No CEO wants to lose $100B of market value because of an AI mishap. Is now the right time for brands to jump in and fully invest in ChatGPT and other generative AI experiences?

To make that decision, brands should consider the six weaknesses of generative AI and to what extent these disadvantages impact brand goals.

The 6 Weaknesses

Misinformation

ChatGPT has the potential for misinformation, depending on the data source used or the topic in question. One example may be due to a lack of data freshness (i.e: a stroller recall). This information may not be factored in if the LLM is trained on 2021 data sources. For brands where consumer safety is dependent on current information, this can be a deal-breaker.

Hallucinations

In AI, a “hallucination” refers to information that the LLM perceives to be true, but in reality fabricated or nonsensical due to the bot’s lack of real-world understanding. One example is with targeted advertising, where an AI algorithm may incorrectly assume a user’s interests based on their online behavior or search history.

For instance, an AI algorithm may associate a person’s online searches for camping gear with an interest in hunting, even if the person has never searched for hunting-related content. As a result, the algorithm may display ads for hunting equipment, which may not be relevant…and could even be offensive.

Questionable ethics and legal liability

Generative AI has the potential to revolutionize the way content (text, images, videos, computer code, legal contracts and architectural drawings) is created, but it also comes with several risks, including plagiarism and infringement of copyright, which is particularly important when it comes to intellectual property (IP) rights. While plagiarism is an often-cited concern with regard to generative AI tools, another area of questionable ethics is misleading vulnerable customers.

Information may be influenced by biases that are present in the training data, resulting in a customer purchasing items that don’t align with their beliefs or philosophies. Think misleading responses to product questions on sustainability, animal rights/testing, etc. Whether these responses could be made as purposely misleading and blamed on AI remains a gray area.

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