Implementing Feedback in Workflows

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

Six months ago, a client almost pulled the plug on an AI implementation we were running. Three weeks in. Leadership was aligned. The use case was clear. The tools were live. And yet adoption had started to stall. Usage dropped. Teams quietly slipped back into old workflows. Moments like this define whether an AI project succeeds or dies. At ALTRD, our instinct isn’t to defend the system we built. Our instinct is to investigate the system we missed. So we paused the rollout and audited what was actually happening inside the workflow. What we found was instructive. The training had landed well. But the implementation had been designed around how leadership thought the team worked. Not how they actually worked. Two things were quietly breaking adoption. First, we had optimized the visible workflow but missed an invisible step. There was a key handoff happening informally between two people over WhatsApp. It wasn’t documented anywhere. It never showed up in process charts. But it was where the real decision-making happened. Our redesigned workflow skipped that moment completely. Second, there was a quiet skeptic in the system. The team lead everyone naturally looked to before trying something new had concerns she hadn’t voiced in any meeting. Not because she was resistant, but because she wasn’t convinced the workflow would hold up under real pressure. Once the team sensed that hesitation, adoption slowed down. So we fixed the system. We remapped the actual workflow, not the documented one. Then we worked directly with the team lead. Not to sell the tool, but to understand the operational concerns and redesign parts of the system around them. The engagement expanded. And that project ended up becoming one of the most valuable learning moments for how we implement AI today. Two lessons we now carry into every engagement at ALTRD: Document the informal workflow, not just the official one. And find the quiet skeptic in the room early. They’re rarely the blocker. They’re usually the signal that something important hasn’t been designed properly yet. AI implementation isn’t just a technical system. It’s a human system. And if you want adoption to stick, you have to understand both.

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

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

The two patterns that fixed my agentic coding workflow: The self-correction loop and the scope creep buffer! Building with AI agents requires more than just good prompting; it requires a robust feedback architecture. After testing frameworks like spec-kit and taskmaster-ai and building my own, I’ve found that the specific tool matters less than the process. Currently, my daily driver is gemini-cli on Google Cloud, and there are two specific mechanisms I use to improve outcomes over time: 1. The self-correction loop When the agent provides the wrong output, needs manual redirection, or struggles with a task, I don't just fix the code, I make the agent fix its own instructions. I ask: "What specific instruction do you need to add to your context file to avoid this mistake in the future?" The agent analyzes its failure and writes a new system instruction that it appends to the context file (GEMINI.md). This turns every error into a permanent upgrade, ensuring the agent actually "learns" from the session. 2. The scope creep buffer Agents either try to implement too much at once or leaving things out, leading to halfway-done features. To solve this, I mandate a future_features.md file in my instructions. If the agent identifies an improvement or feature that isn't strictly part of the current task, it is forbidden from implementing it immediately. Instead, it must document it in the markdown file. We then review this file together to plan future iterations, keeping the current context clean and focused. 👀 Coming soon: the data health engine I’m currently wrapping up a technical deep dive on how we solved data quality transparency at Nordnet Bank AB. We built a "data health engine" entirely on GCP (Cloud Run + BigQuery + Looker) that tracks data quality incidents and their downstream impact. It even surfaces "health badges" directly on Looker dashboards and an overall data health status page so end-users know exactly what the state of the data is. I’ll be sharing the architecture and implementation details soon! #GoogleCloud #Gemini #AgenticAI #DataEngineering #Nordnet

Years ago, when we shipped one of our first containers of shoes overseas, I thought we had everything figured out. Everything looked great on paper. Only after our partner received the container did the feedback not go so well. It’s easy for leaders to lean into dashboards and what I call EKG reports with lots of lines showing performance. But that alone isn’t essential. So are rapid feedback cycles for fast decision-to-action timelines. When our partner received the shipment, everything was right, with solid packaging and tight systems. Still, our partners told us that packaging wasn’t working due to the country’s humidity, and the unloading conditions were much harsher. I knew they wanted to continue to work with us, and they weren’t complaining. They were informing. I didn’t defend the system, I simply turned to our team and said since they’re the experts, so listen and adapt to our partner needs. Within a week, the team redesigned how shoes were sorted and packed, and soon it became the global standard for us. Execution doesn’t happen in a boardroom. It happens in real places, with real people who see what leaders miss. Here’s what I learned about a fast feedback loop: ✅ Listen early and often. Feedback loops can’t wait for scheduled meetings. Stay tuned in. ✅ Empower your team. When a challenge arises, allow your team to speak up and do the work. ✅ Adjust rapidly. A strong feedback loop allows you to get critical feedback. Use it to innovate and execute faster. Listening at all times. Feedback loops are essential—make sure you become a master. Always: listen, listen, listen. It’ll allow you to fix problems, adjust faster, and scale your business.

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.

