Artificial intelligence (AI) hallucinations occur when AI systems, such as large language models (LLMs), generate information that is incorrect or irrelevant but present it as fact. This phenomenon poses significant challenges for marketers who increasingly rely on generative AI tools to produce content.
Hallucinations typically arise when AI lacks sufficient or accurate data to answer a query. Instead of admitting uncertainty, the model generates plausible-sounding but false information. According to NoGood, "When AI generates incorrect, nonsensical information and presents it as reality, it’s called a hallucination." The company further explains that these errors are not intentional: "AI isn’t conscious; therefore, it isn’t 'lying' on purpose, and it definitely isn’t having a genuine hallucination in the human sense."
Examples of AI hallucinations include fabricated facts—such as Deloitte providing the Australian government with a report containing made-up footnotes and references—and misleading statistics or incorrect attributions. These mistakes can be subtle and difficult to detect without careful review.
Several factors contribute to AI hallucinations. Inadequate or biased training data can lead models to repeat inaccuracies they have learned. LLMs may also lack knowledge about new or niche topics, resulting in invented responses rather than admissions of ignorance. The lack of “grounding”—an inability to connect output directly to verified facts—also increases the risk of hallucinations. Furthermore, technical objectives during model training may prioritize fluent answers over factual accuracy.
The implications for marketers are substantial. Trust in marketing is already low, and uncorrected AI-generated errors can erode consumer confidence further. NoGood states: "The most immediate and damaging consequence is the erosion of trust...when AI-generated content containing factual errors or misleading claims slips through your vetting process, it validates that existing distrust." A damaged reputation can take years to repair and may expose brands to legal risks if inaccurate claims violate industry regulations.
While generative AI promises efficiency by speeding up content creation, these gains are negated if time must be spent thoroughly checking outputs for errors. If not carefully managed, reliance on unchecked AI output can turn an asset into a liability.
To mitigate these risks, NoGood recommends several strategies:
- Fact-check all AI-generated content before publication.
- Use clear and structured prompts when interacting with AI tools.
- Integrate a “human-in-the-loop” workflow where humans review and approve all outputs.
- Rely on human writers for highly technical or sensitive topics requiring deep expertise.
NoGood concludes: "Every piece of content you put out, whether written by a human or an AI, reflects on your brand’s integrity...always have a human-in-the-loop."
The company emphasizes that while AI can improve efficiency in marketing tasks, maintaining trust ultimately depends on rigorous human oversight.
