The Problem
Somewhere along the way, "AI" became a marketing term. A rules-based chatbot? AI. A spreadsheet formula that filters data? AI-powered. A search bar with autocomplete? AI-driven.
What AI Actually Means
At minimum, AI should involve systems that:
- Learn from data (not just follow pre-written rules)
- Generalize to new situations (not just match exact patterns)
- Improve with more data or experience
A decision tree with 50 hand-coded rules is automation. It's useful. But it's not AI.
Why It Matters
When everything is "AI," nothing is. It creates:
- False expectations — Customers expect intelligence, get automation
- Diluted investment — Real AI research competes with "AI-powered" landing pages
- Public confusion — People can't evaluate AI risks if they don't know what AI is
A Simple Test
Before calling something AI, ask:
- Does it learn from data?
- Can it handle inputs it wasn't explicitly programmed for?
- Does it improve over time?
If the answer to all three is no, it's software. Good software, maybe. But not AI.
The Fix
Be specific. Say "machine learning model" or "neural network" or "automated workflow." Precision in language leads to precision in thinking.