In February 2024, a Canadian tribunal handed down a ruling that should be required reading in every boardroom deploying AI. A man named Jake Moffatt was booking a flight with Air Canada after his grandmother passed away. He asked the airline’s chatbot about bereavement fares. The bot told him he could book now at full price and apply for a bereavement discount retroactively within 90 days. That policy did not exist. Air Canada’s actual bereavement policy required the discount request before travel, not after. The chatbot invented a rule on the spot, said it with total confidence, and Moffatt believed it — because why wouldn’t he. When he filed for the refund the bot promised, Air Canada refused. Their argument in the tribunal was almost unbelievable: the chatbot is a separate legal entity responsible for its own words. The tribunal did not buy it. The ruling was blunt — a company is responsible for all the information on its website, whether it comes from a static page or a chatbot. Air Canada paid. Why This Case Changed the Conversation This was not a huge financial loss for Air Canada. A few hundred dollars. But the precedent it set is what actually matters, and it is rippling through legal and compliance departments right now. The ruling establishes something simple and uncomfortable: your AI speaking to a customer is legally the same as your company speaking to a customer. There is no separation. There is no “the bot said it, not us.” If your AI tells a customer something, your company told them that thing. For any business running a customer-facing AI tool — and in 2026 that is most businesses — these changes how you have to think about deployment. This Is Already Bigger Than One Airline Air Canada is the case everyone cites because it went to a tribunal and got documented. But it is far from an isolated incident. A car dealership in California had a customer negotiate their chatbot down to selling a vehicle for one dollar. The bot, trying to be agreeable, agreed to the price. The dealership had to publicly clarify the deal was not honoured, but it became a viral embarrassment that spread faster than any of their actual marketing. A major retailer’s AI shopping assistant confidently recommended products that did not exist in their catalogue, describing features and pricing it invented entirely, based on patterns from similar products. Customers who tried to order those items got confused and frustrated, and the support team had no idea why until someone traced it back to the bot’s chat logs. A telecom company’s support AI told a customer their contract could be cancelled without a fee under conditions that were not actually in the customer’s plan. The customer cancelled based on that information. When the fee showed up on their final bill, the company had to decide whether to honour what the AI said or fight it — and increasingly, companies are finding they do not have a strong legal position to fight it. Why This Keeps Happening Large language models are not databases. They do not retrieve facts from a fixed lookup table and return them precisely. They generate the most statistically plausible response to what they were asked, based on patterns learned during training. Most of the time this produces accurate, helpful answers. Sometimes it produces a completely fabricated policy stated with the same confidence as a real one. This is what the industry calls hallucination, and it is a known, unsolved characteristic of how these systems work — not a bug that gets patched away, but a fundamental behaviour that needs to be managed rather than eliminated. The problem is that most customer-facing AI deployments were built to sound confident and helpful. Nobody trained the bot to say “I am not certain, let me check with a human” often enough, because that feels like a worse customer experience in the moment. It is a much better outcome than confidently inventing a refund policy that costs the company money and credibility. What a Defensible AI Customer Framework Actually Looks Like The companies protecting themselves properly are building a few things into every customer-facing AI deployment. Clear scope boundaries. The AI is explicitly restricted from making statements about policy areas that carry financial or legal weight — refunds, contract terms, legal rights — unless those statements are pulled directly from a verified, current policy document rather than generated freely. Confidence-based escalation. When the AI is dealing with a query that touches money, legal terms, or anything outside clearly defined territory, it should default to connecting the customer with a human rather than guessing. The businesses handling this well have accepted that occasional friction is far cheaper than occasional false promises. Retrievable source citation. Instead of letting the AI generate an answer from general knowledge, well-built systems retrieve the actual current policy document and quote from it directly. This dramatically reduces hallucination because the AI is reading a real answer rather than inventing one. Logged and auditable conversations. Every customer-facing AI interaction should be logged and reviewable, so if something goes wrong, the company can see exactly what was said, why, and fix the underlying issue quickly rather than discovering it months later through a complaint or a tribunal filing. Human review of edge cases. A regular sample of AI conversations, especially any that touch policy, pricing, or commitments, should be reviewed by a person. Not every conversation. Enough to catch patterns before they become expensive. The Bottom Line The uncomfortable truth for any business deploying AI at the customer-facing edge is this: your AI is not a separate entity that can say things on your behalf without consequence. It is your company, speaking. Every promise it makes is a promise your company is expected to keep. Air Canada learned that in a tribunal ruling that cost them a small refund and a much larger amount of public embarrassment. The next company that gets this wrong
