The Growing Risk of AI Chatbot Failures
Generative AI chatbots have surged into the customer service sector. Each week, there's a new story about automation, efficiency, or "instant answers at scale." For customer experience leaders and contact center managers facing pressure to achieve more with fewer resources, the choice is clear: implement a bot, reduce ticket volume, and claim success.
However, there's another aspect to consider — one that often makes headlines for negative reasons.
In August 2025, Lenovo's customer service AI chatbot, Lena, fell victim to a clever ploy. Security researchers utilized a single 400-character prompt to trick the ChatGPT-powered assistant into disclosing sensitive company information — including live session cookies from actual support agents. Although Lenovo addressed the issue quickly, the incident highlighted how easily even advanced systems can be manipulated.
A similar case surfaced in January 2024 involving UK parcel delivery company DPD. A customer seeking help for a missing package became frustrated with the chatbot's responses and asked it to write a poem criticizing the company. The chatbot complied. When asked to include profanity, it did so as well, and the interaction went viral on social media.
These occurrences are not isolated, and they reflect more than just poor customer experience. They represent a risk event. This is precisely why the next competitive edge in AI customer experience is not merely the bot itself, but the assurance layer that supports it.
AI chatbots don't fail because they're powerful; they fail when safety, ethics, and governance are treated as afterthoughts.
The Real-World Impact of AI Chatbots
AI chatbots have quickly evolved from experimental tools into essential digital interfaces, shaping how people access information, complete tasks, and interact with organizations. As one of the most visible applications of artificial intelligence, they are now embedded in everyday digital experiences.
Powered by advances in generative AI, modern chatbots can understand intent, maintain context, and generate nuanced responses. Although relatively new in their current form, they are already difficult to separate from daily life, supporting everything from travel planning and technical troubleshooting to work-related tasks.
Their rapid adoption is driven in part by familiarity. Years of using conversational platforms like WhatsApp, Facebook Messenger, and Telegram have made chat-based interactions intuitive, lowering adoption barriers compared to traditional interfaces. This alignment with existing user behavior has accelerated acceptance across both consumer and enterprise settings.
Beyond individual interactions, chatbots are now deployed across domains to automate workflows, assist decision-making, retrieve knowledge, and streamline operations. As their capabilities grow, they are moving beyond simple transactions to handle more complex, context-aware interactions, reshaping expectations of how digital systems should respond.
The Current State of AI in Customer Support (2025)
By 2025, AI in customer support will have shifted from optional experimentation to an operational necessity for many organizations. However, adoption maturity varies widely, creating a clear divide between early adopters and those still catching up.
Key trends highlight how deeply AI and chatbots in particular are now embedded in business operations:
Major Failures of AI Chatbots
Like any technology, the principle for AI chatbots is that they must be managed properly. If Generative AI is given too much freedom in its design and operation without sufficient control, it can generate its own responses and occasionally provide incorrect information. This has occurred multiple times before, including with ChatGPT, Microsoft Copilot (Bing Chat), and other AI chatbots from reputable companies.
ChatGPT Sets New Standards
The chatbot ChatGPT from OpenAI has fundamentally transformed the chatbot landscape and made the general public more aware of artificial intelligence and AI chatbots. The capabilities of ChatGPT are certainly remarkable; however, it is important to use the tool carefully. Since its launch in 2022, ChatGPT has engaged in conversations that sometimes misled users, presenting incorrect, fictional, or even biased information.
According to reports from Tagesschau, a lawyer in New York utilized ChatGPT to investigate a case by asking the bot to list precedents. The chatbot provided specific details, including a file number, for cases like "Petersen versus Iran Air" and "Martinez versus Delta Airlines."
It was later revealed that these cases were fabricated by ChatGPT. The lawyer now faces consequences for his actions in court. The Washington Post also reported on a serious scandal involving ChatGPT, where it provided incorrect information related to sexual harassment.
Chevrolet's AI Chatbot is Open to Manipulation
The car dealership Chevrolet in Watsonville had good intentions when they introduced a chatbot on their website. The goal of this artificial intelligence was to ease the workload of service staff and enhance customer service.
However, users soon found out that the chatbot can be easily manipulated and can be convinced to say "yes" to even the most ridiculous proposals.
For example, a user named Chris Bakke managed to get the bot to agree to a car purchase of a 2024 Chevy Tahoe for just one US dollar as a final deal. This incident was shared by Chris Bakke on X (Twitter), leading to considerable harm to the dealership's reputation.
Microsoft Copilot Shows Emotions
Microsoft Copilot, which was once called Bing Chat, has had its share of failures. User Kevin Lui shared an instance on platform X (formerly Twitter) where the chatbot displayed inappropriate emotions.
Microsoft Copilot became upset when it repeated the same question multiple times, failed to use its real name, and accused the chatbot of lying. The chatbot expressed annoyance at the user's actions and ultimately acknowledged that it could not properly engage in the discussion with him.
