When AI Makes Things Up, and How Businesses Stop It

Ask an AI agent a question it does not actually know the answer to, and it will rarely say "I do not know." More often it invents something that sounds completely reasonable, and says it with full confidence. For a hotel, clinic or dealership running calls through AI, that single habit can turn one wrong sentence into a refund, a complaint, or a customer who never calls back.

The call that never happened

Take Waleed, who coordinates the call desk for a polyclinic group with branches in Al Khuwair and Bausher (a composite example). A caller asked the AI agent handling overflow calls whether the orthopaedic specialist saw patients at the Bausher branch on Thursday evenings, the way the same doctor does at Al Khuwair. Bausher does not run evening clinics. The AI agent said yes anyway, because Thursday evening clinics are common across the group's other branches. The caller arrived at 6pm to a locked front desk.

Why AI agents guess in the first place

This behaviour has a name: hallucination. It means an AI produces an answer that sounds confident and reasonable but is not actually true. The technology behind most AI agents, a large language model, does not look answers up in a filing cabinet. It predicts the most likely next words based on patterns it learned from enormous amounts of text. Most of the time that produces a sensible answer. When the model has nothing solid to draw on, it fills the gap with something plausible rather than admitting it does not know.

The gap gets wider with Arabic. Most AI models studied far more English and formal written Arabic than the Omani Arabic your customers actually speak, and none of them ever studied your business's own prices, room types or doctor rosters. Left ungrounded, an AI guesses hardest in exactly the calls your customers are having right now.

Confident and wrong costs real money

This is not a hypothetical risk. In 2024, a tribunal in British Columbia ordered Air Canada to honour a bereavement discount its website chatbot had invented, one that did not exist in the airline's real policy. Air Canada argued the chatbot was responsible for its own words. The tribunal disagreed, and ruled the airline liable for what its AI told a customer, the same as if a staff member had said it.

A business is responsible for what its AI agent tells a customer, whether that answer came from a real document or from a guess.

The same logic applies to a hotel quoting a room rate, a dealership quoting a service price, or a clinic quoting a doctor's hours. If the AI's answer is wrong, the business owns the consequence, not the software.

Grounding the answer in what is actually true

The first guardrail is grounding. That means connecting the AI agent to a knowledge base: a single set of documents holding your real prices, policies, room types or doctor schedules, so the AI answers only from what you actually gave it. Ask it something outside that document, and a properly grounded AI says it is not sure instead of inventing an answer. CustomerCare.OM's knowledge base works this way: you upload your own price list or policy document, and the AI is trained to work from that, not from general patterns it picked up elsewhere.

Knowing when to hand off

The second guardrail is escalation: rules that tell the AI when a question is outside its depth, so it hands the caller to a person instead of guessing. Some questions will always need a human, a medical judgment call, an upset customer, a request the knowledge base genuinely does not cover. Rules built around those triggers are what make the handoff automatic rather than accidental.

Waleed's clinic now runs both guardrails together: each branch's real schedule loaded into the knowledge base, and any question about symptoms or timing outside standard hours routed straight to the call desk. The Thursday evening mix up has not happened again.

What this means for you

• Ask any AI vendor two plain questions: what does the AI answer from, and what happens the moment it does not know.
• Test it yourself. Ask your own AI agent something true about your business that you never gave it in writing, and watch whether it admits the gap or invents an answer.
• Confirm escalation actually reaches a person during your working hours, not a message that queues silently until morning.

The bottom line

An AI agent that never says "I do not know" is not more capable, it is more dangerous. Grounding it in your real documents and giving it clear rules for when to hand a caller to a person turns confident guessing into an answer your customers can actually trust.