When an AI chatbot answers on behalf of your company, it speaks with your brand's authority. If it fabricates a return policy, promises a discount that does not exist, or invents a warranty claim process, the customer reasonably believes it. And you are on the hook for what your bot promised.
Air Canada's AI chatbot told a customer they could book a full-price ticket and retroactively apply for a bereavement discount within 90 days. This policy did not exist. When the customer tried to claim the discount, Air Canada refused. A tribunal ruled that Air Canada was liable for its chatbot's fabricated policy - the company is responsible for the accuracy of its automated agents.
Any policy a bot can state, it can invent: a return window, an exchange rule, a warranty. When a customer arrives in-store citing the chatbot, staff have no record of the policy, and the company must choose between honouring a promise it never made and turning the customer away.
AI assistants in banking have described account features, interest rates, and fee structures that do not match the customer's actual account. In regulated financial services, providing incorrect information about financial products is not just a customer experience problem - it is a compliance violation.
The AI never invents. It says what it thinks is right. But "what it thinks" comes from training data and pattern matching - not from your actual policies, your actual inventory, or your actual customer's account.
Policy changes faster than training data. Companies update policies regularly. The AI's knowledge of your policies is frozen at whenever it was last trained or had its RAG documents updated. If your return policy changed last week, the bot may still cite the old one - or worse, fabricate a hybrid of old and new.
Customer context is missing. A customer asks "Can I return this?" The correct answer depends on what they bought, when they bought it, which region they are in, and what their specific account terms are. The bot does not have access to all of this. So it fills the gaps with the most plausible answer - which may not be the correct one for that specific customer.
The sycophancy problem. Customer service bots are trained to be helpful and positive. Sycophancy means the model prefers to say "yes" rather than "no." When a customer asks "Can I get a refund?", the model's training pushes it toward an affirmative, accommodating answer - even when the truthful answer is "no, based on your purchase terms."
Edge cases compound. Most support queries are routine. But edge cases - discontinued products, regional policy differences, grandfathered terms, promotional exceptions - are where humans would escalate and the AI fabricates. The bot does not know what it does not know.
Legal liability. As the Air Canada ruling showed, a company can be held liable for what its AI agents tell customers. A hallucinated promise can become a contractual obligation.
Brand erosion. Every fabricated answer that a customer later discovers was wrong erodes trust - not in the bot, but in the company. Customers do not distinguish between "the bot said" and "the company said."
Agent workload increase. When bots give wrong answers, customers escalate to human agents. But now the human agent is not just solving the original problem - they are also correcting the bot's mistake and managing the customer's frustration about being misinformed.
Regulatory exposure. In regulated industries - financial services, insurance, healthcare - providing incorrect information to customers is not just bad service. It is a compliance violation with potential fines. The EU AI Act requires that people are told when they are interacting with an AI system (Article 50).
The chatbot needs access to the actual, current state of: your policies (as they exist right now, not last month), the customer's specific account (their plan, their purchase history, their region), and your inventory and service status (what is actually available).
This is grounding. The bot reads reality before it responds. Then its response is validated against that reality before it reaches the customer.
Check applies this idea to AI coding agents: a check against reality before their actions run. A customer-service bot needs the same pattern, with your policies and account data as the source of truth.
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