The Unexpected Feedback Coach: Is AI helping Us Master Radical Candor
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The Unexpected Feedback Coach: Is AI helping Us Master Radical Candor

Ever wrapped feedback in so many pleasantries it became a gobstopper? After years of watching product teams navigate the feedback forrest - from retros to feature demos - I've discovered an unexpected ally in mastering radical candor: artificial intelligence. And no, I'm not talking about AI replacing human interaction (we have enough "AI will replace X" hot takes already).


When Feedback Gets Messy

Most of us in product learned about giving feedback through situations that still make us wince. Whether it was that time you tried to tell a developer their architecture wouldn't scale (but wrapped it in so many pleasantries they missed the point entirely), or when you had to explain to a designer why their perfect solution wasn't aligned with a single user need. That delicate balance between personally caring and directly challenging is surprisingly hard to nail.

Here's what's fascinating: while we obsess over metrics like user retention and time to value, we often overlook how our communication patterns impact these very metrics. Through running product teams, I've seen how better feedback loops drive measurable improvements in team performance.

From my experience, clear feedback directly impacts:

  • Feature rework rates - catching misalignments early through direct feedback
  • Technical decision timelines - when teams feel comfortable challenging assumptions
  • Cross-functional collaboration - when people aren't afraid to speak up

These improvements aren't just anecdotal - they showed up in our sprint metrics, team satisfaction scores, and ultimately our business outcomes.


The Feedback Lab

AI is creating what you could call "feedback laboratories" - consequence-free spaces where product teams can practice delivering tough messages with both care and clarity. Think of it like running A/B tests on your communication style.

Are you the PM who wraps scope changes in so many caveats they need a full blown index? Or maybe you're the engineer who'd rather refactor an entire codebase than explain technical debt to stakeholders? This is where practice comes in handy.

Some key opportunities I see for different product roles:

  • Engineers could practice explaining technical debt implications to stakeholders (we all know how those conversations usually go...)
  • Designers might refine how they explain user-centered decisions (especially when faced with the dreaded anecdotal "I just don't like it" or "it won't work" feedback)
  • PMs could work on delivering scope changes without sugar-coating (my nemesis - I used to wrap these in so many caveats they were a gobstopper)

I've added a few examples of these to the prompt library for you to copy/paste!


From Theory to Practice

Here's where we get practical. Start with common scenarios - sprint retros, design critiques, those fun technical debt discussions (you know the ones). Track the impact through team velocity, collaboration scores, and project alignment. The key is treating this like any other product feature - measure, iterate, improve.

Through experimentation with product teams, we've identified clear patterns in effective feedback delivery:

  • Opening with shared understanding of user/business goals
  • Maintaining directness without diminishing team morale
  • Following up to ensure alignment
  • Adjusting approach based on team dynamics


For product folks ready to dive in:

  1. Baseline your current feedback patterns (Are you avoiding technical discussions? Sugarcoating timeline concerns?)
  2. Practice specific scenarios with AI simulation
  3. Track improvements in both directness and care
  4. Measure impact on team outputs and collaboration


Looking Forward

As someone obsessed with optimizing product experiences, I see huge potential in using AI to help product teams master radical candor. We're moving toward systems that can help us:

  • Identify when we're slipping into ruinous empathy during planning
  • Practice challenging assumptions constructively
  • Maintain care while pushing for excellence

I'm curious though - what's your biggest feedback fail? Drop a comment about your most memorable one - we've all got them, and they're usually the best teachers.

#ProductManagement #Leadership #ArtificialIntelligence

Bonny Morlak

?? Founder Coach | Startup Mentor | Angel Investor

2 个月

Interesting. As a German born Australian, Radical Candor is how I grew up communicating. You might call it blunt. We feel it's simply honest. As long as you are saying it with kindness in your eyes, it's OK. In fact Germans find too much politeness unsettling - we want to know the truth as clearly as possible. Reading your insight on your experiences from a polite person's point if view is so interesting to me. I feel that if I ask you for your feedback, give it to me straight, so I can improve. You are helping me become a better version of myself. Maybe AI can help us finding a way to communicate with compassion and clarity...

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