AI Content Creation: Are You Aware of These 7 Risks

There are numerous use cases for generative AI in marketing, including industry and article research, faster first drafts, creating content briefs, brainstorming titles and headers, angles, and introductions, programmatic SEO, repurposing content from one format to another, and personalizing ABM campaigns. However, each of these use cases comes with its own set of risks that should be considered before implementing generative AI technology.

Risks Associated with Generative AI in Marketing

The risks associated with generative AI in marketing include factual inaccuracy and hallucination, copyright infringement and legal challenges, Google penalization, and the potential for producing mediocre content that does not help the bottom line.

Google Risk and Updated Guidance

There has been some debate over whether Google penalizes sites that create AI-generated content. In April 2022, Google's John Mueller stated that AI-generated content goes against the webmaster guidelines and could be seen as spam. However, in February 2023, Google issued updated guidance that suggests the focus should be on creating helpful, problem-solving content rather than how it's created. While there is no guarantee that any action will be immune from penalization, most use cases are expected to be safe.

Channel Risk

Generative AI may pose a risk to Google and SEO by creating a vast amount of content that competes for keywords and potentially lowers the returns of search. This is a speculative risk, but one worth considering, especially as chat becomes the primary mode of interaction and organic search-focused content may become less valuable for businesses.

Hallucination Risk and Remedies

When using generative AI, there is a risk that content may contain falsehoods and fictional information, known as "hallucinations." These errors are an intrinsic property of generative AI, which is designed to predict the next-best word in any given sequence of words. However, this risk can be remedied by putting a human in the loop to analyze the validity of the content and catch any mistakes.

Reputation Risk

The use of generative AI in marketing can also pose a reputation risk. Customers and stakeholders may view AI-generated content as impersonal or disingenuous, leading to a decrease in trust and loyalty. It's important to consider the impact on brand perception before implementing generative AI technology in marketing strategies.

Ethical Risks

Ethical concerns surrounding generative AI in marketing include the potential for bias and discrimination, as well as the use of AI-generated content to spread disinformation or propaganda. It's crucial to establish ethical guidelines and ensure that generative AI is used in a responsible and transparent manner.

Operational Risks

There are also operational risks associated with generative AI in marketing, such as the cost of implementing and maintaining the technology, as well as the time required to train the model and ensure its accuracy. It's essential to evaluate the feasibility of using generative AI in marketing and weigh the benefits against the potential operational cost.

Legal Risk

One significant risk associated with using generative AI in marketing is the potential for copyright infringement and other legal challenges. This is particularly true when the AI is trained on copyrighted material or uses copyrighted images or videos without proper licensing. Additionally, there is a risk that the AI-generated content may contain defamatory statements or otherwise violate intellectual property laws. It is essential to have legal counsel review any AI-generated content to ensure compliance with copyright and other legal requirements.

Quality Risk

Although generative AI can create content quickly and efficiently, there is a risk that the quality of the content will suffer. AI-generated content may lack the creativity and nuance that comes with human-generated content, leading to mediocre or bland content that does not resonate with the target audience. It is crucial to carefully evaluate the quality of the AI-generated content and refine the AI algorithms continually to produce higher-quality output.

Also, Brand voice consistency - Generative AI may struggle to capture a brand's unique tone and voice, leading to inconsistent messaging and branding. This risk can be mitigated by providing clear guidelines and oversight.