2.1. Analyzing Requirements for AI Solutions

Before selecting platforms or designing agents, you need to determine what problems AI should actually solve. The exam tests whether you can distinguish between problems that benefit from agentic AI and problems better solved with traditional automation — and whether you can assess the data readiness that underpins any AI solution.

Skipping requirements analysis is the most expensive mistake in AI projects. Organizations that jump straight to building agents without validating their use cases and data quality end up with impressive demos that fail in production.

Think of requirement analysis as a filter: start with all potential AI use cases, then eliminate based on four dimensions — task complexity, data readiness, integration complexity, and ROI. What survives the filter is worth building.

⚠️ Common Misconception: Any business data can be used for AI grounding without preparation. In reality, grounding data must meet criteria for accuracy, relevance, timeliness, cleanliness, and availability — unprepared data produces confidently wrong answers.