Feedback

What you did not ask for, but should hear

Feedback that arrives unsolicited, in comments, in reactions, in offhand remarks, is often the most accurate signal you will get, and the least likely to be saved.

There is a category of feedback that is more valuable than any formal review: the thing someone says when they are not trying to be diplomatic. The annotation in a shared doc. The Slack message after a presentation. The comment in a team retro that captures what everyone was thinking. This feedback is candid, specific, and time-stamped to the moment it was most relevant. It is also almost never systematically captured.

The challenge with informal feedback is that it arrives across many surfaces and often in passing. A manager says something in a one-to-one that changes how you approach a project. A colleague annotates a draft in a way that reveals a fundamental misunderstanding. A client mentions something in a call that suggests the brief was off. Each of these is a signal, and each is likely to exist only in the memory of the person who received it.

For an AI tool to give useful feedback on your work, or to help you improve over time, it needs access to the patterns in feedback you have already received. What recurring themes have come up. What has been consistently praised or consistently flagged. This is a layer of context that most tools have no way to reach today.

Make it visibleStart a simple 'feedback log' in a note or doc your AI tool can reach. For the next two weeks, add one item each time you receive feedback that was not formally solicited: what the feedback was, on what, and from whom. Review it at the end of the two weeks.

Why AI needs this

Each part of your work matters to AI in a specific way. Some of it is context a tool needs before it can help, some of it is work a tool can take on, and some of it is judgment that should stay with you.

Pattern recognition

A single piece of feedback is an anecdote. A body of feedback captured over time is a pattern a tool can help you see. The investment is in routing informal feedback to one place rather than letting it evaporate.

Unfiltered signal

Formal feedback processes tend to produce sanitised responses. The informal feedback that arrives outside those processes is often the most accurate, and capturing it gives a tool access to a more honest picture of how work lands.

Improvement loops

If a tool can see what feedback your work typically receives, it can help you address the most common issues before a deliverable goes out. But only if you have a habit of capturing that feedback somewhere reachable.

Blindspot surfacing

Feedback you did not ask for is by definition a blindspot. Keeping a log of unsolicited feedback, sorted by theme, is one of the best ways to give an AI the context it needs to help you improve in the areas that matter most.

Questions to explore

Use these on your own or in a group. There are no right answers, only better conversations.

Think about the most useful piece of feedback you received in the last month. Where is it now?
What informal feedback have you received repeatedly, from different people, that you have never formally recorded?
If an AI could analyse all the feedback you have received on your work this year, what pattern do you think it would find?
How do you currently distinguish between feedback that is worth retaining and feedback that is noise?
Where in your work does unsolicited feedback arrive most often, and how much of it gets saved versus lost?

Readiness traps

Capturing all feedback equally treats a quick emoji reaction the same as a substantive critique. Being selective, favouring specific, unexpected, or repeated feedback, makes the captured set more useful to a tool.
Feedback about other people's work or performance is sensitive. A log of informal feedback that mixes professional critique with personal observations requires clear boundaries before any of it is routed to an AI.
The value of a feedback log drops sharply if it is not reviewed. Capturing feedback that never gets read is storing noise. Build in a regular moment to review and draw out what the patterns are.