When to Use AI in Your Product (And When Not To)
AI isn't always the answer. Here's a framework for deciding when AI adds value and when simpler solutions work better.
The AI Hype Problem
Every product roadmap now includes "add AI" somewhere. But AI is a tool, not a strategy. Using it wrong wastes money and creates technical debt.
Here's how we evaluate whether AI is right for a feature.
The Decision Framework
Use AI When:
1. The problem is fuzzy
AI excels when inputs vary unpredictably. Good examples:
2. Rules would be endless
If you'd need thousands of if-statements, AI might be simpler:
3. You have quality data
AI needs data to learn. No data = no AI. Questions to ask:
Don't Use AI When:
1. Deterministic logic works
If you can write the rules, do it:
2. Explainability is required
AI decisions are often black boxes. Avoid AI when:
3. You can't tolerate errors
AI makes mistakes. All AI makes mistakes. If 95% accuracy isn't acceptable:
Real Examples
Good AI Use Case: Document Classification
A client had 50+ document types to route. Rules-based approach required constant maintenance as document formats changed. AI classifier achieved 98% accuracy and handles new variations automatically.
Bad AI Use Case: Lead Scoring
Another client wanted AI for lead scoring. But they only had 500 leads with outcomes. We built a simple rules-based scoring system instead. It works, it's explainable, and it can be tuned by sales without engineers.
Implementation Principles
The Bottom Line
AI is powerful but not magic. Use it when the problem genuinely requires learning from data. Use rules when logic is clear. The best systems often combine both.