Feedback without action? Wasted opportunity. Here’s how to change that.👇: Here’s how to turn feedback into real, actionable steps: 1️⃣ Listen with an Open Mind ➟ The first step to turning feedback into action is truly hearing it.   ✅ Take a deep breath, focus on understanding, and ask questions for clarity if needed. 2️⃣ Separate Emotion from Information ➟ Focus on the content, not the delivery.   ✅ Jot down key takeaways objectively, leaving emotions out. Review it later when you’re calm. 3️⃣ Identify Key Themes ➟ Look for patterns in your feedback.   ✅ Notice if similar feedback comes up frequently—this is where small changes can lead to big improvements. 4️⃣ Prioritize What Matters Most  ➟ Not all feedback requires immediate action.   ✅ Use the “80/20” rule: focus on the 20% of feedback that will drive 80% of your growth. 5️⃣ Set Clear, Achievable Goals  ➟ Transform feedback into specific, actionable goals.   ✅ Instead of “communicate better,” set a goal to “speak up in meetings once per week” or “clarify tasks with teammates.” 6️⃣ Create a Plan and Timeline ➟ Real progress comes from consistent action.   ✅ Use a tool like a calendar or task app to track your progress and stay accountable to your timeline. 7️⃣ Follow Up and Ask for Feedback ➟ Growth is ongoing, and feedback should be too.   ✅ Schedule regular check-ins with a mentor or manager to review your progress and get updated feedback. 📌 PS...Remember, feedback is only as valuable as the action you take from it. ♻️ Share this with your network to help them give better feedback too! 🚀 Follow Harry Karydes for more daily tips to lead high-perfomring teams through mindset, habits and systems. 🔥 Do you want a high-res pdf of 125 of my top infographics? ➡ Go Here: https://lnkd.in/gaewRGyj

There is absolutely no point in gathering feedback from employees without creating feedback loops and USING feedback to inform improvement. WHAT IS AN EMPLOYEE FEEDBACK LOOP❓ An employee #feedbackloop is just one of many feedback loops in organizations. It is a structured process where people provide input or comments about their work experiences, job satisfaction, or performance, and this #feedback is used by the organization to make improvements, enhance employee engagement, and create a better working environment. That last part is where many organizations fall down... they don't USE feedback to inform improvements. This is a huge missed opportunity!! Employee feedback is a powerful way for organizations to identify improvement areas. Also, when people see that they are taken seriously, they feel heard and valued, and this feeling can significantly enhance engagement and job satisfaction. There is a real danger in asking for people's feedback and then ignoring it...or failing to acknowledge it. People lose engagement and trust... slowly they stop giving feedback.... and the organization struggles to improve. 💥 CREATING FEEDBACK LOOPS 💥 If you are thinking of creating/improving your employee feedback loops, here are some high-level steps to guide you: 1️⃣ Identify the type of feedback required 2️⃣ Select Feedback Methods 3️⃣ Regularly Collect the Feedback 4️⃣ Analyze and Share Results 5️⃣ Take Action 6️⃣ Follow Up 7️⃣ Track Progress: 8️⃣ Celebrate Successes 9️⃣ Iterate and Improve: Every single one of these 9 steps are important. And not very difficult. All it takes is good leadership and organization. Remember that feedback should not be a once-off effort. It is important to aim towards creating a feedback culture, where regularly giving and receiving feedback is encouraged and valued. Consistency is key! _______________________________________ I'm Catherine McDonald- Lean Business and Leadership Coach. Follow me for insights on Lean, Leadership, Coaching, Strategy and Organizational Behaviour

I've helped teams build stronger communication cultures. (sharing my proven framework today) Building open communication isn't complex. But it requires dedication. Daily actions. Consistent follow-through. Here's my exact process for fostering feedback culture: 1. Start with weekly 30-min team check-ins → No agenda, just open dialogue → Everyone speaks, no exceptions → Celebrate small wins first 2. Implement "feedback Fridays" → 15-min 1:1 sessions → Both positive and constructive feedback → Action items for next week 3. Create anonymous feedback channels → Digital suggestion box → Monthly pulse surveys → Clear response timeline 4. Lead by example (non-negotiable) → Share your own mistakes → Ask for feedback publicly → Show how you implement changes 5. Set clear expectations → Document feedback guidelines → Train on giving/receiving feedback → Regular reminders and updates 6. Follow up consistently → Track feedback implementation → Share progress updates → Celebrate improvements 7. Make it safe (absolutely crucial) → Zero tolerance for retaliation → Protect confidentiality → Reward honest feedback Remember: Culture change takes time. Start small. Build trust. Stay consistent. I've seen teams transform in weeks using these steps. But you must commit fully. Hope this helps you build stronger team communication. (Share if you found value) P.S. Which step resonates most with you? Drop a number below. #team #communication #workplace #employees