When NOT to use AI
AI is powerful, but it is not always the right solution. Knowing when to hold back is as important as knowing when to apply it.
Today’s AI insight
AI should be used purposefully. Applying it without clear goals or reliable data can lead to wasted time, wrong conclusions, or unexpected biases.
Why this matters
- Not every problem benefits from AI
- Using AI without sufficient quality data can produce misleading results
- Simpler methods are often faster, cheaper, and more interpretable
A simple example
Imagine you want to predict student grades using a complex neural network, but you only have 20 past records.
A linear regression or simple statistical analysis would likely perform better and be easier to interpret.
Try this today
Ask yourself before every AI project:
“Could a simpler approach solve this problem just as well?”
Reflection
AI is not a universal solution. Recognizing its limits keeps your research, projects, and decisions effective and trustworthy.