A demand forecast can look plausible and still miss what supply chain managers know about supplier delays or local weather. If they quietly ignore its recommendations, the product team loses both adoption and a chance to find the problem. User feedback turns those experiences into evidence for improving the product. section 12.3.5
Feedback is information users provide about their needs, experiences, preferences, and problems. It serves two jobs in an AI product: finding needs the team has not addressed and revealing unexpected behavior after launch. It can guide changes to both the user experience and AI performance, but it should not be treated as an automatic verdict on what to build. section 2.1.2 section 10.3
Collect evidence in more than one way:
- Listen directly. Product reviews, sales and support notes, interviews, and in-product prompts can surface pain points and requests. Compare what people say with what they do: someone who says they want to study daily may open a learning app only on weekends. section 2.1.2
- Observe implicit feedback. Log interactions such as clicks, use of controls, and edits to AI output. Structure the data so the team can identify friction or features to improve. Usage is scalable evidence, but it is a proxy for needs and may miss needs the product does not yet serve. section 10.3.1
- Ask for explicit feedback when users can judge the result. A thumbs-up/down control, a comment, or a reason for an incorrect prediction can make a reaction actionable. Prompt after the intended value has been delivered rather than before users have experienced it. section 5.4.4 section 10.3.2
From complaint to model improvement
When forecast adoption fell, supply chain managers initially reported problems through scattered tickets and emails. An in-platform Needs Improvement button let them flag a forecast, select a reason, and add a comment. Their reports exposed missing factors such as weather-dependent demand and variable supplier delays. After the team reviewed the input and adjusted the model, accuracy improved by 12%. The useful signal was not simply that users disliked a prediction, but why. section 12.3.5
Close the loop deliberately. Connect feedback to the output or decision it concerns; review it alongside satisfaction, engagement, conversion, and other behavior measures; then test whether a change improves the user’s experience. Feedback can also enrich task-specific datasets and evaluations. Make requests easy to answer, explain their impact, and show users how their input shapes the system. That makes feedback a form of co-creation rather than a box to tick. section 5.4.4 section 8.3 section 10.3.2 section 12.4
