Feedback Tools and Technologies

The PMs who win in the next wave won't be the ones who figured out how to prompt to build. They'll be the ones who figured out how to run 10x the customer learning with the same team. Here's why that matters right now. AI has handed engineering teams a jetpack. Cursor. Codex CLI. Claude Code. The delivery side of product development — build, specify, launch — is being automated at a breathtaking pace. But as Andrew Ng recently pointed out, the real bottleneck today isn't coding. It's discovery. While everyone raced to accelerate shipping, the question mark moved upstream. We now have the ability to build faster than we've ever been able to learn. And building fast on the wrong insight isn't speed — it's just expensive mistakes, sooner. The good news: the same AI revolution is quietly making discovery dramatically more powerful too. A few of the emerging use cases: 1️⃣ Analyzing feedback at scale. What used to require a researcher and two weeks can now be done by a PM in an afternoon — feeding thousands of NPS verbatims, support tickets, or app reviews into an AI and getting back a structured synthesis of themes, patterns, and verbatim quotes. 2️⃣ Automating feedback rivers. Tools like Reforge Insights, Enterpret, and Kraftful now continuously monitor customer feedback across every channel and surface actionable signals without anyone having to manually triage. 3️⃣ AI-moderated user interviews. Platforms like Reforge and Listen Labs are making it possible to run interviews at a scale that was never feasible with human moderators — turning what used to be 10 interviews into 100. 4️⃣ Discovery via prototypes. With vibe-coding tools like Lovable, v0, and Bolt, PMs can now build functional prototypes and gather real behavioral data — heatmaps, drop-offs, in-product surveys — before a single line of production code is written. 5️⃣ Natural language metric analysis. Ask your database a plain-English question, get a chart back. No SQL. No waiting for a data analyst. The feedback loop between a hypothesis and an answer just collapsed from days to minutes. The teams that wire these workflows together won't just be better informed. They'll develop a sharper product intuition — the kind that David Lieb (Founder of Google Photos, Partner at YC) described as "the world's most sophisticated machine learning model ever created." Join me Thursday, March 5th at the Lean Product Meetup with Dan Olsen in Mountain View, CA where I'll be sharing the exact 10 AI discovery workflows I now rely on to help me decide what's worth building faster 👉 https://lnkd.in/gfrJVsd3

Most teams drown in feedback and starve for insight. I’ve felt that pain across CX, SaaS, retail—and especially in gaming, where Discord, reviews, and LiveOps telemetry never sleep. The unlock wasn’t “more data.” It was AI turning feedback → insight → action in hours, not weeks. Here’s what changed for me: Ingest everything, once. Tickets, app reviews, Discord threads, calls, streams—normalized and de-duplicated with PII handled by default. Enrich automatically. LLMs tag topics, intent, and aspect-level sentiment (what players love/hate about this feature in this build). Act where work happens. Copilots draft Jira issues with evidence, propose fixes, and close the loop with customers—human-in-the-loop for quality. Measure what matters. Not just CSAT. In gaming: retention, ARPDAU, event participation. In other industries: conversion, refund rate, cost-to-serve. Gaming example: a balance tweak drops; AI cross-references sentiment from Spanish/Portuguese Discord channels with session logs and flags a difficulty spike for new players on Android. Product gets a one-pager with root cause, repro steps, and a recommended hotfix—before social blows up. That’s the difference between a rocky patch and a win. This isn’t just for studios. Healthcare, fintech, DTC, SaaS—same playbook, different telemetry. I put my approach into a 2025 AI Feedback Playbook: architecture, workflows, guardrails, and a 30/60/90 rollout you can start tomorrow. If you lead Product, CX, Support, or LiveOps, it’s built for you. 👉 I’d love your take—what’s the hardest part of your feedback loop right now? Link in comments. 💬 #AI #CustomerExperience #Gaming #LiveOps #ProductManagement #VoiceOfCustomer #LLM #Leadership #CXOps

