Member-only story
100 Days of Data Engineering on Databricks Day 4: Traditional AI vs Machine Learning vs Generative AI
Understanding the Key Differences
The AI Landscape: Many Flavors, One Goal
The term “Artificial Intelligence” often feels like a catch-all phrase. But under the hood, there are very different paradigms powering today’s intelligent systems:
- Traditional AI (Rule-Based Systems)
- Machine Learning (ML)
- Generative AI (GenAI)
Each has its own logic, architecture, and best-fit use cases — and all three continue to play vital roles in enterprise AI, especially when dealing with structured datasets like those from SAP systems and Customer 360 platforms.
Let’s break down these paradigms, compare their strengths, and show how they complement each other using real-world business examples.
What is Traditional AI?
Traditional AI, or Symbolic AI, is based on explicit rules and logic created by human experts. It doesn’t learn from data — it follows instructions.
“If customer buys A and B, recommend C.”
“If temperature > 38°C, flag as fever.”
