Practical AI adoption
Garbage In, Garbage Out
Why AI fails before it starts.
You've heard the phrase before, even if you're not a software developer.
It applies to everything, from logo creation to healthcare and everything in between. And yes, it applies to AI.
AI is designed to answer any question it's asked, as confidently as it can. Sometimes, that means it will make something up just to satisfy the asker.
Those made-up answers are called "hallucinations." A very human word for something built from billions of algorithms.
Here's the important part:
AI doesn't hallucinate because it's broken. It hallucinates because we ask vague, incomplete, or misaligned questions.
Think about it this way:
Doctor: "How are you feeling today?"
You: "Kinda cruddy."
You wouldn't expect a diagnosis from that. And you'd be concerned if the doctor said, "Here, take these pills," without asking another question or getting more context.
It's the same with AI.
Bad inputs lead to confident nonsense. Clear inputs lead to surprisingly useful results.
Most people assume AI fails because it made something up, wasn't accurate, or didn't understand the business.
In reality, AI usually fails because the goal wasn't clear, context wasn't provided, or the problem was ill-defined.
AI doesn't replace thinking. It amplifies it, for better or worse.
Before asking, "Which AI tool should we use?" try asking, "What problem are we actually trying to solve?"
I put together a short, free worksheet to help businesses clarify where AI can truly help, and where it can't.