AI projects work best when they begin with a well-defined task rather than a technology trend. Look for a repetitive process where better summarization, classification, search, or decision support could save time or improve consistency.
Choose a narrow use case
Describe the current workflow, its pain points, and the expected improvement. Decide how a human will review important outputs and what should happen when the system is uncertain.
Set data and privacy boundaries
Use only data that is appropriate for the task. Check provider terms, retention settings, access permissions, and any requirements that apply to your industry or customers.
Evaluate quality before rollout
Test representative examples, including edge cases. Measure accuracy and usefulness against a baseline, and ask the people who will use the result to review it.
Keep a feedback loop
Monitor errors, user feedback, and changes in the underlying task. AI should support accountable workflows, not remove ownership from the people responsible for the outcome.
Temporary demo article: replace or delete this sample before your public launch.