Not every business problem needs Machine Learning (ML). Sometimes, a few simple rules do the job better. The trick is knowing when to use which.

When Simple Rules Work Best

Simple rules are great when:

• You have limited data.

• The logic is clear and deterministic.

• The system needs to be transparent and easy to explain.

Example:
“If the order value > ₹10,000, send to manager approval.”
No need for ML — a rule works fine.

When to Use Machine Learning

ML is powerful when:

• Patterns are too complex for manual rules.

• You have enough historical data.

• You want predictions or personalization.

Examples:

• Predicting customer churn.

• Product recommendation.

• Fraud detection.

The Balanced Approach

Start with rules, gather data, and evolve into ML later. In many real-world systems, hybrid models work best — rules handle the predictable, ML handles the uncertain.

Use rules for clarity, ML for discovery.

Wiselink Global’s AI experts can help you identify which approach fits your problem and design scalable solutions for both.