RULE BASED vs AI vs ML | Let's Brainstorm

BRAINSTORMING

Background of the Topic:

• Digital transformation has changed how companies manage data and make decisions.
• Organizations increasingly rely on ICT-based systems to improve efficiency, accuracy, and competitiveness.
• Different computational approaches are used, including rule-based systems, artificial intelligence, and machine learning.
• Each approach has different characteristics, strengths, and limitations.

Rules-Based Systems

• Operate based on predefined rules created by humans.
• Follow “if–then” logic to produce decisions.
• Ensure consistency and predictability in business processes.
• Easy to understand, explain, and audit.
• Commonly used in compliance-driven environments (finance, quality control).
• Limited flexibility when conditions change.
• Require manual updates and maintenance.
• Not suitable for handling uncertainty or complex patterns.

Artificial Intelligence (AI)

• Refers to systems designed to simulate human intelligence.
• Capable of reasoning, pattern recognition, and decision support.
• Processes large and complex datasets.
• Automates tasks that previously required human judgment.
• Improves efficiency and accuracy in decision-making.
• Can be applied in risk analysis, customer service, and automation.
• Raises ethical concerns such as bias, transparency, and accountability.
• Requires governance, monitoring, and ethical guidelines.

Machine Learning (ML)

• A subset of artificial intelligence.
• Learns from historical data instead of relying on fixed rules.
• Continuously improves performance as more data is collected.
• Useful for prediction, classification, and optimization tasks.
• Applied in fraud detection, demand forecasting, and recommendation systems.
• Highly dependent on data quality and quantity.
• Risk of model drift over time.
• Requires regular evaluation and retraining.

Comparison Between Systems

• Rule-based systems emphasize control and transparency.
• AI emphasizes intelligent automation and reasoning.
• ML emphasizes adaptability and learning.
• Combining these systems can enhance organizational performance.
• Hybrid systems can balance stability and flexibility.