How to handle AI hallucinations about my brand

AI making things up: Understanding the roots and risks of AI hallucinations in brand visibility

As of April 2024, up to 37% of AI-generated responses about brands contained inaccurate or fabricated information, according to a recent study by an ai brand monitoring industry analytics firm. It might seem odd, but AI hallucinations, where AI systems generate false or misleading statements, have become a growing problem, especially when it comes to brand representation. Think about it: consumers and stakeholders increasingly rely on chatbots like ChatGPT or Google’s Bard to answer questions or recommend products. But when these systems produce content that doesn’t align with a company’s actual offerings or values, it can damage reputation or mislead honest customers.

AI hallucinations happen because language models generate plausible text based on patterns learned during training, not by verifying facts in real-time. This means chatbots might confidently assert something your company never said or claim product features that don’t exist. For example, last March, a mid-sized tech firm discovered that a popular AI chatbot repeatedly mentioned a “premium support” feature that wasn’t launched yet, confusing their client base. This hallucination took weeks to correct as misinformation proliferated online.

So how do you manage these AI-made mistakes without wasting time chasing shadows? Brands need to monitor and interpret AI’s narrative about them, what I call the “AI Visibility Score.” This score reflects how accurately and positively an AI system portrays your company across channels. It ranges from factual representation to wild invention. Google’s Knowledge Graph has improved accuracy but still struggles with emerging startups or niche products. Meanwhile, ChatGPT offers fast responses but sometimes invents citations or statistics out of thin air.

Let me share a few recent cases I’ve tracked. One e-commerce brand found that Perplexity and ChatGPT described their eco-friendly packaging as made from “100% recycled glass” instead of recycled paper, which is factually incorrect. The mistake was subtle but problematic for consumers demanding sustainable practices.

Another situation involved a well-known SaaS provider. AI chatbots generated incorrect pricing tiers that promised levels of service not offered anywhere, some potential customers even signed up before realizing the error. Fixing ai visibility tracking software that required not only updating the official website but also pushing corrected messaging to major AI data sources.

Understanding the core causes demands recognizing AI’s reliance on probability models over fact-checking. It’s like a confident salesperson telling you a story sounding perfectly plausible, but you’re left wondering if they actually know the product inside out.

Addressing AI hallucinations involves ongoing investment in monitoring tools, content adjustments, and direct interaction with AI platforms. Initial monitoring can take 48 hours to highlight pressing errors. Content corrections and outreach can stretch over four weeks. Cost varies widely, larger brands with complex product lines spend six figures annually on continuous AI visibility management, smaller brands less.

To curb AI inaccuracies, companies need a centralized, vetted knowledge base updated quarterly. This includes product specs, FAQs, recent changes, and validated press statements. Creating and maintaining such documentation forces coordination between marketing, product, and legal teams, sometimes a slow and frustrating endeavor but absolutely critical.

Handling AI hallucinations isn't just reactive cleanup; it demands proactive correction strategies. The process has evolved beyond “hope the chatbot updates itself.” Instead, brands undertake a Monitor -> Analyze -> Create -> Publish -> Amplify -> Measure -> Optimize cycle to regain control. I’ll break this down with three major approaches currently in play.

The first approach demands budget for vendor relationships and patience. The second is resource-intensive with content creation teams needing SEO and AI literacy. The third intersects with reputation management and social strategy costs. Nine times out of ten, a hybrid method blending direct corrections with authoritative content wins, while social media efforts serve as support.

Expect vendor corrections to take 4-6 weeks for visible effect. Content reinforcement might require months before AI assimilates changes fully. Social media corrections are immediate but fragile. Experience shows fewer than 50% of AI hallucination requests get completely resolved exclusively via vendor channels, it’s never a simple “submit and forget” scenario.

Dealing with chatbot inaccuracies can often feel like playing whack-a-mole. Last September, I advised a fintech startup that routinely saw new AI hallucinations about their compliance protocols every time ChatGPT rolled out an update. Monitoring was the first practical move, they used custom tools to track AI “mentions” roughly every 48 hours. Their process wasn’t perfect; for instance, one monitoring tool flagged benign conversations as “issues,” creating noise.

After sifting through the noise, they developed a prioritized correction plan targeting the most damaging hallucinations first, like incorrect loan approval limits. They realized early on that fixing the root cause isn't just about issuing a statement. Instead, it encompasses updating website content, FAQs, and external knowledge bases simultaneously. It's human creativity combined with machine precision.

One useful trick: involve subject matter experts (SMEs) in crafting corrections. The SME might spot nuances or ambiguous phrasing that could unexpectedly trigger hallucinations later. But don’t expect perfection. Some false claims persist because AI models train on historical data that can’t be fully erased.

Brands should maintain an up-to-date set of key documents:

Some companies hire specialized AI visibility managers or consultants, experienced in SEO and AI prompt engineering, to shape AI narratives actively. These licensed agents understand how chatbots ingest content and can design prompts or responses mitigating misinformation. But these services can be pricey and still require in-house coordination. They’re worth it mainly if your brand faces high-stakes misinformation harming conversions or reputation.

Set realistic timelines: initial AI scans every 48 hours, in-depth analysis every 2 weeks, public corrective content published monthly . Expect at least 3-4 cycles before seeing meaningful reduction in hallucinations. Keep stakeholders informed with dashboards or reports showing AI visibility score improvements.

AI visibility management now extends beyond fixing chatbot lies about my company. Emerging strategies aim to elevate brand presence actively within AI ecosystems, turning correction into competitive advantage. Google and Microsoft recently launched tools encouraging brands to submit structured data helping AI understand entities better. For example, Google’s Business Profiles let brands add rich metadata directly influencing AI answers.

But here's a twist: not all brands benefit equally. Startups or niche players often struggle with the complexity and cost of implementing these systems, while large companies with established SEO teams have a head start. Furthermore, the jury’s still out on how AI hallucinations will evolve as multimodal AI, combining text with images or videos, becomes mainstream over the next 1-2 years.

During COVID lockdowns, some brands used AI to generate FAQs and test messaging, surprisingly effective but sometimes resulted in unexpected hallucinations requiring rework. This highlights human creativity combined with machine precision as crucial. In practice, AI amplifies even tiny narrative slips. You have to be meticulous correcting, then constantly optimizing.

Google’s Knowledge Graph update in early 2024 expanded its data sourcing, improving accuracy but increasing the volume of data brands must police. AI chatbot vendors like ChatGPT now allow “user feedback” flags, a new lever albeit imperfect. These updates suggest the process of Monitor -> Analyze -> Create -> Publish -> Amplify -> Measure -> Optimize will tighten further.

Advanced AI visibility management also intersects with compliance and legal planning. Misinformation about pricing or contractual terms can lead to regulatory scrutiny. Some brands partner with legal teams to audit outgoing AI-facing data regularly, a surprisingly underrated tactic ensuring factual consistency.

This combination helps brands stay ahead of AI errors, reduce risk, and safeguard trust. Look, AI isn’t magic, and its hallucinations don’t vanish on their own, management requires continual vigilance.

First, check your brand’s presence across major AI platforms to assess your current AI visibility score. Don’t rush into broad fixes before pinpointing what’s most damaging or widespread. Whatever you do, don’t ignore the issue hoping it’ll fade away, it only grows. Instead, start gathering accurate, verifiable content and prepare to engage AI vendors and your internal teams in a no-nonsense, ongoing process that’s equal parts technical and creative. Keep a detailed timeline and stay ready to adapt. Missing even small hallucinations can cost you real customer trust soon enough.