We’ve all asked a chatbot about a company’s services and seen it respond inaccurately, right? These errors aren’t just annoying; they can seriously hurt a business. AI misrepresentation is real. LLMs could provide users with outdated information, or a virtual assistant might provide false information in your name. Your brand could be at stake. Find out how AI misrepresents brands and what you can do to prevent them.
How does AI misrepresentation work?
AI misrepresentation occurs when chatbots and large language models distort a brand’s message or identity. This could happen when these AI systems find and use outdated or incomplete data. As a result, they show incorrect information, which leads to errors and confusion.
It’s not hard to imagine a virtual assistant providing incorrect product details because it was trained on old data. It might seem like a minor issue, but incidents like this can quickly lead to reputation issues.
Many factors lead to these inaccuracies. Of course, the most important one is outdated information. AI systems use data that might not always reflect the latest changes in a business’s offerings or policy changes. When systems use that old data and return it to potential customers, it can lead to a serious disconnect between the two. Such incidents frustrate customers.
It’s not just outdated data; a lack of structured data on sites also plays a role. Search engines and AI technology like clear, easy-to-find, and understandable information that supports brands. Without solid data, an AI might misrepresent brands or fail to keep up with changes. Schema markup is one option to help systems understand content and ensure it is properly represented.
Next up is consistency in branding. If your brand messaging is all over the place, this could confuse AI systems. The clearer you are, the better. Inconsistent messaging confuses AI and your customers, so it’s important to be consistent with your brand message on various platforms and outlets.
Different AI brand challenges
There are various ways AI failures can impact brands. AI tools and large language models collect information from sources and present it to build a representation of your brand. That means they can misrepresent your brand when the information they use is outdated or plain wrong. These errors can lead to a real disconnect between reality and what users see in the LLMs. It could also be that your brand doesn’t appear in AI search engines or LLMs for the terms you need to appear.
At the other end, chatbots and virtual assistants talk to users directly. This is a different risk. If a chatbot gives inaccurate answers, this could lead to serious issues with users and the outside world. Since chatbots interact directly with users, inaccurate responses can quickly damage trust and harm a brand’s reputation.
Real-world examples
AI misrepresenting brands is not some far-off theory because it has an impact now. We’ve collected some real-world cases that show brands being affected by AI errors.
All of these cases show how various types of AI technology, from chatbots to LLMs, can misrepresent and thus hurt brands. The stakes can be high, ranging from misleading customers to ruining reputations. It’s good to read these examples to get a sense of how widespread these issues are. It might help you avoid similar mistakes and set up better strategies to manage your brand.
Case 1: Air Canada’s chatbot dilemma
- Case summary: Air Canada faced a significant issue when its AI chatbot misinformed a customer regarding bereavement fare policies. The chatbot, intended to streamline customer service, instead created confusion by providing outdated information.
- Consequences: This erroneous advice led to the customer taking action against the airline, and a tribunal eventually ruled that Air Canada was liable for negligent misrepresentation. This case emphasized the importance of maintaining accurate, up-to-date databases for AI systems to draw upon, illustrating a major AI error in alignment between marketing and customer service that could be costly in terms of both reputation and finances.
- Sources: Read more in Lexology and CMSWire.
Case 2: Meta & Character.AI’s deceptive AI therapists
- Case summary: In Texas, AI chatbots, including those accessible via Meta and Character.AI, were marketed as competent therapists or psychologists, offering generic advice to children. This situation arose from AI errors in marketing and implementation.
- Consequences: Authorities investigated the practice because they were concerned about privacy breaches and the ethical implications of promoting such sensitive services without proper oversight. The case highlights how AI can overpromise and underdeliver, causing legal challenges and reputational damage.
- Sources: Details of the investigation can be found in The Times.
Case 3: FTC’s action on deceptive AI claims
- Case summary: An online business was found to have falsely claimed its AI tools could enable users to earn substantial income, leading to significant financial deception.
- Consequences: The fraudulent claims defrauded consumers by at least $25 million. This prompted legal action by the FTC and served as a stark example of how deceptive AI marketing practices can have severe legal and financial repercussions.
- Sources: The full press release from the FTC can be found here.
Case 4: Unauthorized AI chatbots mimicking real people
- Case summary: Character.AI faced criticism for deploying AI chatbots that mimicked real people, including deceased individuals, without consent.
- Consequences: These actions caused emotional distress and sparked ethical debates regarding privacy violations and the boundaries of AI-driven mimicry.
- Sources: More on this issue is covered in Wired.
