, the only strategy is to increase the volume and quality of accurate information about the brand online, so the model has more reliable data to draw from and less reason to fill gaps with fabrications.

Some AI platforms offer mechanisms for reporting errors (Google’s feedback tools, ChatGPT’s thumbs-down interface). These do not guarantee corrections, but they create a documentation trail. A more effective strategy is to fix the sources the model relies on rather than trying to influence the model directly.

Wikipedia corrections are possible but work only indirectly: Wikipedia can only be edited based on published, reliable sources. If the only available sources about a company contain errors, a Wikipedia edit will not pass editorial review. First fix the sources (through earned media), then update Wikipedia.

Prevention Is Cheaper Than Correction

The same principles that drive correction also work preventatively when applied before problems emerge.

• Publish regularly. Models favor recent content. If the last PR material about your company is 18 months old, models will rely on whatever is available, even if it is three years out of date.
• Maintain entity consistency. The same company name, the same product names, the same executive names with titles, across every source. Inconsistency is an invitation for hallucination.
• Do not block AI crawlers. Check your robots.txt: are GPTBot, PerplexityBot, and ClaudeBot allowed access? If they are blocked, models cannot see your own content and rely entirely on what others say about you.
• Monitor AI responses systematically. Do not wait for a client or prospect to tell you that ChatGPT is saying something wrong about your company. Detect it first. Regular AI visibility monitoring catches problems before they influence purchasing decisions.

The EU AI Act: Regulatory Context for Brand Accuracy in AI

For brands operating in European markets, AI hallucinations intersect with emerging regulatory obligations. The EU AI Act’s Article 50 transparency requirements (enforceable from August 2, 2026) mandate that AI-generated content be appropriately labeled (EU AI Act, Art. 50). While the Act does not currently impose direct liability on AI providers for hallucinated brand information, the regulatory trajectory suggests increasing attention to accuracy and accountability.

The ICCO Warsaw Principles (2023) provide a PR-specific ethical framework that includes fact-checking and transparency obligations relevant to AI-generated content about brands. For European communications teams, proactive management of AI accuracy is both a reputation protection measure and a compliance alignment step.

Among large US companies, reputation is already the most frequently cited AI risk: 38% identify it as their primary concern, ahead of cybersecurity at 20% (The Conference Board / ESGAUGE, 2025).

Frequently Asked Questions

Insightland conducts Brand Search Presence audits that include hallucination identification, error source mapping, and specific corrective recommendations across ChatGPT, Gemini, Perplexity, and Copilot. Our AI Search Optimization service provides ongoing correction and prevention as part of a partnership model designed for PR teams and agencies.