Most of the time, when you ask an AI chatbot a question or have it summarize your data, the responses are smart, helpful, and surprisingly human.
But how do you know if they’re accurate answers? Sometimes, AI generates answers that seem correct—but are completely made up.
That’s what’s known as an AI hallucination: when a model confidently presents false information as fact. In lower-stakes scenarios, this might mean summarizing meeting notes incorrectly, or inventing a new social media holiday that sounds just real enough..
But in a business setting, the stakes are much higher. What if your AI tool misreports quarterly revenue? Or pulls the wrong data into a customer-facing dashboard? Suddenly, a small hallucination becomes a big problem with real financial, legal, and reputational risks.
So why do these hallucinations happen? And more importantly, how do you keep them from putting your business at risk? Let’s unpack it.
Table of contents:
AI hallucinations happen when an AI system, especially a large language model (LLM) generates information that sounds correct but is factually inaccurate, misleading, or entirely fabricated.
The term ‘hallucination’ might sound strange, since hallucinations are something we associate with people, not machines. But this metaphor makes sense. Just like a person might see or hear something that isn’t really there, AI can generate outputs that seem perfectly logical on the surface, but are completely disconnected from reality.
Think of large language models (LLMs) like a really advanced version of autocomplete. They don’t think, reason, or verify facts on their own. Instead, they generate responses by predicting the next most likely word or phrase based on patterns in the data they were trained on.
So if you ask for words, you’ll get words. But that doesn’t mean they’ll be right.
Take math, for example. An LLM doesn’t understand that ‘2 + 2 = 4’. It just generates that response because it’s seen it more often than ‘3 + 2 = 4.’ When it gets a math problem right, it’s usually just pattern recognition, not real understanding.
The same goes for facts. Ask who directed Inception, and it’ll probably say Christopher Nolan because that answer shows up consistently in the training data. But ask about a lesser-known movie or something with fuzzy or conflicting info, and it might spit out a completely made-up name - or even Christopher Nolan. It’s not trying to deceive you intentionally; it’s just making its best guess.
That’s why AI hallucinations aren’t a bug. They’re built into how these models work.
What are the hidden costs of AI hallucinations?
McKinsey research shows that 63% of industry leaders view GenAI as an important business priority. But 91% admit they don’t feel very prepared to implement it responsibly.
This gap comes at a cost. Because when AI gets it wrong, the consequences aren’t just technical; they’re deeply human, and they can have cascading impacts. Here’s what that might look like in your business:
1. Lost credibility and trust
Imagine your AI assistant confidently reports a 12% jump in quarterly revenue to your board, only for you to discover the real number is way off. That one error can derail major strategy discussions, shake leadership’s trust in you and the data team, and even trigger legal and ethical consequences.
In our recent webinar on AI hallucinations, our CDAO Cindy Howson sat down with Jennifer Belissent, Principal Data Strategist at Snowflake, to break down how even small AI missteps can snowball into credibility crises—and what you can do to catch them before they cause damage.
💡 Want to avoid AI mistakes? Watch the on-demand webinar on how to mitigate hallucinations.
2. Biases at scale
AI bias happens when a model learns from skewed, incomplete, or unrepresentative data. Maybe the data comes mostly from one region, excludes underrepresented groups, or reinforces outdated patterns from the past.
But here’s the danger: AI doesn’t just reflect that bias, it amplifies it.
What used to be a localized, fixable data issue becomes a systemic problem, showing up in dashboards, decisions, and customer experiences everywhere.
Here’s a real-world example of how fast bias can spiral:
1. Build a solid data foundation
Jennifer Belissent said it best: “There is no AI strategy without a data strategy.”
What this really means is: you can’t simply bolt AI onto a shaky data foundation and expect enterprise-grade accuracy. If your internal data is siloed, incomplete, or inconsistent, every AI initiative you launch will reflect and amplify those flaws.
Think of it as: garbage in, hallucinations out.
To get real value from your AI initiatives, you’ve got to go beyond collecting data. That means:
Connecting data across clouds
Breaking down silos
Treating it like a product with clear ownership, rigorous documentation, and reusable building blocks.
