Choosing the Solution Type: Rules, Predictive AI, GenAI, and AWS
Explore how to identify the appropriate AI solution type by distinguishing between rule-based systems, predictive AI for pattern recognition, and generative AI for content creation. Understand when to use AWS managed services such as Amazon Quick, Bedrock, and SageMaker according to business requirements and data complexity. This lesson helps you apply structured decision-making for AI investments and aligns AWS tools with specific business use cases.
A request for AI is not automatically a reason to use AI. In the complaint backlog case, leadership wants AI to route complaints, yet the routing policy is a short, stable set of if-then conditions. That requirement calls for rule-based automation, not a model.
Rule-based automation applies predetermined logic to an input. If a complaint requests a refund above the approved threshold, route it to a supervisor. The same input produces the same result, so the decision is deterministic, auditable, and straightforward to explain.
Use rules when the business can state the logic completely, the policy changes infrequently, and consistent execution matters more than pattern recognition. Rules are usually less costly to operate because they do not need historical training data or ongoing prediction-quality evaluation.
Use predictive AI when the desired decision depends on patterns that are too numerous or variable to express as a stable rule set. For example, complaint categorization may need to infer the likely category from free-text emails, transcripts, and historical outcomes. Predictive AI can ...