Editorial
Generative AI is everywhere in customer service these days. It runs chatbots, virtual assistants, smart help centers and even tools that draft emails for support agents. Most of the time, it works well.
However, a significant problem often goes unnoticed — AI hallucinations.
Hallucinations occur when AI generates answers that sound plausible but are factually incorrect or entirely fabricated. In live customer conversations, these hallucinations can cause serious trouble. Incorrect answers undermine customer trust, damage brand credibility and trigger additional work for the entire support team.
Common Consequences of AI Hallucinations in Customer Service
Hallucinations aren’t just technical hiccups — they impact trust, compliance, and operational load.
| Consequence | Example | Impact |
|---|---|---|
| Loss of Customer Trust | AI falsely states a refund policy that doesn't exist | Frustration, churn and reputational harm |
| Legal Risk | Chatbot provides incorrect regulatory guidance | Compliance violations, lawsuits, fines |
| Increased Workload | Hallucinated answers trigger follow-up tickets | Higher support costs, agent burnout |
| Public Backlash | Social media exposure of an AI mistake | Brand damage, viral criticism |
Over the past year, as generative AI became integral to model development, I encountered these hallucinations frequently. I want to share what I observed, why common safety checks do not always catch them and what CX leaders can do to mitigate this risk.
Table of Contents
- The High Stakes of AI Hallucinations in Customer Service
- Why Do These AI Hallucinations Usually Happen?
- How Smart Companies Are Handling This
- Where CX Leaders Should Focus Now
- Core Questions About Preventing AI Hallucinations in Customer Service
The High Stakes of AI Hallucinations in Customer Service
Customer service is unpredictable. Having worked with AI in support settings long enough, I know these tools handle tough, emotional and urgent chats in real time. When AI invents information in that context, it is not just a glitch; it can break laws, cause safety issues or leave customers frustrated.
A 2025 McKinsey report found that 50% of U.S. employees cite inaccuracy, including hallucinations, as the top risk of GenAI. This concern is well founded. In one legal case, a federal judge uncovered fabricated citations created by a GenAI tool, resulting in sanctions for the involved attorneys.
Closer to customer service, a developer using Cursor’s AI-powered support chatbot discovered the system invented a subscription policy limiting devices per account, a policy that never existed, leading to user frustration and public backlash.
When I implemented my first GenAI project building an AI email drafting tool, I faced the same issue. Occasionally, the AI confidently suggested fixes or policies that sounded plausible but did not exist in our official knowledge sources. This forced us to redesign the process and safeguards to address hallucination risks before deploying the tool for customer-facing applications.
Related Article: Exploring Air Canada's AI Chatbot Dilemma
Why Do These AI Hallucinations Usually Happen?
Root Causes of Hallucinations in Customer Service AI
Understanding the technical and environmental drivers helps CX leaders prevent failure before it happens.
| Cause | Description | Source/Example |
|---|---|---|
| Low-Quality or Outdated Data | Model is trained on stale or inaccurate information | Google Cloud, IBM |
| Generative Limitations | LLMs generate fluent text without built-in fact checking | Intrinsic to all GenAI tools |
| Weak Retrieval Mechanisms | RAG models retrieve irrelevant or incomplete sources | Shelf.io case studies |
| Ambiguous Inputs | AI misinterprets vague language or slang | Common in customer chat environments |
| Adversarial Prompts | Malicious inputs cause intentional hallucinations | Emerging security concern |
Industry experts attribute AI hallucinations to a few common causes:
Training Data Issues
- Insufficient or Low-Quality Data: AI models learn from large datasets. If the data is limited, biased or incomplete, the model generates unreliable responses. Google Cloud noted that poor training data is a significant contributor to hallucinations.
- Overfitting: When a model becomes too specialized to its training data, it struggles to generalize to new situations. IBM explained that overfitting causes models to "memorize noise" rather than understand patterns.
- Outdated Data: AI systems pulling from stale knowledge bases may fill gaps with incorrect assumptions.
Model Limitations
- Generative Nature: Generative AI predicts the next word in a sequence without inherently verifying facts.
- Context Window Constraints: Large language models (LLMs) have limited context windows, which can cause them to lose track of conversation details.
- Faulty Model Architecture: Poorly designed AI systems with flawed attention mechanisms or assumptions risk hallucinations. Data.world pointed out that overly complex or incorrectly configured AI models can generate inaccurate responses.