Hear straight from leading experts why laying the right foundation matters:
2. Leverage unstructured data
Here’s something worth noting: nearly 90% of your company’s data is unstructured.
Think about all the PDFs, contracts, meeting notes, support emails, and product manuals scattered across your organization. That’s a goldmine of context most AI systems never touch.
Why? Because that unstructured data doesn’t typically live in neat rows and columns. It comes in different formats, across disconnected systems, with no easy way to query or join it with the rest of your data. That’s made it historically difficult—and expensive—to integrate into your everyday analytics.
With ThoughtSpot’s new MCP Server, you can finally bring semi-structured and unstructured data into the tools you and your team use every day. It works by connecting AI agents directly to your business data so they don’t just access more information; they understand it in context. That means fewer hallucinations, more accurate answers, and insights you can actually trust.
💡 Want accurate, trustworthy answers from your AI tools? See how the MCP Server makes that possible.
3. Improve accuracy with RAG architecture
Your AI tools—whether you’re using an agent, a co-pilot, or an automated assistant—can’t just scan raw files and instantly know what they mean. They need help to make sense of your data.
This is where a robust RAG architecture and a clear semantic layer come in. These aren’t just nice-to-haves; they’re foundational to agentic AI to make it understand real-world context.
Here’s how to make your AI smarter, more reliable, and truly business-ready:
a. Start with a RAG architecture
Retrieval-Augmented Generation (RAG) grounds your models in trusted internal knowledge. Instead of guessing, it pulls context from sources like support tickets, product docs, or internal reports.
b. Define your business language
Even with the right documents, the tool you use needs to understand what your organization means by terms like “revenue,” “churn,” or “active user.” These definitions aren’t universal, and that nuance matters.
c. Add a semantic layer
Think of this as a translator. The semantic layer maps complex, technical data into business-friendly language, so the AI can respond in ways that actually make sense to your team.
d. Include a human-in-the-loop feedback system
By incorporating human oversight into AI processes, you can catch errors, refine results, and continuously improve the system’s understanding of your data and goals.
4. Enforce data governance
When it comes to AI, operating without clear rules can cause real damage. Unlike traditional chatbots that are trained once, large language models (LLMs) learn and adapt in real time, based on every prompt they receive. That means you have to keep a close eye on what the model sees, remembers, and might reuse with proper governance.
Ask yourself:
What if someone unknowingly enters sensitive data into a prompt?
Can the model retain it?
Could it show up later in someone else’s conversation?
Good governance answers these questions before they become problems. It sets clear guardrails for how data is handled, who can access what, and how you track and audit AI outputs. It’s the difference between an AI system you can trust and one that puts your business at risk.
💡 Get candid tips from the ThoughtSpot execs leading with AI innovation and governance at enterprise scale.
5. Embrace diversity in AI models
Fixing biases in data isn’t just about fine-tuning algorithms; it starts with embracing diversity at every level. That means not just looking at the data itself, but also where it comes from, who’s shaping it, and how it’s being validated.
This means:
Bringing in external data to complement internal blind spots
Synthesizing datasets to reflect underrepresented voices
Reviewing model outputs through diverse human perspectives
Allowing the team behind the tech to reflect the customers and communities they serve
The more you scale AI, the more important it is to scale inclusion alongside it. You’ll be able to create models that are not only smarter, but safer and more useful to everyone.
According to Gartner, by 2027, companies that prioritize AI literacy for all will outperform their peers financially by 20%.
But enterprise-grade literacy isn’t just about understanding how AI works—it’s also about knowing when not to trust an output, and what should be done about it. Preventing hallucinations starts with teaching people how to ask better questions, spot red flags, and react correctly when something feels off.
Here’s what that really looks like:
Real examples that show how AI fits into their daily tasks and processes
Consistent clarity on roles and ownership around data and AI
A culture of curiosity where it’s encouraged to ask questions
You don’t need every user to be a data scientist. You need to know that every user can make confident, informed decisions on their own.
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