It’s no secret that generative AI tools have become powerful assistants in modern work. From drafting emails and reports to generating insights and summarising complex documents, the productivity gains are real.
But there’s a catch. A big one.
Generative AI needs a human eye to check it.
Generative AI, for all its promise, has a well-documented habit of… well, making things up.
These are called hallucinations—and they’re not bugs in the system; they’re byproducts of how these tools are designed. That means no matter how confident an answer sounds, it might not be grounded in fact.
And if you’re using AI to assist with business content, decision-making, research, or client communication, that’s something you can’t afford to ignore.
Why AI Hallucinates
Unlike a search engine, generative AI doesn’t look up information. It generates language based on patterns in the data it was trained on. It’s essentially predicting what “should” come next in a sentence—not verifying it.
So, if you ask it to reference a white paper or provide a statistic, it might create one that sounds legitimate. The formatting will look right. The tone will be on point. But the source? It could be entirely fictional.
That’s not deceit. It’s just how the system works. And unless we apply human oversight, hallucinations can easily sneak into outputs unnoticed—especially in fast-paced environments where people are under pressure to move quickly.
The Danger of Repetition: When Hallucinations Compound
There’s another layer to this: the more hallucinated content gets used, shared, or published —especially online — the more likely it is to re-enter the training loop of future AI models.
If a made-up source is quoted in a blog post, and that post gets indexed and included in future datasets, the hallucination starts to look like fact. AI models can begin to reinforce their own errors, reusing falsehoods simply because they’ve been seen before.
This creates a kind of feedback loop where misinformation becomes self-sustaining.
It’s one reason why generative AI sometimes doubles down on its own invented claims—even when questioned. Left unchecked, hallucinations can propagate across documents, systems, and decisions. What starts as a seemingly harmless mistake can evolve into a widely accepted falsehood.
What This Means for Business
In professional settings, AI hallucinations can lead to misinformation, reputational damage, or poor decisions. Imagine relying on AI-generated content that references a made-up regulation. Or quoting statistics that don’t exist in a board paper. Or using a case study the AI invented from thin air.
That’s why we treat AI as an assistant, a co-pilot, not a replacement. We use AI to support thinking—not to replace it. And that means every AI-assisted output goes through human checks for accuracy, relevance, and risk.
How We Keep It Grounded
Here’s how we apply human oversight to generative AI:
- Verify sources: If AI provides a source, we look it up ourselves. If it can’t be verified, it doesn’t get used.
- Check facts: We validate key statistics and claims against trusted references or data we already know to be accurate.
- Watch for tone and context: AI sometimes misinterprets the nuance of sensitive topics. We edit for intent and impact.
- Use AI as a starting point: We often treat AI’s first draft like a whiteboard—full of ideas, not final answers.
- Stay critical: If something feels too polished, too vague, or too confident, we dig deeper.
What You Can Do
If you’re using AI tools in your role—whether you’re in strategy, sales, communications, operations, or delivery—keep these in mind:
- Always review before using – Never treat an AI output as final.
- Cross-reference facts and stats – Don’t assume something is true just because it sounds true.
- Ask follow-up questions – If the AI makes a claim, prompt it to explain further or cite evidence.
- Trust your instincts – If something feels off, it probably is.
- Know when not to use AI – Some tasks still need the full attention of a human brain.
The Bottom Line
Generative AI is an incredible enabler. But it’s not infallible—and it doesn’t understand truth the way we do. That’s why human judgement still matters. When used well, AI enhances our thinking. When used blindly, it risks distorting it—and repeating that distortion again and again. We’re committed to using AI responsibly. That means blending the speed of machines with the critical thinking of people—so we can work smarter and more accurately.