- Weak Data Retrieval (in RAG Models): In Retrieval-Augmented Generation models, incomplete or irrelevant retrieved information can produce hallucinated answers.
External Factors
- Adversarial Attacks: Malicious users can manipulate inputs to intentionally provoke hallucinations.
- Nuanced Language: Ambiguous phrasing or slang often trips up AI models, causing misinterpretations.
How Smart Companies Are Handling This
The good news is that several companies are actively refining their AI safety practices:
- Human Feedback Loops: CVS Health implemented additional human reviews after AI occasionally provided questionable medical advice, reinforcing the importance of keeping humans in the loop for high-stakes scenarios.
- Retrieval-Augmented Generation (RAG): DoorDash adopted RAG techniques with three elements — the RAG system, the LLM guardrail and the LLM judge to ground AI responses in verified knowledge bases, create safeguards and monitor performance over time, improving accuracy.
- Seamless Human Handoffs: Experts at eGlobalis emphasize maintaining clear operational playbooks for when AI should defer to human agents, particularly for sensitive or complex queries. NICE Ltd. found success programming AI to flag uncertain responses for human review before they reach customers.
Where CX Leaders Should Focus Now
If you manage customer experience, consider these steps to reduce hallucination risks:
- Prioritize High-Quality, Real-Time Data: Train GenAI models with accurate, current and context-specific data for your products and services. Set up pipelines to refresh data periodically to ensure the AI retrieves the most up-to-date information.
- Implement a Human-in-the-Loop Approach: Embed human agents into the AI workflow to review and approve sensitive responses. Program AI to escalate interactions when its confidence drops below a defined threshold.
- Define Clear AI Objectives and Guardrails: Limit AI’s scope and clarify what it should and should not attempt to answer.
- Conduct Thorough Testing and Evaluation: Regularly test AI systems using hypothetical customer scenarios and monitor metrics such as CSAT scores and escalation rates.
- Continuously Monitor and Iterate: Track hallucination cases as you would service outages or data breaches. Use the insights to refine your model.
- Be Transparent with Customers: Inform users when AI is involved and offer easy options to connect with a human agent.
AI is rapidly becoming central to customer experience operations. While hallucinations may seem like rare anomalies, they can inflict lasting damage when they happen. The companies handling this responsibly recognize that AI systems need continuous oversight, proactive safeguards and strong collaboration between technology and human teams.
Here is the bottom line: AI should not be treated as a “set it and forget it” solution. It requires constant attention, tuning and a smart process for escalating tough cases to humans. That is not a weakness. It is good leadership and smart system design.
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
Webinar
On demand
Insights to Action Rethinking the Contact Center for Real Business Impact
Join our exclusive webinar to hear CX executives share their innovative strategies for transforming service delivery.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
Core Questions About Preventing AI Hallucinations in Customer Service
Editor's note: AI hallucinations aren't just technical glitches — they’re business risks. These questions help CX leaders probe how to reduce false or misleading AI outputs before they impact customers, compliance or brand trust.
Start by testing generative AI in controlled, real-world customer scenarios. Monitor for inconsistencies, invented terms or advice that cannot be validated in your internal knowledge systems. It's also critical to simulate edge cases — including emotionally charged or ambiguous inputs — where hallucinations are more likely. Metrics like escalation rate, agent overrides and customer satisfaction (CSAT) can help surface patterns indicating hallucination risks.
Poor-quality or outdated training data is a top driver of hallucinations. If a model is trained on stale or irrelevant content, it may “fill in the blanks” with incorrect assumptions. Real-time knowledge retrieval allows models to access current, validated information instead of relying solely on what they memorized during pretraining. This dynamic pairing significantly reduces the chance of hallucinated responses, especially in fast-moving industries like healthcare, finance or tech.
One of the most effective safeguards is Retrieval-Augmented Generation (RAG), which grounds AI responses in a verified knowledge base. This helps prevent the model from generating “plausible-sounding” but inaccurate answers. AI systems can also be configured to flag uncertain responses for human review, especially in high-stakes scenarios. Guardrails should include both technical constraints (limiting response types or topic domains) and business rules that define when AI must defer to humans.
Escalation should be triggered when the model’s confidence score drops below a defined threshold, or when it detects signals of emotional distress, regulatory topics or complex edge cases. Some organizations define a “no-go zone” for AI — topics it’s never allowed to handle — and instead default to a human agent. The key is to design workflows where escalation is seamless and fast, so the customer experience remains uninterrupted while maintaining accuracy and safety.
Learn how you can join our contributor community.
