04AI prototypes feel fast because they temporarily ignore the hardest product questions: whose data may be used, what happens when the answer is wrong and who remains responsible for the action that follows.
Select a workflow, not a technology
Begin with repetitive information work, a decision bottleneck or a difficult knowledge journey. Define the user, source material, desired action and consequence of error before choosing the model or interface.
A narrow workflow with reliable context is more likely to create value than a general assistant expected to understand the entire organisation.
Design the information boundary
Production AI needs explicit rules for what information can be retrieved, which user may see it and how source changes are reflected. Permission-aware retrieval and clear data ownership are product requirements.
The system should show enough evidence for users to understand why an answer was produced, especially where the information supports a consequential decision.
Keep accountability visible
Automation should reflect the consequence of the task. Low-risk preparation may be largely automated; material recommendations, approvals or external actions may require structured human review.
Do not hide this review behind a generic disclaimer. Build it into the workflow, roles and status of the product.
Evaluate the complete product
Model quality matters, but so do retrieval accuracy, latency, cost, usability, security and the rate at which people accept or correct the output.
- ✓Use representative test cases and known edge conditions
- ✓Measure groundedness and task completion, not only fluent responses
- ✓Capture user feedback at the point of review
- ✓Monitor cost, performance and failure patterns
- ✓Create a controlled route for improving prompts, sources and models
Key takeawayA dependable AI product is a carefully designed operating system around a probabilistic capability—not a model endpoint with a polished interface.