Case 5: LLMs generating misleading financial predictions
- Case summary: Large Language Models (LLMs) have occasionally produced misleading financial predictions, influencing potentially harmful investment decisions.
- Consequences: Such errors highlight the importance of critical evaluation of AI-generated content in financial contexts, where inaccurate predictions can have wide-reaching economic impacts.
- Sources: Find further discussion on these issues in the Promptfoo blog.
Case 6: Cursor’s AI customer support glitch
- Case summary: Cursor, an AI-driven coding assistant by Anysphere, encountered issues when its customer support AI gave incorrect information. Users were logged out unexpectedly, and the AI incorrectly claimed it was due to a new login policy that didn’t exist. This is one of those famous hallucinations by AI.
- Consequences: The misleading response led to cancellations and user unrest. The company’s co-founder admitted to the error on Reddit, citing a glitch. This case highlights the risks of excessive dependence on AI for customer support, stressing the need for human oversight and transparent communication.
- Sources: For more details, see the Fortune article.
All of these cases show what AI misrepresentation can do to your brand. There is a real need to properly manage and monitor AI systems. Each example shows that it can have a big impact, from huge financial loss to spoiled reputations. Stories like these show how important it is to monitor what AI says about your brand and what it does in your name.
How to correct AI misrepresentation
It’s not easy to fix complex issues with your brand being misrepresented by AI chatbots or LLMs. If a chatbot tells a customer to do something nasty, you could be in big trouble. Legal protection should be a given, of course. Other than that, try these tips:
Use AI brand monitoring tools
Find and start using tools that monitor your brand in AI and LLMs. These tools can help you study how AI describes your brand across various platforms. They can identify inconsistencies and offer suggestions for corrections, so your brand message remains consistent and accurate at all times.
One example is Yoast SEO AI Brand Insights, which is a great tool for monitoring brand mentions in AI search engines and large language models like ChatGPT. Enter your brand name, and it will automatically run an audit. After that, you’ll get information on brand sentiment, keyword usage, and competitor performance. Yoast’s AI Visibility Score combines mentions, citations, sentiment, and rankings to form a reliable overview of your brand’s visibility in AI.
See how visible your brand is in AI search
Track mentions, sentiment, and AI visibility. With Yoast AI Brand Insights, you can start monitoring and growing your brand.
Optimize content for LLMs
Optimize your content for inclusion in LLMs. Performing well in search engines is not a guarantee that you will also perform well in large language models. Make sure that your content is easy to read and accessible for AI bots. Build up your citations and mentions online. We’ve collected more tips on how to optimize for LLMs, including using the proposed llms.txt standard.
Get professional help
If nothing else, get professional help. Like we said, if you are dealing with complex brand issues or widespread misrepresentation, you should consult with professionals. Brand consultants and SEO experts can help fix misrepresentations and strengthen your brand’s online presence. Your legal team should also be kept in the loop.
Use SEO monitoring tools
Last but not least, don’t forget to use SEO monitoring tools. It goes without saying, but you should be using SEO tools like Moz, Semrush, or Ahrefs to track how well your brand is performing in search results. These tools provide analytics on your brand’s visibility and can help identify areas where AI might need better information or where structured data might enhance search performance.
Businesses of all types should actively manage how their brand is represented in AI systems. Carefully implementing these strategies helps minimize the risks of misrepresentation. In addition, it keeps a brand’s online presence consistent and helps build a more reliable reputation online and offline.
Conclusion to AI misrepresentation
AI misrepresentation is a real challenge for brands and businesses. It could harm your reputation and lead to serious financial and legal consequences. We’ve discussed a number of options brands have to fix how they appear in AI search engines and LLMs. Brands should start by proactively monitoring how they are represented in AI.
For one, that means regularly auditing your content to prevent errors from appearing in AI. Also, you should use tools like brand monitor platforms to manage and improve how your brand appears. If something goes wrong or you need instant help, consult with a specialist or outside experts. Last but not least, always make sure that your structured data is correct and aligns with the latest changes your brand has made.
Taking these steps reduces the risks of misrepresentation and enhances your brand’s overall visibility and trustworthiness. AI is moving ever more into our lives, so it’s important to ensure your brand is represented accurately and authentically. Accuracy is very important.
Keep a close eye on your brand. Use the strategies we’ve discussed to protect it from AI misrepresentation. This will ensure that your message comes across loud and clear.
The post When AI gets your brand wrong: Real examples and how to fix it appeared first on Yoast.
