The term “AI hallucinations” comes up in meetings, in workplace policies and in every conversation about responsible use of technology. The term sounds dramatic, but the idea behind it is quite simple.
A hallucination happens when an AI tool gives an answer that sounds correct but is actually wrong. It does not do this on purpose. It produces a response that fits the pattern of what it has learned, even when the pattern is incomplete.
When asked to name the most cited economics paper of all time, an AI chatbot produced a detailed answer that looked correct, but the entire reference was made up. This is a common pattern. When the model does not know something, it generates the most likely answer instead of admitting uncertainty.
These mistakes are not rare. The New York Times has reported that some newer AI systems can produce these kinds of errors in up to 79% of tests, and they do it with a lot of confidence.
Why these mistakes happen
AI models learn by analysing extremely large collections of text and identifying statistical patterns. When they generate a response, they do not “look up” facts. They predict the next likely word again and again until a full sentence appears. This process is powerful for tasks like summarising or drafting, but it also means the model can produce statements that sound accurate without confirming whether they are true.
Mistakes also come from the data the model was trained on. Most models learn from sources that are easy to collect at scale, such as Wikipedia and other widely circulated articles. These sources cover mainstream topics very well but leave gaps in areas that are less documented or underrepresented. When the model is asked about something that sits in one of these gaps, it tries to complete the missing information using patterns from unrelated topics. This often results in confident but incorrect answers.
The way we prompt the model also shapes the outcome. A broad prompt gives the system too much room to guess. For example, “Explain our HR policies” may lead the model to invent details because it does not have access to the exact policies. A clear and specific prompt, such as “Summarise these three HR policies for onboarding,” gives the model boundaries to work within and leads to more reliable responses.
Is “hallucination” the right word
Some researchers question whether “hallucination” is the best term. In humans, a hallucination involves a sensory experience that is not real. AI systems do not see, hear or understand the world. They produce text by recognising patterns, not by perceiving reality. Calling these errors hallucinations can make AI seem more human-like than it is. A clearer explanation is that the model generated an incorrect or fabricated answer because the patterns it relied on were incomplete.
Why workplaces should care
These mistakes can create real problems when people rely on AI for important tasks. AI systems have invented legal cases, created false news stories and produced incorrect technical instructions. In one case, a radio host was falsely linked to financial wrongdoing because a chatbot generated a made-up explanation of a case. Incidents like this show why organisations need safeguards in place.
The World Economic Forum has listed AI-generated misinformation as a major global risk. As AI becomes part of daily work, the chance of mistakes increases unless organisations set clear boundaries and controls.
How to reduce the risk at work
There are practical steps organisations can take.
- Choose the right tool: Some AI models are designed for specific fields such as law or finance. These models usually make fewer mistakes in those areas. A general model may not be reliable for specialised work.
- Add strong human review for the output: Even when AI produces a well-written answer, it may still contain errors. Build a simple review step where a person checks the output for accuracy before it is used.
- Train employees on how to write clear prompts: A simple, well-structured prompt can reduce errors. Teams can use prompt templates for tasks they repeat often.
- Set boundaries for when AI can and cannot be used: High-stakes decisions, sensitive topics and complex calculations should always involve human judgement.
Serein helps organisations move from uncertainty to confidence in their AI journey. With hands-on learning, ethical safeguards, and tools to assess readiness, we ensure AI becomes safe and practical for everyone. Reach out to hello@serein.in to learn more.