AI Hallucinations at Work: What Causes Them and the Fixes That Hold

An AI hallucination is a confident answer that is wrong: an invented figure, a feature your product lacks, a policy that does not exist. Models hallucinate partly because training rewards guessing over admitting uncertainty. In business work, most hallucinations come from missing facts. The fixes that hold are approved facts, sourced output and a person approving what goes out.

What is an AI hallucination?

An AI hallucination is output that sounds plausible and confident but is factually wrong or made up. Typical examples in business include invented statistics, quotes nobody said, product features that do not exist, wrong prices, and policies or legal details stated with authority but no basis.

The defining feature is confidence. A model rarely signals that it is guessing. It writes an invented figure in exactly the same tone as a real one, which is why hallucinations slip through review. Bad output gets caught; plausible output gets sent.

Why do AI models hallucinate?

Language models generate the most likely next words, not verified facts. When they lack the information, they produce something that fits the pattern. OpenAI researchers argued in 2025 that training and evaluation make this worse by rewarding guessing: a model graded only on accuracy scores better by guessing than by saying "I don't know".

According to the 2025 paper "Why Language Models Hallucinate", by Adam Tauman Kalai and colleagues at OpenAI, the problem is structural. Rare, arbitrary facts cannot be predicted from patterns, so errors arise even from clean training data. Benchmarks that only count correct answers then encourage models to guess rather than abstain, like a student filling in every multiple-choice answer.

The scale varies by task. According to Stanford HAI's 2024 study, general-purpose models hallucinated on 69% to 88% of specific legal queries. Everyday business writing is less demanding than legal research. Still, the mechanism is the same.

Why do hallucinations happen more in business work?

Because business work depends on specific facts the model was never trained on: your products, prices, customers, policies and past decisions. When those facts are missing, the model fills the gap with what similar businesses typically say. The more company-specific the task, the more it guesses.

Consider a follow-up email after a demo. The model does not know what the prospect saw, what they objected to or what your product actually integrates with. So it writes a plausible email, perhaps promising an integration that sounds standard for your category but that you do not offer.

People sense the gap. According to Atlassian's 2026 Pulse survey, only 12% of workers say their AI understands their organisation like a tenured senior colleague. A senior colleague does not hallucinate your product features, because they know them.

Which fixes for AI hallucinations actually hold?

Three fixes hold up in practice. Give AI the company's approved facts before it writes, so it has no gap to fill. Require it to cite which fact each claim came from and to flag anything it cannot source. Keep a named person approving anything published, sent or spent. Together, they make errors rare and visible.

1. Approved facts. A maintained list of product facts, prices and policies, each with a source and a flag showing whether it may be published. The guide to approved claims shows how to build one.

2. Sourced output. Ask for every factual claim to be tied to an entry in the approved facts, and for unsourced claims to be flagged rather than written. This also speeds up review. Retrieval techniques help here: according to the 2020 paper that introduced retrieval-augmented generation, grounding answers in retrieved text made them more factual.

3. A person at the gate. Review cannot be skipped for anything external. The guide to human in the loop covers where to place it.

Prompting tricks, such as telling the model "do not hallucinate", help little on their own. The model cannot avoid inventing facts it does not have.

How do you catch hallucinations before they go out?

Check every number, name, quote and product claim against a trusted source before publishing. Make it easy by having AI list the sources it used, and treat any unsourced specific as suspect. Pay particular attention to statistics, legal or regulatory statements, prices, and anything about a named person or company.

A reviewer's quick checklist:

• Numbers: does each figure trace to a named source?
• Product claims: is each one on the approved list?
• Quotes and names: did that person or company really say or do this?
• Rules and regulations: is the statement current and from an official source?
• Links: does each link go where it claims?

Personal information needs extra care. Australia's privacy regulator notes that personal information AI generates or infers, including inaccurate content about an identifiable person, is covered by the Australian Privacy Principles.

The approved facts belong in a company brain, which every person's AI reads before writing.

Skein builds that shared knowledge layer into every Company OS, with agents that cite their sources and stop for approval. That suits a business where several people produce customer-facing work with AI. Someone using AI only for private brainstorming faces much lower stakes.

Frequently asked questions

What causes AI hallucinations?

Language models predict likely words rather than look up verified facts, so when information is missing they produce something plausible. OpenAI researchers argued in 2025 that training and evaluation reward guessing over admitting uncertainty. In business work, most hallucinations fill gaps where the AI lacks the company's facts.

How do you stop AI from hallucinating?

You cannot eliminate it entirely, but three fixes hold: give AI the company's approved facts before it writes, require it to cite which fact each claim came from and flag anything unsourced, and keep a named person approving anything published or sent.

How common are AI hallucinations?

It depends heavily on the task. Stanford HAI found general-purpose models hallucinated on 69% to 88% of specific legal queries in its 2024 study. Rates are lower on general tasks and with newer models, but hallucinations still occur, especially on company-specific facts the model was never given.

Can RAG prevent AI hallucinations?

Retrieval-augmented generation reduces hallucinations by giving the model relevant text to answer from, and the 2020 paper introducing it found answers more factual. It works only if the retrieved documents are accurate and consistent. Retrieval over outdated or conflicting files can still produce confident errors.

Who is responsible when AI gets something wrong?

The business that publishes or sends it. AI output carries the company's name once it goes out, so a person should approve anything external. Australia's privacy regulator also notes that inaccurate personal information generated by AI is covered by the Australian Privacy Principles.

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