Most AI projects do not fail on the model. They fail on the framing, the data and the integration. Run through this checklist before you commit.
1. Frame the use case
- What specific problem are you solving, and for whom?
- What does success look like as a measurable result?
2. Check the data and inputs
- What data or inputs do you actually have, and in what shape?
- Who is allowed to access it, and how is that enforced?
3. Choose the right capability
- Extraction, classification, generation, retrieval, or a mix?
- Where does the answer need to be grounded in your sources?
4. Plan the integration
- Which systems and workflows does it plug into?
- How will people actually use it day to day?
5. Build in oversight and limits
- Where does a human review or approve the output?
- How do you handle model limitations and hallucination risk?
- Is the system auditable and documented?
Answer these and you have the outline of an AI discovery, which is exactly where we start.