Building the Future of Feedback: Lessons from AI for Humans
In tech, getting feedback that is both compassionate and actionable is still surprisingly rare. Too often, it’s either vague encouragement or blunt criticism — neither of which helps people grow.
But what if we made high-quality feedback the default operating system of our teams?
What AI Has Taught Us About Feedback
AI has forced us to get serious about two things: observability and evaluation.
- Observability in AI means continuously monitoring a system’s real-time behavior, surfacing insights into performance and health. Think of it like a medical monitor for the AI — constantly checking vitals.
- Evaluation is the structured process of analyzing that observability data against specific goals. It answers the bigger question: Did it work?
Both are essential. Without observability, you miss signals. Without evaluation, you miss meaning.
The Analogy for People
Humans need both, too.
- Observability for people looks like regular check-ins, listening, and noticing day-to-day patterns — how someone is showing up, where they’re struggling, where they’re thriving. It’s about being present, not just waiting for problems.
- Evaluation for people is the structured reflection that turns those signals into growth: “You did well here. Here’s what could improve. Here’s how it ties to our goals.”
When leaders neglect one or the other, growth stalls.
- Only “observing” without clear feedback feels like being watched without support.
- Only “evaluating” without ongoing care feels like being judged without context.
The magic is in combining both — continuous attentiveness (observability) and structured, compassionate guidance (evaluation).
Shaping the Future of Work
If we want AI + humans to thrive together, we can’t just build smarter systems. We need to lead with smarter feedback cultures:
- Embed care + clarity. Every piece of feedback should carry both empathy and a clear action step.
- Make it continuous. Just as AI systems are monitored in real time, humans need frequent signals and encouragement.
- Anchor in outcomes. Evaluation should tie back to bigger goals, not just individual moments.
- Treat feedback as investment. Observability keeps AI safe and reliable. Feedback keeps people resilient and inspired.
Closing Thought
The future of work won’t just be defined by AI models improving. It will be defined by humans and AI both having the feedback loops they need to thrive.
We already accept that AI needs observability and evaluation to function. The opportunity now is to give humans the same care — continuous attention and compassionate, actionable reflection.
That’s how we’ll build teams — and products — that truly last.
