*AI is best used when*: 1. The task requires understanding or generating human language, images, or complex media. 2. The goal is to create something new (summaries, code, designs) rather than just categorize what already exists. 3. The problem requires synthesizing information from multiple unstructured sources (e.g., "Read these 100 PDFs and tell me the common theme"). 4. The "rules" are subjective or context-dependent (e.g., determining if a tone is "empathetic" or "professional"). 5. You don't have a perfectly labeled dataset, but you have access to a pre-trained foundation model that already understands the world. 6. The system needs to interact naturally with humans in a conversational or adaptable way. 7. The process requires multi-step reasoning or following a chain of logic to reach a conclusion.a 8. Speed to deployment is critical and can be achieved by prompting an existing model rather than building an architecture from scratch. **AI shouldn’t be used when**: 1. Absolute factual accuracy is non-negotiable (e.g., calculating taxes or medical dosages where "hallucinations" are fatal). 2. The logic must be fully transparent and auditable (e.g., "black box" reasoning is not allowed for regulatory reasons). 3. The latency or cost of running a large model outweighs the value of the automation (e.g., using a massive LLM to do basic math or simple string formatting).