When AI Gets It Wrong: Managing AI Hallucinations in Customer Support – by Nataliia Onyshkevych, CEO of EverHelp
In support environments, AI hallucinations are not just a messaging error — it’s a customer-trust breaker. When AI responds incorrectly yet confidently, it can confuse a person and lead to reputational and credibility damage.
According to the new McKinsey’s 2025 State of AI report, 51% of organizations using AI have already experienced at least one negative consequence, and nearly one-third of those incidents are linked to AI inaccuracy.
In this article, we’ll explore the growing challenge of AI hallucinations in customer support, their causes, consequences, and management approaches.
What Is AI Hallucination?
When AI “hallucinates,” it generates a confident and plausible response that, in reality, isn’t factual. Much like the human brain, AI prioritizes coherence over accuracy and tends to complete missing information with its best statistical estimate. The difference is that humans can sense doubt — AI cannot.
In customer support, such hallucinations often appear as:
- Incorrect details, product information, and timelines
- Inventing or altering policies
- Off-brand tone and inappropriate replies
- Wrong troubleshooting steps
Such AI “behavior” can lead to severe consequences, from damaging customers’ trust to legal liability.
The Air Canada case shows how AI hallucinations can escalate into reputational and legal risks. In 2022, the airline’s support chatbot gave a passenger the wrong information about the bereavement fare policy, stating that a refund could be requested within 90 days after the flight, even though refunds aren’t available for completed trips. When the company clarified the mistake, the customer took the case to court and won compensation.
Another example involves DPD’s support chatbot that attracted public attention after generating off-brand and inappropriate responses. Instead of assisting customers, it deviated from expected behavior and produced messages inconsistent with the company’s tone of voice. While the incident was resolved quickly, it illustrates how AI can damage brand trust within minutes if proper guardrails are not in place.
Why Does AI Hallucinate in Customer Support?
It’s tempting to consider that the hallucinations are a model problem only, hoping that if we “fix the AI,” they will disappear. But in reality, the issue is more complicated.
Here are the main reasons behind AI hallucinations in customer support:
- Incomplete or outdated knowledge base
An AI assistant rarely says “I don’t know” or “I’m not sure,” unless it’s specifically designed to. If product or policy information is fragmented or incomplete, the model will “fill the gaps,” often confidently, but at the expense of factual accuracy. - No continuous learning
The customer support environment changes constantly. Such things as pricing, product features, terms, and exceptions evolve on a daily basis. If AI doesn’t learn from new tickets, agent feedback, and documentation updates, it will rely on out-of-date or generic information. - Ambiguous or insufficient guardrails
AI needs to have clear instructions and patterns in its training data. Brand tone of voice, policy boundaries, and compliance rules must be deliberately engineered, not assumed. You can’t give AI vague information and expect it to understand intent by itself. - Lack of human guidance
When AI is left to operate on its own, mistakes and hallucinations become inevitable. In our work at EverHelp, we’ve seen that AI performs best under human guidance — supporting teams rather than replacing them. So, treat it as a partner that needs direction, not as a free-running employee.
As we can see, hallucinations are rarely the fault of the model itself. They often happen when AI is deployed without a solid knowledge structure and proper human oversight.
How to Manage AI Hallucinations in Customer Support
AI hallucinations are inevitable to some extent, but the key is to learn how to contain them. It’s less about fixing AI and more about training the organization around it. The following strategies outline practical steps to minimize AI hallucinations in customer support environments.
- Strengthen the data foundation
The first step to reducing hallucinations is building a strong knowledge base. Keep it clear, organized, and alive. Your product guides, policies, and FAQs should evolve with the product itself. The more complete and well-structured the knowledge base, the fewer mistakes the AI makes. Prepare it in advance and review the content regularly, so the system never relies on guesswork. - Set up clear instructions
A solid knowledge base alone isn’t enough — AI needs to understand how to use it. That’s why providing straightforward and detailed instructions is essential. They guide the system’s reasoning and significantly reduce the chance of inaccurate responses. Concise and context-rich instructions deliver more reliable results. Also, it’s equally important to define tone of voice, data boundaries, and escalation logic, giving AI the clarity it needs to stay aligned with brand policies. - Provide human oversight
Combining automation with human review is another crucial step in managing AI hallucinations effectively. A “human-in-the-loop” workflow allows agents to catch and correct errors before they can lead to reputational damage or legal consequences. When inquiries fall outside of the knowledge base, the system automatically redirects requests to human agents, especially if cases are more complex. This approach ensures that AI doesn’t improvise where human judgment is required. - Track and learn from mistakes
When a hallucination occurs, treat it as a performance metric rather than an occasional glitch. This approach helps identify recurring patterns and possible weak spots in your instructions or training data. Regularly audit AI conversations to review both factual accuracy and tone-of-voice alignment. Over time, this kind of quality control turns into a continuous improvement cycle, where every mistake helps the system perform better next time.
Conclusion
AI hallucinations in customer support may seem frustrating, but they don’t have to be a deal-breaker. With a strong data foundation, a clear knowledge structure, and frequent human oversight, organizations can turn AI from a potential risk into a reliable partner.
The key to successful customer service isn’t replacing human agents with AI but combining their strengths intelligently. And by learning how to deal with AI hallucinations, companies can build systems that are not only faster and more scalable but also more trustworthy.
Author’s bio:
Nataliia Onyshkevych is the CEO of EverHelp, a customer experience outsourcing company helping brands deliver support that is both cost-efficient and human. She is a member of the Forbes Business Council with nearly 10 years of hands-on experience — from frontline agent to CEO. Nataliia shares practical insights on CX, the role of people in an AI-driven world, and how businesses can leverage automation without losing empathy or service quality.
