Feedback loops and continuous improvement
Building structured mechanisms for capturing human corrections, measuring AI accuracy over time, and systematically improving system performance — turning every human override into a learning opportunity.
Why it matters
AI systems don't improve on their own in production. Without feedback loops, the same errors recur, human reviewers lose confidence, and the system stagnates. Structured feedback turns human review from a cost into an investment that compounds over time.
Where it shows up
finance
Every analyst correction to AI-generated commentary is logged with the reason. Monthly accuracy reports show which variance types the AI handles well and where it needs improvement. Prompt templates are updated quarterly based on error patterns.
hr
HR corrections to policy guidance responses are tracked and used to improve the knowledge base. Patterns in escalations reveal policy gaps or areas where the AI's training data needs updating.
procurement
Procurement team overrides to vendor scoring are recorded with rationale. Over time, the scoring model incorporates these corrections and the override rate decreases for well-understood categories.
Common mistakes
- Collecting feedback without acting on it — the loop must close
- Not distinguishing between AI errors and legitimate differences of opinion
- Updating the AI model without testing whether the changes improve overall performance
- Making feedback collection burdensome so that reviewers skip it
Signals that a workflow needs this pattern
- The team reviews the same types of AI errors repeatedly
- There's no mechanism to measure whether AI accuracy is improving or declining
- Human reviewers have stopped trusting the AI because it keeps making the same mistakes
- The organization wants to scale AI usage but can't demonstrate reliability improvements