From raw feedback to actionable insights: My AI-powered workflow. I'm running an AI-Native PM training and for each cohort I like to close the feedback loop in a more dynamic, engaging, and collaborative way. Here’s the 3-step, AI-powered, collaborative process I use. Step 1: Capturing the raw feedback with Google Forms. It starts with a simple Google Form to gather candid feedback on the training. Step 2: Transforming raw feedback into an engaging video with Notebook LM. This is where the magic happens. Instead of manually combing through the feedback and creating slides, I took a different approach. I uploaded all the raw, anonymized feedback directly into Notebook LM and then prompted it to act as a product manager synthesizing user research, asking it to identify the core positive themes, the most critical areas for improvement, and to structure these findings into a concise video. Step 3: Uploading the video to Loom for sharing and collaboration. Numbers are great, but a video is more personal and engaging. This final step is key because Loom transforms a one-way summary into a two-way conversation. By sharing a Loom link with my stakeholders, they can: • Watch the summary on their own time. • Leave comments and reactions tied to specific moments in the video. • Engage in threaded discussions right on the video timeline. This workflow didn't just save me time but created a richer, more collaborative way to understand and act on valuable feedback. It’s a simple and fun example of how we can use AI tools not just to build products, but to improve how we communicate and share learnings.

we spent $2M building "the most comprehensive patient experience dashboard in the industry." hospital executives loved the demos. the visualizations were beautiful. the data was clean. nobody used it. three months in, I finally understood why: we'd built a quantitative masterpiece that ignored qualitative reality. our dashboard could predict average length of stay across thousands of patients. but it couldn't tell our clinical leads what she actually needed at 2 PM on a Tuesday — whether patient A in room 123 was getting anxious about discharge. That's the trap most data product teams fall into. We pick a side: the quant folks build dashboards and A/B tests. Great for "what" questions but terrible for "why." the qual folks run user interviews and read support tickets. Rich context but doesn't scale. Both miss the magic that happens when you combine them. Here's what changed for us: we built what Sachin Rekhi calls "feedback rivers" — continuous streams of customer feedback that merge quantitative signals with qualitative context in real time. (didn't have for a name for it back then) Traditional approach: schedule focus groups, design surveys, manually dig through tickets. Takes weeks. our nlp-powered feedback system surfaced this in 30 minutes: → dozen support tickets: "confusing medication reminders" → multiple support calls: "managers don't understand the app" → Interview quote: "its pretty but i don't know what to do about it" we simplified the interface. Two weeks later: → 30% improvement in completion rates → 25% increase in adherence scores it was about connecting quantitative signals with qualitative context instantly. i just published a deep dive on this: how to build your own feedback river, avoid common pitfalls (drowning in data, over-relying on AI summaries), and create a culture where stories and stats inform each other. also includes a 30-day action plan to get started. Link in comments. 👇

Teams with continuous feedback programs show 23% higher profitability and 18% greater productivity than those relying on outdated annual performance reviews. AI ALPI research has uncovered a critical shift in top-performing HR departments. While 76% of organizations still rely on annual reviews, market leaders are leveraging technology-enabled continuous feedback loops that drive real business outcomes. → Weekly micro-feedback sessions are replacing quarterly or annual reviews, creating psychological safety and real-time course correction ↳ This approach reduces employee anxiety and creates 3x more actionable insights than traditional methods → AI-powered tools now enable performance tracking without the administrative burden ↳ HR leaders implementing these systems report 42% reduction in management time spent on performance administration → Human-centered leadership training has become a critical enabler ↳ Organizations investing in empathy-driven feedback skills see 37% higher retention rates among high performers Companies that implemented continuous feedback systems initially saw a temporary 15% drop in satisfaction as managers adjusted to more frequent, meaningful conversations. By month three, both engagement and productivity metrics surpassed previous levels by significant margins. 🔥 Want more breakdowns like this? Follow along for insights on: → Getting started with AI in HR teams → Scaling AI adoption across HR functions → Building AI competency in HR departments → Taking HR AI platforms to enterprise market → Developing HR AI products that solve real problems #ContinuousFeedback #HRTech #FutureOfWork #LeadershipDevelopment #PerformanceManagement

Launch Week Day 4: Expert Feedback 📝 Building AI in regulated industries means getting expert input right. But right now, the feedback process is a mess - scattered across tools, hard to track, and difficult to action. That's exactly why we built Expert Feedback with 3 core capabilities: 1️⃣ Centralized feedback collection where experts can comment, score, and override decisions 2️⃣ Organized tracking that links feedback to specific outputs and evaluation categories 3️⃣ Direct pathways to implement improvements through model fine-tuning and workflow optimization With this, clinicians can now provide structured inputs that directly shape model development and improve AI output quality. And this is only just the beginning 💪🏽 P.S. If you’re building AI in regulated or high-stakes industries, how are you currently managing expert feedback? Would love to hear your thoughts!