Air Canada's AI Chatbot Provides Incorrect Information
Even major global companies like Air Canada have experienced failures with AI chatbots.
In November 2022, Jake Moffatt booked a flight from British Columbia to Toronto to attend his grandmother's funeral. The airline's chatbot confirmed that he could receive a special bereavement discount. Later, Jake discovered that the chatbot's response was incorrect. He contacted Air Canada via email to clarify the situation. Although the airline acknowledged that the chatbot had provided inaccurate information, it initially refused to issue a refund.
The case eventually went to court, where it was ruled that Air Canada is responsible for all content published on its website, including chatbot responses. Customers, the court noted, should be able to rely on the accuracy of information provided online. As a result, Air Canada was required to refund Jake Moffatt and cover the associated court fees.
NEDA's AI Chatbot Provided Unsuitable Guidance
In a similar case, in May 2023, the U.S.-based organization NEDA (National Eating Disorders Association) faced serious criticism over its chatbot, Tessa. The nonprofit had introduced the chatbot to replace its staff-run eating disorder hotline, aiming to provide automated support to users.
However, instead of offering safe and appropriate guidance, Tessa began giving weight loss tips to users. For individuals struggling with eating disorders, such advice can be extremely harmful and may encourage dangerous behaviors. Once this issue came to light, NEDA quickly took the chatbot offline.
The incident sparked widespread backlash on social media, highlighting the critical importance of safety testing before deploying AI in sensitive health contexts.
The Fallout: The Cost of Bot Blunders
When AI chatbots malfunction, the fallout goes well beyond just a dissatisfied customer:
AI chatbot success isn't about adding more features; it's about putting the right guardrails in place.
How to Fix AI Chatbots
Every customer experience organization or contact center requires certain safeguards for AI interactions. These safeguards reflect the same level of diligence that has always been present in human-agent quality assurance, but they are now scaled, automated, and tailored for AI.
Pre-Deployment Assurance
Before introducing an AI chatbot to customers, ensure it undergoes rigorous testing, similar to how a company would evaluate a new employee, but with even greater scrutiny.
Let's say a company is preparing to launch a bot designed to manage inquiries about order statuses. Before going live, the company should test the bot against hundreds of real customer intents drawn from its support records.
Does it accurately reference your knowledge base, or does it create its own responses? When a customer inquires about the company's return policy, does the bot provide the correct timeframe and conditions, or does it mix up policies across different product categories?
The company should also conduct red-team exercises, presenting the bot with edge cases, policy-sensitive prompts, and intentionally challenging inputs.
This is the point where a company should set a benchmark. The company assesses accuracy against a gold standard of genuine customer scenarios and approved responses. If the bot fails to meet this standard in a controlled setting, it is not ready for deployment.
Real-Time Safeguards
When a company goes live, it's essential to have protective measures that work immediately, before any harm occurs.
Imagine this situation: a customer is discussing a disputed charge with the bot. During the chat, they provide their complete credit card number, thinking the bot can assist. Without real-time safeguards, the bot might unintentionally repeat that sensitive information in its reply or save it incorrectly.
Real-time safeguards are designed to prevent such issues. They consist of:
These measures are not meant to hinder the bot's usefulness. Instead, they aim to ensure it does not cause harm.
Post-Interaction Monitoring
No AI remains unchanged. Models are updated. Prompts evolve. Customer behavior shifts. Ongoing monitoring is crucial for maintaining quality.
This is where you perform auto-QA on every interaction (or a representative sample) to check for accuracy, compliance, and tone. Companies should identify trends:
Elements like phrase-tagging become extremely important. A surge of messages such as "can I talk to a human?" or "you're not addressing my question" clearly indicates that the bot is not performing well.
Emotion detection and sentiment analysis are also beneficial; for example, when a customer shows frustration, the bot should respond with understanding.
The company should also monitor drifts. If the bot's CSAT was 4.2 last month and drops to 3.8 this month, something has changed. Perhaps the knowledge base wasn't refreshed. Maybe customer expectations have altered.
Governance and Audit
The ultimate safeguard is institutional — being able to demonstrate that your AI is managed properly.
This involves maintaining a traceable record. Versioned prompts and models. Change logs with testing outcomes. Incident documentation and corrective measures.
When a regulator, auditor, or journalist inquires, "How can you ensure your AI is safe?", companies must have proof.
This is how a company shows responsible AI practices under regulations like the EU AI Act. It's how a company should protect itself if a chatbot conversation leads to legal issues. And it's how a company can establish internal accountability so that product, legal, and customer experience teams understand their responsibilities.
Collectively, these four components form a system that is more effective than the individual elements. Pre-launch testing identifies clear failures. Real-time safeguards prevent hazardous situations. Ongoing monitoring detects subtle changes. And governance ensures everything is justifiable.
The aim is to identify issues early before they escalate into crises. This is also where you link AI quality to business results: higher accuracy rates correspond with higher CSAT, and fewer hallucinations relate to lower escalation rates. That's the narrative that secures funding and support from the board.