Quick answer
Add AI to an existing product by starting with one repetitive, easily verifiable task, grounding the model in your own data with citations, logging every response, and keeping a human fallback.
AI projects fail for boring reasons: unclear goals, no evaluation, and a demo that never becomes a workflow. The teams that succeed start smaller than they expect.
Start with a task, not a technology
Pick one task that is repetitive, text-heavy and easy to check. Drafting replies, summarising tickets, extracting fields from documents. If a human cannot verify the output in seconds, it is a bad first project.
Ground the model in your data
Generic answers feel impressive and are often useless. Retrieval over your own documents, plus clear instructions and citations, turns a chatbot into a tool people trust.
- Define the source of truth for every answer
- Return citations so users can verify
- Log every request and response for review
- Fall back to a human whenever confidence is low
If you cannot measure whether the output was good, you are not shipping AI — you are shipping vibes.
Design the escape hatch
Every AI surface should make it easy to ignore the suggestion, edit it, or hand off to a person. Trust grows when users feel in control, not replaced.